Excerpts From:

MOTIVATING CRITICAL COMPUTER SYSTEMS OPERATORS:JOB CHARACTERISTICS, CONTROLS, AND RELATIONSHIPS

A THESIS SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL

OF THE UNIVERSITY OF MINNESOTABY

DALE HARRISON MCKNIGHT

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

Chair: Gordon B. Davis Co-Advisors: Norman L. Chervany and Fred D. DavisCommittee Members: Frank Miller, Akbar Zaheer

December, 1997

Note -- These excerpts include:--The first in-depth examination of the Critical Information Systems Operator job--Incrementing the Job Characteristics Model with Relationships/Trust --Incrementing Management Contols theory with Relationships/Trust--New Grounded Theory validity methods--Demonstrations of thorough survey Construct Validity methods--Empirical results that explain what motivates critical systems operators--An explanation of the paradoxical results found for managerial controls

Copyright Dale Harrison McKnight 1997All Rights Reserved

MOTIVATING CRITICAL COMPUTER SYSTEMS OPERATORS:JOB CHARACTERISTICS, CONTROLS, AND RELATIONSHIPS

ABSTRACT

This study expands the explanatory power of two theories of motivation: the

Hackman and Oldham Job Characteristics Model (JCM) and the economics-based

Management Controls model (MCM). The JCM predicts worker motivation as a

function of the worker’s job characteristics (e.g., skill variety), while the MCM predicts

motivation as a function of managerial controls (e.g., incentives). These motivation

theories each omit an explicit account of the roles of: a) supervisor/subordinate

relationships, and b) workplace fairness perceptions, relying instead on how the job or its

incentives are structured. This study adds explanatory power to these theories through

two constructs: ‘Relationships’ (worker trust and liking towards the supervisor) and

‘System Trust’ (worker beliefs about the fairness structures of the workplace). The

target application of this research is the critical computer systems operator. ‘Critical’

means the extent to which business transactions are interrupted when these systems are

not available to their users.

This research was conducted in two phases at one site. Phase I explored factors

important to keeping critical computer systems available to users almost 100% of the

time. “Grounded theory” methods were used to analyze the semi-structured interviews.

In Phase II, a questionnaire was administered to eighty-six operators to test the extent to

which adding Relationships and System Trust to the JCM and MCM helped these models

predict operator motivation.

i

The study contributes to research in four ways. First, Relationships and System

Trust added predictive power to the JCM. Second, Relationships and System Trust

added predictive power to the MCM. Relationships and System Trust supplement

traditional views that job characteristics or management controls alone produce

motivated workers. Third, the study validates measures for two newly conceptualized

constructs: Relationships and System Trust. Fourth, it describes the highly motivating

nature of the critical computer systems operator job.

This study also contributes to practice. Two paradigms have dominated recent

corporate motivation practices: worker empowerment (based on the JCM) and incentive

pay (based on the MCM). This research suggests that these paradigms will yield

inadequate results unless worker/manager relationships and workplace fairness are also

considered.

ii

MOTIVATING CRITICAL COMPUTER SYSTEMS OPERATORS:

JOB CHARACTERISTICS, CONTROLS, AND RELATIONSHIPS

TABLE OF CONTENTS

PageAbstract ii

Chapter One: Introduction and Overview 1

Overview and Research Question 1

The Nature of the Critical Systems Operator Job 4

Advancing the Job Characteristics Model 6

Advancing the Management Controls Model 9

Summary and Contributions 12

Roadmap for the Study 13

Chapter Two: Methodology and Construct Validation 15

Approach of the Study 15Phase I Conceptual Model Building Methodology

20 Grounded Theory 20 Phase I Research Framework 22

Phase II Model Building and Testing Methodology 24

Measurable Constructs Used 25 Instrument Pretest 27 Instrument Pilot 31

Construct Validation Results 33

Hypothesis Testing Methodology 45

Research Site for the Study 49

iii

Chapter Three: Nature of the Critical Systems Operator Job 55

Theory Building 56 Nature and Importance of the Critical Computer System

56 Management Information Systems Literature 59 Management of Technology Literature 62 Conceptual Model Building 64 Hypotheses 66

Methodology Detail 76

Results of Hypothesis Testing 79Discussion of Results

84

iv

TABLE OF CONTENTS (continued)

Chapter Four: Job Characteristic Model--Adding Relationships 88

Theory Building 89 JCM Related Research 89 JCM Hypotheses 90 Relationships and System Trust Related Hypotheses

91Methodology Detail

96Results of Hypothesis Testing 96Discussion of Results

100

Chapter Five: Incentive Controls--Adding Relationships 103

Theory Building 104

Definitions 104 Controls Theory Overview 105 Conceptual Model Building-Incentives 107 Scientific Model Building-Incentives 110 Hypotheses-Incentives 119

Methodology Detail 123

Results of Hypothesis Testing 125Discussion of Results

127

Chapter Six: Other Controls--Adding Relationships 133

Theory Building 134

Conceptual Model Building-Accountability 134 Scientific Model Building-Accountability 135

v

Conceptual Model Building-Feedback 137 Scientific Model Building-Feedback 137 Conceptual Model Building-Micromanagement 140 Scientific Model Building- Micromanagement 141 Conceptual Model Building-Autonomy 142 Scientific Model Building- Autonomy 142 Scientific Model Building-Work Outcomes

143 System Trust’s Impact on Motivation 145

Methodology Detail 145

Results of Hypothesis Testing 146Discussion of Results

153

vi

TABLE OF CONTENTS (continued)

Chapter Seven: Contributions, Limitations, and Future Research 157Contributions

158 To Theory

158 To Practice 160

Study Limitations 163

External Validity 164Future Research 167

References 172

Appendix A Examples of open and axial coding 191

Appendix B Questionnaire Items by Construct 193

Appendix C Operator Questionnaire 201

Appendix D Supervisor Questionnaire 224

Appendix E Pretest Instrument A--Matching 227

Appendix F Pretest Instrument B--Categorization 228

Appendix G Pretest Instrument C--Sorting 229

Appendix H Pairwise Intercorrelation Matrices 230

Appendix I Descriptive Statistics 244

Appendix J Pairwise Intercorrelation Matrices--High Level Concepts 245

vii

LIST OF TABLESPage

Table 1 Instruments for Testing Management Controls/Relationships 26 Model

Table 2 Pilot Reliability Analysis 32

Table 3 Construct Level Cronbach’s Alpha Reliabilities 34

Table 4 Intercorrelations of Trust Constructs and Liking 38

Table 5 Mono-Trait, Mono-Method Analysis for Autonomy 41

Table 6 Correlations among CTE, Performance, and Two Autonomy Types 43

Table 7 Correlations among JCM Variables and Two Autonomy Types 43

Table 8 Reliabilities for High Level (Second Order) Concepts 46

Table 9 Intrinsic Motivation Orientation (IMO) Scale 78

Table 10 Job Characteristics Comparisons 79

Table 11 Intrinsic Versus Extrinsic Factors Reported 81

Table 12 Correlations between Less Secure Group and Other Attributes 82

Table 13 Job Characteristics Model Test Results 97

Table 14 Relationships and System Trust Test Results 99

Table 15 Effects of Extrinsic Motivation on Intrinsically Motivating Tasks 115

Table 16 Management Controls / Relationships Model—Correlation Tables 147

Table 17 Management Controls / Relationships Model—Regression Results 148

Table 18 Sensitivity Analysis for Relationships Moderation of Accountability 150

Table 19 Sensitivity Analysis for Relationships Moderation of Feedback 151

viii

LIST OF TABLES (continued)

Table 20 Sensitivity Analysis for Relationships Moderation of Micromanagement 151

Table 21 Sensitivity Analysis for Relationships Moderation of Autonomy 152

COMMONLY USED ABBREVIATIONS

JCM Job Characteristics Model

MCM Management Controls model

CSO Critical (computer) System Operator

MIS Management Information Systems

XYZCo Organization for the research site

GNS Growth Need Strength

CPS Critical Psychological States

ix

LIST OF FIGURES

PageFigure 1 Job Characteristics Model (JCM) 2

Figure 2 Motivating Nature of the Critical Systems Operator Job 6

Figure 3 Expanding the Job Characteristics Model 9

Figure 4 Management Controls Model 10

Figure 5 Advancing the Management Controls Model 12

Figure 6 Roadmap for the Study 14

Figure 7 The Operations Research Model 19

Figure 8 Phase I Research Framework 23

Figure 9 Management Controls / Relationships Model—Detailed Level 25

Figure 10 Job Characteristics Model (JCM)—Detailed Level 27

Figure 11 Nomological Network for Trust Constructs 37

Figure 12 Model of Construct Creation 192

Figure 13 Model of Construct Linkages 192

x

CHAPTER ONE: INTRODUCTION AND OVERVIEW

This chapter previews the topic, propositions, general

methodology, and contributions of the study. It begins with a research

overview that introduces the research question. Then it creates the

broad propositions that later chapters will test in detail. Finally, it

summarizes the contributions of the study and presents a guide that

organizes the contents of later chapters.

OVERVIEW AND RESEARCH QUESTION

This study introduces two constructs, Relationships and System

Trust, that improve the predictive power of the Job Characteristics

Model (JCM) and the Management Controls model (MCM) of worker

motivation. System Trust means the belief that proper impersonal

structures are in place to enable one to anticipate a successful

endeavor (Lewis & Weigert, 1985; Shapiro, 1987; Zucker, 1986). In

this study, the Systems Trust construct was operationalized as the

worker’s belief that structures (i.e., processes, procedures) support or

encourage fairness in one’s work environment. Relationships means

the extent to which one holds positive feelings, beliefs and intentions

towards another person. The Relationships construct was

operationalized as trust in, and liking of, one’s supervisor. The

Relationships definition carries a quality-of-relation focus that differs

from the traditional definitions of relationships in: a) sociology, which

focus more on behavioral and role interdependence (e.g., Blau, 1964), 1

and b) social psychology, which focuses more on the ability of parties

to influence each other (e.g., Berscheid, 1983).

As depicted in Figure 1, the Hackman and Oldham (1975) Job

Characteristics Model (JCM) posits that worker perceptions of their Job

Characteristics (e.g., Skill Variety) lead to Critical Psychological States

(e.g., Felt Responsibility) that, in turn, lead to motivational Work

Outcomes (e.g., Job Satisfaction). These model linkages are

moderated by the worker’s Growth Need Strength, an individual

characteristic variable. The JCM focuses on the nature of the job itself,

ignoring social or structural aspects of the worker’s environment.

Figure 1 Job Characteristics Model (JCM)

Growth Need Strength Critical

Job Characteristics Psychological Work States (CPS) Outcomes

In contrast, Management Controls models (e.g., Ouchi, 1979)

posit that incentives or other controls improve worker motivation. The

term “controls” means methods of attempting to ensure desired

outcomes by trying to influence other people (Anthony, 1965; Lawler

& Rhode, 1976). Management control occurs when managers use

methods to try to influence employees to behave in certain ways.

Control models generally ignore social and structural factors, focusing

instead on extrinsic rewards or behavior control. For example, 2

managers try to entice employees to work faster by offering them

contingent financial incentives.

This study’s subjects were critical systems operators. Critical

systems are computer systems that must be kept available to users,

or else numerous business or operational transactions are interrupted.

Transaction processing systems, used to conduct a firm’s daily

business, often fall in the category of critical systems (Laudon &

Laudon, 1995). Managers of critical systems try to keep their systems

continuously available to system users. Hence, critical systems

operators (CSOs) must be constantly alert to problems that might

threaten the system. When a critical systems crashes, the operators

are charged with restoring it within seconds or minutes, not hours.

The researcher studied critical computer systems operators (CSOs) in

two stages: exploratory (Phase I) and confirmatory (Phase II). The

systems these operators ran were considered critical because

thousands of users required that the systems be continuously

available so they could perform their daily job function.

During the study’s Phase I interviews, it became evident that

CSOs were clearly motivated by the nature of their job, but that

controls and incentives did not have consistent, positive motivational

effects on CSOs. In analyzing Phase I data, it became clear that

worker relationships with superiors and their beliefs about the work

environment also influenced their motivation. Some evidence for this 3

effect also comes from the management literature (e.g., Cook & Wall,

1980; Locke, Latham & Erez, 1988). Therefore, the study’s research

question is:

Do operator/supervisor Relationships and System Trust

improve the ability of the Job Characteristics Model and the

Management Controls model to predict critical systems

operator motivation and motivational outcomes?

In other words, this study tested the extent to which

operator/supervisor Relationships and System Trust added predictive

value to the JCM and the MCM in the critical systems operator context.

THE NATURE OF THE CRITICAL SYSTEMS OPERATOR JOB

The critical computer systems operator (CSO) is a subset of the

class of information systems workers called “computer operators.” A

literature search revealed that very little research has been done on

computer operators. The management information system (MIS)

literature focuses on system development, implementation,

maintenance, and use issues, while covering few system operation

issues (Berkeley, 1984; Ives, Hamilton and Davis, 1980; Swanson &

Ramiller, 1993). Lyytinen & Hirschheim's (1987) exhaustive review of

the MIS failure literature reported almost no research on system

operation issues.

In fact, in the 1970s and 1980s, the traditional computer operator job was viewed

as a quasi-clerical function that did not merit intensive study (Couger & Zawacki, 1980). 4

In their survey of over 1200 computer operations employees, Couger

and Zawacki (1980: 33) reported that “employees in DP operations

perceive their jobs to be deficient in the key characteristics that

produce motivation and lead to increased productivity. The

motivating potential score (MPS) of these jobs is lower than that of

any of the other 500 jobs in the Hackman/Oldham data base.” MPS,

derived from the scores of the five JCM job characteristics, represents

how motivating a job is. Describing computer operations as a data

processing ‘stepchild,’ Couger and Zawacki suggested that only “the

‘sledgehammer’ of a catastrophic event such as a flood or bombing”

could “draw attention to computer operations.” (1980: 34)

This study draws attention to the job of the critical systems operator—a job that

does not fit the Couger and Zawacki computer operator profile. In the critical system

context, the threat of catastrophic system downtime is so large that it produced

motivating potential scores for the eighty-six critical systems operators in this study that

were more than double that of the traditional computer operator Couger and Zawacki

measured. This study’s informants operated three critical transaction processing systems

at a large U. S. corporation fictionally name XYZCo. During Phase I interviews, critical

systems operators (CSOs) at XYZCo were found to be highly skilled and motivated

individuals who performed an extremely interesting and challenging job. For example,

the task of diagnosing and fixing system outages was reported to be exhilarating,

satisfying, and yet full of pressure. These CSOs were found to be primarily intrinsically

motivated, in that they more often mentioned that they enjoyed their job and its challenge 5

than they mentioned extrinsic job rewards. From Phase I data (discussed in more detail

in Chapter Three), it was proposed that (see Figure 2):

Proposition 1: The nature of the critical systems operator (CSO) job is such

that: a) JCM measures for the CSO will be significantly higher than was found among

traditional computer operators in the Couger & Zawacki (1980) study; and b) CSOs will

be more intrinsically motivated than extrinsically motivated.

Figure 2 Motivating Nature of the Critical Systems Operator Job

Nature of the Critical Systems High Levels of Operator Job Motivation

The CSO job is therefore considerably different from the jobs of the traditional

computer operators Couger and Zawacki (1980) studied. The CSO subjects of this study

are not representative of computer operators in general, but are representative of

operators of computer (and other) systems that are required to stay available nearly 100%

of the time. Therefore, rather than generalizing to the job of computer operators, the

results of this study will shed light on: a) the jobs of critical computer system operators

(e.g., for transaction processing systems—Laudon & Laudon, 1995; Weick, 1990); and

b) the jobs of those who operate critical systems like nuclear power plants or aircraft

carriers (e.g., Perrow, 1984; Weick & Roberts, 1993).

ADVANCING THE JOB CHARACTERISTICS MODEL

The Job Characteristics Model posits that jobs may be designed to maximize

6

motivation (e.g., Hackman, 1980). JCM forms the theoretical basis for worker

empowerment (e.g., Peters, 1992) and the related process “reengineering” (Hammer &

Champy, 1993) paradigms, which have dominated recent motivation practices of

corporations. JCM has also been widely adopted and discussed in the Management and

MIS literatures (e.g., Couger & Zawacki, 1980; Roberts & Glick, 1981). Evidence

developed by those who have studied information systems jobs (e.g., Couger & Zawacki,

1980; Ives & Chervany, 1983; Lending, 1996) generally supports the application of the

JCM to the information systems worker. Therefore, (see Figure 1)

Proposition 2: The job characteristics of critical systems operators will be

positively associated with their Critical Psychological States (CPS), which, in turn, will

be positively associated with their Work Outcomes. Both linkages will be moderated by

Growth Need Strength (GNS).

Over the past twenty years, significant evidence has accumulated that social

relationships also motivate workers. The original JCM (Hackman & Lawler, 1971)

contained social needs factors that were later removed, probably because they did not

receive as much empirical support as did the job characteristics part of the model

(Lending, 1996). However, some researchers have continued to include some aspect of

sociality in their testing of the JCM (e.g., Couger & Zawacki, 1980; Lending, 1996).

Further, Salancik & Pfeffer (1978) offered their Social Information Processing (SIP)

model as a JCM alternative. SIP posits that worker perceptions of their jobs are

influenced through social cognitive processes rather than through job characteristics. 7

Lending (1996) and Couger & Zawacki (1980) used forms of social needs in their

studies, based on Hackman & Lawler (1971). These needs have not always been found

to be closely related to CPS or Work Outcomes.

Note that measurements of social needs or social cognitions are indirect ways of

measuring the ‘goodness’ of relationships between people in the work place. That is,

measuring social need fulfillment refers to how well a relationship fulfills a person’s

need, rather than measuring the quality of the relationship (i.e., trust and liking between

the people) directly. Similarly, social cognition embodies how cognitive frames are

formed, but does not directly measure people relationships. However, if social need

fulfillment and social cognition are important to motivation, then it seems reasonable that

people relationships measured directly could be even more important. In fact, Smits,

McLean and Tanner (1997) found that the relationship with one’s supervisor was one of

the two most significant predictors of the motivational variable called organizational

commitment. Similarly, Lending (1996) found that one relationship variable,

“Satisfaction with Supervisor,” improved her ten-factor JCM index’s prediction of

system developer Job Satisfaction from an adjusted R-squared of .22 to .33. System

Trust, because it is part of the family of trust variables that are positively related to

motivation (Locke, Latham & Erez, 1988), is also likely to be related to motivational

outcomes. For example, how one feels about the structures encouraging equity in the

work environment (System Trust) should be related to one’s Job Satisfaction (a Work

Outcome). Therefore (see Figure 3):

8

Proposition 3: In the critical systems operator job, operator/supervisor

Relationships will be predictive of CPS and Work Outcomes beyond the predictive

power of JCM constructs. System Trust will be predictive of Work Outcomes beyond

the predictive power of JCM constructs.

Figure 3 Expanding the Job Characteristics Model

Growth Need Strength Critical

Job Characteristics Psychological Work States (CPS) Outcomes

Relationships System Trust

In light of the strong job characteristics motivation of the CSO job (Proposition

1), Proposition 3 is a strong test. Proposition 1 implies that CSOs will be highly

motivated by job characteristics. The strong salience of the job characteristics factors

makes it less likely that, in the presence of job characteristics factors, Relationships and

System Trust will be significant predictors of CPS and Work Outcomes. That is, in the

CSO context, job characteristics factors are more likely to dwarf the effects of

Relationships and Systems Trust than would occur in another work context. Thus,

Proposition 3 is a strong test of the efficacy of Relationships and System Trust.

ADVANCING THE MANAGEMENT CONTROLS MODEL

Management Controls research (e.g., Ouchi, 1979; Eisenhardt, 1985) has

typically linked controls to desired outcomes like motivation. For example,

accountability control should lead to higher motivation (Tetlock, 1985). Also, agency 9

theory proposes that, to be successful, principals should contract with the agent such that:

a) their objectives are aligned (typically through offering the agent incentives); or, b) the

agent’s behavior can be monitored. The latter constitutes a behavioral control, while the

former is an outcome control (Kirsch, 1992).

The typical Management Controls model (MCM) is economics-based, and

assumes that people are self-interested and not socially influenced. The MCM is a

theoretical basis for the long-standing paradigm of incentive compensation that

permeates corporate America today (see Figure 4). The logic is that incentives provide

employees the proper motivation for achieving such motivational outcomes as improved

market share, profitability, and stock price. Accountability and Feedback (e.g., Cusella,

1982) also positively influence motivation, which in turn affect motivational outcomes.

Thus:

Proposition 4: In the critical systems environment, Management Controls will

be positively associated with CSO Motivation, which will, in turn, be positively

associated with Motivational Outcomes.

Figure 4 Management Controls Model

Management Motivation Motivational Controls Outcomes

10

Paradoxically, Management Controls have sometimes had negative outcomes.

Whereas incentives, or other controls, have sometimes been found to improve worker

motivation and performance (e.g., Henderson & Lee, 1992), they have also been found to

have dysfunctional side effects (e.g., Lawler & Rhode, 1976; Simons, 1995). For

example, Powers and Dickson (1973) found negative perceived effects of project controls

on system development outcomes. However, they did not explain why this occurred.

Phase I data indicated that the worker relationship with the manager is likely to

have an effect on the worker’s motivation. In two Phase I instances, the relationship

moderated the effects of controls on worker motivation. In another instance, the

relationship directly affected the worker’s motivation.

Some evidence exists in the literature that Relationships can moderate the effect

of Controls on Motivation. For example, Steers & Porter (1979) said that merit pay

systems work best when management and workers have a good relationship. Lawler

(1971) said that pay-for-performance systems don’t work when worker/management trust

is low. Tetlock (1985) and Cummings and Anton (1990) also found evidence that

accountability is motivating only when the relationship between the two parties is

positive. Hence, Relationships moderates the effects of Management Controls on

Motivation. System Trust will also likely be a motivator. As operationalized here,

System Trust relates closely to structural workplace fairness. Logically, a worker’s

perceptions of workplace fairness could affect the worker’s motivation. Because System

Trust relates to structural fairness, System Trust will be positively related to Motivation.

Therefore (see Figure 5):

11

Proposition 5: In the critical systems environment, operator/supervisor

Relationships will moderate the effects of Management Controls on Motivation. System

Trust will be predictive of Motivation beyond the predictive power of Management

Controls.

Figure 5 Advancing the Management Controls Model

Management Motivation Motivational Controls Outcomes

Relationships System Trust

SUMMARY AND POTENTIAL CONTRIBUTIONS

The introduction presents the study as a test to see if Relationships and System

Trust add predictive power to the popular JCM and MCM theories. Just as Hirschman

(1984) argued that adding variables to economic models that are too parsimonious can

improve understanding, so this study argues that adding Relationships and System Trust

to the JCM and the MCM can improve prediction of motivation.

The primary research contributions of the study are:

improving the prediction of the dependent variables of the JCM by using

Relationships and System Trust as independent variables;

improving the prediction of the dependent variables of the MCM by

using Relationships and System Trust as independent variables;

12

describing for the first time the nature of the critical computer systems

operator job; and

validating the new conceptualizations of Relationships and System

Trust.

The primary practical contributions of the study are:

exposing worker/manager relationships and structural workplace fairness

as critical understanding gaps that need to be filled to successfully

implement practices like incentive awards, reengineering, and

empowerment, which stem from the JCM and MCM;

explaining the relative importance of the JCM, MCM, relationships, and

workplace fairness factors for the motivation of CSOs; and

explaining that incentives may actually de-motivate, rather than

motivate, workers. The detailed understanding this study provides

of one organization’s experiences with incentives can help guide a

reasoned use of incentives in organizations with similar conditions.

Roadmap for the Study

Figure 6 maps Propositions 1-5 (“Prop.”) and related models to the chapters

13

(“Ch”) that address them. The roadmap will be repeated at the beginning of

Chapters Three through Seven.

Figure 6 Roadmap for the Study

Ch Prop: Content or Model

2 -- Methodology and Construct Validation

3 1 Nature of the Critical Systems High Levels of Operator Job Motivation

4 2, 3 Growth Need Strength

Critical Job Characteristics Psychological Work

States (CPS) Outcomes

Relationships System Trust

5 4, 5

Incentive Motivational Controls Effect

Relationships

6 4, 5

Other Motivation Motivational Controls Outcomes

Relationships System Trust

7 -- Contributions, Limitations, and Future Research

14

CHAPTER TWO: METHODOLOGY AND CONSTRUCT VALIDATION

First, this chapter outlines and justifies the general approach

taken in the study. Next, the methodologies for Phases I and II are

discussed. This is followed by the results of construct validation

efforts. Finally, a brief description of the research site is given.

APPROACH OF THE STUDY

Research models may be built in at least two different ways.

Using Method 1, a researcher searches the scientific literature for what

has been done in the area of interest (e.g., Kaplan, 1964). By analysis

of what has already been done, a researcher deductively builds a

model for testing. Using Method 2, a researcher visits the research

site and observes what is happening (e.g., Glaser & Strauss, 1967;

Glaser, 1978). By analyzing some subset of the complex

phenomenon, the researcher inductively creates a conceptual model

of the phenomenon. Method 1 has the advantages that it builds upon

earlier work and results in a readily testable model. Its disadvantage

is that the model may not adequately reflect what is occurring in the

research setting. Method 2 has the advantage of more closely

matching the phenomenon chosen. Its disadvantages are that it can

create models that are: a) hard to connect with existing models in the

literature, and b) difficult to test scientifically. This study combines

Methods 1 and 2 to take advantage of the benefits of each.15

This study was conducted in two phases. Phase I explored the

research problem using semi-structured interview data analyzed via

grounded theory methods (Glaser & Strauss, 1967; Strauss, 1987).

Phase II tested the model produced by Phase I, using telephone

questionnaire data primarily analyzed with correlation and regression

techniques.

16

Why was this two-phased approach was taken? First, from an

initial literature search, no studies were found that addressed the

critical systems operator’s (CSO’s) job within the context of the related

management controls and people relationships. This decreased the

researcher’s confidence that hypotheses developed from the literature

would hold; rather, the judgment was that such hypotheses would be

conjectural. Given this judgment, it would be likely that, even after

testing, the resulting model would not explain many of the interacting

factors found in this area of practice. This issue is a concern because

both MIS and reference discipline scholars have said that complex,

interacting factors determine system reliability (Hale & Glendon, 1987;

Lyytinen & Hirschheim, 1987). For example, Lucas (1975) said, “...a

number of variables are involved in the design and operation of

successful systems. The complex relationships among technical,

behavioral, situational, and personal factors all must be considered. If

any variable is ignored, systems are likely to fail.” (1975: 110)

Second, an exclusively deductive model building approach would likely

lead to “Type III” errors (Kirk & Miller, 1986), which occur when a

researcher misses important issues for study in the setting. This is

especially important to new fields of study, such as the critical

computer system.

17

Third, the contribution of a deductive model building / model

testing effort is likely to be very limited. To make a major

contribution, one needs to go beyond a small, incremental addition to

the literature, which Weick compared to swimming toward “the white

cliffs of the obvious” (Mintzberg, 1979). Meehl (1978) argued that

“science does not, and cannot, proceed by incremental gains achieved

through statistical significance testing of hypotheses” (Kaplan &

Duchon, 1988: 572). Mintzberg argued that serious exploratory work

is needed for progress to be made: “Simplification squeezes out the

very thing on which the research should focus” (1979: 586). Further,

solely deductive research tends to prevent the discovery of new

insights (Kirk & Miller, 1986).

18

For these reasons, the researcher felt it important to first

develop conceptual models of the phenomenon through an inductive

approach. A conceptual model may, or may not, be quantitatively

testable. Often, these models are developed at a high level of

abstraction that needs further delineation in order to be tested. At the

least, a conceptual model provides a clear description of what factors

are important in explaining the target outcomes of the study. This

approach lies within the tradition of creating models from case study

work (Applegate, 1991; Eisenhardt, 1989a). The resulting conceptual

models need to be: a) made testable and b) tested empirically. This

is important if the resulting models are to add to the body of

scientifically tested knowledge. Through literature searches, the

researcher can make the conceptual models specific enough to be

tested. This is done by justifying variable-level hypotheses that can

be tested by existing or new quantitative scales. Hence, this intensive

study builds theory by integrating the strengths of exploratory and

testing methods, much as Lee (1991a) recommended integrating

positivist with post-positivist research.

This study's overall structure can be understood in terms of

Sagasti & Mitroff's (1973) diamond model, which represents four

"bases" of research (Figure 7). The bases are (from "3rd base"

clockwise to "home plate"): (1) the real world problem; (2) the

conceptual model of the problem; (3) the scientific model; and (4) the 19

model's solution. Sagasti & Mitroff argued that the four bases are

connected by four scientific research processes--conceptualization,

modeling, model solving/testing, and implementation (see Figure 7--

[a],[b],[c],[d]). By linking these four bases, one can produce, from

everyday reality, [a] conceptual models that can be refined into [b]

scientific models that, when [c] tested, can be used as helpful input

[d] to the problem again.

The danger of not pursuing part [a] of the process is producing irrelevant or

unrealistic models (Mintzberg, 1979). As Dubin said, “observation and description of

the real world are the essential points of origin for theories” (1976: 18). Warning against

the use of reality-starved methodologies, Cook & Campbell (1979: 92) remarked that

exclusive reliance on statistical or experimental methods can have “disastrous” effects on

a study. Crozier said that “premature rigor” can keep a theory “from being adequately

comprehensive.” (1964: 5). Oversimplifying phenomena through excessive

mathematical modeling eliminates key elements, such that “every similarity to reality is

gone” (Hofstede, 1967: 89). Researchers should preserve reality by resisting models that

are not founded on a thorough prior understanding of the real world phenomenon.

20

Figure 7 The Operations Research Model

Science

Research Processes:

Conceptual [a] = Conceptualization Model

[a] [b] [b] = Modeling

[c] = Model Solving/Testing

Reality, [e] Scientific [d] = ImplementationProblem Model

Situation [e] = Validation

[d] [c]Source: Sagasti & Mitroff, 1973

Solution

In order to stay true to the critical systems context, the research

undertaken in this study includes three of the four scientific processes

indicated in Figure 7:

[a] building conceptual models of real world critical

computer systems situations through interviews,

using inductive analysis,

[b] creating a scientific model by Hegelian (dialectic)

contrast of the conceptual models and the literature

(Crozier, 1964), and

[c] testing the scientific model through questionnaire

data, analyzed with regression analysis.

This study’s approach to the dialectic of inductive and deductive

theory building does not rely completely on the qualitative data (as do

21

grounded theorists—Glaser, 1992), but synthesizes the grounded

empirical results and the existing literature into testable models.

Research step [a] ensures that the resulting theoretical contribution is

grounded in real world situations. Step [b] ensures that conceptual

models are translated into scientific models that [c] are rigorously

tested. Following these steps strengthens the study’s contribution,

because the resulting models will be applicable to practice ([a]) and

the study will add to the body of scientifically validated models ([b]

and [c]).

PHASE I CONCEPTUAL MODEL BUILDING METHODOLOGY

Phase I data consisted of transcripts of twenty semi-structured

interviews of managers and operators at a computer site described in

the last section of Chapter Two. Observations of operators in action

were limited to two cases of less than thirty minutes each. The

interviewees consisted of a convenience sample selected in

consultation with research site management. A grounded theory

approach (Glaser & Strauss, 1967; Strauss & Corbin, 1990) was used

to develop the conceptual model that resulted in the

controls/relationships model (Figure 5), but without the System Trust

construct. Due to time constraints, only six of the twenty interviews

were analyzed with grounded theory methods to produce the model.

The six were selected because they were felt to be the richest sources

22

of what seemed key concepts in Phase I: controls, motivation,

teamwork, and relationships.

Grounded Theory

Grounded theory is a qualitative method from sociology (Glaser

& Strauss, 1967) that enables one to build theory from a rigorous

analysis of observational or interview data. Grounded theory employs

the “usual canons of ‘good science’...significance, theory-observation

compatibility, generalizability, consistency, reproducibility, precision,

and verification” (Denzin, 1994: 508), and has been used effectively in

MIS research (Orlikowski, 1993).

A full grounded theory study was not done; rather, the

researcher used three methods from grounded theory: theoretical

sensitivity, open coding, and axial coding. Theoretical sensitivity

means that the researcher modifies the specific research topics as key

aspects become apparent from the data already gathered. This is

especially important to exploratory research like Phase I. The

researcher used theoretical sensitivity to focus attention on specific

research concepts (e.g., motivation, controls) that seemed important,

based on the initial few interviews at the research site. Using a

modifiable interview instrument facilitated use of theoretical

sensitivity. That is, the researcher added and deleted specific

questions from one interview to the next in order to focus on the key

concepts. Open coding means that the researcher abstracted 23

theoretical concepts from segments of the transcribed interview data.

This was done by reading a transcribed sentence, phrase, or word and

asking questions like, “What is this an instance of?” (Kearney, Murphy

& Rosenbaum, 1994: 353), or “What kind of concept does this refer

to?” Axial coding means to analyze the data a second time, relating

one concept to another. Through axial coding, the relationships

between concepts that form a conceptual model are developed.

Examples from the research data of open and axial coding are

included in Appendix A.

Grounded theory was selected because:

It is considered a rigorous method (Denzin, 1994),

compared with other qualitative research

techniques;

It is widely used in the social sciences (Denzin, 1994) and

in MIS research (e.g., Kaplan & Duchon, 1988);

It is well suited for building models (Strauss & Corbin,

1990), that reflect reality; and,

The use of the theoretical sensitivity technique enables

researchers to follow the line of study that appears

most important in the research setting.

Phase I Research Framework

Before entering the field to collect data, the researcher

documented the research framework guiding Phase I interviews 24

(Figure 8). At this point, the research design was not fully specified,

as is common in studies combining qualitative and quantitative

methods (Kaplan & Duchon, 1988).

This framework assumes that the systems approach to understanding the complex

and interactive causes of computer failure is the most productive one (Lyytinen &

Hirschheim, 1987). In particular, several complex systems (sets of factors) interact in the

setting to produce the system availability1 results. To understand the interactive effects

of management strategies, the researcher used the framework shown in Figure 8, which

synthesizes the frameworks of Bostrom & Heinen (1977) and Orlikowski (1992). The

framework assumes that the effects of management strategies on system availability will

be mediated by the interacting systems shown. In particular, the effects of strategies are

translated into performance (i.e., system availability) by these systems’ processes and

interactive effects. The Technical System includes the computer system, its physical

environment, and the tools the operators use to run it. The Social System refers to the

informal interaction roles and relationships that exist among workers and management.

The Structural system means the formal aspects of organizations (e.g., official roles,

procedures, and official measurement/incentive systems). The Individual System is

comprised of the perceptions, traits, knowledge, and capabilities of people.

Figure 8 Phase I Research Framework

Technical System

1    ?For simplicity, availability is defined to be measured at the central computer site. Availability equals the total time possible (24 hours/day, 7 days/week) minus the summed duration of all computer site outages (planned or unplanned), divided by total time possible.

25

Manage- ment System AvailabilityStrategy Social System Structural System

Individual System Organizational/ Technical Context

Based on the above framework, the original semi-structured questionnaire

covered management strategies that related to keeping the system running, the roles of

operators, team relationships, and technical issues important to keeping the system

running. As the researcher learned more about the environment from initial interviews,

the questionnaire began to focus on management controls, worker/management

relationships, worker motivation, and teamwork issues, since these seemed most

important to keeping the systems running. Phase I resulted in the high level conceptual

model shown in Figure 5 (without System Trust). This Controls/Relationships model is

considered “high level” because each model concept is broad and needs further

specification before measurement can be done. For example, in the literature, the term

“Controls” can refer to many different things--from incentives to budgeting systems to

surveillance. Specifics on the creation of the conceptual and testable versions of Figure 5

are contained in later chapters.

PHASE II MODEL BUILDING AND TESTING METHODOLOGY

In general, Phase II refined the Controls/Relationships model by

decomposing it into measurable form. This was done by: a)

26

decomposing the high level concepts into measurable constructs,2

each associated with a questionnaire instrument, and b) developing

testable hypotheses, based on a combination of literature search and

qualitative analysis of the Phase I interviews. The reasons for

choosing the particular constructs is explained in the theory building

sections of Chapters Three through Six. Similarly, the JCM concepts

shown in Figure 3 were broken down according to the JCM literature.

The researcher telephoned one hundred operators for the phone

questionnaire. Eighty-six of the one hundred participated. Only

fourteen declined.

Measurable Constructs Used

This section describes how instruments were developed for

testing the hypotheses, which are presented in Chapters Three

through Six. First, midrange constructs were taken from the literature

to form constitutive parts of the high level concepts of Figure 5 (see

Figure 9). A questionnaire instrument was found for each construct,

generally adapted from existing instruments (see Table 1). Each

construct was measured with either three, four, or five items. Most

scales had seven points, from Strongly Agree to Strongly Disagree.

Two scales used five point scales because they were worded in terms

2 In general, the term “concept” refers to the high level entities (e.g., Motivation) comprised of several measured constructs (e.g., Intrinsic Motivation, Job Satisfaction). The term “construct” refers to measurable (mid-range) entities (Autonomy, Feedback, Trusting Intention,...).

27

of amount instead of agree/disagree. Final items and questionnaire

item order are shown in Appendix C for the operator questionnaire.

Figure 9 Management Controls / Relationships Model—Detailed Level

Worker Relationship with Superior Individual

Management Computer Contribution Controls Worker to Team

Motivation Effectiveness

Feedback Liking Intrinsic Motiv.-Enjoyment Contribution to CommunicationAutonomy Trusting Intention Intrinsic Motiv.-Self-Esteem Contribution to Conflict ResolutionAccountability Trusting Belief- Benevolence Experienced Meaningfulness Contribution to CooperationMicromanagement Trusting Belief-Competence Job Satisfaction Contribution to Team Effectiveness

Organizational Commitment

Individual Performance

Table 1 Instruments for Testing Management Controls / Relationships Model

Construct Instrument SourceFeedback Henderson & Lee, 1992Autonomy Aiken & Hage, 1966Accountability Van de Ven & Ferry, 1980Micromanagement Van de Ven & Ferry, 1980Liking Rubin, 1973Trusting Intention Dobing, 1993Trusting Belief-Benevolence Wrightsman, 1991Trusting Belief-Competence Wrightsman, 1991System Trust NewIntrinsic Motivation-Enjoyment NewIntrinsic Motivation-Self-Esteem Lawler & Hall, 1970; Van de Ven & Ferry, 1980Experienced Meaningfulness Hackman, 1980Job Satisfaction Hackman, 1980Organizational Commitment Mowday, Steers & Porter, 1979Contribution to Communication O’Reilly & Roberts, 1975Contribution to Conflict Resolution NewContribution to Cooperation Georgopoulos & Mann, 1962Contribution to Team Effectiveness NewIndividual Performance New

28

Respondents for Contribution to Team Effectiveness (CTE) items

consisted of the direct supervisors of the operators. CTE means the

extent to which a worker contributes to team proficiency in key team

attributes. These measures were formulated to represent three key

attributes of team effectiveness—communication, cooperation, and

conflict resolution. Each CTE construct was measured with two items,

using two methods (see Appendix D). The first method employed the

same seven point Likert scale used in the operator questionnaire. The

second method was to have the supervisor rank the operators best-to-

worst on the construct. Individual Performance was also measured by

asking the supervisors to rank the operators best-to-worst on

performance. This was a quasi-objective measure. That is, the

supervisor was asked to give the report based on the group’s latest

official best-to-worst rankings. Supervisors with small groups reported

the ranking from memory. The others were heard accessing a ranking

file as they prepared to answer over the phone.

Similarly, the detail constructs shown in Figure 10 enabled the

JCM to be measured. Items from the Hackman/Oldham instrument

were transformed into only positively-phrased items, in order to avoid

the problems found in Idaszak & Drasgow (1987—also see Lending,

1996).

29

Figure 10 Job Characteristics Model (JCM)—Detailed Level

Growth Need Strength (GNS)

Job Characteristics Critical Work Psychological Outcomes

States (CPS)

Skill Variety Task Identity Experienced Work Intrinsic Motivation Job Significance Meaningfulness Job

Satisfaction Work Performance

Autonomy Felt Responsibility

Job Feedback Knowledge of Results

Instrument Pretest

This section describes how instruments were refined. In order

to assure that the instruments would provide reliable and valid

measures of the theoretical constructs, several pretests and a pilot

were conducted. The pretest entailed the following steps, based on

Davis (1989):

1. Created a document listing each construct’s definition and

items. The researcher and three faculty members successively

reviewed this document for face validity. Changes were made and the

document revised after each of the four reviews. Most changes were

wording items that clarified or simplified the items. For example, an

item that was found to address two ideas was simplified to only

address one idea. Since the Job Characteristics and Motivation 30

instruments had already undergone significant testing by others (e.g.,

see Lending, 1996; Mowday, Steers & Porter, 1979; Van de Ven &

Ferry, 1980), the next pretest steps concentrated on improving the

Controls and Relationships constructs.

2. Pretest instrument A was a matching instrument (Appendix E).

This instrument was given to four Ph. D. Students and one department

clerical person. Respondents were asked to match items to construct

names/definitions and then to point out which items (up to three

items) didn’t fit well with the definition. Pretest A was analyzed in

terms of the number of respondents who incorrectly categorized each

item. Respondent comments about which items didn’t fit were

quantified by assigning points to each of the items. A worst item

comment was given a 3, second worst item a 2, and third worst a 1.

An overall ranking of best-to-worst items was developed by equally

weighting the results of these two analyses. Those items within each

construct that had low rankings were reworded.

3. Pretest B was a categorization exercise (Appendix F). The two

Pretest B versions (one each for trust and control) were each

administered to forty-eight MBA students. Respondents were asked to

place sixteen statements into three to five categories by placing A, B,

C, D, or E next to the statement. At the bottom of the page,

respondents were asked to define each construct. The questionnaire

included improved directions versus the previous pretest, and asked 31

for the item numbers that were difficult to analyze. Pretest B was

analyzed for number of respondents correctly categorizing each item

and for the items identified as hard to categorize. Eighty-nine percent

of the Relationship items and seventy percent of the Controls items

were categorized correctly. The major problem with Controls was the

two negatively worded items that caused respondents to categorize in

terms of degree of control instead of type of control. These were

reworded positively. Several other changes were made based on

Pretest B.

4. Pretest C was drafted as an item sorting exercise. Forty-one

MBA students were given an envelope with fifteen slips of paper with

items on them--twenty-four respondents for trust constructs and

seventeen for control. The students were asked to sort the items into

three to five categories and then to tell what the categories mean

(Appendix G). The data were analyzed for difficult items and changes

to the instruments were made. The trust instruments (ninety-two

percent correctly classified) again did better than the controls

instruments (seventy-two percent correct).

5. After the instrument changes were made, the questions were

ordered by major topic (e.g., Job Characteristics) and by construct

within topic for the pilot. All items of a construct were asked together,

in order to improve internal consistency (Davis & Venkatesh, 1994). In

addition, a preface sentence introducing each construct was placed 32

before the first question in the series. For example, before the

Feedback questions, the interviewer said, “The next few questions relate to

supervisory feedback.” (see Appendix C for other examples). The

questionnaire mechanics were based on Dillman’s (1978)

recommendations. In particular, respondents were first asked whether

they agreed with, disagreed with, or were neutral toward, the

statement; then they were asked whether they (dis)agreed strongly,

moderately, or slightly. This technique enabled use of a seven point

scale without producing cognitive overload among respondents

(Appendix C).

The questionnaire was done by telephone because telephone

interviews, per Dillman (1978):

have high response rates, both for individual items and the

entire instrument;

allow the researcher to control fully the sequence of questions;

are less expensive to conduct than face-to-face interviews;

are almost unlimited in terms of the number of items one may

ask;

facilitate transitions that indicate when a new construct is

being covered;

enable researchers to gauge the feelings of the respondents;

provide less social desirability bias than face-to-face

interviews, and about the 33

same as written questionnaires;

facilitate use of open-ended questions.

Because social desirability was still considered a possible validity

threat, the researcher included in the questionnaire’s introduction

assurances that: a) there were no right or wrong answers to the

questionnaire; b) he was not an agent of management; and c) the

respondents’ answers would not be shared with anyone else (see

Appendix C).

Instrument Pilot

The pilot consisted of administering the revised pretest

instruments in full telephone questionnaire form to ten computer

troubleshooters in another company (not the research site). The pilot

group consisted of troubleshooters organized into a self-directed

team. These troubleshooters were not actually computer operators,

but were the technical support people for a number of software

products. When customers called the help desk with difficult software

problems, debugging tasks were assigned to these troubleshooters.

Hence, their job functions were somewhat similar to those of the

operators at the research site, providing a realistic pilot test for the

instruments. The researcher made notes of respondent difficulties

with, or comments about, the individual items. For example, if the

respondent paused before answering, the researcher wrote “pause”

by the question. These notes were then analyzed to identify items 34

needing rework. To further identify rework items, those items were

identified whose average score varied the most from the average of

all item scores for the construct. Reliability analysis of each construct

also identified rework items. Table 2 displays reliability results from

the pilot.

From the pilot, a number of changes were made. First, several

items were reworded slightly (e.g., in Job Significance, Task Identity).

Second, since respondents seemed to have trouble with the first set of

questions of the questionnaire (i.e., Job Significance was the first set in

the Job Characteristics series), the researcher placed the most reliable

Job Characteristics construct (Skill Variety) at the beginning of the

questionnaire. Third, items were substituted in some instruments

(e.g., Accountability), in order to improve reliability. The instrument

was administered to eighty-six computer operators at the research

site.

Table 2 Pilot Reliability Analysis

ConstructCronbach’s Alpha

Liking .95Trusting Belief-Benevolence .92Trusting Belief-Competence .99Trusting Intention .99System Trust .60Feedback .87Autonomy .74Accountability .66

35

Skill Variety .72Job Significance .14Task Identity .48Job Feedback .98Experienced Meaningfulness .60Knowledge of Results .81Felt Responsibility .66Job Satisfaction .63Growth Need Strength .60Organizational Commitment .78Intrinsic Motivation-Self-Esteem

.79

Intrinsic Motivation-Enjoyment

.93

CONSTRUCT VALIDATION RESULTS

The psychometric tests consisted of internal consistency

reliability and simple construct validity tests on the data from the

eighty-six questionnaires. Nomological validity was done for the trust

constructs and mono-method bias was tested for the Autonomy

construct.

Reliability. Cronbach’s alpha (Cronbach, 1951) was used as

the indicator of internal consistency reliability. Reliability refers to the

ratio of “true” variance to total variance in a set of measures obtained

from a respondent (Schwab, 1980). True variance means systematic,

error-free variance. While the true variance can’t be calculated, it

can be estimated by assuming that the available items are a random

sample of a population of items that would give a true measure of the

variable if all the items were answered (Cronbach, 1951). Reliability is 36

a necessary, but not a sufficient, condition for construct validity. This

is because unreliable measures cannot be depended upon to

consistently reflect the same conceptual meaning. Table 3 shows that

nearly all the constructs were unidimensional at, or almost at, the 0.70

level generally endorsed (Nunnally, 1978).

Only Job Significance and Growth Need Strength (GNS) did not

come close to 0.70. Both constructs appeared to have reached a

ceiling effect, with very low variances. On seven point scales, GNS

and Job Significance items had average means of 6.82 and 6.77,

respectively. Their standard deviations were 0.36 and 0.45. Most

other constructs had standard deviations above 1.0. Because of these

high means and low standard deviations, the researcher decided to

use GNS and Job Significance as unitary constructs, even though their

internal consistency score was low. Descriptive statistics for all

constructs are shown in Appendix I.

Table 3 Construct Level Cronbach’s Alpha Reliabilities (n=86 research site respondents; number of items in parentheses

CONTROLS: AlphaJOB CHARACTERISTICS: (not included elsewhere) Alph

aAutonomy Granted (4 items) 0.79 Skill Variety (3 items) 0.67Micro Management (4) 0.85 Job Significance (3*) 0.62Feedback (4) 0.98 Task Identity (3) 0.77Job Accountability (2*) 0.69 Job Feedback (3) 0.85

Knowledge of Results (3) 0.92RELATIONSHIPS: Growth Need Strength (3) 0.44Liking (4 items) 0.94 Felt Responsibility (3) 0.73Trusting Intention (4) 0.99Trusting Belief--Benevolence (4) 0.97 CONTRIBUTION TO TEAM

37

EFFECTIVENESS:**Trusting Belief--Competence (3*) 0.95 Contrib. to Overall Team Effectiveness

(2)0.70

Contrib. to Coordination Effectiveness (2) 0.71MOTIVATION: Contrib. to Communication Effectivns.

(2)0.68

Experienced Meaningfulness (3*) 0.92 Contrib. to Conflict Resolution (2) 0.67Organizational Commitment (4) 0.84Intrinsic Motivation--Enjoyment (4) 0.92 OTHER TRUST-RELATED:Intrinsic Motivation--Self-Esteem (4) 0.77 System Trust (4) 0.94Job Satisfaction (3*) 0.86 Dispositional Trust (3) 0.91

*Questionnaire contained additional items that did not highly correlate with items in the constructs shown.**Note: These alphas are probably deflated because two different methods were used to collect them.

Construct Validity. Adequate construct validity means that

the measures of a variable correspond closely to the conceptual

meaning of the variable (Schwab, 1980). Construct validity addresses

“the approximate validity with which we can make generalizations

about higher-order constructs from research operations” (Cook &

Campbell, 1979: 38). This is important because no true implications

can be drawn at the construct level from measures that do not

adequately represent the meaning of the construct. Reliability is

generally considered a necessary, but not sufficient, condition for

construct validity. Further evidence is required, in terms of

convergent and discriminant validity. Convergent validity means the

extent to which responses from different measurements of the same

construct are highly correlated (Schwab, 1980). Discriminant validity

means the extent to which a construct is distinct from other

constructs. Therefore, discriminant validity means one construct’s 38

measurements should be distinct from measurements of other

constructs.

Convergent and discriminant construct validity were

demonstrated by pairwise intercorrelation matrices of constructs

within each high level concept (Appendix H). For example, the first

pair contrasts correlations within and between Autonomy and

Micromanagement, two Controls constructs. The intra-construct

correlations are consistently higher than the correlations between

constructs. Appendix H reports the intra- and inter-correlation

averages, and highlights intercorrelations that exceed the smallest

intra-construct correlation. This analysis was done to show, in the

simplest possible fashion, how the constructs hold together internally

while being distinguished from similar constructs, much as a factor

analysis would do. This method was chosen over factor analysis

because factor analysis is based on correlation analysis, but uses

somewhat arbitrary cut-off values that may obscure what the actual

correlations indicate. These results show that each construct is

internally cohesive (convergent validity) and differs from similar

constructs (discriminant validity). This is a strong test of discriminant

validity, since one would expect high correlations among four different

types of Motivation, for example.

Of the Controls constructs, only Accountability shows construct

validity problems (see bold highlighting of items in Appendix H). 39

However, when item 4 is removed, the construct demonstrated

discriminant validity. For hypothesis testing, the researcher dropped

item 4 and treated Accountability as a two item construct.3 The

resulting reliability improved from 0.65 to 0.69 when this was done.

Among the Relationship constructs, Liking, Trusting Intention, and

Trusting Belief-Benevolence had high intercorrelations with each

other. However, the average intracorrelations were consistently

higher than the average inter-correlations, providing evidence that

these constructs can be distinguished. These constructs were also

kept separate at this point because of the theoretical basis for treating

them as separate constructs (McKnight & Chervany, 1996; McKnight,

Cummings & Chervany, 1996). The intercorrelation matrices for the

Motivation and Job Characteristics constructs provide significant

evidence that these are unitary constructs.

Nomological Validity. Because System Trust is a new

operationalization and the other trust constructs are re-formulations,

nomological validity of these constructs was analyzed. Nomological

validity means that one assesses (theoretically and empirically) the

relationships between a construct and other constructs (Schwab,

1980). Hence, nomological validity is also tested in later chapters,

when the hypotheses are tested. In this chapter, the researcher

3 Item two had already been removed in pilot testing.40

looked at nomological validity in terms of the relationships among

System Trust and other trust-related variables.

McKnight & Chervany (1996) and McKnight, Cummings &

Chervany (1997) hypothesized the relationships among trust variables

shown in Figure 11. This theory has not previously been tested, so all

the links are tentative. Trusting Belief-Benevolence and Trusting

Belief-Competence were selected for this study because of their

importance to the trust literature in general (e.g., Barber, 1983;

Mayer, Davis & Schoorman, 1995) and the technical worker

specifically (Crozier, 1964).

Figure 11 Nomological Network for Trust Constructs

Other empirical work has shown that Trusting Beliefs are related

to Trusting Intention (e.g., Dobing, 1993). Tests of the links from

Dispositional Trust have had mixed results (e.g., Johnson-George &

Swap, 1982), so these are shown as weak links using dotted lines. In

addition to the relationships shown in Figure 11, Liking should be

Trusting Intention System Trust

Dispositional Trust

Trusting Belief-- Benevolence

Trusting Belief-- Competence

41

highly related with the Trusting Beliefs and Trusting Intention, but less

highly related with System- and Dispositional Trust (since the latter

are not social constructs). Liking was selected because it has

traditionally been an important interpersonal variable that generalizes

much of the emotional tie one person has for another (Rubin, 1973).

Table 4 shows the correlations among these variables.

Table 4 Intercorrelations of Trust Constructs and Liking(correlation / [significance])

Trusting Intention

Trusting Belief-Benevolence

Trusting Belief-Competence

System Trust

Dispositional Trust

Liking

Trusting Intention

1.0

Trusting Belief-Benevolence

.85/[.000]

1.0

Trusting Belief-Competence

.75/[.000]

.79/[.000] 1.0

System Trust

.51/[.000]

.59/[.000] .42/[.000] 1.0

Dispositional Trust

.18/[.049]

.05/[.340] .04/[.345] .16/[.066]

1.0

Liking .82/[.000]

.80/[.000] .83/[.000] .42/[.000]

.15/[.087] 1.0

In general, the results support nomological validity. System

Trust and the Trusting Beliefs are highly correlated with Trusting

Intention, as expected. Dispositional Trust is correlated with System 42

Trust at p=.066. However, instead of being related with Trusting

Beliefs, Dispositional Trust is related directly (at p=.049) to Trusting

Intention. This is a little surprising, and indicates that Dispositional

Trust can be a determinant of one’s willingness to depend on the

other party (Trusting Intention) irrespective of one’s Trusting Beliefs in

that party.

The relationships between Liking and the trust constructs are as

expected, in that Liking is highly related to the Trusting Beliefs and

Trusting Intention, but very little related with Dispositional Trust.

However, the fact that Liking is significantly related with System Trust

indicates that the study’s operationalization of System Trust ties it

more closely to feelings about one’s supervisor than the theory

projects. This is probably because System Trust was operationalized

to represent structures supporting fairness in one’s environment, and

the supervisor is one of the prime administrators of fairness in the

work environment. The high correlations between System Trust and

the Trusting Beliefs constructs may be explained in the same way. So

while the theoretical System Trust variable is quite impersonal, the

operationalization of it is quite closely related with operator feelings

regarding their supervisor. Note that System Trust does not equate to

fairness or equity, such as constructs in the organizational justice

literature (e.g., Greenberg, 1993), but is the belief that the workplace

has features that encourage fairness.43

Mono-method bias. For purposes of this study, mono-method

bias refers to the use of a single informant type: the CSO or the

supervisor. Though mono-method bias has been pointed out as a

potential problem with JCM research (Roberts & Glick, 1981), most

researchers have accepted it as a given, since employees are the best

informants of their own beliefs and feelings about their own job

characteristics and related motivation. Although this argument has

significant merit, the laissez-faire approach of accepting it fully is not

completely satisfying. Thus, two separate efforts addressed mono-

method bias in the study. First, the Contribution to Team

Effectiveness and Individual Performance dependent variables used

supervisors as informants, while the JCM, Controls, Relationships,

System Trust, and Motivation variables had CSOs as informants. This

means that tests of links between constructs gathered from these two

different sources constituted stronger tests. However, it also means

that tests of links within informant constituted relatively weaker tests.

The relative weakness or strength of these tests is demonstrated by

the very high correlation (see Chapter Four) between the supervisor

variables Contribution to Team Effectiveness and Individual

Performance versus the weak correlation between CSO-informed

variables and Individual Performance. This result emphasizes the

large difference a different informant can make. But it leaves

unanswered the question of which informant’s view is most correct.44

Second, the researcher tested the results when one variable

(Autonomy) was measured with both methods. Table 5 displays a

variation of the Campbell & Fiske (1959) multitrait-multimethod

analysis (cf. Henderson & Lee, 1992). The informant is represented as

a method, while the item is represented as a trait. Note that the

correlations within methods (in bold) are generally higher than the

correlations between methods. Average correlations are also shown,

as in the pairwise matrices of Appendix H. From Table 5, the two

methods appear to be related (based on the cross-correlations), but

also appear to be somewhat separate constructs from each other

(based on higher within-method correlations). Exploring further, we

did Cronbach’s Alpha measures for each of the methods separately,

and one that joined them. The result was that joining them raised the

alpha from .75 (Supervisor informant) or .79 (CSO informant) to .80

(combined). Since joining the constructs together as one did not

degrade internal consistency, they are probably not two distinct

constructs. The average intercorrelation of Table 5 items overall

is .41. This is a significant correlation, and is higher than the average

correlation among Motivation constructs (.36), which was treated as

one second order construct. Based on this analysis, the Autonomy

items from both informants could effectively form one construct.

Table 5: Mono-Trait, Mono-Method Analysis for Autonomy

45

(Methods) Method 1: Supervisor

Report

Method 2:Operator Report

(Traits)

Item 1 Item 2 Item 1

Item 2 Item 3 Item 4

Method 1: Item 1

1.00 Means:

Supervr.

Operator

Cross

Supervisor Report

Item 2

.65 1.00 .65 .55 .29

OverallMethod 2: Item

1.14 .23 1.00 .41

Operator Item 2

.26 .36 .74 1.00

Report Item 3

.30 .25 .41 .60 1.00

Item 4

.42 .32 .39 .51 .62 1.00

A look at the wording used for each informant (Appendix B)

revealed that the Supervisor question was worded in more general

terms than were the Operator questions. Each of the four Operator

questions was worded specifically and differed slightly from each

other. The differences between these constructs is also accentuated

in that while the Operator measure of Autonomy used a seven-point

agree/disagree/neutral format only, the Supervisor measure used both

the seven-point scale and a one-to-N ranking of the employee against

all other employees in the supervisor group (see Appendix B, B.

Questions Asked Supervisors). These wording and scaling differences

probably accentuate the level of overall method bias that exists.

46

Since CTE and Individual Performance used the same informant

as the Supervisor-reported Autonomy construct, a correlation was run

among these constructs and Operator-reported Autonomy, in order to

isolate how much difference the informant method would make to the

correlation. Table 6 shows the result. Supervisor-reported Autonomy

was correlated with CTE and Individual Performance almost as

strongly (.72, .79) as CTE and Performance are with each other (.84),

while CSO-reported Autonomy was only correlated with CTE at .37 and

Individual Performance at .30. Similarly (Table 7), Operator-reported

Autonomy was correlated with other (Operator-reported) job

characteristics and CPS variables at an average of r = .20, while

Supervisor-reported Autonomy was correlated with the same job

characteristics and CPS variables at only an average of r =.10. So the

autonomy construct was, on average, twice as highly correlated with

the JCM variable if it had the same informant as the JCM variable. This

is another indication that some level of method bias exists, and needs

to be addressed in future research of this kind. To see if using the

combined Autonomy variable mattered to the prediction of Motivation,

the six items were merged into one variable. Combined Autonomy

only correlated with Motivation at r = .069, which means combined

Autonomy did not predict Motivation any better than did CSO-reported

Autonomy (see Chapter Six).

47

Table 6 Correlations among CTE, Performance, and Two Autonomy Types

Supv-reported

AutonomyCSO-

reported Autonomy

Contribution to Team

EffectivenessIndividual

Performance

Supv-reported Autonomy

1.00

CSO-reported Autonomy

.40 1.00

Contribution to Team

Effectiveness

.72 .37 1.00

Individual Performance

.79 .30 .84 1.00

Table 7 Correlations among JCM Variables and Two

Autonomy Types

(CSO-reported) JCM variables

Supv-reported Autonomy

CSO-reported Autonomy

Job Significance -.03 -.03Job Identity -.01 .33Job Feedback .16 .35Skill Variety .22 .16Experienced Meaningfulness

-.05 .00

Knowledge of Results .14 .32 |Mean| = .10 |Mean| = .20

In summary, mono-method (common informant) bias is a

concern that was addressed for equations predicting CTE and

Performance, but only partly tested for equations predicting

Motivation, CPS, and Work Outcomes. The testing of Autonomy for

mono-method bias revealed some differences between methods. 48

Overall, however, the items from the two methods can be successfully

merged into a single construct, lending confidence to the results of

this study. Just as important, this study was more concerned with the

operator’s own perceptions of their JCM, Motivation, Relationships, and

System Trust constructs. It is highly doubtful that the supervisor can

accurately report on what the operator perceives and feels about

these constructs. Hence, for the purposes of this study, the CSO-

reported data was justifiably used for these constructs.

First-Order versus Second-Order Concept Formation.

Once the reliability and validity of the constructs were tested, the

items were summed to their respective constructs. Because the

Figures 3 and 5 models are shown at two levels—second-order

(concept) level (e.g., Controls) and the first-order (construct) level that

defines the concept operationally (e.g., Feedback, Autonomy)--the

researcher needed to determine at which level to test the model. An

analysis was performed similar to that done to analyze second order

factor models (e.g., Hunter & Gerbing, 1982; Kumar & Dillon, 1990). It

was decided that those concepts (e.g., Controls) whose construct

components (e.g., Feedback, Autonomy, Accountability, and

Micromanagement) were internally consistent and had convergent

validity as a set would be tested at the second-order (concept) level.

This was judged by performing reliability and intercorrelation matrix

analyses on the constructs within each high level category. The first 49

rule for use of the second-order concept was for the concepts to be

internally consistent at the Cronbach’s alpha .70 level, just as for

the operational constructs. For this test, each construct was treated

like an item and a reliability analysis was performed (see Table 8) for

the set of constructs. The second rule was for the concepts to display

the same kind of convergent and discriminant validity among similar

concepts as was demonstrated in Appendix H for items. For this test,

Appendix J displays the intercorrelation matrix analysis. The reliability

analysis demonstrated adequate support for treating Relationships,

Motivation and Contribution to Team Effectiveness as unitary

constructs, with alphas of .94, .73, and .97, respectively. By

comparing internal to cross-correlations, Appendix J shows that

Relationships, Motivation and Contribution to Team Effectiveness are

internally cohesive and separate from the other concepts. In contrast,

based on the same intercorrelation analysis, Controls, Job

Characteristics, Critical Psychological States, and Work Outcomes are

not unitary. Further they had alphas of .52, .44, .48, and .22,

respectively. Hence, the researcher chose to test hypotheses using

Relationships, Motivation and CTE as unitary concepts, while Controls,

Job Characteristics, CPS, and Work Outcomes were tested at the

construct level.

HYPOTHESIS TESTING METHODOLOGY

50

To test the hypotheses, correlation and regression analyses

were done, using SPSS. In some cases, qualitative analysis

supplemented the correlational analyses, as detailed in Chapters 3-6.

The model relationships were tested with regression. The coefficient

beta used by regression gives a clear interpretation of the magnitude

of the effect of each independent variable on the dependent variable.

All hypotheses are stated and tested at the individual operator level of

analysis. Specific hypothesis testing techniques will be discussed in

detail in succeeding chapters.

Table 8 Reliabilities for High Level (Second Order) Concepts(N=86)

Model Concepts # Items Alpha Item IntercorrelationControls4 4 .52 .31

.35 .12

.18 .24 .37Relationships5 4 .94 .82

.80 .85

.83 .75 .79Motivation6 5 .73 .22

.49 .44

.32 .22 .41

.51 .47 .34 .17Contribution to Team Effectiveness7

4 .97 .86.85 .85.90 .92 .86

Job Characteristics8 5 .44 .16 .45 -.09-.15 .14 .09 .16 -.03 .35 .33

Critical Psychological States9 3 .48 .40

4 Controls = Autonomy, Feedback, Accountability, and Micromanagement5 Relationships = Liking, Trusting Intention, Trusting Belief-Benevolence, Trusting Belief-Competence6 Motivation = Experienced Work Meaningfulness, Organizational Commitment, Intrinsic Motivation-Enjoyment, Intrinsic Motivation-Self-Esteem, Job Satisfaction7 Contribution to Team Effectiveness = Contribution to Team Coordination Effectiveness, Contribution to Communication, Contribution to Conflict Resolution, Contribution to Overall Team Effectiveness8 Job Characteristics = Skill Variety, Job Significance, Job Feedback, Job Identity, Autonomy

51

.24 .34

Work Outcomes10 3 .22 .10.17 .12

In general, the hypotheses in this study were tested at the alpha

= .05 level. This level is appropriate for three reasons. First, this is a

field study, not a controlled experiment. Because of the complexity of

things happening in a field setting, links between variables will be

harder to find than in an experiment, in which alpha = .01 may be

appropriate. Second, this study covers virgin conceptual territory

through new, and sometimes speculative, hypotheses. Third, the use

of an alpha of .01 or .001, while decreasing the chance of a Type I

error, severely increases the chance of a Type II error. That is, using a

very small alpha decreases the chance that researchers will think they

have found an effect when they didn’t (Type I error). But it greatly

increases the chance that one will think there is no effect when there

really is one (Type II error), per Cohen (1988), who recommends a

moderate choice of alpha for significance testing. Cohen illustrated

this using an alpha of .001 for a given scenario. In Cohen’s scenario,

the .001 significance level for Type I errors implied that the Type II

rate is .90. The ratio of importance between the two is .90 divided

by .001, or 900 to one. Hence, this assumes that “mistakenly

rejecting the null hypothesis…is 900 times more serious than 9 Critical Psychological States = Experienced Work Meaningfulness, Felt Responsibility, Knowledge of Results10 Work Outcomes = Job Satisfaction, Individual Performance, Intrinsic Motivation-Self-Esteem

52

mistakenly accepting it” (Cohen, 1988: 5). Cohen gave a more

moderate scenario that used an alpha of .05, in which the ratio

was .20 divided by .05, or four to one. Using alpha = .05 reflects this

Type II moderation while still providing a challenging alpha for study

of new phenomena in a field setting.

Regression analysis assumes that multicollinearity and non-

constant variance are not present (Neter, Wasserman & Kutner, 1990).

Multi-collinearity of the independent variables was checked for each

regression using the variance inflation factor (VIF) statistic in SPSS.

The few regressions found to have multi-collinearity are reported in

the Results sections of Chapters Three through Six. Non-constant

variance was analyzed through a test devised by Weisberg (1985).

None of the study’s equations had the problem of non-constant

variance. These tests provided evidence that the regression

assumptions were met.

To test the model’s moderation structure, regression was used

as outlined by Baron & Kenny (1986). For moderation

(X=independent variable, Y=dependent variable, Z=moderator

variable), the study assumes linear effects of X on Y. So Y is regressed

on X, Z and XZ. Moderator effects were considered to be indicated if

XZ is significant while X and Z are controlled. The interaction terms

were created by multiplying standardized terms together, as

53

recommended by Aiken & West (1991), in order to minimize

multicollinearity.

Because moderation effects are much more difficult to

demonstrate for field data than for laboratory data (McClelland & Judd,

1993), an additional moderation analysis was done for the

Management Controls / Relationships model. The data were split into

two groups reflecting respondents with high- and low-Relationships

scores. For example, in the high Relationships conditions, a means

test was performed to see whether Motivation was higher or lower for

those in low or high Controls (e.g., Autonomy) groups. Two-by-two

tables were constructed to show the Motivation means under the four

Relationships (HI-LO) and Controls (HI-LO) conditions. An interaction

was considered to have taken place when, under conditions of HI

Relationships, the effect of Controls was high; yet under conditions of

LO Relationships, the effects of Controls was low. If the effects of

Controls did not significantly differ under the two Relationships levels,

then there was no interaction.

Cook & Campbell (1979) said that researchers who eliminate

plausible alternatives to the constructs in their model increase the

likelihood that they have found a valid relationship among constructs

(‘internal validity’). To improve confidence in the internal validity of

this study’s findings, a number of plausible alternatives were added to

the models tested to see if they added predictive power. These 54

included the normal demographic variables (e.g., age, gender, and

education) and several others suited as alternatives to the constructs

used in the study (see Appendix B). These were entered into the JCM-

and Management Controls-related models to see if they improved the

models’ prediction of the motivation-related dependent variables.

RESEARCH SITE FOR THE STUDY

This study intensively researches one field site, because:

the literature lacks a reasonably complete understanding

of the critical computer systems phenomenon;

understanding the detailed context in which the operator

phenomenon exists will improve whatever

knowledge exists of how the phenomenon really

works (Kaplan & Duchon, 1988; Lending, 1996; Van

Maanen, 1979b);

logically, a more complete understanding would have the

effect of generating more--and better focused--

research; and, therefore,

studying a single site intensively would enable this

deeper level of initial understanding (Mintzberg,

1979), laying a firmer foundation for future research.

A general description of the research site is presented below.

The details of the research site are discussed throughout Chapters 3-6

55

in order both to provide a context for the study and to support

hypotheses.

The description of the research site supplied vital data about the

context in which the critical systems phenomenon takes place, aiding

the researcher in asking the right questions. Lending (1996), for

example, found that the organizational context was important in her

study of systems analyst use of CASE tools. The organizational

context can be a major factor in how work is accomplished. For

example, in stressful jobs like nuclear plant operation or air traffic

control, the context can influence individual worker ability to

accomplish the work (Mowday & Sutton, 1993). Context is also

important because questionnaire data can best be understood in light

of the specific environment of the organization (Mintzberg, 1979). The

contextual environment includes both the nature of the tasks done by

the workers and the manner in which the company is run by

management. It also includes the workers’ norms and customs and

assumptions. Because time was limited, the researcher did not do a

complete ethnographic study. Therefore, the description of these

environmental factors is incomplete. The site description effort was

guided by Barley (1990), Denzin (1978, 1989), Lincoln & Guba (1985),

Lofland (1971), Sanjek (1990), and Van Maanen (1979a,b).

The research site studied was the computer operations

department (comprised of approximately 100 hardware and software 56

operators and their nine supervisors), which operates a large host

computer system in a firm to be called XYZCo. “Host” computer

system means that thousands of workers throughout XYZCo’s industry

continually use the system for daily business transactions--not just

employees within the host system’s parent corporation. XYZCo

employees take pride in the high level of system availability--better

than 99.9% up-time at the computer site. The operations department

also operates several other smaller systems. The largest of these is

the test/development system, used by the company to test new and

modified applications software, most of which is developed in-house.

Finally, one subgroup within the organization operates another

company’s system. Those who support the main system are

separated into hardware and software groups. Hardware operators

are assigned to a single shift, but the software operators for the

largest of the systems rotate among the three shifts. This means that

the set of people watching for system problems on a given shift differs

from day to day. Specific duties (e.g., watching monitors, handling

utilities) are also shifted among hardware and software operators on a

given shift. But hardware operators do not handle software duties,

and software operators do not handle hardware duties.

Because the system is used internationally, operators try to

keep the system fully available not only on first shift, but also during

second and third shifts. Over the past ten or fifteen years, system 57

unavailability due to scheduled maintenance (on second or third shift)

has been greatly reduced. Unavailability due to system outages has

also been significantly reduced. Nearly every year for the past ten

years, the average system availability has improved over the prior

year.

XYZCo is very customer-conscious; it tries to please every user

of the system. Hence, the operators take pride in their role in keeping

the system available. The computer division of XYZCo incorporated

Total Quality Management practices in 1991. A quality improvement

team meets after each operator-caused outage to analyze the root

causes of the outage and to discuss what can be done to prevent this

type of outage from happening again.

XYZCo has traditionally provided operators a favorable

workplace. CSOs have typically stayed in their positions for many

years. Some have transferred due to promotions or lateral

opportunities, both of which have been adequate or even plentiful.

During Phase I, the researcher noted a (then) recent

management decision to automate many of the hardware operator

functions. The researcher expected this action to increase the level of

job insecurity among hardware operators. In addition, a new

management incentive system was installed as Phase I began that

encouraged stricter cost controls by management. The incentive

system provided operators financial bonuses, which had previously 58

only been provided to managers. The awarding of these bonuses was

contingent upon the profitability of the overall organization.

The role of the critical system operator (CSO) is interesting and

challenging. Depending on the situation, the CSO plays three primary

roles, which resemble the roles of a detective, a doctor and a fire

fighter. In the detective role, the CSO monitors the computer system,

proactively looking for problems that could potentially harm the

system. In this role, the CSO uses both the system’s monitoring

consoles and system “dumps” that report small or large internal

computer events. CSOs may also interface with system help desk

personnel to try to head off individual-level problems that could be

symptoms of larger system problems. The curious, persistent,

somewhat suspicious CSO is best suited to the detective’s task.

When the system incurs a serious, but unknown problem, the

CSO becomes like a frenzied doctor trying to diagnose a patient who

will die within minutes if not properly treated. In the doctor role, CSOs

try to very quickly diagnose the cause of the problem. This task can

involve interpretation of “dumps,” system monitor cues, or help desk

news. In those cases in which a new problem arises (Weick, 1990),

the diagnosis task requires intensely imaginative brainstorming. In

this highly unstructured situation, some who are not as good at

everyday tasks excel. Some CSOs can imagine the step-by-step

process the computer takes as it undergoes various problems (Weick 59

& Roberts, 1993). Describing the creative scenario-generating

prowess of one of the software operators, a co-worker said, “[name]

can BE the computer.” The abstract thinker with a great imagination

does the diagnosis task best.

Once the root problem is confidently diagnosed, the CSO

becomes like a fire fighter. When the building is burning, every

second counts. Realizing the urgency of restoring the system which

thousands are impatiently waiting to use, the CSO takes rapid, but

calculated, actions. The proactive, quick-to-act, experience-assured

CSO performs the fire fighting task best.

It should be noted that the CSO job often involves technological

equivoque, which Weick (1990: 2) defined as “something that admits

of several possible or plausible interpretations and therefore can be

esoteric, subject to misunderstandings, uncertain, complex, and

recondite.” Weick said that technological equivoque occurs for three

reasons. First, because stochastic, random events occur to cause the

system problems. Second, Weick explained that the randomness of

these events causes worse problems when the event is not

understood. Hence, a large store of knowledge and skill is required

among CSOs and the technical specialists they must immediately

access during a system outage. Third, since the internal workings of

these critical systems are obscure and hard to visualize (Brooks, 1987;

Weick, 1990), operators must deal with abstract, almost 60

incomprehensible, events. Because of the nature of critical systems,

CSOs need an aptitude for “high attention to work processes, rapid

response to emergencies, ability to stay calm in tense environments,

and early detection of malfunctions” (Weick, 1990: 13).

61

CHAPTER THREE:

NATURE OF THE CRITICAL SYSTEMS OPERATOR JOB

Ch Prop: Content or Model

2 -- Methodology and Construct Validation

3 1 Nature of the High Levels of Critical Systems Motivation Operator Job

4 2, 3 Growth Need Strength

Critical Job Characteristics Psychological Work

States (CPS) Outcomes

Relationships System Trust

5 4, 5

Incentive Motivational Controls Effect

Relationships

6 4, 5

Other Motivation Motivational Controls Outcomes

Relationships System Trust

7 -- Contributions, Limitations, and Future Research

Chapter Three first reviews the nature of critical systems from

the research literature. From this review and from Phase I data,

62

hypotheses are developed. The methods used to test the specific

hypotheses are outlined. The results are then presented and

discussed.

THEORY BUILDING

The Nature and Importance of the Critical Computer System

To understand the CSO job, one must first understand what

critical computer systems are like. The critical nature, importance,

and complexity of these systems is now discussed.

Critical Nature. Some computer systems are so critical to the

operations of an organization that when they become unavailable,

they create major problems. One class of such systems is the

transaction processing system (TPS). "A transaction processing

system is a computerized system that performs and records the daily

routine transactions necessary to conduct the business..." (Laudon &

Laudon, 1995: 37) As an example of system criticality, Lucas (1975:

16) said that “...interruption of on-line service in a reservation system

can drastically affect the functioning of other departments in the

organization.” Now that airlines share their reservation systems with

travel agents, system downtime can interrupt thousands of travel

businesses across the globe. As another example, automated bank

teller systems can be critical for conducting personal business (e.g.,

withdrawing money from a bank). In terms of the need for continuous

operation (Weick, 1990), the critical computer system resembles the 63

critical nature of nuclear power plants, air traffic control systems, or

the aircraft carrier. Hence, critical computer systems can be classified

as a member of the general family of critical technology systems

(Perrow, 1984; Rasmussen, 1986; Weick & Roberts, 1993). However,

the critical computer systems studied here did not have life-

threatening consequences. Instead, their consequences consisted of

work stoppages for large numbers of people and significant customer

inconveniences, both of which could hurt those businesses that

depended on the systems.

Importance. When a TPS fails, a company may lose sales,

upset important customers, or decrease productivity for itself and its

customers. Upsetting customers is becoming dangerous as more and

more firms compete on the basis of service quality (Schlesinger &

Heskett, 1991). The decreased productivity caused by TPS failures is

costly--and ironic, since companies use such systems to try to increase

productivity (Drucker, 1991). But TPS failures are even more harmful

because they often cause total work stoppages for those dependent

on the system (Weick, 1990; Zuboff, 1985). Without the use of its TPS,

a company may not be able to transact its daily business. In the

extreme case, "...TPS failure for a few hours can spell the demise of a

firm and perhaps other firms linked to it" (Laudon & Laudon, 1995:

37). An interesting parallel to the critical nature of the TPS failure was

found in Crozier’s (1964) account of plant machine stoppages. Crozier 64

said these stoppages were crucial because: a) they are unpredictable;

b) impersonal rules can’t be applied to fix them; and c) only skilled

maintenance people can cope with them.

Internet-related computer systems are also becoming more

critical to business. For example, well over 1 million people use

America Online (AOL) to conduct business (Reuters News Service,

1996). These people have suffered such business interruptions as a

nineteen hour outage on August 7, 1996 (Wall Street Journal, 1996).

Because of this and subsequent AOL availability problems, the Wall

Street Journal online edition hosted a discussion group regarding AOL

problems for several months during 1996 and 1997, further

accentuating the public relations problems related to the AOL outages.

Another recent example of the business consequences resulting from

system failure involved a small Internet provider used by a number of

firms to transact business (personal communication with a subscriber).

During the summer of 1996, this provider incurred a series of long

outages. A number of businesses, that depended on the network’s

continuous availability to be able to conduct daily operations, were

severely hurt. Soon, this internet provider lost about 10% of its

customer base and a higher percentage of its revenues.

Complexity. Like all large computer systems, critical systems

are generally complex. As Brooks (1987: 11) said, “Software entities

are more complex for their size than perhaps any other human 65

construct because no two parts are alike…In this respect, software

systems differ profoundly from computers, buildings, or automobiles,

where repeated elements abound.” Complexities of systems are

compounded when: a) systems interact with each other, and b)

systems are operated by automated systems (Zuboff, 1985, 1988).

Weick (1990) posited that technology can be very hard to control

when it is comprised of many automated, interacting parts.

Complexity places a burden of escalating cognitive demands on

operators that can lead to operator errors.

Interactive effects between complexity and criticality are also

possible. Critical systems have the added burden of having many

people dependent upon them. Zuboff (1985: 13-14) warned: “Such

dependence on automation means that the problems of reliability will

be critical. Automatic controls that can provide fail-safe measures to

guard against systems errors will be needed, since the ripple effects of

such failures can escalate with alarming speed in a highly automatic

and interdependent machine system.”

Management Information Systems Literature

The MIS literature contains several research streams that relate

to computer operations. These bodies of research helped inform

Phase I exploration of the critical computer systems operators

phenomenon. A large body of literature (e.g., Bailey, 1982; Galitz,

1980; Norman, 1983) addresses what might be called “user interface 66

requirements,” (Davis & Olson, 1985) or the engineering of computers

to match “human factors” (Shneiderman, 1980). User interface

research addresses an important issue: how to design systems with

which humans can effectively and efficiently interact. This literature

stresses the importance of designing appropriate computer interfaces

for operators in order to minimize errors at the operator console.

Davis & Olson (1985) mentioned three types of controls to

improve information system availability: physical facilities control (to

prevent the risk of access to the computer site by undesirables),

terminal access control (to protect against illegal access), and backup

and recovery controls (to recover from errors). Further, Davis & Olson

discussed procedures and duties performed by information systems

personnel to monitor system quality in terms of errors, downtime,

reruns, and application repair maintenance. They also discussed

preventive controls (i.e., quality application development and

adequate testing) and detective controls (e.g., redundant parity bits).

These controls emphasize ways to protect the system. They

encompass both technical and structural approaches.

DeGreene (1970) discussed the Semi-Automatic Ground

Environment (SAGE) air defense system set up by the Air Force to

detect and destroy enemy bomber aircraft. This system was the

“granddaddy” of the electronic control systems (DeGreene, 1970: 12).

However, DeGreene almost exclusively discussed the system in terms 67

of the lessons learned from developing—not operating--SAGE.

Similarly, Lyytinen & Hirschheim’s (1987) detailed review of system failures

reports almost no studies of operations failures. It does, however,

point out the importance of operations in terms of keeping the system

running, because “errors...are hard to pin down and correct” when

systems are so complex (1987: 281).

Perhaps closest to this study’s domain is research in technical

support and computer maintenance. Amit Das (1994) and Brian

Pentland (1992) studied the technical support done at computer user

help desks. Das took a problem solving (i.e., Simon, 1981) approach

to technical support work, explaining that the failure mode leads to

the types of tasks and problem solving moves used. Pentland’s work

described how technical problem solvers interpret and coordinate

(e.g., assign, refer, escalate) the trouble calls they receive. Das and

Pentland showed that helping users is a dynamic process that requires

effective teamwork.

In the area of computer maintenance, Lientz & Swanson (1980)

and Swanson & Beath (1989) have taken a combined technical and

organizational approach. For example, Swanson (1984) has looked at

the impact of alternate organizational designs on software

maintenance. Others have also taken up the topic of software

maintenance (e.g., Slaughter, 1995; Banker, Datar, Kemerer & Zweig,

1993), particularly in terms of the economics of enhancing and 68

maintaining software. These studies have primarily addressed the

enhancement of application software, while this study researches

large infrastructure systems that include both the systems software

and the related applications, and focuses on pure maintenance (fixing

the system when it breaks).

Couger and Zawacki (1980) surveyed over 1200 computer

operations employees. From analysis of their data, Couger and

Zawacki concluded that the computer operator job is one of the least

motivating jobs in industry. By contrast, Couger and Zawacki found

that the job of the system developer was more motivating than the

average industry job.

In sum, the MIS literature is helpful in framing the boundaries for

this study, and introducing the researcher to the complex nature of

the task, the opportunity for human error, and the importance of

teamwork in the critical systems operator context. The Couger &

Zawacki (1980) study provided a benchmark view of the traditional

operator job that could be compared with Phase I findings regarding

the critical systems operator.

Management of Technology Literature

Perrow (1984) described the Three Mile Island nuclear power

disaster as a “normal accident” that occurs when complex, interacting,

and hard-to-visualize systems combine with human limitations.

Perrow argued (1984: 31) that neither “better organization..., [nor] 69

more money and resources for better people and equipment” will help

reduce the risk of accident in such systems. Per Perrow, only taking

steps to simplify the system will help. This is a structural view of

system problems that says people, interpersonal relationships, and

the organization of workers’ roles do not matter: simplification is the

only possible answer.

Rasmussen, like Perrow, studied the operation of nuclear power

plants. Rasmussen focused on the human-system interactions,

primarily using cognitive decision-making as his research paradigm

(e.g., Rasmussen, 1986). However, some studies sponsored or

reported by Rasmussen allude to the importance of people

relationships or motivation in keeping nuclear plants operating (e.g.,

Quantanilla, 1987). This alerted the researcher to the importance of

social issues in keeping systems running.

Zuboff (1985, 1988) discussed how computers affect people and

management in terms of controls and power issues. Computers may

be used by management to both control or even replace people,

making the computer divisive to the worker-management relationship.

Highlighting power issues between workers and management, Zuboff

(1985) explained that giving information to workers takes away some

measure of manager control, constituting a threat to management.

These descriptions helped the researcher be aware that: a)

automating the critical systems operator function may have some 70

drawbacks and b) power issues may exist between technicians and

their management.

Weick & Roberts (1993) explained how things work on the flight

deck of a military aircraft carrier. The flight deck is extremely

complex, interactive and risky, “a million accidents waiting to

happen.” In this context, Weick & Roberts portrayed the people and

interactive roles as the glue that kept things together, “but only a few

[accidents] do [happen].” Weick & Roberts posited that the combined

problem solving capability of the high-reliability organization enables

it to hold at bay its potentially hazardous environment. In contrast to

Perrow’s belief that the environment limits people, Weick & Roberts

posited that the combination of people, organization, and relationships

is able to overcome almost any degree of structural complexity.

Weick & Roberts’ belief in the ability of teams to handle complex

situations inspired the researcher to wonder if highly alert, motivated,

and team-oriented operators might be a key to keeping critical

systems running.

This dissertation research fills an important gap by examining

how computer operator motivation and teamwork function within an

organizational and social context. To some extent, researchers have

studied critical computer systems (DeGreene, 1970), beginning with

air defense systems. But they have primarily focused on technical or

human/computer interaction factors. The same focus permeates the 71

analogous literatures on industrial safety (Hale & Glendon, 1987) and

nuclear plant availability (Rasmussen, 1986; Rasmussen, Duncan, &

Leplat, 1987), leaving a gap in the area of social/organizational issues.

This gap is important to fill, since people and organizations are part of

the interacting systems components that determine the success of

computer systems (e.g., Lucas, 1975; Lyytinen & Hirschheim, 1987;

see Figure 8 in Chapter Two).

Conceptual Model Building

In part, reducing computer outages is complex because

computer system components are themselves complex (Brooks,

1987). This was confirmed at XYZCo. The main system consisted of

many thousands of interacting segments of computer code. Once

each week, many new and revised segments were implemented, each

carrying the potential to take the system down through programming

errors. Pre-implementation testing of software was extremely

rigorous.

72

Phase I found that each operator must learn as much as possible

to be prepared for almost any contingency. This is because: a)

operators cannot predict the type of system problem they will next

face; b) system repair knowledge is highly specialized and dispersed

among numerous people; c) differing levels of experience, abilities,

motivation, and teamwork exist among operators; and, d) because of

rotating shifts among software operators and the unpredictability of

the timing of outages, management cannot predict which set of

operators will be onsite when an outage occurs.

The operators take pride in being key to keeping the system

going. But, since system outages damage so many peoples’ ability to

do their job, they also feel intense pressure from the task to do things

right. Most of the hardware and software operators have college

degrees, but they primarily obtained their technical knowledge on the

job.

The operators continuously and alertly monitor the health of the

system, proactively investigating anything that could bring the system

down. They act on such cues as messages on the operator console

and calls from the help desk. When the system does crash, they

immediately respond in cohesive team fashion to bring the system up

again--hopefully in a matter of a few minutes. One operator said, “...an

outage looks very chaotic. But everybody basically knows what to do...” The

researcher found that what one does during an outage also depended 73

on who else was present and what the outage symptoms were.

Operators reported that adrenaline rushes are common as they fix

system problems. Total attention is focused on getting the system

running. When asked about how the presence of management felt in

an outage, one interviewee replied,

Respondent: That's tough, because when you are in the middle of an outage,

I think that the pressure is so great that you don't particularly think of it. I

don't particularly care for popcorn, okay? But if you give me a bag of

popcorn in the middle of an outage, I'll eat the whole thing.

Interviewer: Just because you're so nervous?

Respondent: Right. Well, it's not even.. [pause].. It's like energy.

Another XYZCo employee said the operator job is “an

adrenaline junkie’s dream.” While system outages occurred more

frequently during the 1980s, they now only occur about once every

two weeks. Between fixes, some team members search for potential

problems in existing software or in hardware or software that will soon

be brought online. Others “tune” the large body of application and

system software for increased efficiency. Still others monitor the

utilities that create off-line files for testing and disaster-recovery

storage.

74

The operators are proud of the fact that the system has historically improved to

over 99% availability. In many cases, their greatest motivation appears to be the

challenge of keeping the system continuously up and running. In talking with several

employees, this feeling of pride is especially strong among the “old-timers.” One of

these (whom we’ll call Tom), for example, began with the company in the early 1960s,

when the system was in its infancy. After working as a software operators for about

twenty years, Tom moved to the applications group in the 1980s. In spite of this job

change, he has maintained a close relationship with the current operators. Tom also

spends about half an hour each day looking for things that might harm the system, much

as he did when in the operator group.

Hypotheses

These findings can be related to the Hackman/Oldham JCM (see

Figure 2). These hypotheses expand upon Proposition 1 (see Chapter

One), which stated that CSOs will be more highly motivated than

traditional computer operators. Each job characteristic will be

italicized in the following discussion. Based on the findings at XYZCo,

the job of the critical systems operators appeared to be highly

significant to the operators, because the entire organization and

thousands of customers depend on each individual operator to keep

the system running. Arguably, these jobs would be more job

significant than the job of a system developer, since developers

typically have fewer continually dependent constituents. Because so

many different activities are possible during outages and periods of 75

calm, the job appeared to be replete with skill variety. Critical

systems operator skill variety is probably higher than that of system

developers, who tend to work on one task for a longer period of time.

Because most CSOs possess significant knowledge and skill, they

appeared to be given much autonomy by management in carrying out

their complex functions. Because the system itself tells CSOs

immediately whether or not they succeeded in fixing an outage, the

task potentially carries high levels of job feedback. Job feedback

should be higher than for system developers, who do not receive

feedback as often, and don’t receive complete feedback until their

system is tested and implemented. Given the significant pressure on

the critical systems operator and the length of time it takes to learn

the job, CSOs will probably have higher levels of growth need strength

than will systems developers or traditional operators. Therefore:

76

Hypothesis 1: The nature of the critical systems operator job

is such that the levels of Job Significance, Skill Variety, Autonomy, Job

Feedback, Growth Need Strength, and Motivating Potential Score will

be significantly higher for XYZCo operators than was found among

traditional: a) computer operators and b) system developers in the

Couger & Zawacki (1980) study.

Note that systems developers have been found to be highly motivated employees

(e.g., Couger & Zawacki, 1980; Lending, 1986). For example, Couger and Zawacki

found that system developers had an average Motivating Potential Score that was fifty-

five percent higher than that of the computer operator. Hence, the comparison of CSOs

to Couger & Zawacki operators is an easy test, but the comparison of CSOs to Couger &

Zawacki system developers constitutes a difficult test. Also note that if CSOs are

strongly motivated by job characteristics, they are probably not as strongly motivated by

other factors, such as social relationships. Thus, Hypothesis 1, if true, enables a strong

test of whether Relationships and System Trust add to the JCM’s prediction of

motivation constructs (tested in Chapter Four).

The Lending (1996) study of system developers was included as a comparison

group to help remove the objection that the time period (1980 to 1997) was the major

differentiating factor between the CSO measures and those of the Couger & Zawacki

(1980) computer operator study.

Hackman and Oldham’s Task Identity construct refers to the extent to which

workers see the task as a whole or complete task, as opposed to some component part of 77

an entire task. Since XYZCo workers often get interrupted by outages or new potential

problems to explore, their Task Identity should be relatively low. Therefore:

Hypothesis 2: The nature of the critical systems operator job

is such that the levels of Task Identity will be significantly lower for

XYZCo operators than was found among traditional: a) computer

operators and b) system developers in the Couger & Zawacki (1980)

study.

Based on JCM theory, these four job characteristics—job

significance, skill variety, autonomy, and job feedback--should result in

high levels of experienced meaningfulness, felt responsibility, and

knowledge of results. In fact, interviews indicated that operators feel

their job is very meaningful and that they feel keenly their

responsibility to keep the system available. Note that these four will

far outweigh the effects of Job Identity, which is hypothesized to go

the other direction. Therefore:

Hypothesis 3: The nature of the critical systems operator job

is such that the levels of Experienced Meaningfulness, Felt

Responsibility, and Knowledge of Results will be significantly higher for

XYZCo operators than was found among traditional: a) computer

operators and b) system developers in the Couger & Zawacki (1980) 78

study.

Based on the JCM, the Critical Psychological States will lead to

motivational Work Outcomes. Supporting this theory, Phase I

interviews found significant levels of job satisfaction and intrinsic

motivation. Therefore:

Hypothesis 4: The nature of the critical systems operator job

is such that the levels of intrinsic motivation and job satisfaction will

be significantly higher for XYZCo operators than was found among

traditional: a) computer operators and b) system developers in the

Couger & Zawacki (1980) study.

To motivate its workers effectively, management should know the extent to

which the workers are motivated by intrinsic factors (i.e., the characteristics of the job)

or extrinsic factors (e.g., incentives) (Steers & Porter, 1977). Hackman and Oldham

found that the worker’s Growth Need Strength moderated the effects of Job

Characteristics on their motivating psychological states (Figure 1). In Phase I interviews,

workers were asked what motivated them to do a good job. In the vast majority of cases,

the answers were intrinsic, rather than extrinsic, in nature. This provided initial evidence

that critical computer systems operators are more intrinsically motivated than

extrinsically motivated. In Phase II (Appendix C, questions 48. and 49.), CSOs were 79

asked to compare their current level of commitment to work hard for the company to

their commitment level three years previously (or less, if they had less than three years of

tenure). While CSOs probably had trouble remembering their commitment level of three

years ago, this question broadly measured the upward or downward trend in their

commitment levels. The CSOs were then asked why they are more (or less) committed

today. Their answers provided qualitative data on what motivates the CSOs to be

committed. Because this question asks about their motivation indirectly, the responses

should be less subject to social desirability bias than would result from a direct question

about their motivation. Therefore:

Hypothesis 5: When asked why they are more (or less)

committed to work hard for the organization today than they were

three years ago, most of the reasons CSOs provide will be intrinsic,

rather than extrinsic, in nature.

To the Phase I question of what motivated them to do a good job, some CSO

answers indicated that people relationships, either with supervisors or peers, motivated

them. From this, the researcher projected that a significant portion of the responses to

the question of why the CSO is more/less committed today would involve people

relationships. If over half of the responses are expected to refer to intrinsic factors

(Hypothesis 5), it is reasonable to assume that a significant portion of the remaining

responses would be about twenty percent of the remaining responses, or ten percent of

the total responses. Therefore:80

Hypothesis 6: When asked why they are more or less

committed to work hard for the organization today than they were

three years ago, greater than ten percent of CSO responses will

indicate that critical systems operators are motivated by people

relationships.

Age, grade level, and job security probably affect the worker’s

choice between intrinsic and extrinsic factors. Herzberg (1966) said

that what motivates someone is what they want that they don’t have

(Steers & Porter, 1977). Hence, a junior employee is more likely to be

motivated by money or promotions (extrinsic factors) because they

are usually at a lower salary and grade level early in their career.

Similarly, the employee with a low grade level is more likely to choose

extrinsic factors like promotion over a challenging job. Employees

with low job security are likely to want job security, or will at least

want to be compensated for the lack of job security by receiving

greater compensation or promotional opportunities. Moreover, based

on needs-based motivation theories (e.g., Herzberg, 1976; Maslow,

1954), the lack of job security (a low-order need) will direct operator

attention from higher order needs like job satisfaction to lower order

needs like compensation. Therefore:

81

Hypothesis 7: When asked what motivates them to work hard

and do a good job, those critical systems operators a) who possess a

higher grade level, and b) have greater job security will be more likely

to choose intrinsic over extrinsic motivators.

While XYZCo has found that improved technical tools helped, the

proactive teamwork of the highly motivated operations “fire-fighters”

has contributed much to improving uptime. For example, one

management person said that the number one factor for keeping the

system up was “lots of teamwork.” In part, this is because the system

is so complex and has so many interacting parts that, as one operator

said, “There’s too much for one person alone, for even just a handful of people alone.

Everybody has their part.” Hence, teamwork is essential to CSO performance.

In the CSO setting, teamwork is essential because no one knows

what problem is going to threaten system availability next. Some

members of the team specialize in particular parts of the system (e.g.,

data base software or system utilities), since no one can comprehend

it all. Thus, diagnosing and fixing the system often requires the onsite

personnel to access by phone a virtual network of experts. Knowing

who to call for what purpose and being quick to respond to a call

become incredibly important as the seconds of downtime tick into

minutes. Those interviewed on-site named many people who were

critical to keeping the systems available. The names of those critical 82

came from many different parts of the organization--from on-site

hardware vendor technicians, to system engineering department

gurus who specialized in tape or DASD (Direct Access Storage

Devices), to applications programmers like Tom. In spite of being in a

new job in a different city, Tom still reported getting four or five calls

from the software group per month. Given how important such

teamwork is, management probably views an operator’s contribution

to team effectiveness as an essential ingredient in the operator’s

individual performance. Therefore:

Hypothesis 8: Because of the nature of the critical systems

operator task, teamwork will be highly valued at XYZCo. This will be

manifested by a high correlation between the supervisor’s evaluation

of the operator’s contribution to team effectiveness and the operator’s

performance rating.

Pfeffer (1981) said that those workers who are most critical in

meeting an organizational contingency will have the most power,

particularly when they are hard to replace in the function. Pfeffer

cited Crozier’s (1964) study of French maintenance engineers who

controlled “the one remaining uncertainty confronting the

organization, the breakdown of machinery” (Pfeffer, 1981: 113).

Crozier found that maintenance engineers held significant power over 83

assistant plant managers, for example, and were able to exert more

influence on the plant manager than did the assistant manager. As a

group, the operators of XYZCo’s critical systems hold this type of

power.

However, levels of power differ between software and hardware

operators. Software operators have significant expert power (French

& Raven, 1968) because: a) they have no written procedures for

diagnosing problems, since the possible problems are too complex to

document, and b) the amount of knowledge it takes to become

relatively competent at this task can only be learned on-the-job over a

period of about two years. To become expert at the job requires much

more time, however. The interview with Tom highlighted this. Though

no longer a CSO, he still was frequently called on issues within his

area of expertise. Tom also pointed out three software operators who,

in Tom’s opinion, were “becoming” expert. Each of the three had

been software operators for ten years or more!

Hardware operator power levels appears to be lower than for

software operators. While the task of the hardware operators is also

complex, portions of the hardware job are sufficiently standardized to

make automation possible. Hence, management has pursued a “lights

dim” initiative to replace enough hardware functions to make it

unnecessary to have operators physically located in the computer

room. By making the hardware operators partially replaceable, “lights 84

dim” efforts constituted a threat to hardware operator power

(McKnight, 1996). One informant reported that the operators resisted

programmers who came to the operators to develop specifications for

automating their function. This resistance demonstrates the use of

operator power against actions that threatened their power.

Arguably, the worker/management relationship will begin to

erode when management makes attempts to eliminate worker jobs or

erode worker power. This relationship erosion will be manifest in

terms of the trust and liking of the workers for their management.

Even though such policies generally come from higher management

levels, such actions will likely taint hardware operators’ relationships

with their immediate supervisors. Therefore:

Hypothesis 9: Critical systems operators in groups whose jobs

will likely be eliminated over time will have lower levels of trust and

liking toward their supervisors than will operators in other groups.

Workers whose jobs will be eliminated will likely have lower

organizational commitment and job satisfaction. They are likely to get

less enjoyment from their job because of job insecurity, as they focus

more on lower-order, rather than higher-order, needs (Deci & Ryan,

1985; Maslow, 1954)

85

Hypothesis 10: Critical systems operators in groups whose

jobs will likely be eliminated over time will have lower levels of

Organizational Commitment, Intrinsic Motivation-Enjoyment, and Job

Satisfaction than will operators in other groups.

METHODOLOGY DETAIL

Hypotheses 1-4 (motivation levels comparisons with other

groups) were tested by means comparison tests.

Hypotheses 5 and 6 (intrinsic and relationships motivation

orientation) were tested by grounded theory’s open coding method

(Glaser & Strauss, 1967). The qualitative responses were open coded

into categories. Once the categories were identified from the data,

the researcher went back through the coding process again, making

several minor coding changes. Hypothesis 5 indicates that a majority

of comments would be intrinsic. For testing purposes, “majority” was

assumed to be 50% or more. Hypothesis 6’s “significant percentage”

was interpreted to be over 10% for testing purposes. Given that over

50% were projected to be intrinsic, then 10% is at least 20% of the

remaining 50% of the comments.

Hypothesis 7 (grade/job security motivation orientation) was

tested by correlating grade and secure group membership with a

construct developed to represent the operator’s choice of intrinsic

versus extrinsic motivation for why they work hard and do a good job. 86

This construct was named IMO, for Intrinsic Motivation Orientation.

The data for IMO was gathered by asking questions #50-52 in

Appendix B, as shown in Table 9. Questions 50-51 present four

choices for why the employee works hard, two intrinsic and two

extrinsic. Respondents are also offered the choice of “5. Something

else (specify:)___” The intrinsic and extrinsic choices were selected

from theory. Question 52 offers three extrinsic choices, choice “5.

Something else,” and one choice that mixes extrinsic and intrinsic:

“Appreciation from your boss.” This choice is partly extrinsic (Kohn,

1993b), in that the stimulus comes from outside the worker, and

partly intrinsic in that appreciation relates to the employee’s self-

esteem. Responses that indicated “5. Something else” were further

probed, and the responses recorded on the questionnaire. These

answers were coded as intrinsic, extrinsic, or mixed. IMO was placed

on a 1-7 scale as follows. To the minimum score of 1.0, two points

were added for each answer on questions 50-52 that was an intrinsic

motivator. Exceptions: a) one point was added for “Appreciation from

your boss;” b) the scores from mixed answers to “5. Something else”

probes were scored through open coding methods explained above.

Since IMO was formulated by summing the intrinsic responses to each

of the three questions, IMO could not be tested for reliability.

Table 9 Intrinsic Motivation Orientation (IMO) Scale

Q# Question Text Category87

50. From the following list, please select the one reason that best represents why you try to work hard and do a good job:1. Opportunities for a promotion Extrinsic2. The challenge of the task Intrinsic3. Merit pay increases Extrinsic4. A feeling of accomplishment Intrinsic5. Something else (specify) Either

51. From the following list, please select the one reason that best represents why you try to work hard and do a good job:1. [incentive plan name] bonuses Extrinsic2. Solving the incident, outage, or potential problem Intrinsic3. Achievement award programs Extrinsic4. Enjoyment of the job Intrinsic5. Something else (specify) Either

52. From the following list, please select the one reason that best represents why you try to work hard and do a good job:1. Opportunities for a Promotion Extrinsic2. Appreciation from your boss Extrinsic/

Intrinsic3. Merit pay increases Extrinsic4. [incentive plan name] bonuses Extrinsic5. Something else (specify) Either

Hypotheses 8 (CTE importance) and 9-10 (job security effects on

relationships, motivation) were tested using correlations, with a one-

tailed significance test. Hence, Hypotheses 9-10 tests for differences

between employees whose functions were going to be eliminated and

those in the other groups by correlating insecure group membership

with Relationships and Motivation variables.

RESULTS OF HYPOTHESIS TESTING

Table 10 shows the results related to Hypotheses 1-4

(motivation levels).

Table 10 Job Characteristics Comparisons (averages)

TYPE OF JOB: SYSTEM COMPUTER

88

DEVELOPMENT OPERATIONS

H:Support

?STUDY:

Lending, 1996

Couger &Zawacki, 1980

Couger &Zawacki, 1980

XYZCo, 1997

1 Yes Job Significance 5.37 5.75 5.62 6.771 Yes Skill Variety 5.76 5.55 3.98 6.281 Yes Autonomy 5.31 5.31 4.08 5.971 Yes Job Feedback 5.09 5.20 4.62 5.951 Yes Growth Need Strength 5.29 5.91* 5.78 6.821 Yes Motivating Potential Score 150 154* 99 2162 No Task Identity 4.98 5.37 4.53 4.833 Yes Felt Responsibility n/a 5.31 4.08 6.883 Yes Experienced Meaningfulness n/a 5.56 4.71 6.623 Yes Knowledge of Results n/a 4.59 4.33 6.384 Yes Job Satisfaction 5.10 5.10 4.94 6.294 Yes Intrinsic Motivation 5.70 n/a 5.71 6.46

*Based on combined programmers and analysts; other column entries are analysts only

Lending results shown here only to demonstrate that System Development scores have not changed greatly from 1980 (Couger & Zawacki study) to 1996 (Lending study). Means tests did not involve Lending results.

Hypothesis 1 (CSO JCM measures higher than those of

comparison groups) was consistently supported. Each Hypothesis 1

job characteristics measure for critical systems operators at XYZCo is

nominally higher than the Lending or Couger results. Note that the

contrast is greatest between XYZCo CSOs and Couger’s computer

operators. For each variable, a T-test was performed, comparing the

XYZCo results to the Couger & Zawacki System Developer score

(Keller, Warrack & Bartel, 1988). At alpha = .05, each variable

showed a significant mean difference. XYZCo’s figures are also

significantly higher than the operator figures, since the System

Developer figures each exceeded Couger & Zawacki’s operator figure.

The CSO Motivating Potential Score was more than double that of the

89

computer operator, and over sixty points higher than that of the

System Developers. Hence, Hypothesis 1 was fully supported.

Hypothesis 2 (CSO Task Identity lower than those of comparison

groups) was partially supported. XYZCo’s Task Identity mean was

nominally lower than the means of Lending and Couger & Zawacki

system developers, but was higher than that of the Couger & Zawacki

computer operators. T-test results showed that XYZCo’s average Task

Identity score of 4.83 was significantly (alpha = .05) lower than the

Couger & Zawacki average for system developers (5.37), supporting

Hypothesis 2. However, T-tests showed that XYZCo’s average Task

Identity score of 4.83 was not significantly different from the Couger &

Zawacki average for Computer Operators (4.53) or the Lending

average for system developers (4.98).

Based on alpha = .05 significance T-tests, Hypotheses 3 and 4

(CSO CPS and Work Outcome measures higher than those of

comparison groups) were also supported. While most T-tests

compared the XYZCo mean with that of Couger & Zawacki’s System

Developers, the Intrinsic Motivation T-test compared the XYZCo mean

with that of the Couger & Zawacki operators, since that was the only

number available.

T-tests were also done to compare Hypotheses 1 and 4 XYZCo

results with those of Lending (1996). For each variable, the average

XYZCo score was significantly higher than that of Lending. Hypothesis 90

2 Lending results were reported above, and Lending did not report

data on Hypothesis 3 variables.

Table 11 presents the strongly supportive results of Hypotheses

5 and 6 (intrinsic and relationships motivation orientation). In support

of Hypothesis 5, Table 11 shows that intrinsic factors were strongly

favored over extrinsic factors—52.9% to 8.9%. In support of

Hypothesis 6, the worker’s relationship with either his/her boss or

coworkers was mentioned almost 20% of the time, which was more

than twice as often as extrinsic factors. Even the sum of job security

and hygiene factors was a higher percentage than extrinsic factors

overall.

Table 11 Intrinsic versus Extrinsic Factors Reported(Hypotheses 5 and 6 Results)

% Factor Reported52.9% Intrinsic factors (job related)

8.9% Extrinsic factors (pay, promotions, bonuses)19.5% People relationship factors

8.0% Job security3.0% Shift work (hygiene factor)

7.7% Other hygiene factors (overtime, work conditions)

100.0%

Hypothesis 7 (grade/job security motivation orientation) was

supported. Grade level was correlated with IMO (r = .200) with a

significance of p=.033. This provides evidence that those with higher

grade levels are more likely to choose intrinsic factors. Being in one of 91

the more job-secure groups was correlated with IMO scores (r = .405),

at p=.000 level. This strongly indicates that lack of job security led

hardware operators to think more in terms of extrinsic rewards, rather

than intrinsic ones. As an alternative to grade level, age was tested.

Age was not a factor (r = .073, p=.253).

Hypothesis 8 (CTE importance) was strongly supported.

Contribution to Team Effectiveness was highly correlated with

Individual Performance (r = .84, p=.000). In terms of prediction, CTE

predicted Individual Performance at an adjusted R-squared level

of .71. A caveat of this result is that CTE and Individual Performance

were both reported by the supervisor (see Mono-method bias in

Chapter Two). However, Individual Performance was based on written

performance appraisal documentation.

Hypothesis 9 (job security effects on relationships) was

supported, but Hypothesis 10 (job security effects on motivation) was

not (Table 12). That is, those in less secure groups had lower levels of

trust in their supervisor. Yet their motivation levels were no different

than those in more secure groups.

Table 12 Correlations between Less Secure Group and Other Attributes (Hypotheses 9 and 10)

Less Secure Group correlations with:

r p Trusting Belief-Benevolence .261 .008Trusting Belief-Competence .219 .021Liking .258 .008

92

IM-Enjoyment -.035 .376Organizational Commitment -.076 .244Job Satisfaction .135 .108

Eliminating Plausible Alternatives

In order to establish these hypotheses’ internal validity with

greater confidence, the researcher entered a number of plausible

alternatives into the equations predicting the CPS and Work

Outcomes. These included demographic variables (age, grade level,

education), individual situation variables (number of recent

promotions, number of recent pay raises, percent of time keeping

systems available, duration of time worked with supervisor), and

variables providing possible alternative explanations (interaction with

team members, interaction with supervisor, relationship with team

members). These variables added little predictive value to the Work

Outcomes models’ most significant equations (i.e., predictions of IMSE

and JobSat). Only grade level helped ‘predict’ Performance. However,

good Performance is more likely to cause higher grade levels than

vice-versa. So grade level was eliminated from consideration as a

Performance predictor. Because these plausible alternatives were

eliminated, one can have greater confidence in the internal validity of

the best equations for the Work Outcomes models (see Table 14).

While none of the plausible alternatives helped predict Felt

Responsibility, two alternatives successfully entered the equations 93

predicting the other CPS. Education level was strongly (negatively)

predictive of Experienced Meaningfulness (beta = .317, p = .000), and

the CSO’s relationship with team members was predictive of

Knowledge of Results (beta = .244, p = .015). The fact that the CSO’s

relationship with the team was related to Knowledge of Results

indicates that having a good relationship with peers helps CSOs know

how they are doing on the job. Perhaps they receive considerable

feedback from their peers, as well as from the job. This was also

indicated in the answers to the questions about pressure on the job.

Many respondents, after indicating that they did not feel significant

pressure to perform well from managers or supervisors, said they felt

more pressure from the job itself and from their peers. The highly

negative correlation between education level and Experienced

Meaningfulness can be interpreted as follows. It is possible that

education broadens one’s views of what is important in the workplace

generally. If so, those with more education would be less ‘impressed’

by the importance or meaningfulness of their current job because they

would have more knowledge of other interesting jobs in the economy.

DISCUSSION OF RESULTS

The results of Hypotheses 1-4 underscore the highly motivating

nature of the CSO job. In particular, while it is impressive that

XYZCo’s motivating potential score more than doubled that of

Couger’s computer operators, it is even more impressive that the 94

operator job is significantly more motivating than that of the system

developer—a job which has received much more research attention in

the past. These results confirm that the CSO job is very different from

that of the traditional computer operator, placing it in the general

class of critical technology systems jobs (e.g., nuclear plant

operators).

Critical systems operators are primarily motivated to work hard

for the organization through intrinsic factors like the job’s challenge

(53%) and through people relationships (20%), rather than through

extrinsic motivation (9%). Those with higher grade levels and secure

positions are significantly more intrinsically motivated than their

counterparts. Operator Contribution to Team Effectiveness (CTE) is

closely related to operator Performance rating, showing how important

supervisors consider CTE to be. Operators in secure groups had

higher trust and liking towards their supervisor, but did not report

significantly higher levels of motivation than did their counterparts.

This latter finding shows the over-arching power of the critical systems

job to motivate the operator. Apparently, the job’s characteristics are

powerful enough to lead to high levels of job enjoyment,

organizational commitment, and job satisfaction in spite of being in an

insecure group. This agrees with the relatively infrequent mention

(8%) of job security as a motivational driver. Additional analysis

revealed that Experienced Meaningfulness and Intrinsic Motivation—95

Self-Esteem were not significantly correlated with secure group either.

One explanation of this is that workers who stay in insecure groups (or

companies) tend to reconcile their feelings about such groups. This

would happen in order for them to reconcile their feelings about

continuing to work there. Some evidence exists that workers are

beginning to adapt to the fact that the American dream of job security

is no longer the same (Wall Street Journal, 1995).

If the CSO job is highly motivating, the question remains: which

specific tasks are CSOs most motivated to do? It was suggested to the

researcher by an advisor that a highly visible and appreciated task like

fixing the system may be more attractive to the CSO than an almost

invisible task like preventing problems. This was informally termed

the “Red Adair versus Maytag repair” syndrome. The CSO reward

system is likely to favor fixing the system, as opposed to preventing

system problems. At the extreme, a CSO may feel a disincentive to do

preventive maintenance, for three reasons. First, fixing the system is

probably more intrinsically motivating and brings greater job

satisfaction than the prevention job because it is more challenging.

Second, the ‘honor and glory’ is more likely go to the CSO in heroic

fire-fighter mode, because of the high visibility of an outage (and the

longer the outage, the more visible it is). Third, the fix-it task

preserves the power of the operator (Crozier, 1964), as discussed

earlier. The researcher found no evidence that this phenomenon was 96

taking place at XYZCo. On the contrary, the researcher found that

when a new manager asked who had fixed a particularly troubling

outage, the supervisor refused to give out an individual name, stating

that it was a team effort. The fact that fix-it successes were identified

as team, rather than individual, successes suggests that the Red Adair

versus Maytag repair syndrome may not exist at XYZCo.

In sum, the picture of the critical systems operator job reflects:

extremely high motivating potential,

the job itself (intrinsic factors) as the primary motivator

and relationships the secondary motivator,

extrinsic and job security factors less important,

job security positively related to operator/supervisor

relationships, and

Contribution to Team Effectiveness a paramount virtue in

supervisors’ eyes.

The highly intrinsically motivating nature of the operator job is

likely to impact additional parts of this study. For example, because

this job is so highly motivating, job characteristics are likely to be

especially important to worker motivational outcomes. Hence, the

basic tenets of the JCM (tested in Chapter Four) are likely to hold. For

the same reason, however, Relationships and System Trust are not

likely to be as important to worker motivational outcomes as job

characteristics. Chapter Three evidence on what motivates operators 97

supports this prediction. Even though about 20% of comments

mentioned relationship issues, nearly three times as many referred to

job-related / intrinsic motivational factors. Chapter Four examines

further the relative importance of relationship and intrinsic factors.

98

CHAPTER FOUR:

JOB CHARACTERISTICS MODEL--ADDING RELATIONSHIPS

Ch Prop: Content or Model

2 -- Methodology and Construct Validation

3 1 Nature of the Critical Systems High Levels of Operator Job Motivation

4 2, 3 Growth Need Strength

Job Characteristics Critical Work

Psychological Outcomes States (CPS)

Relationships System Trust

5 4, 5

Incentive Motivational Controls Effect

Relationships

6 4, 5

Other Motivation Motivational Controls Outcomes

Relationships System Trust

7 -- Contributions, Limitations, and Future Research

99

THEORY BUILDING

Chapter Four first builds hypotheses regarding the Job

Characteristics Model (JCM), expanding upon Chapter One’s

Propositions 2 and 3. These hypotheses are supplemented by

hypotheses on the incremental predictive power of Relationships and

System Trust. The methodology for testing the hypotheses is detailed

and the research results are presented and discussed.

JCM Related Research

The hypotheses in Chapter Four primarily come from literature,

supplemented by Phase I results. The Hackman and Oldham Job

Characteristics Model posits that the worker’s perceptions of five job

characteristics (Job Significance, Task Identity, Skill Variety, Autonomy,

and Job Feedback) will predict the Critical Psychological States that, in

turn, affect Work Outcomes. The Critical Psychological States are

Experienced Work Meaningfulness, Felt Responsibility, and Knowledge

of Results.

While the Job Characteristics Model has had some criticism (e.g.,

Roberts & Glick, 1981), significant amounts of evidence support the

model (e.g., Hackman & Oldham, 1976; Hackman, 1980). In MIS

research, Couger and Zawacki (1980) applied the Job Characteristics

Model. In general, their research supported the tenets of the JCM.

The recent dissertation study of Lending (1996) also supported the

basic premises of the JCM. Lending also confirmed earlier research 100

findings that combining the job characteristics in an additive way

predicts better than in the multiplicative way that Hackman & Oldham

prescribed. Note that Hackman & Oldham’s (1975) version of the JCM

focuses solely on job characteristics without employing the effects of

controls or relationships, as does the Management Controls /

Relationships model. Lending (1996) pointed out that the JCM

originally (Hackman & Lawler, 1971) included two interpersonal

characteristics (friendship opportunities, dealing with others) that

were later removed.

The alternative offered to the JCM by Salancik and Pfeffer

(1978), Social Information Processing (SIP), says that task attitudes

are socially constructed from organizational influences rather than

from the characteristics of the job, as the JCM posits. While the JCM

theory has generally found more support than SIP (Glick, Jenkins &

Gupta, 1986), the void created by removing interpersonal issues was

partially filled by SIP. Rather than looking directly at the effects of

management controls and relationships between people, as this study

does, however, SIP looks at how people’s perceptions of their jobs are

influenced socially through cognitive processes. Because some have

found that the leader/worker relationship is as important to job

motivation as the task itself (e.g., McIntosh, 1990), this study later

tests the effects of relationships on motivation.

JCM Hypotheses101

The hypotheses of this section follow the detailed version of the

Hackman/Oldham model, as depicted in Figure 10 in Chapter Two.

Justification for the original hypotheses may be found in the

Hackman/Oldham studies (e.g., Hackman, 1980; Hackman & Oldham,

1975). Chapter 2 reported that the constructs in the JCM did not

summarize reliably at the concept level (see Table 8). Hence, instead

of testing some hypotheses at Figure 1’s level of Job Characteristics,

Critical Psychological States, and Work Outcomes, JCM hypotheses

were formed at the construct level (e.g., Felt Responsibility, Job

Satisfaction—see Figure 10 in Chapter Two). Therefore:

Hypothesis 11: Skill Variety, Task Identity and Job Significance

will each be positively associated with Experienced Work

Meaningfulness, moderated by Growth Need Strength.

Hypothesis 12: Autonomy will be positively associated with

Felt Responsibility, moderated by Growth Need Strength.

Hypothesis 13: Job Feedback will be positively associated with

Knowledge of Results, moderated by Growth Need Strength.

102

Hypothesis 14: Experienced Meaningfulness, Felt

Responsibility, and Knowledge of Results will each be positively

associated with Intrinsic Motivation-Self-Esteem, Job Satisfaction, and

Work Performance, moderated by Growth Need Strength.

Relationships- and System Trust Related Hypotheses

As briefly discussed in Chapter One, both the originators of the

JCM (Hackman & Lawler, 1971) and its competitors (Salancik & Pfeffer,

1978) have recommended the use of social factors in predicting work

motivation. Hackman & Lawler (1971) used social needs (dealing with

others during work, friendship opportunities) to help predict

motivational outcomes. While these social needs were found to

correlate significantly with job satisfaction, they did not correlate with

other work outcomes, defined as motivation, performance, and

reduced absenteeism (Lending, 1996).

In MIS studies, social aspects of Hackman & Oldham’s (1975) Job

Diagnostic Survey (JDS) have been employed. Couger & Zawacki

(1980) used feedback from supervisors, and dealing with others to

represent the social side. Lending (1996) used three JDS social

variables in her study, dealing with others, friendship opportunities,

and feedback from agents. Lending grouped these with other job

characteristics into a ten-factor index. She did not report the

individual predictive power of these social variables. They probably 103

added predictive power, however, because the ten-factor index out-

predicted the traditional five-factor index. This was especially true for

Job Satisfaction, in that the ten-factor index’s adjusted R-squared

was .22, while the additive five-factor index had an R-squared of

only .10. However, Lending’s exploratory model building included a

construct called “Satisfaction with Supervisor” that raised the ten-

factor index explanation of Job Satisfaction from an adjusted R-

squared of .22 to .33. Expanding her model with a Job Security

construct further increased the adjusted R-squared to .36. Lending

reported that both Satisfaction with Supervisor and Job Security

worked best as moderators in these equations. Couger & Zawacki

(1980) did not report large predictive power from dealing with others,

compared to the core job characteristics variables. However, Couger

& Zawacki reported that feedback from supervisors was correlated

highly (r= .41) with internal work motivation, which was higher than

correlations of any of the core job characteristics variables. Thus,

evidence from Lending (1996) and Couger & Zawacki (1980) provided

strong incentive to employ worker/supervisor relationships as a factor

in predicting operator motivation.

Salancik & Pfeffer (1978) related social cognitive processes to

worker perceptions of the job. They (and others—e.g., Griffin, 1983)

have found support for their Social Information Processing (SIP) model.

However, overall, the Hackman/Oldham model, which excluded social 104

needs, had greater predictive power than the SIP model, based on

Taber and Taylor’s (1990) meta-analysis.

In exploring why social factors inconsistently predicted work

outcomes in the original JCM and SIP theories, two aspects of the JCM

and SIP’s treatment of sociality became clear. First, those variables

did poorly that looked at employee lateral (peer) sociality, rather than

the vertical (employee/boss) relationship. In contrast, Couger &

Zawacki’s (1980) vertical construct, feedback from supervisors, did

better than the peer-related variables. Second, each theory primarily

considers relationships indirectly. SIP examined how cognition is

influenced socially. The original JCM tested how social needs

influenced motivational outcomes rather than directly examining the

relationships between people.

This research is based upon the premise that looking directly at

the worker/supervisor relationship could add greater incremental

predictive power than the approaches employed by the original JCM or

SIP studies. Some evidence from the literature encouraged this

thinking. First, such relationship variables as trust demonstrated a

surprisingly strong impact in some organizational settings (e.g.,

Atwater, 1988). Worker/supervisor relationships has been found to be

significantly correlated with organizational commitment (e.g., Tansky,

1993), a motivational outcome. Second, Smits, McLean and Tanner

(1997) found that the worker/supervisor relationship was one of the 105

two most significant predictors of organizational commitment of

information systems people. This was particularly impressive because

their study included a large number of other independent variables.

Therefore:

Hypothesis 15: Critical systems operator/supervisor

Relationships will be predictive of CPS and Work Outcomes (in the

positive direction) beyond the predictive power of Job Characteristics

Model variables.

Relationships and System Trust were defined in Chapter Two.

This study conceptualizes Relationships in terms of Liking and three

types of trust (see Table 3 in Chapter Two). Liking represents the

affective dimension of the relationship, while the two Trusting Beliefs

represent the cognitive dimension. Finally, Trusting Intention reflects

one’s willingness to depend on the supervisor. Hence, Relationships is

a balanced set of variables that represent how one feels, believes, and

intends to act toward, one’s supervisor. System Trust means a

person’s belief about the structures supporting success in the work

environment. In a sense, System Trust communicates what the

operator believes about the organization or organizational subgroup of

which s/he is a part. For this reason, one might say that System Trust

reflects an operator’s relationship with the organization. Just as the 106

Relationships construct means the extent to which one holds positive

feelings, beliefs and intentions towards another person, so System

Trust refers to one’s feelings/beliefs about the organization. How one

feels about the organization probably motivates one to be committed

to it or to desire to work hard for it. Therefore, System Trust will likely

be positively associated with such motivational Work Outcomes as

contained in the JCM.

System Trust will probably not be strongly related to CPS,

however, since CPS reflect how one feels towards specific aspects of

the job, not the overall work situation. For example, Experienced

Meaningfulness means that one experiences the work as being

important. Similarly, Knowledge of Results means that one

understands the outcomes of one’s job. These are narrowly focused

on job-related psychological states. System Trust, which focuses on

general perceptions of the work environment, is more likely to be

related with the Work Outcomes of CPS, since Work Outcomes reflect

less narrow views of the worker’s motivation, such as Job Satisfaction

or Intrinsic Motivation.

Hypothesis 16: System Trust will be predictive of Work

Outcomes (in the positive direction) beyond the predictive power of

Job Characteristics. System Trust will not add to Job Characteristics’

prediction of Critical Psychological States.107

Given how strongly predictive job characteristics were (see

Chapter Three), Hypotheses 15 and 16 represent strong tests of the

impact of Relationships and System Trust in a work environment.

That is, in a work environment that is so intrinsically motivating, the

effects of social or relationship constructs are very likely to be

overpowered by the intrinsic motivators present. Therefore, if

Hypotheses 15 and 16 are affirmed in this environment, they are even

more likely to hold in less intrinsically motivating environments.

METHODOLOGY DETAIL

Hypotheses 11-16 were tested using regression analysis. To

avoid the multicollinearity problem, the interaction terms were

created by multiplying standardized terms together (Aiken & West,

1991).

RESULTS OF HYPOTHESIS TESTING

Table 13 summarizes the results of the tests of Hypotheses 11-

14. Hypothesis 11 (Job Characteristics Experienced Meaningfulness)

was strongly supported. All three of the hypothesized job

characteristics, GNS, and one interaction entered the equation

significantly. Hypothesis 12 (Autonomy Felt Responsibility) was not

supported. Since Job Feedback predicted Knowledge of Results,

Hypothesis 13 (Job Feedback Knowledge of Results) was supported.

Hypothesis 14 (CPS Work Outcomes) was partially supported. Both 108

Job Satisfaction and Intrinsic Motivation were predicted by some

combination of Experienced Meaningfulness and GNS. However,

Performance was not predicted by any of the CPS or GNS.

Table 13 Job Characteristics Model Test Results

H#

Independent Variables

Dependent Variables R 2 ad

jFstat

Significant Constructs p

11

Skill Variety+ Job Identity+ Job Significance+ GNS+ interactions

Experienced Meaningfulns.

.366 .000Job SignificanceSkill Variety Job IdentityGNSSkill Variety X GNS

.323.180.217.211.242

.005.049.017.049.019

12

Autonomy+ GNS + interaction

Felt Responsibility

-.014

.616 -- -- --

13

Job Feedback+ GNS + interaction

Knowledge of Results .204 .000 Job Feedback .46

6.000

14

Experienced Meaningfulns. + Felt Responsibility + Knowledge of Results+ GNS + interactions

Intrinsic Motivation .170 .003

Experienced Meaningfulns.

GNS

.425

.256

.002

.023

14

Experienced Meaningfulns. + Felt Responsibility + Knowledge of Results + GNS + interactions

Job Satisfaction .343 .000

Experienced Meaningfulns.GNS X Experienced Meaningfulns.

.725

.399

.000

.001

14

Experienced Meaningfulns. + Felt Responsibility + Knowledge of Results + GNS + interactions

Individual Performance

-.035

.760 -- -- --

109

Table 14 shows the results from Hypotheses 15 and 16. For

Hypothesis 15, Relationships with supervisors enters the equations

predicting Experienced Work Meaningfulness (15a) and Felt

Responsibility (15b). However, Table 14’s equation predicting Felt

Responsibility is still nonsignificant, based on the F-statistic. In the

equation predicting Experienced Meaningfulness (15a), the beta for

Relationships is higher than that of any other variable, and raises the

adjusted R-squared from .366 to .492. Separate from Table 14,

however, it was found that when the interaction terms and the non-

significant Skill Variety construct are removed from this equation, Job

Significance has a higher beta (.455) than Relationships (.335). So

Relationships is not as predictive as Job Significance. In the equation

predicting Knowledge of Results (15c), Relationships is not significant

because it is highly correlated with Job Feedback (r = .465).

Relationships does not predict the Work Outcomes (15d,e,f), except

Performance. Based on the F-statistic, however, the Performance

equation (15f) is not significant.

In support of Hypothesis 16, System Trust enters the equations

predicting Job Satisfaction (16b) and Performance (16c). However,

based on the F-statistic, the equation predicting Performance is not

significant overall. System Trust does not help the prediction of

Intrinsic Motivation (16a). As predicted, System Trust did not predict

CPS (16d,e,f). System Trust has only modest predictive power for 110

Experienced Meaningfulness (16d), and no predictive power for Felt

Responsibility (16e) and Knowledge of Results (16f).

111

Table 14 Relationships and System Trust Test Results

H# Independent VariablesDependent Variables R 2 ad

jFstat

Significant Constructs p

15a

Skill Variety+ Job Identity+ Job Significance+ GNS + interactions + Relationships

Experienced Meaningfulns.

.492

.000

Job SignificanceJob IdentityGNSSkill Variety X GNSRelationships

.355.198.258.340.394

.001.016.008.000.000

15b

Autonomy+ GNS + interaction+ Relationships

Felt Responsibility

.023

.209

Relationships .220

.045

15c

Job Feedback+ GNS + interaction+ Relationships

Knowledge of Results .19

4.000

Job Feedback .471

.000

15d

Experienced Meaningfulns. + Felt Responsibility + Knowledge of Results+ GNS + interactions+ Relationships

Intrinsic Motivation .17

7.003

Experienced Meaningfulns.

GNS

.355

.243

.016

.030

15e

Experienced Meaningfulns. + Felt Responsibility + Knowledge of Results+ GNS + interactions+ Relationships

Job Satisfaction .33

5.000

Experienced Meaningfulns.

GNS X Experienced Meaningfulns.

.713

.392

.000

.001

15f Experienced Meaningfulns. + Felt Responsibility + Knowledge of Results+ GNS + interactions+ Relationships

Individual Performance

.029

.247

Relationships .297

.016

16a

Experienced Meaningfulns. + Felt Responsibility + Knowledge of Results+ GNS + interactions+ System Trust

Intrinsic Motivation .16

5.004

Experienced Meaningfulns.

GNS

.405

.266

.004

.020

16b

Experienced Meaningfulns. + Felt Responsibility + Knowledge of

Job Satisfaction .41

5.000

Experienced Meaningfulns.GNS X Experienced

.648

.000

112

Results+ GNS + interactions+ System Trust

Meaningfulns. System Trust

.354.284

.001.002

16c

Experienced Meaningfulns. + Felt Responsibility + Knowledge of Results+ GNS + interactions+ System Trust

Individual Performance

.006

.395

System Trust .234

.046

113

Table 14 (Continued)16d

Skill Variety+ Job Identity+ Job Significance+ GNS + interactions + System Trust

Experienced Meaningfulns.

.393

.000

Job SignificanceJob IdentityGNSSkill Variety X GNSSystem Trust

.337.184.267.266.197

.003.041.014.009.038

16e

Autonomy+ GNS + interaction+ System Trust

Felt Responsibility

.013

.282

-- -- --

16f Job Feedback+ GNS + interaction+ System Trust

Knowledge of Results .19

4.000

Job Feedback .468

.000

DISCUSSION OF RESULTS

First, the hypotheses related to the JCM itself are discussed.

Since most of the equations predicting CPS and Work Outcomes are

significant, the basic tenets of the Job Characteristics Model were

largely supported by the study’s data. The fact that few interaction

terms were significant in the six models is consistent with earlier

findings (Griffin, Walsh & Moorhead, 1981). In the critical systems

environment, Experienced Work Meaningfulness emerged as a very

important psychological state in terms of predicting two of the three

Work Outcomes. Job Significance was the most important predictor of

Experienced Work Meaningfulness. Job Feedback successfully

predicted Knowledge of Results. But neither Knowledge of Results nor

Felt Responsibility had any effect on the Work Outcomes. However,

exploratory regression analysis revealed that Knowledge of Results 114

was significantly correlated with Felt Responsibility, which in turn was

significantly correlated with Experienced Meaningfulness.

Second, hypotheses concerning Relationships and System Trust

are discussed. Relationships and System Trust have significant

predictive effect in two of the equations. In light of the highly

intrinsically motivating job studied, this finding is remarkable.

Relationships adds predictive power to the JCM equation predicting

Experienced Work Meaningfulness. This by itself is important, since

Experienced Work Meaningfulness is the most powerful predictor of

Job Satisfaction and Intrinsic Motivation. System Trust adds predictive

power to the JCM equation predicting Job Satisfaction. In the critical

systems setting, as expected, the characteristics of the job appear to

be the most important factors in predicting Critical Psychological

States (CPS). One CPS, Experienced Meaningfulness, was the most

important factor in predicting two Work Outcomes—Job Satisfaction

and Intrinsic Motivation. Since Relationships and System Trust add

predictive value to two of the more important of the JCM equations,

they represent a vital missing element in the current configuration of

the JCM.

While none of the Table 14 equations predicted Performance,

exploratory analysis found that Relationships by itself predicted

Performance with an R-Squared of .043, Beta = .233, and p = .031.

Hence, Relationships was a better predictor of Performance than any 115

of the JCM variables. However, even Relationships did not predict

Performance well. From Chapter Three results, Contribution to Team

Effectiveness was a major predictor of Performance. Performance

may also not be predicted by motivation-related variables because

variables not measured, like the CSO’s skill, knowledge, and ability,

were much more important predictors of performance. Another

possible explanation is that supervisors (the informant for

Performance) may distinguish employees more via skill, knowledge,

and ability than via motivation levels. This seems plausible in light of

the high mean scores and low standard deviations of the CPSs (Felt

Responsibility, Experienced Meaningfulness, and Knowledge of

Results--see Appendix I).

In summary, Chapter Four provides evidence that:

the JCM applies in the Critical Systems Operator (CSO)

environment;

the job’s significance and related Experienced Work

Meaningfulness are the most important factors in

predicting CSO intrinsic motivation and job

satisfaction, and, therefore, the work context makes

a difference;

by increasing the variance explained from .366 to .492

(34%), Relationships with the supervisor adds

116

significant power to the JCM’s prediction of

Experienced Meaningfulness;

by increasing the variance explained from .343 to .415

(21%), System Trust adds significant power to the

JCM’s prediction of Job Satisfaction.

Chapter Four’s evidence that relationships matter beyond the

power of the JCM constructs is especially striking in light of Chapter

Three’s evidence that the CSO job is highly extrinsically motivating.

Further, while Chapter Three demonstrated that Relationships are

qualitatively important to CSO motivation, Chapter Four quantifies

that importance using an entirely different method. Together, these

two methods provide triangulated evidence (Kaplan & Duchon, 1988)

for the importance of relationships.

117

CHAPTER FIVE:

INCENTIVE CONTROLS--ADDING RELATIONSHIPS

Ch Prop: Content or Model

2 -- Methodology and Construct Validation

3 1 Nature of the Critical Systems High Levels of Operator Job Motivation

4 2, 3 Growth Need Strength

Critical Job Characteristics Psychological Work

States (CPS) Outcomes

Relationships System Trust

5 4, 5

Incentive Motivational Controls Effect

Relationships

6 4, 5

Other Motivation Motivational Controls Outcomes

Relationships System Trust

7 -- Contributions, Limitations, and Future Research

Chapter Five first reviews theory about Incentive Controls.

Hypotheses are then tailored to the XYZCo environment. The

118

methodology for testing the hypotheses is detailed, and then the

results are presented and discussed.

THEORY BUILDING

Definitions

One view of management controls is to measure performance

against some comparison standard (Davis & Olson, 1985: 319). This

definition of controls is broad enough to fit several control

mechanisms, such as monitoring, feedback and accountability. Other

controls definitions augment the comparison-versus-standard view of

control. At the organizational level, the term "controls" means the

process of assuring that resources are used effectively to accomplish

an organization's goals (Anthony, 1965). This is a broad, structural

definition. At an interpersonal level, the term "controls" means the

processes used "to direct, to influence, or to determine the behavior of

someone else" (Lawler & Rhode, 1976: 1248). Combining the

meanings from Anthony and Lawler & Rhode, this study defines

controls as methods of attempting to ensure desired outcomes by

trying to influence other people. Management controls occur when

managers use various methods to try to influence employees to

behave in ways that lead to outcomes desirable to management. This

definition is similar to Kirsch’s (1992: 9) interpretation of Anthony’s

view of control: “motivating individuals to act in accordance with

organizational objectives.”119

Control is distinguished from related concepts as follows.

Influence is a descriptive term that means that one person causes

changes in another person’s behavior, emotions or thoughts (Huston,

1983; Tannenbaum, 1968). Following Huston, this study defines

power as the ability (whether used or not used) to achieve desired

ends through influence. Translating the researcher’s definition of

controls in light of the definitions of influence and power, control

means trying to utilize power through influence attempts. Dominance

exists when influence is asymmetrical over a broad range of activities

(Huston, 1983). Dependence means one’s interest (what is at stake)

in satisfactions provided by the other person (Walton, 1968). Power,

controls, influence, dominance, and dependency exist in actual and

perceived form (Walton, 1968).

Controls Theory Overview

Existing controls theories (e.g., organization theory--Ouchi,

1979; agency theory--Eisenhardt, 1989b) have largely been used to

test the link between types of controls and desired outcomes. For

example, Ouchi’s (1979) controls theory said that outcome-, behavior-,

and clan controls each produce different outcomes under different

kinds of conditions. Agency Theory says that an agent will do what

the principal wants as long as: a) the contract aligns the objectives of

the principal and the agent through bonding mechanisms and/or b)

the principal can monitor the agent’s behavior (Barney & Ouchi, 120

1986). The assumptions underlying Ouchi’s control theory, Agency

Theory, and Transaction Cost Economics (Williamson, 1975) are from

the long-standing tradition in economics: people are boundedly

rational, probably opportunistic but definitely self-interested, and not

influenced socially. In other words, these theories assume that people

are not to be trusted, and therefore they should be controlled.

Organization theory and agency theory provide significant light

about how controls work in terms of what leads to the use of different

kinds of controls, and which types of controls work best on what.

However, because these theories largely ignore organizational, social,

and interpersonal factors, they have trouble explaining how controls

achieve outcomes. That is, controls work through peoples’ attitudes

towards their job, management, and the company. Controls affect,

and are affected by, the social/organizational issues (e.g., motivation,

teamwork, and trust) that are often key to positive outcomes. Several

researchers have pointed out how important workplace relationships

are toward accomplishing tasks (e.g., Ring & Van de Ven, 1994;

Gabarro, 1990). Granovetter (1985) argued that researchers should

find a balance between over-socialized (i.e., relationships are

paramount) and under-socialized (i.e., relationships don’t matter)

depictions of organizational phenomena. Granovetter maintained that

many controls theories (e.g., agency theory and transaction cost

economics) are under-socialized. To the extent that they are under-121

socialized, control theories do not adequately address the linkage

between controls, social relationships, and worker motivation.

Moreover, more work is needed to understand the mechanisms

behind the effects of controls on organizational outcomes. Powers &

Dickson (1973) found that system development project controls were

perceived to have negative effects on system development success.

However, they did not conclude why or how this occurred. Henderson

& Lee (1992) found that controls had positive effects. However, their

operational definitions of controls primarily reflected only positive

types of controls (e.g., helping behaviors) instead of the full range of

possible control mechanisms. Lawler & Rhode (1976) discussed briefly

the negative impacts of tight financial control mechanisms (e.g.,

budgets) on employee behavior, but did not explain the mechanisms

behind it. Simons (1995) said incentives stimulate initiative and

opportunity-seeking, but may have dysfunctional side-effects. Simons

did not explain this statement further.

Conceptual Model Development--Incentives

XYZCo’s context provided some clues about why controls have

dysfunctional side-effects. At XYZCo, management is concerned

enough about system availability to want to control critical systems

operator (CSO) behavior in order to improve availability. The CSOs’

management is active and involved in the day-to-day affairs of the

unit. The senior management team is keenly interested in keeping 122

the system continually available. The company has norms for doing

things right, for succeeding, and for not accepting excuses for failure.

These norms act like controls; management inculcates these norms to

try to influence workers to do things right. The company also has a

norm for rapidly fixing problems that need to be addressed, as well as

a norm for promptly reporting the problem cause and both short-term

and long-term solutions to management. Management requests

significant levels of detail on the reasons behind every outage and

what is being done to see that these underlying causes never recur.

These actions are a form of the feedback- and accountability controls

treated in Chapter Six.

Based on Phase I data, XYZCo management’s concern to keep

the systems operating primarily manifested itself through five control

mechanisms: incentives, accountability, feedback, autonomy

granting, and management involvement. The conceptual and

scientific model development and test results for incentive controls

will be discussed in this chapter. The other control mechanisms will be

covered in Chapter Six.

In 1994, senior corporate management installed a bonus award

system for all employees. Overall, the bonuses were made contingent

on division profitability. However, each work group also had its own

set of performance goals that largely determined its members’ annual

bonus awards. The division was profitable in 1994 and 1995, so 123

bonuses were given. During this period, management focused on cost

controls in order to assure high division profitability. Two interviewees

perceived this as a change from past management strategy, because

the division’s first emphasis had always been to spend enough money

on system infrastructure to assure the system would be kept highly

available. In 1996, the new management team changed the bonus

distribution from a team-based method to an individual merit basis.

The team-based method tied the bonus to specific team availability

goals, while the individual-based method was not as specific.

Interviewees said that the focus on cost savings that began about

1994 decreased morale of some operators. In part, morale decreases

were due to employees’ perceptions that management was de-

emphasizing quality of their system by spending less on it. CSOs had

always been proud of the system’s quality. Therefore, reduced

spending called into question what had been the highest priority of

employees. Also, CSOs translated events like cuts of budgeted

positions11 into job insecurity feelings. However, these slight to

moderate changes in morale did not appear to have seriously

jeopardized system availability. When the computer went down,

workers still quickly got it going again--in a very cooperative, highly

motivated manner.

11 No layoffs of any size had taken place.124

However, the incentive system did produce the potential for

negative side effects in the overall company (McKnight, 1996). Groups

that had different bases for their incentives tended to have more

conflicts with each other than they did before the incentives were

implemented. For example, the major incentive for the programming

division was now to produce more new systems, with no incentive to

maintain old systems or to keep existing systems running. Hence, the

management of the operations group felt the incentives motivated the

programmer group to implement new systems before they were

adequately tested, thereby endangering system availability.

However, the good interpersonal relationships, developed over time,

between specific programmers and operators ameliorated the conflicts

that resulted from the incentive system, such that it appeared to have

no negative effects on computer system availability.

This example supports the conceptual version of the

controls/relationships model (Figure 5—Chapter One). In this

example, interpersonal relationships moderated the effects of controls

(incentives) on system availability. Evidence in Phase I interview data

lent credence to Relationships’ moderation of the Incentive Controls

Motivation link in Figure 5. Those interviewees who appeared to have

the worst relationship with management also appeared to be most

negatively affected, in terms of morale, by the incentive plan.

125

Several interviewees also mentioned that the incentives had

made both management and technicians so cost-conscious that they

were afraid to spend money even for the “infrastructure” that would

keep the system at a high level of availability. If continued,

interviewees noted, this trend could eventually have serious

implications. One interviewee claimed that low infrastructure

spending had already resulted in at least one extended system

availability problem. Two other informants said that incentives were

encouraging the operations people to focus only on things that were

true outages, but to ignore things that inconvenienced the customer

without being outages for which operations was accountable.

From the data above, the researcher felt: a) that incentives may

have negative, as well as positive, effects on CSO motivation at

XYZCo; b) the changes in the incentive plan distribution methods may

change the plan’s effectiveness; and, c) the CSO/supervisor

relationship may influence how incentives affect CSO motivation.

Scientific Model Development-- Incentives

Earlier research has linked controls and motivation. Three most

applicable examples follow: a) scientific management theories, b)

cognitive choice theories, and, c) need-motive-value theories. From

these theories, the intrinsic motivation literature will be discussed in

more detail.

126

Scientific Management Theories. Frederick Taylor’s (1911)

research on “scientific management” of physical tasks resulted in

recommendations that workers be motivated by piece rate incentives.

“In Taylor’s view, workers would only respond to financial incentives

based on defined performance standards” (Simons, 1995: 22). To

Taylor, effective controls came through the strategic placement of a

quantified carrot. Taylor’s work coincides with a large body of

literature in learning psychology on how animals and people can be

controlled by offering them reinforcing rewards (e.g., Skinner, 1953).

Out of this tradition have come many studies in organizational

behavior on related topics such as operant learning theory (e.g.,

Hamner, 1974). As shown in Figure 4, many studies support the

proposition that incentive controls improve worker motivation (see

Hamner, 1991).

Cognitive Choice Theories. Control was the theme in early

animal experimental work in psychology. Experimenter control

assumed, however, that the animal was merely a responder to stimuli,

not a purposeful, thoughtful being. Only the actions of the animals

were studied, since their thought processes were assumed away for

purposes of empirical rectitude (Hergenhahn & Olson, 1993). While

most early work in the psychology of motivation studied behavior

only, Tolman (1932) hypothesized that motivating rats and people

involves not only behaviors, but behavior supported by cognitive 127

processes. The cognitive revolution in psychology spawned a number

of what Kanfer’s motivation review article (1990:75) called “cognitive

choice” theories of motivation, such as expectancy/value theory

(Vroom, 1964), attribution theory (Weiner, 1974), dynamics of action

theory (Atkinson & Birch, 1970) and “self-regulation-metacognition”

theories, such as goal setting (Locke, 1968), social learning (Bandura,

1986) and cybernetic controls theories (Carver & Scheier, 1981).

These theories do not agree with the assumptions behind the narrow

behavioral view of human motivation espoused by operant learning-

and scientific management theorists.

Need-based Theories. Also at odds with scientific

management are the need-motive value motivation theories (Kanfer,

1990). In his famous Western Electric Hawthorne research, Elton

Mayo reported that workers were motivated by the support and

sentiment of social interaction in the workplace (Mayo, 1949). Social

control by meeting worker sociality needs was the key motivator to

Mayo. The Hawthorne studies relate to what Kanfer (1990) called the

need-motive-value research in motivation. This broad area of

research includes need fulfillment theories (e.g., Maslow, 1943;

Alderfer, 1969), intrinsic motivation (e.g, Deci, 1975), and equity

theories (e.g., Adams, 1963) of motivation. For example, Maslow’s

hierarchy of needs theory said that people are first motivated to fulfill

basic needs, such as for food and safety. Once these needs are 128

fulfilled, they no longer motivate. At this point, one desires to fulfill

higher order needs, such as love, self-esteem, and finally, self-

actualization.

One underlying difference between the operant learning and

need-based motivation theories is important for this study. The

assumptions underlying the scientific management and operant

learning theories are that humans are disconnected from each other,

self-interested, and fully rational (Simons, 1995). Note that these

assumptions are also reflected in the economic controls theories

discussed previously (e.g., Eisenhardt, 1989b). By contrast, the need-

motive-value theories assume that people are socially connected, both

self- and other-interested, and not always economically rational. In

particular, intrinsic motivation researchers have tried to reconcile the

economics-based, gain-seeking motivation perspective with the idea

that people are motivated by other needs and desires.

Intrinsic Motivation. Intrinsic motivation means motivation

that “results from an individual’s need to be competent and self-

determining” (Steers & Porter, 1979: 249). Intrinsic motivation

signifies that the outcome/reward for the work behavior comes from

inside the person (e.g., personal satisfaction). Extrinsic motivation

signifies that the outcome of the behavior comes from the outside,

such as a monetary incentive or a job promotion (Kanfer, 1990). The

inference is that a person is intrinsically motivated when outside 129

forces are not present in enough force to move one to action.

However, “[w]hen there are strong [external] forces bearing on the

individual to perform an activity, there is little reason to assume that a

behavior is self-determined...” (Staw, 1976).

Management controls constitute some of these outside forces.

That is, a control like behavior monitoring can act as an extrinsic

motivation factor. For example, Strickland found that supervisors who

watched their employees (i.e., a behavioral control, per Kirsch, 1992)

more frequently felt that the employees’ good behavior was caused by

the supervisor’s monitoring. Staw (1976) cited Strickland (1958) as an

example of how people interpret another person’s behavior as

extrinsically motivated when the other person is being controlled.

Hence, employing managerial controls can affect the manager’s views

of the worker’s motivation.

Bem (1967) said that this principle can also be applied to self-

perception: how one views the motivation behind one’s own behavior.

If one acts in the presence of strong external rewards, s/he is likely to

attribute her/his behavior to external controls. If these outside

rewards are not strong, s/he will probably assume her/his behavior is

due to his/her own interest in the activity. The same point was made

by deCharms (1968: 328): “...when a person perceives the locus of

causality for his behavior to be external to himself (that he is a Pawn),

he will consider himself to be extrinsically motivated.” Staw (1976) 130

argued that when intrinsic rewards are high and extrinsic rewards

(e.g., pay, bonuses) are low, people will perceive themselves as being

intrinsically motivated. That is, intrinsic motivation is perceived to

justify the person’s action to her/himself. However, when both

intrinsic and extrinsic rewards are high, the workers will be faced with

an unstable perception. The perception is unstable because it is

“oversufficiently justified” (Staw, 1976: 255). That is, the person feels

more than fully justified for the action taken. S/he is likely to reason,

therefore, that since the external reward by itself would have justified

the action, s/he was extrinsically motivated to perform the activity,

and therefore, the task was not that enjoyable--or intrinsically

motivating--after all. This drop in intrinsic motivation may be crucial if

the nature of the task was intrinsically motivating before: total

motivation may be decreased and task performance may therefore

suffer.

Three early studies provided evidence of this concept (see Table

15).

Table 15 Effects of Extrinsic Motivation on Intrinsically Motivating Tasks

Study Task Subjects Effects of Experimental ConditionDeci, 1971 Solving

puzzlesCollege students

Lower intrinsic motivation (i.e., less time playing with the puzzle during free time) after being paid for solving a puzzle

Lepper, Greene, and Nisbett,

Playing with Magic Markers

Nursery school children

Lower intrinsic motivation (i.e., less time playing with the markers during free-play period) after a contingent reward

131

1973Kruglanski, Freedman and Zeevi, 1971

Creativity and Memory tasks

Teenagers Less satisfaction with the task, less likely to volunteer for future tasks, lower task performance after being offered the extrinsic reward (i.e., a free tour of the laboratory facility)

Deci (1971, 1972) hypothesized that only rewards contingent on

a high level of task performance will adversely affect intrinsic

motivation. This was supported by Lepper, Greene & Nisbett (1973) in

that those in the unexpected reward condition were not as affected as

those in the expected (i.e., contingent) reward condition. In all three

of the studies summarized in Table 15, what seemed to change the

cognitive orientation of the person from intrinsic to extrinsic was the

contingent reward for a given level of output.

Eisenhardt (1985) challenged Deci’s hypothesis that extrinsic

rewards diminish intrinsic motivation. Eisenhardt used data from

specialty retailers to support her economics-based hypothesis that

incentives motivate. She found that the intrinsic motivation of those

in sales jobs who were given salient, contingent sales incentives was

not decreased. Eisenhardt interpreted this result as a contradiction of

Deci’s overjustification hypothesis. However, Eisenhardt did not

report the specific levels of intrinsic motivation her respondents

possessed, making it impossible to know whether their level of

intrinsic motivation was high enough for the overjustification

hypothesis to work.

132

Why Extrinsic Rewards Can Demotivate. The literature

suggests two reasons why extrinsic rewards can demotivate. First, the

use of extrinsic rewards may change the person’s mind about why

they are doing the intrinsically motivating task. Before the reward

was offered, the person may have been doing the task primarily

because the task was enjoyable. The extrinsic reward convinces them

that they are doing the task for the sake of obtaining the reward

instead of for enjoyment. This causes them to dislike the task: “In

fact, the more you want what has been dangled in front of you, the

more you may come to dislike whatever you have to do to get it.”

(Kohn, 1993b: 83) Second, the use of extrinsic rewards harms the

person’s view of themselves by moving their locus of control from

internal to external. People have a desire to be efficacious and

autonomous (de Charms, 1968). They like to control their own

destiny. When the extrinsic reward moves their locus of control

outside of themselves, they may become less interested in the task.

But they also wonder: Why does my manager believe I cannot control

myself? This is not perceived as a compliment! Arguably, those who

already have a poor relationship with their management are more

likely to interpret controls negatively than are those with a good

relationship with their management.

Conditions for Proper Application of Cognitive Evaluation

Theory. Per Staw (1976, quoted in Steers & Porter, 1979: 261), 133

researchers still need to take these early findings and “determine the

exact conditions under which they might be expected to hold.” The

following discusses five conditions. These studies help by predicting

more precisely when extrinsic rewards will affect intrinsic motivation.

Condition 1—Incentive Salience. Ross (1975) showed

through two experiments that the reward offered had to be salient, or

it would not affect intrinsic motivation. Deci (1975) explained that

rewards may not be salient enough to affect negatively intrinsic

motivation because they are perceived to be informational rather than

controlling. This may occur when the reward is interpreted as

providing information related to one’s competencies--which may

enhance, rather than hurt, one’s feelings of control (Kanfer, 1990).

Evidence for this was found by Harackiewicz, Manderlink & Sansone

(1984). Similarly, Freedman, Cunningham & Krismer (1992) noted

that the greater the incentive offered, the more it will decrease

intrinsic motivation.

Condition 2—Norms. Staw, Calder & Hess (1976) found that

rewards decrease intrinsic motivation only when there is a situational

norm not to give extrinsic rewards for the task. Fisher (1978) found

that the same held for societal norms.

Condition 3—Pre-existing Level of Intrinsic Motivation.

Calder & Staw (1975) found that extrinsic rewards only hurt intrinsic

motivation when intrinsic motivation is high. When intrinsic 134

motivation is low, the rewards had a reinforcement effect that

increased overall motivation. Staw (1976) commented that most

industrial work settings do not meet the conditions for when extrinsic

rewards will hurt intrinsic motivation, because many work tasks are

not highly intrinsically motivating and extrinsic rewards are the norm.

Condition 4—Competence/Control Impact of Incentive.

Two studies found that incentives can increase intrinsic motivation if

they increase the task’s level of perceived challenge, provide the

worker additional competence information, or increase the perception

of personal control over performance (Tripathi, 1991; Lopez, 1981).

Condition 5—Perceived Reason for Incentive. Calder &

Staw (1975) also cautioned that the perception of why the reward

being offered is a key. “For example, if a financial reward is perceived

as a bonus for good work rather than as an inducement to keep

people on the job, it may not have a deleterious effect on the valence

of intrinsic outcomes” (Campbell & Pritchard, 1976: 104).

In summary, how an incentive is perceived is just as important

as its objective attributes. In particular, it is likely that when the

situation involves a highly intrinsically motivating task and extrinsic

rewards are not the norm, a salient, contingent extrinsic reward will

lead to lower intrinsic motivation for the task. In contrast, extrinsic

rewards will not negatively affect intrinsic motivation if: a) they are

not salient or contingent; b) they are already the norm; c) the task is 135

not intrinsically motivating; d) the reward is perceived to increase the

worker’s feelings of competence or control; or, e) the reward is

perceived to be a compliment for good work. Applying these factors

to XYZCo: a) The incentive award was potentially large enough in

monetary value to be salient. b) The award was the norm since 1994,

but the method of distributing the award changed in 1996 from the

previous norm of team-based to individual performance-based; c) The

task is intrinsically motivating in the extreme; d) The incentive award,

since no longer tied to specific actionable measures, did not increase

worker feelings of competence or control; e) The incentive award

could probably be more clearly interpreted as a compliment for

individual performance now. But since it was no longer team-based, it

could also be interpreted as more of a management “carrot,” and less

of a compliment for good team performance. These applications to

XYZCo will influence the hypotheses tested.

Hypotheses-Incentives

Phase I’s qualitative data indicated that when the original

incentive system was installed in 1994, the incentives were tied to

challenging team goals. However, the method for distributing rewards

changed in 1996. The literature indicates that challenging goals need

to be quantifiable. Quantifiable goals provide the worker with greater

perceived control over performance (and related rewards). The

qualitative data indicated that the new award distribution method was 136

relatively subjective, making it likely to be perceived as non-salient.

Therefore:

Hypothesis 17: Since the incentive award system was recently

changed from specific, quantifiable team goals to a non-quantified

individual performance at XYZCo, most CSOs will say that the

incentive plan goal was not challenging for them. Hence, the

incentive will not be perceived to be highly challenge salient, even

though, in absolute dollar terms, the goal is large enough to be

considered monetarily salient.

Because the incentive will probably not be perceived as salient,

and because the workers probably have high levels of intrinsic

motivation, the incentive will probably be considered to have neutral

or negative effects on CSO motivation. The changes that were made

to the incentive award structure will probably cause negative

reactions in many of the workers. Therefore:

Hypothesis 18: When asked: a) if achieving their incentive

plan goal was challenging for them, and b) if the incentive plan has

any other effects on them or their team, the majority of the responses

will indicate that the incentive plan has either little-to-no effect or a

negative effect on motivation.137

The recent changes at XYZCo in how XYZCo’s incentive awards

are divided will also mean the awards will probably not be regarded as

being highly motivational. Rather, they will be considered only “nice-

to-have” by some workers, but punitive by those who receive smaller

than expected awards. This is because the workers’ views of an

award will probably change from a bonus for overall good team

performance to a vehicle to reward differentially “good versus bad”

employees. The new way of awarding bonuses may violate employee

norms for how things should be done. Therefore:

Hypothesis 19: When asked: a) if the incentive plan has a

positive motivating effect on them and the team; b) if the incentive

plan has a positive effect on their own and the team’s

conscientiousness; and, c) if the incentive plan has a positive effect on

their own and the team’s work effort, respondents will be significantly

more negative than they were for the other questions in the survey.

Several researchers have indicated that the relationship

between workers and management impacts the effectiveness of

incentives in motivating employees. In the context of budget controls,

Hofstede said that “the interpersonal relation and communication

between superior and subordinate is of much greater importance for 138

the functioning of the organization than the power relationship”

(1967: 58). Steers & Porter (1979: 547) said that merit pay systems

work best when trust and openness exist between workers and

management. Indeed, other studies suggest that pay-for-performance

plans may not work because of a lack of worker/management trust

(Lawler, 1971; Steers & Porter, 1979: 526-531). Lawler said that “No

plan can succeed in the face of low trust and poor supervision, no

matter how valid it may be from the point of view of mechanics”

(1971: 163). Steers & Porter (1979: 386) said that the perceived

helpful intent of controls leads to employee liking of the boss, which

leads to greater productivity.

The literature reviewed above indicated that the effects of

incentives also depend on worker perceptions of why the incentive

was given. Phase I’s qualitative data indicated that worker

perceptions are often influenced by worker relationships with

management. Hence, worker relationships with management

probably moderate the effects of the incentive on the worker’s

motivation, as Figure 5 indicates. Therefore:

Hypothesis 20: Responses to the questions in Hypothesis 19

will be significantly more positive for those CSOs with a better

relationship with management than those with a worse relationship

with management.139

Another important factor will be whether the groups have a

feeling of security about their jobs. Workers in groups whose jobs are

going away are more likely to feel less motivated by the incentive

than those whose jobs are secure. Therefore:

Hypothesis 21: Responses to the questions in Hypothesis 19

will be significantly less positive for CSO groups with insecure

positions.

It is likely that respondents’ answers about the incentives’ effect

on motivation are to some extent driven by the level of motivation

they possess about the job itself. That is, how intrinsically motivated

they are will probably be related to how they feel about their own

level of motivation. If they are highly intrinsically motivated, they will

more likely feel the incentive plan provides positive motivational

effect. This hypothesis makes intuitive sense, but is speculative,

because it is not based on prior research. Therefore:

Hypothesis 22: The CSOs’ intrinsic motivation will be

positively associated with answers about the effects of the incentive

plan on them and their team.

140

METHODOLOGY DETAIL

Chapter Five’s hypotheses were tested through a combination of

qualitative and quantitative methods.

To determine whether the incentives were perceived as

challenge salient (H17), the respondents to the telephone

questionnaire were asked (on a 7-point scale) the extent to which they

agreed that achieving their most recent bonus goal was challenging

for them. Scores of four or less were considered “not challenging”

responses, while those over four were coded as challenging. A simple

majority of “not challenging” responses was considered adequate

support for Hypothesis 17.

H18 (incentive plan Motivational effect) was tested by coding

the researcher’s notes from respondents’ open-ended responses to

the two questions implied in the hypothesis (Appendix C, questions 43

and 90). By open-ended is meant the responses that CSOs used to

comment on their agree/disagree answer to these questions. The

coding of these responses was done twice, once to capture whether

answers were positive or negative toward the incentive, and a second

time to capture answers that specifically stated that the incentive plan

had little or no effect on the respondent’s or the team’s motivation.

These questions were asked of the respondents over a period of time

from about the date the award was given to about two months after

the award was given. Hence, the most recent award was fresh in their 141

minds. A simple majority of negative responses is considered support

for Hypothesis 18.

H19 (incentive planMotivational effect) was tested by asking

the respondents the questions implied in the a), b), and c) parts of

Hypothesis 19. Each of the three topics was asked with two items.

One item addressed the individual’s feelings about the team, and the

other question asked them to respond about themselves (see

Appendix B, questions 84-89). The six items could be joined into one

construct called Motivational Effect with a Cronbach’s alpha of 0.92.

However, for this hypothesis, the answers will each be analyzed

separately by pair of questions. This is because the first pair asks for

general motivation effects, while the second and third ask for two

specific types of motivational effects: conscientiousness and work

effort. The hypothesis was tested by comparing the average

responses to these three sets of questions with the average responses

to the other questions in the survey. Hypothesis 19 will be considered

supported if: a) each of the three mean scores is in the bottom

quartile when compared with the mean scores in Appendix I; b) each

of the three mean scores is significantly below the average of all mean

scores shown in Appendix I. Test a) is probably the stronger of the

two tests. The test for b) will be an alpha = .05 significance T-test of

the difference between two means, as used to test Hypotheses 1-4

(Keller, Warrack & Bartel, 1988).142

To test whether relationships with management made a

difference (Hypothesis 20), the data were divided at the mean into a

good relationship group and a poor relationship group. The mean

scores for opinions on the motivational effect of the incentive awards

were calculated, and then a one-way anova test performed. The same

test was done for those who were in secure versus insecure groups

(Hypothesis 21). The insecure group consisted of the hardware

operators, whose functions management had decided to largely

automate.

Hypothesis 22 (Intrinsic MotivationMotivational effect) was

tested by correlating the degree of both enjoyment- and self-esteem-

based intrinsic motivation (Appendix C, average of questions 20-27)

with respondents’ beliefs about the effects of the incentive plan

(Motivational Effect--average of questions 84-89 in Appendix C).

RESULTS OF HYPOTHESIS TESTING

Hypothesis 17 (incentive salience): H17 was supported.

Fifty of eighty-six (58.1%) of the respondents felt the goals were not

challenging to obtain. Hence, the incentive was not salient in terms of

challenge. Prior year goals appeared to present a mild to moderate

challenge, based on responses. This occurred even though the annual

award was found to be anywhere from zero to as much as somewhat

143

above ten percent of employee annual salary. Thus, the incentive was

monetarily salient but not challenge salient.

Hypothesis 18 (negative effect of incentive): H18 was

supported. Eighty-four percent of the responses were negative and

16% of the responses were positive about the incentive plan in

answers to questions 43 and 90. Although the researcher did not

specifically solicit this comment, 44% (37 of 84) of the respondents to

these two questions also stated that the incentive plan had little or no

effect on their motivation or actions, or those of the team.

Hypothesis 19 (Incentive planMotivational Effect): H19

was strongly supported. The mean score for the sum of the six

questions asked about the incentive was 4.03 on a 1-7 scale, which is

very low compared to the mean scores of other variables (Appendix I).

The detailed questions were then analyzed to obtain a more complete

view. On the question of the incentive’s general effect on motivation,

scores were somewhat more positive (mean = 4.58), while they were

quite negative on how the incentive specifically affected

conscientiousness (mean = 3.78) and work effort (mean = 3.72).

Comparing these scores to the means in Appendix A, all three

Motivational Effect scores were in the bottom quartile of scores. In

fact, excluding Micromanagement (reverse-scaled), Accountability

(five-point scale) and Performance (scaled to be close to 4.0 on

average), Motivational Effect mean scores were the lowest of all the 144

variables in the study. T-tests revealed that each of the three

Motivational Effect scores was significantly below the average mean of

all other Appendix I variables, which was 5.74. Hence, H19 was fully

supported.

Hypothesis 20 (effect of Relationships): H20 was weakly

supported. Responses were more positive for those employees with

better relationships with management (mean = 4.36; n=43) versus

those with worse relationships (mean = 3.69; n=43). However, a one-

way ANOVA revealed that the means were different at the moderately

significant p=.072 level.

Hypothesis 21 (effects of job security): H21 was not

supported. Contrary to prediction, responses were more positive for

those in insecure groups (mean = 4.64) versus secure groups (mean =

3.79). These two means differed at the significant p=.038 level.

H22 (intrinsic motivation motivational effect) results:

The respondents’ intrinsic motivation was significantly correlated with

their beliefs about the motivational effectiveness of the incentive plan

(Motivational Effect), at r= .243; p=.012. Intrinsic Motivation-

Enjoyment and Intrinsic Motivation-Self-Esteem were about equally

correlated with Motivational Effect.

DISCUSSION OF RESULTS

145

Based on the results of Hypotheses 17-19, CSOs felt that since

the incentives were not challenging, the incentives only had general

motivational effects rather than specifically improving their

conscientiousness or work effort. Those CSOs with higher intrinsic

motivation and better relationships with their supervisors felt the

incentives had greater motivational effect. To understand these

results better, the following discusses the findings in light of other

qualitative analysis of the questionnaires.

From the questionnaire interviews, CSO perceptions of the

incentive plan had changed since it was installed in 1994. While at

first it was almost like a profit sharing award tied to team goals, such

as system availability, it became more of a carrot for management to

use to try to influence behavior by rewarding or punishing individual

performance. The reward value came through for those who received

medium to large awards. Qualitative responses from those who were

dissatisfied with the incentive bonus consistently indicated that it had

a punitive effect for them, as Kohn (1993a,b) and Simons (1995: 79)

predicted. A number of respondents who were not satisfied with their

own award indicated that they felt the bonus award system was not

equitable (e.g., it worked like a “good old boy” system). Since

incentive systems that punish have never proven to be effective

motivators (see review in Hergenhahn & Olson, 1993), XYZCo’s

system has a decidedly negative motivational effect on those who 146

received lower than expected rewards, in spite of the money devoted

to it.

To further understand CSO views on how motivating the

incentives were, the researcher split the data in questions 84-89 by

the perceived effect of the incentives on the CSO versus on the team.

Paradoxically, even though 69% of the CSOs were satisfied (scores

above 4.0) with their own recent incentive award, only 28% felt that

most of their co-workers were satisfied. This is probably because the

comments they heard around the shop were primarily negative. If so,

this indicates a rumor-mill type of effect that is similar to how second-

hand knowledge about a person can exaggerate the effects of various

factors on peoples’ trust of that person (Burt & Knez, 1996). The

preponderance of negative (84%) comments given the researcher also

supports the rumor-mill effect.

Tests of Hypothesis 19 showed that the incentive plan had more

general effects (e.g., morale boosting--”it’s nice to have a bonus”), as

opposed to helping the team’s specific work effort motivation or its

conscientiousness. The qualitative questionnaire data supported this.

Many respondents said they rarely thought about the incentive except

just before and just after it was given. Hence, the incentive probably

had very little day-to-day motivational effect, even on those who were

positive toward it. Rather, it probably only acted like a general and

temporary morale booster.147

The results contradicting Hypothesis 21 (job security lower

motivational effect of incentives) can be explained as follows. Even

though those in the insecure groups are just as highly intrinsically

motivated as those in the other groups (Table 12), Chapter Three

found that those in insecure groups were more likely to explain what

motivates them with extrinsic, rather than intrinsic, factors (p= .000).

Because of this tendency, those in insecure groups were more likely to

believe that extrinsic controls have motivational effect.

Overall, the incentive plan tended to have either neutral or

negative effects. While none of the interviewees suggested that the

incentive be done away, the workers’ consensus was two-fold: a) the

incentive did not significantly effect their specific motivation to work

harder or be more conscientious; and b) the 1996 changes made to

the incentive plan primarily had negative effects on worker morale.

The latter effect was more pronounced among those who were

dissatisfied with their own award.

Incentives fit this study’s definition of controls in that incentives

are used by management to influence the work behavior of

employees. Figure 5 posits that the operator’s relationship with the

supervisor moderates how effective the operator felt the incentive was

in motivating her/him and the team. Results from Hypothesis 20

support the controls/relationship model, in that the mean Incentive

Salience for those in the low group was 3.69, while it was 4.36 for 148

those in the high relationship group. Though the difference is not

significant at p = .05, this provides modest evidence that

Relationships moderate the effects of controls on worker motivation in

the critical systems environment.

The literature search pointed out that controls may have either

informational or controlling effects, depending on how they are

interpreted. The qualitative data showed that a few workers felt that

incentives were used by management as a carrot. These employees

said things like, “I don’t need a bonus to work hard.” Most employees

did not say this directly. However, the fact that 44% made the

unsolicited comment that incentives had little or no influence on their

work motivation reflects a less than favorable attitude toward either

the bonus or the way it was awarded.

Since this study’s data showed that Relationships may moderate

the effect of incentives on motivation, it makes a step towards

resolving a paradox in the literature. The literature on controls has

been mixed on whether, or when, controls improve motivation.

Scholars have tried to explain contradictory results (e.g., Harackiewicz

& Larson, 1986; Pittman, et al., 1980; Ryan, 1982) by describing the

feedback as controlling versus informational. But this ignores the

relationships between the controller and the controllee. This study

contends that the literature has been unable to unravel this because

of their neglect of interpersonal relationships. Adding personal 149

relationships into the analysis helps predict when controls will hurt

motivation (i.e., when a poor relationship exists). The relationship

probably provides a lens by which the worker views the control

mechanism as either controlling or complimentary/informational.

In sum, Chapter Five found evidence that in XYZCo’s CSO

environment:

the incentive plan was perceived to have more negative

or neutral effects than positive effects;

the incentive plan’s effects on motivation consisted more

of general and temporary morale boosting than

increases in CSO work effort or conscientiousness;

CSO relationships with their supervisors modestly

moderated the effects of the incentive plan on

perceived work motivation;

CSOs in insecure groups were more likely to believe the

incentives had a positive effect on worker motivation

—probably because they are more extrinsically

oriented;

those CSOs with higher levels of intrinsic motivation were

generally more positive about the effects of the

incentive plan on CSO motivation;

since CSOs were generally satisfied with their own

incentive award, many of the negative perceptions 150

they had about the incentive probably related to

how they felt other CSOs perceived the incentive

award process, a “rumor-mill” effect.

151

CHAPTER SIX: OTHER CONTROLS—ADDING RELATIONSHIPS

Ch Prop: Content or Model

2 -- Methodology and Construct Validation

3 1 Nature of the Critical Systems High Levels of Operator Job Motivation

4 2, 3 Growth Need Strength

Critical Job Characteristics Psychological Work

States (CPS) Outcomes

Relationships System Trust

5 4, 5

Incentive Motivational Controls Effect

Relationships

6 4, 5

Other Motivation Motivational Controls Outcomes

Relationships System Trust

7 -- Contributions, Limitations, and Future Research

This chapter first develops hypotheses relating four types of

Controls, Relationships, and System Trust to critical systems operator

Motivation. The Motivation construct consists of Job Satisfaction,

Experienced Work Meaningfulness, Organizational Commitment, 152

Intrinsic Motivation--Self-Esteem, and Intrinsic Motivation--Enjoyment.

Then, the methods are detailed and the results are reported and

discussed.

THEORY BUILDING

Conceptual Model Building--Accountability

Accountability at XYZCo. Accountability means being held

responsible for an action or event. This occurs either by receiving

some consequence from the event (Tetlock, 1985), or by being asked

to give a verbal or written account or explanation of the event

(Cummings & Anton, 1990). When an outage occurs at XYZCo,

someone begins to account for it immediately. The supervisor is

contacted right away, and outages over five minutes are reported to

higher levels of management. At the end of the shift, an incident

report is created by the supervisor to explain why the outage occurred

and what was done about it. Those in higher levels of management

felt keenly aware of the need to account for the health of the system

on a daily basis. The Vice President and division President read a

system situation report first thing each morning. However, when the

system went down, these executives required almost minute by

minute reporting so that they could communicate up the line to their

corporate leaders. In other words, accountability is a constant in this

critical systems organization.

153

AccountabilityMotivation. This type of accountability

appears to have positive motivational consequences in terms of

making workers aware that their job was important. CSOs appear to

interpret constant accountability as a signal of management interest

in system health. Since operators know how keenly management was

interested in keeping the system up, they know their jobs are critically

important. This raises their level of pride in their work, and positively

affects their self-esteem. Overall, then, this type of accountability

primarily has positive motivational effects at XYZCo.

Relationships as Moderator. However, accountability also

had negative effects. For example, one employee involved in

accounting to a disliked boss appeared to be de-motivated by the

accounting process. This appeared to happen because the process

had a negative effect on her/his self-esteem. Thus, accountability

controls can have either positive or negative effects on motivation.

What helped resolve this paradox was the relationship between the

CSO and the manager who held the CSO accountable. When the CSO

had a good relationship with the manager, the accountability control

had positive impacts. When the CSO had a poor relationship with the

manager, the accountability control had a negative impact on the

worker’s motivation. Hence, as Figure 5 shows, the relationship

moderates the effects of Management Controls on Motivation.

Scientific Model Building--Accountability154

AccountabilityMotivation. Cummings & Anton (1990) said

that accountability leads to felt responsibility (a JCM motivation

construct). Steers & Porter (1979, p. 324) reported that whatever

leads to definite expectations leads to felt responsibility, which results

in organizational commitment (another motivation construct). Since:

a) strong accountability leads to definite expectations in terms of

having to account for oneself, and b) definite expectations lead to felt

responsibility and organizational commitment, then c) accountability

should lead to felt responsibility and organizational commitment.

Tetlock (1985) agreed that accountability should lead to higher

motivation in terms of experienced work meaningfulness. Therefore:

Hypothesis 23: The perception of accountability by the CSO

will be positively related with the CSO’s Motivation.

Relationships as a moderator. Tetlock pointed out that the

relationship between the two parties could make a difference. He said

that having a good relationship makes one want to account for their

task. Having a bad relationship makes accountability threatening,

especially if job insecurity is present. Cummings & Anton (1990) came

to a similar conclusion. They theorized that the worker’s perceptions

of management’s attitude toward them determine whether

accountability becomes a mentoring or a controlling/monitoring 155

system. The person held accountable will respond in accordance with

the motives s/he perceives in management. This suggests that

Relationships will moderate the effects of Accountability on Motivation.

Therefore:

Hypothesis 24: Relationships will moderate the effect of

Accountability on Motivation. Those CSOs with positive Relationships

with their supervisors will more likely have significant positive links

between Accountability and Motivation.

Conceptual Model Building—Feedback

Feedback at XYZCo. In this study’s context, feedback refers to

information the supervisor gives the critical systems operator about

how s/he is doing on the job. Most feedback comes to CSOs in an

informal way during day-to-day interaction with their supervisors.

Only one instance of feedback was found in the qualitative data. In

this instance, the feedback was negative, and the existing relationship

between supervisor and CSO was negative. The CSO indicated that

the experience had a negative effect on the CSO’s morale.

Scientific Model Building--Feedback

FeedbackMotivation. In general, feedback has been found

to have positive effects on motivation (Gear, Marsh & Sergent, 1985),

motivation-related productivity (Gallegos & Phelan, 1977; Pritchard & 156

Montagno, 1978), and job satisfaction (Sarata & Jeppesen, 1977).

Feedback has been found to be correlated positively with

organizational commitment (Ivancevich & McMahon, 1982). Feedback

has had positive correlations with intrinsic motivation (Cusella, 1982;

Ivancevich & McMahon, 1982; Shanab, Peterson, Dargahi & Deroian,

1981).

Hypothesis 25: Perceived feedback from the supervisor will be

positively related with the CSO’s Motivation.

However, feedback has not always been found to positively

affect Motivation. Formal feedback in the form of performance

appraisals caused the organizational commitment of satisfactory (less

than outstanding) employees to drop (Pearce & Porter, 1986). Some

have found that negative or controlling feedback hurts intrinsic

motivation (Deci, 1972; Harackiewicz & Larson, 1986; Ryan, 1982).

These findings suggest that there may be a need for a moderator in

the Feedback Motivation equation.

Relationships as a Moderator.

Although several treat topics close to Relationships (e.g., social

mediation) and feedback (Guzzo, 1979), Harackiewicz and Larson

(1986) come the closest to connecting feedback, superior/subordinate

relationships, and intrinsic motivation. They proposed that the 157

supervisor’s feedback style impacts intrinsic motivation. A controlling

feedback style will undermine intrinsic motivation, while a supportive

feedback style enhances it. In their experiment, Harackiewicz and

Larson operationalized controlling feedback style by the printed

messages subject supervisors chose to give to their subordinates. In

this way, the control mechanism became part of the experiment.

Harackiewicz & Larson did not hypothesize or test any effect of the

relationship between the superior and the subordinate. Interestingly,

the Harackiewicz & Larson study found that in the no-reward-for-

subordinate condition, the control mechanism had a positive effect on

intrinsic motivation, contradicting prior results (Pittman, et al., 1980;

Ryan, 1982). The authors gave the plausible explanation that the

controlling behavior contained informational feedback, which may

have overpowered the negative effect of the controlling behavior. No

measure of the subordinate’s feeling of being controlled was made.

But some who have read this important study felt there may be

more to it. Kanfer commented on the Harackiewicz & Larson (1986)

study: “As suggested by Dyer and Parker (1975), normative beliefs

associated with the broader context in which behavior occurs appear

to influence the interpretation of events and intrinsic motivation. In

the Harackiewicz and Larson (1986) study, subordinates performing a

novel task might have construed the situation as one in which the

supervisor’s feedback was designed to help the subordinate master 158

the task, thus reducing the perception that the feedback was

controlling.” (1990: 91) The point here is that the positive social and

structural context surrounding the task can make enough difference

to reverse the expected results of this highly controlled experiment.

A worker with a good relationship with the supervisor would be more

likely to interpret the supervisor’s feedback as helpful, rather than

controlling.

This study contends that one of the key contextual variables not

being taken into account in these feedback experiments is the

relationship between the superior and subordinate. There are hints in

the literature that relationships are important. Earley (1986), for

example, found that feedback is more effective in influencing a

worker’s performance if the worker trusts the feedback giver. Earley

(1988) found that the feedback source (i.e., supervisor or computer)

influenced a person’s level of trust in the feedback. Lawler & Rhode

(1976) found that, in order to motivate positively, feedback should

come from a trusted source. Therefore:

Hypothesis 26: Relationships will moderate the effects of

Feedback on CSO Motivation. Those CSOs with positive Relationships

with their supervisors will more likely have significant positive links

between Feedback and Motivation.

159

Conceptual Model Building--Micromanagement

MicromanagementMotivation. Micromanagement means

that a supervisor gets so deeply involved with the worker’s task that

s/he takes over the task. From Phase I interviews, the extent to which

CSOs were micromanaged could not be ascertained. One worker

reported that a former supervisor knew so much about certain topics

that s/he would “take over” for the worker. The interviewee reported

that this made him/her feel bad about being in that job, and he soon

found a better position. S/he said that in general s/he had a good

relationship with the boss, but that these actions had put a strain on

the relationship.

Relationships as a Moderator. One interviewee described

his/her reactions to the widely varying management styles of a former

and current boss. One of the bosses used a hands-off approach, while

the other was very hands-on. The hands-on manager would actually

come in and take over the job for the operator on occasion. The

interviewee’s reactions to the hands-on manager was intriguing.

Whereas the researcher would have predicted that this obtrusive

management style would demotivate the worker, s/he reported feeling

good about the manager’s actions. S/he interpreted the manager’s

actions as a kind of training/helping function. In further questioning,

the researcher found that the operator had worked with the hands-on

supervisor for many years, and had a very good relationship with the 160

supervisor. This led the researcher to speculate that the operator’s

relationship with the supervisor moderated the potentially negative

motivational effects of the supervisor’s micromanagement actions.

Scientific Model Building--Micromanagement

MicromanagementMotivation; Relationships as a

Moderator. Standing by a worker without taking an active part can

lead to higher felt responsibility by the worker, per Steers & Porter

(1979:323). But standing over the worker may reduce the worker’s

intrinsic motivation--enjoyment (Steers & Porter, 1979: 323) or job

satisfaction (Ouchi & Maguire, 1975). Similarly, Creed & Miles (1996)

said that overmanagement may lead to lower morale. It is speculated

that Relationships make a difference in how Micromanagement affects

Motivation. Therefore:

Hypothesis 27: Perceived micromanagement from the

supervisor will be negatively related with the CSO’s Motivation,

moderated by Relationships.

161

Conceptual Model Building--Autonomy

AutonomyMotivation. Autonomy is not the opposite of

Micromanagement. Micromanagement refers to doing work, while

autonomy refers to decision making. This study defines Autonomy as

the extent to which an employee is allowed to make decisions

pertaining to his/her own job functions. One employee reported that

s/he was constrained from making his/her own decisions. S/he

contrasted this with the autonomy s/he was given by a former boss

when s/he was in a less responsible position. S/he said s/he felt better

about his/herself and his/her authority in the less responsible position

than s/he felt now. S/he saw the lack of autonomy in his/her current

job as a clear sign that the present supervisor did not trust him/her.

The lack of autonomy granted appeared to have a negative effect on

the worker’s morale.

Scientific Model Building--Autonomy

AutonomyMotivation. Several researchers have found that

autonomy leads to greater job satisfaction (e.g., Jayaratne, Vinokur-

Kaplan & Chess, 1995; Kakabadse, 1986). The JCM has connected

autonomy with felt responsibility and other general job attitudes

(Hackman & Lawler, 1971; Hackman & Oldham, 1975). Research on

Autonomy has provided fuel for the practical business press on

empowering or liberating workers (e.g., Peters, 1992). Steers & Porter

(1977) and Rosin & Korabik (1991) found that autonomy is positively 162

related to organizational commitment. Deci and Ryan (1987) found

that autonomy promoted intrinsic motivation, as have others (e.g.,

Goudas, Biddle & Underwood, 1995; Green & Foster, 1986). Therefore:

Hypothesis 28: Perceived autonomy from the supervisor will

be positively related with the CSO’s Motivation.

Relationships as a Moderator. As already justified in Chapter

Four, Relationships is proposed to moderate the effect of Autonomy on

motivational variables. Therefore:

Hypothesis 29: The effects of Autonomy on Motivation will be

moderated by Relationships. Those CSOs with positive Relationships

with their supervisors will more likely have significant positive links

between Autonomy and Motivation.

Scientific Model Building--Work Outcomes

Motivation Individual Performance. The motivation

literature shows that highly motivated workers are likely to produce

better outcomes, both in terms of productivity and general

performance (Steers & Porter, 1979). While this linkage does not

always hold (Griffin, Welsh & Moorhead, 1981), it holds often enough

163

to propose a link between a CSO’s motivation and individual

performance. Therefore:

Hypothesis 30: CSO Motivation will be positively associated

with CSO Individual Performance.

Motivation Contribution to Team Effectiveness. This

study linked Motivation to the CSO’s Contribution to Team

Effectiveness, with subconstructs relating to team cooperation,

communication, conflict resolution, and overall team effectiveness

(see Figure 9). Contribution to Team Effectiveness was also linked to

Individual Performance, measured objectively by the supervisor’s

recall of the latest official rating of the CSO.

To date, very little research has been done to see how

motivation affects functioning as a team. Thus, the linkage between

Motivation and Contribution to Team Effectiveness is considered

speculative. However, it makes sense that highly motivated

employees will like their jobs and will get along with those with whom

they work. Getting along with others on the job is connected with the

construct Contribution to Team Effectiveness (CTE), because CTE

includes communication and cooperation, which are manifestations of

getting along. Therefore,

164

Hypothesis 31: CSO Motivation will be positively associated

with CSO Contribution to Team Effectiveness.

Hypothesis 8 already demonstrated the strong linkage between

Contribution to Team Effectiveness (CTE) and Individual Performance.

165

System Trust’s Impact on Motivation

In Chapter 4, it was argued that System Trust will be related to

such Work Outcomes as Job Satisfaction and Intrinsic Motivation,

which are included in the Motivation construct. Further, System Trust

is probably related to Organizational Commitment, since it reflects

how an operator feels towards the work environment. System Trust

will also probably be related to Experienced Meaningfulness, since an

unfair work environment would tend to cast doubt on how important

the work is. Workplace unfairness can reduce general motivation

levels. For example, from studying budget controls, Hofstede (1967:

56) reported that feelings of unfairness regarding management “are

strongly demotivating.” Thus, System Trust will probably be

predictive of Motivation, comprised of the above discussed constructs.

Therefore:

Hypothesis 32: CSO System Trust will be positively associated

with CSO Motivation.

METHODOLOGY DETAIL

Regression analysis was used to test Chapter Six’s hypotheses,

including the moderation effects. The researcher also used a second

method to test for moderation. The researcher divided the

questionnaire results into groups: those with high Relationship scores 166

and those with low Relationship scores. The means for the Control

and Motivation variables were then calculated and a T-test done to

see if there was a mean difference. This was done with two groups

(low-high) and three groups (low-medium-high) to see how significant,

or close to significant, the moderator was. McClelland and Judd (1993)

said that showing a moderation effect using regression techniques on

survey data is many times more difficult than showing a moderation

effect using ANOVA on experimental data. The sensitivity analysis

employed here seemed appropriate given the difficulty in seeing a

moderation effect on field data using a regression analysis.

RESULTS OF HYPOTHESIS TESTING

Table 16 presents a set of correlation matrices of the

hypothesized variables. Table 16 shows at a glance which variables

are most closely related with Motivation (Feedback, Relationships and

System Trust). These correlations are not intended to test the

hypotheses, but are shown to provide information clarifying the

regression results. Table 17 presents the results of the related

regressions.

Hypotheses 23-32. Relationships and Feedback were the only

variables that were significant in the full regressions for Hypotheses

23-29 (Table 17). Based on Table 17’s results, only Hypotheses 23,

25, and 32 were supported. Relationships was not found to

significantly moderate the linkage between any of the Controls and 167

Motivation. However, Relationships by itself was a significant

predictor of Motivation (as shown in Table 17 between H24 and H25).

Since Feedback was highly correlated with Relationships, only one of

the two could be used as a predictor without introducing

multicollinearity. Hence, the better model would employ only

Feedback, with an

168

Table 16 Management Controls / Relationships Model—Correlation Tables

H# Motivation Accountability

Relationships

Accountability X

Relationships

Motivation 1.023 Accountability .23 1.024 Relationships .28 .40 1.024 Accountability X

Relationships-.27 -.38 -.55 1.0

Motivation Feedback Relationships

Feedback XRelationships

Motivation 1.025 Feedback .32 1.026 Relationships .28 .71 1.026 Feedback X

Relationships-.15 -.40 -.81 1.0

MotivationMicromanage-ment Relationship

s

Micromanage-ment X Relationships

Motivation 1.027 Micromanagemen

t-.20 1.0

27 Relationships .28 .10 1.027 Micromanagemen

t X Relationships-.26 -.12 -.17 1.0

Motivation Autonomy Relationships

Autonomy X Relationships

Motivation 1.028 Autonomy .06 1.029 Relationships .28 .06 1.029 Autonomy X

Relationships-.01 -.07 .11 1.0

Motivation CTE Performance Relationships

Motivation 1.031 CTE .14 1.030 Performance .10 .84 1.0

Relationships .28 .28 .23 1.0

Motivation System

169

TrustMotivation 1.0

32 System Trust .35 1.0

170

adjusted R-squared of .089 and model significance of p=.003.

Relationships by itself produces an adjusted R-squared of .069 and

p= .008.

Table 17 Management Controls / Relationships Model—Regression Results

H#

Independent Variable(s)

Dependent Variables R 2 ad

jFstat

Significant Constructs p

23

Accountability Motivation .040 .036 Accountability .226

.036

24

Accountability + Relationships + Accountability X Relationships

Motivation .075 .025 -- -- --

Relationships Motivation .069 .008 Relationships .283

.008

25

Feedback Motivation .089 .003 Feedback .316

.003

26

Feedback + Relationships + Feedback X Relationships

Motivation .079 .021 -- -- --

27

Micromanagement Motivation -.012

.961 -- -- --

27

Micromanagement + Relationships + Micromanagement X Relationships

Motivation .074 .026 Relationships .263

.016

28

Autonomy Motivation -.008

.582 -- -- --

29

Autonomy + Relationships + Autonomy X Relationships

Motivation .050 .066 Relationships .285

.009

30

Motivation Performance

-.002

.369 -- -- --

31

Motivation CTE .007 .205 -- -- --

171

32

System Trust Motivation .112 .001 System Trust .350

.001

In Hypotheses 30-31, Motivation predicted neither CTE nor

Individual Performance. In exploratory mode, Relationships by itself

was found to be predictive of CTE, with an R-squared of .070, Beta

= .284, and p = .008. Supporting Hypothesis 32, System Trust was

strongly related with Motivation, with a higher R-squared than either

Feedback or Relationships. Only System Trust explained more than

ten percent of the variance in Motivation.

Tables 18 through 21 present the moderation sensitivity analysis

by variable. Tables 18 through 21 show the moderation analysis based

on dividing each variable into halves and then thirds. Using thirds was

done in case the moderation effect related only at the two extreme

values of the data. That is, a moderation effect may only be

significant among those with relatively high or low Relationship scores.

Using the top third and lower third captures this possibility. A

Relationships moderation effect is represented by a table in which

there is a significant difference in Motivation means () between the

left side and the right side of the table. That is, at a given level of the

Controls variable, moving from low to high Relationships resulted in a

significant increase in the mean of the Motivation scores.

172

For example, in the second part of Table 18, which represents

high and low thirds, one sees that in the case of the low Accountability

scores, a change from low to high Relationships scores produced a

significantly higher (p= .04) set of Motivation scores. Therefore,

Relationships is said to have moderated the effects of Accountability

on Motivation. This effect appears in the “thirds” analysis for

Accountability and Feedback, and in the “halves” analysis for

Micromanagement. In each case, the difference is only significant

when the Controls variable is in the “low” condition. This provides

modest evidence that Relationships moderates the effect of these

Controls variables on Motivation, supporting Hypotheses 24, 26, and

27.

Table 18 Sensitivity Analysis for Relationships Moderation of

Accountability

Low Half Relationships

Significance Of Difference

High Half Relationships

Low Half Accountability

= 6.13; n=24

not significant (n.s.)

= 6.45; n=17

Significanceof Difference

n.s. n.s.

High Half Accountability

= 6.30; n=19

n.s. = 6.55; n=26

Low Third Significanc High Third 173

Relationships e Of Difference

Relationships

Low Third Accountability

= 5.95; n=10

p = .041 = 6.51; n=4

Significanceof Difference

n.s. (p=.08) n.s.

High Third Accountability

= 6.36; n=9

n.s. = 6.53; n=30

Note: refers to the mean of the Motivation scores in this cell.

174

Table 19 Sensitivity Analysis for Relationships Moderation of Feedback

Low Half Relationships

Significance Of Difference

High Half Relationships

Low Half Feedback

= 6.11; n=35

n.s. = 6.33; n=11

Significanceof Difference

n.s. (p=.09) n.s.

High Half Feedback

= 6.61; n=8

n.s. = 6.57; n=32

Low Third Relationships

Significance Of Difference

High Third Relationships

Low Third Feedback

= 6.12; n=21

p = .041 = 6.48; n=9

Significanceof Difference

n.s. (p= .053)

n.s.

High Third Feedback

= 6.75; n=1

n.s. = 6.56; n=35

Note: refers to the mean of the Motivation scores in this cell.

Table 20 Sensitivity Analysis for Relationships Moderation of

Micromanagement

Low Half Relationships

Significance Of Difference

High Half Relationships

Low Half Micromanagement

= 6.16; n=21

p = .01 = 6.58; n=22

Significanceof Difference

n.s. n.s.

High Half Micromanagement

= 6.24; n=21

n.s. = 6.44; n=22

Low Third Significanc High Third 175

Relationships

e Of Difference

Relationships

Low Third Micromanagement

= 5.98; n=8

n.s. p = .051

= 6.53; n=11

Significanceof Difference

n.s. n.s.

High Third Micromanagement

= 6.26; n=9

n.s. = 6.46; n=26

Note: refers to the mean of the Motivation scores in this cell.

Table 21 Sensitivity Analysis for Relationships Moderation of

Autonomy

Low Half Relationships

Significance Of Difference

High Half Relationships

Low Half Autonomy

= 6.12; n=24

n.s. (p = .069)

= 6.45; n= 20

Significanceof Difference

n.s. n.s.

High Half Autonomy

= 6.31; n=19

n.s. = 6.56; n= 23

Low Third Relationships

Significance Of Difference

High Third Relationships

Low Third Autonomy

= 6.12; n=8

n.s. = 6.53; n=12

Significanceof Difference

n.s. n.s.

High Third Autonomy

= 6.30; n=8

n.s. = 6.57; n=22

Note: refers to the mean of the Motivation scores in this cell.

Eliminating Plausible Alternatives

176

In order to establish these hypotheses’ internal validity with

greater confidence, the researcher entered a number of plausible

alternatives into the equations predicting Motivation, CTE, and

Performance. These included demographic variables (age, grade

level, education), individual situation variables (number of recent

promotions, number of recent pay raises, percent of time keeping

systems available, duration of time worked with supervisor), and

variables providing possible alternative explanations (interaction with

team members, interaction with supervisor, relationship with team

members). Interaction with, and duration of time worked with, the

supervisor were suggested by research on trust (e.g., Burt & Knez,

1996). Interaction and relationship with team members was

suggested by the social needs emphasis of the JCM. None of these

variables added any significant predictive value to the model’s most

significant equations (i.e., System Trust Motivation; Relationships

CTEs). With these plausible alternatives eliminated, one can have

greater confidence in the internal validity of the best equations for

these models (see Table 17).

DISCUSSION OF RESULTS

Of the Control constructs, only Feedback and Accountability

were significantly related to Motivation. Neither Autonomy nor

Micromanagement had any significant effect on the CSOs’ Motivation. 177

Further analysis revealed that Micromanagement did not significantly

(p=.05, one-tailed test) correlate with any of the motivation-related

variables that comprised Critical Psychological States, Work Outcomes,

or Motivation. Autonomy significantly correlated with only one

(Knowledge of Results, r = .323; p<.01). In light of both the research

cited above and the ground swell of practitioner support for

‘empowering’ or ‘liberating’ workers (e.g., Peters, 1992), this finding is

very surprising. This underscores the possibility that empowering by

itself may not have as strong of an effect on motivation as the

CSO/supervisor relationship.

In contrast, Feedback from the Supervisor appears to be

strongly positively related to the worker’s Motivation, as is

Accountability to a lesser extent. Relationships itself relates positively

to Motivation, and is more strongly related to Motivation than any of

the Controls except Feedback. This is interesting because Feedback is

the control that apparently has the most to do with Relationships,

given its high correlation with Relationships. This high correlation

indicates that either frequency of feedback leads to good relationships

or that good relationships leads to frequent feedback (or both).

Hence, Feedback may be thought of as a characteristic of the

relationship. Hence, an important finding of this study is that

operator/supervisor Relationships (and the related Feedback) have a

more power for predicting Motivation than do the Control types. Even 178

though Relationships has a stronger direct, than interactive, effect on

Motivation, the sensitivity analysis shows that Relationships does have

some impact on the strength with which most of the Controls affect

Motivation. In particular, above average Relationships were

associated with significantly higher Motivation among those CSOs with

below average Micromanagement scores. Similarly, top third

Relationships scores were associated with higher Motivation among

those with lower third Feedback and Accountability.

The fact that Relationships and Feedback frequency are so

highly correlated is in itself important. Argyris (1975) argued that

those who act within controlling environments will develop poor

relationships with the controller and will come to seek little feedback.

It is also likely that a supervisor with a poor relationship with the

employee will have less desire to give feedback. From the other

direction, the infrequency of feedback will leave questions in the mind

of the worker, which will lead to suspicion and, over time, to lower

trust levels (Holmes & Rempel, 1989). The power differences between

the worker and supervisor tend to increase the levels of suspicion and

distrust (Kramer, 1996). Hence, XYZCo data supports the idea that

relationships and feedback tend to reinforce each other over time.

One possible reason why Autonomy had so little predictive

power is that Autonomy is so crucial in the critical system environment

(Weick, 1990) that almost all CSOs have high degrees of autonomy. 179

The data shows some evidence of this (Appendix I), in that the mean

for Autonomy was 5.97 out of 7.00. Also, while answering the

questionnaire, two supervisors said they give all their CSOs full

autonomy on the job.

Also somewhat surprising, Relationships predicted CTE better

than did Motivation. Also, Autonomy predicted CTE and Individual

Performance even better than did Relationships. It is probable,

however, that the causality is the opposite for Autonomy. That is, it is

possible that those who are the best performers are given the most

Autonomy by their supervisors. Hence, Individual Performance is

probably a predictor of Autonomy, rather than the converse. The

finding that Motivation did not predict Individual Performance parallels

the Chapter Four finding that CPS did not predict Individual

Performance. Again, this is probably because other variables, such as

Contribution to Team Effectiveness, skill, knowledge, and ability are

more salient predictors of Individual Performance.

Note that the only Table 17 R-squared that exceeds .10 is the

prediction of Motivation by System Trust, which is only .112. By

contrast, Chapter Four’s significant JCM equations had R-squares in

the .20-.49 range. It appears from this that these controls have far

less motivational impact on CSOs than do the characteristics of the

job. Based on Chapter Five, the same is probably true of incentive

controls.180

In sum, Chapter Six found evidence that:

System Trust was the best predictor of Motivation,

followed by Feedback;

of the other Controls, only Accountability was a

significant predictor of Motivation;

Relationships itself predicted Motivation better than any

Control except Feedback;

Relationships had a modest moderating effect on how

Controls affect Motivation, especially for

Relationships in the top or bottom third;

Relationships predicted CTEs better than did Motivation;

Although a direct comparison cannot be made,

Controls appear to have less effect on CSO

motivation than do the JCM variables.

181

CHAPTER SEVEN:

CONTRIBUTIONS, LIMITATIONS, AND FUTURE RESEARCH

Ch Prop: Content or Model

2 -- Methodology and Construct Validation

3 1 Nature of the Critical Systems High Levels of Operator Job Motivation

4 2, 3 Growth Need Strength

Critical Job Characteristics Psychological Work

States (CPS) Outcomes

Relationships System Trust

5 4, 5

Incentive Motivational Controls Effect

Relationships

6 4, 5

Other Motivation Motivational Controls Outcomes

Relationships System Trust

7 -- Contributions, Limitations, and Future Research

182

CONTRIBUTIONS

This research contributes to both theory and practice.

To Theory

The current literature lacks models fully explaining the paradoxical effects of

controls. That is, controls sometimes have positive effects and sometimes negative

effects (e. g., Harackiewicz & Larson, 1986; Powers & Dickson, 1973). This suggests a

hidden moderator is present (Sitkin & Pablo, 1992). While the nature of the feedback

itself has been examined as a moderator, the contextual relationships between parties,

though suggested by Kanfer (1990), has not been examined. This study developed and

tested a model that used relationships as a moderator of the Management

Controls/Motivation link. Building on existing theory, the Controls/Relationship model

helps explain prior paradoxical empirical findings on Controls by including

Relationships. When CSO/supervisor relationships were positive, Management Controls

had a stronger positive influence on motivation than when these relationships were

negative. Adding the Relationships and System Trust constructs improved the predictive

power of the Management Controls model of motivation.

Through the use of Relationships and System Trust, the study also extended the

predictive power of the Job Characteristics Model. Even though the job of the CSO is

highly intrinsically motivating, Relationships and System Trust were found to be

predictive of JCM dependent variables in the presence of job characteristics predictors.

While this study says Relationships and System Trust are important, it does not claim

that job characteristics are not important. Indeed, job characteristics were the most

salient predictors of CSO motivation in this study. 183

This research also fills a key gap in the management information systems domain

by analyzing and describing the critical computer systems operator (CSO) job vis-a-vis

that of traditional computer operators and system developers. The CSO job is critical

because of the urgent need to restore systems that crash. Two recent five-hour e-mail

blackouts at America Online (Quick, 1997) underscore again the need to keep highly

used systems running 100% of the time. In addition, the System Trust and Relationships

constructs are introduced for the first time in psychologically measurable form in this

study.

This study helps explain the paradoxical research findings

regarding Management Controls. While Controls have been found to

motivate employees (Eisenhardt, 1985; Henderson & Lee, 1992;

Tetlock, 1985), controls often have dysfunctional effects (e.g., Lawler

& Rhode, 1976; Powers & Dickson, 1973). Unraveling paradoxes is a

highly recommended theory-building process (e.g., Poole & Van de

Ven, 1989). Resolving paradoxes often requires that researchers

incorporate moderator or mediator variables between independent

and dependent variables (e.g., Sitkin & Pablo, 1992). Perhaps one

reason the economics literature has been unable to unravel the

control paradox is because it assumes that interpersonal relationships

are not important. This study adds value by positing Relationships as

a moderator of the traditional Management Controls Motivation link.

From the results of this study, it is proposed that Management

Controls have positive effects on Motivation when worker/supervisor 184

Relationships are positive, but negative effects when Relationships are

negative. Adding personal relationships into the analysis helps

explain when controls will hurt motivation (when a poor relationship

exists), but also how (through threatening the self-esteem of the

worker).

This study also calls for broader-based motivation research

approaches. Most studies of motivation have focused narrowly on one

kind of factor (e.g., incentives, job characteristics) or outcome (e.g.,

organizational commitment, intrinsic motivation). This study found

that one can measure the relative strength of several motivational

factors. Further, in a given context, one can test to see which

motivational paradigm (i.e., controls, job characteristics) works best.

To Practice

Two major paradigms of management have dominated U. S. Corporations in

recent years. One, based on the Hackman/Oldham model, says to redesign work to

increase productivity and decrease worker costs. In the process, managers should

empower or ‘liberate’ (Peters, 1992) the workers by giving them more autonomy to do

their job. Similarly, numerous corporations have pursued “reengineering” (Hammer &

Champy, 1993) with the charge to enrich the worker’s job (e.g., Davenport, 1993)--but

without considering people relationships. The ‘gurus’ of the reengineering movement

now admit that they forgot the people part (Wall Street Journal, November, 1996). This

study points out that autonomy by itself may not be nearly as motivating as simply a

good worker/supervisor relationship. Further, a reengineered job may motivate, but that 185

motivation can be enhanced by a positive worker/boss relationship. The other paradigm,

based on economics, said that corporations can be most successful by giving corporate

agents salient incentives to encourage them to do the right thing for corporate principals.

Some are now beginning to show that this paradigm does not consistently work either

(e.g., Kohn, 1993a). This study’s findings say that each of these two paradigms is

inadequate! That is, proper management of people relationships and the fairness of the

company’s work environment are also required for effective corporate management. The

“traditional models of authority” often assume that “managers must closely supervise

their employees and cannot trust them” (Tyler & Kramer, 1996: 6). The relationships

findings of this study shed new light on authority models of management by pointing out

that the manager/worker relationship is itself an overlooked key to the worker’s

motivation. Since trust is the key component of Relationships, managers should work to

improve the level of trust between them and their employees. Creed and Miles (1996:

36, 19) pointed out that, by “taking the initiative in trusting,” management plays “a

central role” in determining a unit’s levels of trust. This study’s System Trust findings

also provide evidence that “managers need to create an environment in which workers

can be trusted” (Ibid.)

Popular management books and articles are also beginning to emphasize trust

between workers and managers (e.g., Covey, 1989; Peters, 1992). Further, the role of

workplace justice is being explored in management books: “[Workers] must trust that

you will treat them fairly if they make mistakes” (Campbell, 1997). This is especially

important in light of evidence that worker/management relationships have eroded

because of the layoffs, downsizing and other insecurity-building management practices 186

(e.g., Associated Press, 1997). One recent study of 215 companies found that “trust has

declined in three out of four workplaces during the past two years” (Jones, 1997: 1). The

lack of worker trust in management is commonly associated with lower worker loyalty to

the company (e.g., Jackson, 1997). Jackson said that the “new contract” between

employees and employers has left employees feeling like they are on their own. Hence,

worker loyalty , based on trust, is at an all-time premium in the corporate workplace. In

the critical system environment, in which a large store of knowledge and skill must be

kept to face the next unpredictable contingency (Weick, 1990), loyalty and retention of

skilled workers is of paramount importance.

This study also emphasizes that giving out incentives may not motivate. Rather

than just giving incentives, managers need to find out first what motivates their workers

and then try to give it to them (Pinder, 1991). In this study’s research site, the operators

were more strongly motivated by intrinsic factors and Relationships/System Trust than

anything else. Hence, managers of CSOs should concentrate on keeping the CSO job

motivating and developing a good relationship with each CSO. They should also take

steps to assure that CSOs feel that workplace structures encourage fair treatment.

Management should be very careful in how they employ incentive systems or alter

existing incentive systems. Mohrman, Resnick-West, and Lawler (1989) emphasized

that pay for performance must be done correctly, or “the positive advantages …are more

than wiped out…” (1989: 174-175). Lawler (1971) recommended that incentive pay not

be used in situations in which worker/supervisor trust levels are low.

By adding Relationships and System Trust to either the JCM or the Controls

model, organizations can more fully explain and more accurately predict motivation and 187

motivational work outcomes. In the context examined in this research, the study outlines

the significant motivational impacts of Job Characteristics, Relationships, and System

Trust for those operating critical computer systems.

This research also contributes to practice by applying the critical technology

systems paradigm (e.g., Weick, 1990) to the information systems field. What has been

learned in this study largely relates to the very challenging CSO job and its environment.

This researcher echoes Weick’s impression of critical systems operators generally:

“Considering what they face, it is remarkable that operators do as well as they do” (1990:

33). This study identifies several personal (e.g., knowledge), interpersonal (e.g.,

Relationships), structural (e.g., System Trust), and technical (e.g., testing) factors that

interact in this environment. While these factors each require additional research, the

immediate contribution to practice is that, in managing CSOs, one should consider all of

these interacting aspects of the CSO environment. Otherwise, unforeseen problems may

arise.

STUDY LIMITATIONS

This section discusses limitations and how they were addressed.

Most research is subject to the researcher’s pre-existing biases (Yin,

1984). However, the use of both inductive and deductive methods

helps reduce the effects of bias in this study.

External Validity

The most important limitation of a study of a single organization

is that its results may not be generalizable to other organizations. The

fact that the study’s data is drawn from operators of three separate 188

critical systems helps minimize the site’s uniqueness in terms of

critical systems, but does not address the uniqueness of XYZCo as an

organization. This study has argued that it is the unique

organizational and systems task-related factors at XYZCo that make it

an informative site. The drawback of this argument is that the results

may not have external validity, limiting this study’s contribution to

both science and practice.

However, the researcher believes that many of the principles

brought out in this study have wider applicability. For example, the

Controls/Relationships model (Figure 5) can be applied to many other

situations, both in information systems settings, and in general

management situations. Likewise, the issues relating to incentive

systems have broad applicability. The new instruments developed

here were designed for broad application. The following briefly

outlines efforts the researcher has already made to test the

generalizability of the Management Controls / Relationships model and

the specific incentive findings.

First Test for External Validity: Controls/Relationships

Model. First, the Management Controls/Relationships model has been

tested in a different setting. Separate from the dissertation study, the

researcher and a colleague have tested the external validity of a

subset of the Controls/Relationships model using an existing data

source, the “world-class manufacturing study.” This study began in 189

1991 at the University of Minnesota’s Carlson School of Management.

Its researchers collected data on a wide range of important variables

from 110 American, Japanese, and Italian manufacturing plants. The

data included variables reflecting Management Controls,

worker/management Relationships, and Motivation. Using these data,

a model of Relationships moderating the effects of Controls on

Motivation was tested. A variable for plant nationality was also placed

in the model as a covariate. This model was found to be strongly

supported. This external validity test provides significant additional

evidence for the usefulness of the Management Controls/Relationships

model. Obviously, more testing is needed.

Second Test for External Validity: Incentives Effects on

Motivation. The pilot organization afforded a second site to further

explore the implications of incentives because of how their reward

structure differed from that at XYZCo. At the pilot company:

incentives were salient in terms of goal challenge, but not as salient

in terms of objective amount;

the incentive was tied to a one time productivity goal over a three

month period;

the award was based on the team meeting its goal; and,

the award was divided evenly among team members.

The researcher hypothesized that the incentive would have negative

side effects.190

Team members were interviewed about the incentive and their

motivation both during and after the three month award period. The

incentive appeared to increase several aspects of their motivation

(and performance) during the award period. However, performance

decreased immediately after the award period to lower-than-normal

levels. In subsequent months, performance gradually increased to

about the same as it had been before the incentive. Post-award

interviews confirmed the researcher’s hypothesis that the incentive

would have some negative impact. Explaining the reduced post-

incentive performance, several interviewees said that the incentive

caused the team to focus so much on the goal that it neglected other

things that were not directly related to the goal. Having to pursue

these neglected items contributed to decreases in goal-related

productivity in the months directly following the goal period. Hence,

the pilot site confirmed that negative effects of controls may occur.

Other Limitations. The fact that most constructs were based

on self-report data makes it difficult to know how valid they are in the

face of threats like social desirability bias (Cook & Campbell, 1979). In

spite of Dillman’s (1978) assurances to the contrary (Chapter Two),

this threat may have been exacerbated by the use of a telephone

questionnaire. This possibility was minimized by assurances the

interviewer provided that tend to decrease social desirability bias (see

Chapter Two, Appendix C-Introduction).191

Another issue is statistical power (Cohen, 1988). Because the

sample size was limited to the number of people available to talk at

one research site, power calculations were not pursued. Intuitively, a

sample size of eighty-six is probably adequate to feel safe about the

main effects of the models. However, the interactive effects probably

require additional power.

Mono-method (common informant) bias is a limitation that was

addressed for equations predicting CTE and Performance, but not for

equations predicting Motivation, CPS, and Work Outcomes. Hence,

mono-method bias should be considered a caveat for this study. The

testing of Autonomy for mono-method bias revealed some differences

between methods, but overall, the items from the two methods could

be successfully merged into a single construct.

Another limitation is that several minor wording changes were

made to the JCM scales in order to increase their reliability. While

these were minor, it is possible that this could have slightly affected

the scores for these variables. It is not at all unusual, however, for

JCM constructs to be operationalized in ways that differ somewhat

from the original (Griffin, Welsh & Moorhead, 1981).

FUTURE RESEARCH

This study raises a number of interesting questions for researchers to pursue. The

most obvious question is, “To what extent will these findings apply in similar and diverse

settings?” A number of direction-of-causality issues were raised in the discussion 192

sections. This study indicates that a number of processes lie behind these cross-sectional

results; the interactional nature of relationships and motivation is intriguing but not clear

at this point. To begin to answer such questions, the following steps are suggested.

First, the constructs of this study need to be studied in other critical computer

systems settings. Second, the direction of causality of a number of construct-to-construct

relationships needs to be studied. These include the extent to which Controls, System

Trust, and Relationships influence each other. Third, the interaction between

Relationships, System Trust, and Motivation should be tested longitudinally--both

experimentally and through questionnaire research. Fourth, the surprising strength of

System Trust and Relationships’ trust constructs makes determining the antecedents of

trust a strong imperative (e.g., see Luhmann, 1991). Fifth, while the relationship

between workers and direct supervisors is important, some research suggests that the

relationship between workers and higher levels of management may also be important—

yet difficult to manage (e.g., Crozier, 1964: 82-83, 95). The effects of these relationships

on worker motivation should also be studied. Sixth, the relative strength of the JCM and

MCM should be tested. Simultaneous model tests (e.g., Davis, Bagozzi & Warshaw,

1989) help science advance.

In future research, method bias should be addressed in two ways. First, the social

desirability bias resulting from telephone questionnaire should be compared directly to

that resulting from written questionnaires by splitting the sample. Second, informant-

related bias should be addressed by gathering independent variables from one informant

and dependent variables from the other informants. Alternatively, one set should be

gathered from both informants.193

This study found that organizational, people, and relationships issues were

important to the operation of critical computer systems. Abstracting to a higher level,

one might say that this study explores the effects of people and organizational issues on

information systems (people + organizations systems). This is the opposite of the

trend in the MIS field, which has primarily focused on how information systems affect

people and organizations (systems people + organizations). For example, George &

King (1991) discussed how computing can drive centralization or decentralization of

organizations, but did not discuss how organizational structure can affect the health of

systems. The results of this study suggest, as emphasized by Kaplan and Duchon (1988:

583), that research “designs should consider the impacts of users, the organization, or

society on the computer information system.” As an example, this study’s results about

incentives imply a need to study cross-sectionally the effects of information system

management incentives on system availability.

In particular, the well-being of critical systems needs to be examined in much

more detail than has been done so far. Since system availability is becoming more and

more important, the following research avenues should be pursued:

The effects of Total Quality Management approaches on critical computer

system availability should be researched. This was suggested by XYZCo’s quality

improvement teams made up of CSOs. Improving critical system availability to six

sigma (99.9999%) quality is a natural fit for the quality management research paradigm.

Thompson (1967) predicted that the structural system will address system

uncertainties by protecting the technical system from environmental threats. Phase I

confirmed this proposition at XYZCo (McKnight, 1996). The effects of structural 194

technical safeguards like redundant equipment, backup software, system environmental

protections, and testing procedures all contributed to XYZCo’s systems availability.

These should be studied in terms of which factors are the most important to system

availability.

The complexity and comprehensibility of an operational environment change in

both nature and degree when the environment is computerized (Lee, 1991b; Perrow,

1984; Zuboff, 1988). In light of efforts to automate CSO roles, the effects of operator

automation on CSO alertness, comprehension, and job characteristics should be studied

(McKnight, 1996).

In the Chapter Three Discussion of Results section, the issue of CSO

disincentives to do preventive maintenance was raised. This area needs further research.

In light of this possibility, the extent to which teamwork and trust among CSOs reduces

the tendency to prefer the ‘glory job’ of system repair over the ‘no glory’ job of

preventive maintenance should also be studied.

As Pentland (1992) found, an individual CSO in troubleshooting mode is likely

to need the help of others on the team to resolve a thorny problem. The network of team

members on whom the CSO can call seems paramount. Research should identify what

attributes of the networked team member are most important to the CSO handling a

given problem type (e.g., particular knowledge, skills), and how the interpersonal

relationships attributes (e.g., Liking, Trusting Beliefs-Benevolence) matter regarding

who the CSO contacts for help.

195

The central computing center probably accounts for far fewer system outages,

as experienced by end users, than the combined communications network and end user

premise environments. These should also be studied in terms of system availability.

While studies have been done of the human-computer interaction (HCI) of

nuclear plant and aircraft carriers operators (e.g., Rasmussen, 1986; Weick & Roberts,

1993), little is known about the HCI of CSOs. At XYZCo, work has been done to

simplify and more clearly present the system control screen information to the CSO. The

HCI domain needs to be explored in much more detail in the uniquely frenetic CSO

arena.

Weick (1990) pointed out that the critical system environment can be a cauldron

of strong emotions. In the context of a critical computer system outage, emotional events

are likely to affect both management, the CSO, and the system user—as they interact.

How these emotions affect, and are affected by, the interpersonal dynamics of the

situation is interesting. How these interactive factors impact CSO performance is an

intriguing question to pursue.

196

REFERENCES

Adams, J. S. 1963. Toward an understanding of inequity. Journal of Abnormal and Social Psychology, 65: 422-436.

Aiken, M. & Hage, J. 1966. Organizational alienation: A comparative analysis. American Sociological Review, 31: 497-507.

Aiken, L. S. & West, S. G. 1991. Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage.

Alderfer, C. P. 1969. An empirical test of a new theory of human needs. Organizational Behavior and Human Performance, 4: 142-175.

Anthony, R. N. 1965. Planning and control systems: A framework for analysis. Boston, MA: Division of Research, Graduate School of Business Administration, Harvard University.

Applegate, L. M. 1991. Technology support for cooperative work: A framework for studying introduction and assimilation in organizations. Journal of Organizational Computing, 1: 11-39.

Argyris, C. 1975. Dangers in applying results from experimental social psychology. American Psychologist, 30: 469-485.

Associated Press. 1997. Job is great-except for boss, workers say. Deseret News archives, September 1: 1.

Atkinson, J. W. & Birch, D. 1970. The dynamics of action. New York: Wiley.

Atwater, L. E. 1988. The relative importance of situational and individual variables in predicting leader behavior: The surprising impact of subordinate trust. Group and Organization Studies, 13: 290-310.

Bailey, R. W. 1982. Human performance engineering: A guide for system designers. Englewood Cliffs, NJ: Prentice-Hall.

Bandura, A. 1986. Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall.

197

Banker, R. D., Datar, S., Kemerer, C. F. & Zweig, D. 1993. Software complexity and software maintenance costs. Communications of the ACM, 36: 81-94.

Barber, B. 1983. The logic and limits of trust. New Brunswick, NJ: Rutgers University Press.

Barley, S. R. 1990. Images of imaging: Notes on doing longitudinal field work. Organization Science, 1: 220-247.

Barney, J. B. & Ouchi, W. G. 1986. (Eds.) Organizational economics. San Francisco: Jossey-Bass.

Baron, R. M. & Kenny, D. A. 1986. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations, Journal of Personality and Social Psychology, 51: 1173-1182.

Bem, D. J. 1967. Self-perception: The dependent variable of human performance. Organizational Behavior and Human Performance, 2: 105-21.

Berkeley, P. E. 1984. Computer operations training: A strategy for change. New York: Nostrand Reinhold, 1984.

Berscheid, E. 1983. Emotion. In Kelley, H. H., Berscheid, E., Christensen, A., Harvey, J. H., Huston, T. L., Levinger, G., McClintock, E., Peplau, L. A. & Peterson, D. R. (Eds.), Close relationships: 110-168. New York: W. H. Freeman.

Blau, P. M. 1964. Exchange and power in social life. New York: John Wiley & Sons.

Bostrom, R. P. & Heinen, J. S. 1977. MIS problems and failures: A socio-technical perspective, Part I: The causes. MIS Quarterly, 1: 17-32.

Brooks, F. P. Jr. 1987. No silver bullet: Essence and accidents of software engineering. IEEE Computer, April: 10-19.

Burt, R. S. & Knez, M. 1996. Trust and third-party gossip. In R. M. Kramer & T. R. Tyler (Eds.). Trust in organizations: Frontiers of theory and research, 68-89. Thousand Oaks, CA: Sage.

198

Calder, B. J. & Staw, B. M. 1975. Self-perception of intrinsic and extrinsic motivation. Journal of Personality and Social Psychology, 31: 599-605.

Campbell, J. 1997. Workplaces that don’t work. Deseret News archives, July 13: 3.

Campbell, D. T. & Fiske, D. W. 1959. Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56: 81-105.

Campbell, J. P. & Pritchard, R. D. 1976. Motivation theory in industrial and organizational psychology. In M. D. Dunnette (Ed.) Handbook of industrial and organizational psychology, 63-130. Chicago: Rand McNally.

Carver, C. S. & Scheier, M. F. 1981. Attention and self-regulation: A control theory approach to human behavior. New York: Springer-Verlag.

Cohen, J. 1988. Statistical power analysis for the behavioral sciences, second edition. Hillsdale, NJ: Lawrence Erlbaum.

Cook, T. D. & Campbell, D. T. 1979. Quasi-experimentation: Design & analysis issues for field settings. Chicago: Rand-McNally.

Cook, J. & Wall, T. 1980. New work attitude measures of trust, organizational commitment and personal need non-fulfilment. Journal of Occupational Psychology, 53: 39-52.

Couger, J. D. & Zawacki, R. A. 1980. Motivating and managing computer personnel. New York: John Wiley.

Covey, S. R. 1989. The seven habits of highly effective people: Restoring the character ethic. New York: Simon & Schuster.

Creed, W. E. D. & Miles, R. E. 1996. Trust in organizations: A conceptual framework linking organizational forms, managerial philosophies, and the opportunity costs of controls. In R. M. Kramer & T. R. Tyler (Eds.). Trust in organizations: Frontiers of theory and research, 16-38. Thousand Oaks, CA: Sage.

Cronbach, L. J. 1951. Coefficient alpha and the internal structure of tests. Psychometrika, 16: 297-334.

199

Crozier, M. 1964. The bureaucratic phenomenon. Chicago: University of Chicago Press.

Cummings, L. L. & Anton, R. J. 1990. The logical and appreciative dimensions of accountability. In S. Srivastva, D. L. Cooperrider and Associates, (Eds.) Appreciative management and leadership: The power of positive thought and action in organizations, 257-286. San Francisco: Jossey-Bass.

Cusella, L. P. 1982. The effects of source expertise and feedback valence on intrinsic motivation. Human Communication Research, 9: 17-32.

Das, A. 1994. Productivity in knowledge work: A study of technical support in supercomputing. Unpublished doctoral dissertation, University of Minnesota.

Davenport, T. H. 1993. Process innovation: Reengineering work through information technology. Boston: Harvard Business School Press.

Davis, F. D. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13: 319-340.

Davis, G. B. & Olson, M. H. 1985. Management information systems: Conceptual foundations, structure, and development, Second edition, New York: McGraw-Hill.

Davis, F. D., Bagozzi, R. P. & Warshaw, P. R. 1989. User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35: 982-1003.

Davis, F. D. & Venkatesh, V. 1994. A critical assessment of potential measurement biases in the technology acceptance model: Three experiments. Unpublished working paper, University of Minnesota.

de Charms, R. 1968. Personal causation: The internal affective determinants of behavior. New York: Academic Press.

Deci, E. L. 1971. The effects of externally mediated rewards on intrinsic motivation. Journal of Personality and Social Psychology, 18: 105-15.

200

Deci, E. L. 1972. The effects of contingent and noncontingent rewards and controls on intrinsic motivation. Organizational Behavior and Human Performance, 3:217-229.

Deci, E. L. 1975. Intrinsic motivation. New York: Plenum.

Deci, E. L. & Ryan, R. M. 1985. Intrinsic motivation and self-determination in human behavior. New York: Plenum.

Deci, E. L. & Ryan, R. M. 1987. The support of autonomy and the control of behavior. Journal of Personality & Social Psychology, 53: 1024-1037.

DeGreene, K. B. 1970. (Ed.), Systems psychology. New York: McGraw-Hill.

Denzin, N. K. 1978. The logic of naturalistic inquiry. In Denzin, N. K. (Ed.). Sociological methods: A sourcebook, second edition, New York: McGraw-Hill.

Denzin, N. K. 1989. The research act: A theoretical introduction to sociological methods. Englewood Cliffs, NJ: 1989.

Denzin, N. K. 1994. The art and politics of interpretation. In N. K. Denzin & Y. S. Lincoln (Eds.). Handbook of qualitative research, 500-515. Thousand Oaks, CA: Sage.

Dillman, D. A. 1978. Mail and telephone Surveys: The total design method. New York: Wiley.

Dobing, B. 1993. Building trust in user-analyst relationships. Unpublished doctoral dissertation, Information and Decision Sciences Department, University of Minnesota, Minneapolis.

Drucker, P. F. 1991. The new productivity challenge. Harvard Business Review, November-December, 69: 69-79.

Dubin, R. 1976. Theory building in applied areas. In M. D. Dunnette (Ed.), Handbook of industrial and organizational psychology, 17-39. Chicago: Rand-McNally.

Dyer, L. & Parker, D. F. 1975. Classifying outcomes in work motivation research: An examination of the intrinsic-extrinsic dichotomy. Journal of Applied Psychology, 60: 455-458.

201

Earley, P. C. 1986. Trust, perceived importance of praise and criticism, and work performance: An examination of feedback in the United States and England. Journal of Management, 12: 457-473.

Earley, P. C. 1988. Computer-generated performance feedback in the magazine-subscription industry Organizational Behavior and Human Decision Processes, 41: 50-64.

Eisenhardt, K. M. 1985. Control: Organizational and economic approaches. Management Science, 31: 134-149.

Eisenhardt, K. M. 1989a. Building theory from case study research, Academy of Management Review, 14: 532-550.

Eisenhardt, K. M. 1989b. Agency theory: An assessment and review, Academy of Management Review, 14: 57-74.

Fisher, C. D. 1978. The effects of personal control, competence, and extrinsic reward systems on intrinsic motivation. Organizational Behavior and Human Performance, 21: 273-288.

Freedman, J. L., Cunningham, J. A. & Krismer, K. 1992. Inferred values and the reverse-incentive effect in induced compliance. Journal of Personality and Social Psychology, 62: 357-368.

French, J. R. P. & Raven, B. 1968. The bases of social power. In D. Cartwright and A. Zander (Eds.), Group Dynamics, third edition, 259-269. New York: Harper and Row.

Gabarro, J. J. 1990. The development of working relationships. In J. Galegher, R. E. Kraut & C. Egido, Intellectual teamwork: Social and technological foundations of cooperative work. Hillsdale, 79-110. NJ: Lawrence Erlbaum.

Galitz, W. O. 1980. Human factors in office automation. Atlanta: Life Office Management Association.

Gallegos, R. C & Phelan, J. G. 1977. Effects on productivity and morale of a systems-designed job-enrichment program in Pacific Telephone. Psychological Reports, 40:283-290.

Gear, T. E., Marsh, N. R. & Sergent, P. 1985. Semi-automated feedback and team behavior. Human Relations, 38: 707-721.

202

George, J. F. & King, J. L. 1991. Examining the computing and centralization debate. Communications of the ACM, 34: 63-72

Georgopoulos, B. S. & Mann, F. C. 1962. The community general hospital. New York: Macmillan.

Glaser, B. 1978. Theoretical sensitivity. Mill Valley, CA: Sociology Press.

Glaser, B. 1992. Emergence vs. forcing: Basics of grounded theory analysis. Mill Valley, CA: Sociology Press.

Glaser, B. & Strauss, A. 1967. The discovery of grounded theory.

Chicago: Aldine.

Glick, W. H., Jenkins, G. D., Jr. & Gupta, N. 1986. Method versus substance: How strong are underlying relationships between job characteristics and attitudinal outcomes? Academy of Management Journal, 29: 441-464.

Goudas, M., Biddle, S. & Underwood, M. 1995. A prospective study of the relationships between motivational orientations and perceived competence with intrinsic motivation and achievement in a teacher education course. Psychology, 15: 89-96.

Granovetter, M. 1985. Economic action and social structure: The problem of embeddedness, American Journal of Sociology, Volume 91, pp. 481-510.

Green, L. & Foster, D. 1986. Classroom intrinsic motivation: Effects of scholastic level, teacher orientation, and gender. Journal of Educational Research, 880: 34-39.

Greenberg, J. 1993. Justice and organizational citizenship: A commentary on the state of the science. Employee Responsibilities and Rights Journal, 6: 249-256

Griffin, R. W. 1983. Objective and social sources of information in task redesign: A field experiment. Administrative Science Quarterly, 28: 184-200.

Griffin, R. W., Welsh, A. & Moorhead, G. 1981. Perceived task characteristics and employee performance: A literature review. Academy of Management Review, 6: 655-664.

203

Guzzo, R. A. 1979. Types of rewards, cognitions, and work motivation. Academy of Management Review, 4: 75-86.

Hackman, J. R. 1980. Work Redesign, Reading, MA: Addison-Wesley.

Hackman, J. R. & Lawler, E. E. 1971. Employee reactions to job characteristics. Journal of Applied Psychology, 55: 259-286.

Hackman, J. R. & Oldham, G. R. 1975. Development of the job diagnostic survey. Journal of Applied Psychology, 60: 159-170.

Hackman, J. R. & Oldham, G. R. 1976. Motivation through the design of work: Test of a theory. Organizational Behavior and Human Performance, 16: 250-279.

Hale, A. R. & Glendon, A. I. 1987. Individual behaviour in the control of danger, Amsterdam: Elsevier.

Hammer, M. & Champy, J. 1993. Reengineering the corporation: A manifesto for business revolution. New York: Harper Business.

Hamner, W. C. 1974. Reinforcement theory and contingency management in organizational settings. In H. L. Tosi & W. C. Hamner (Eds.), Organizational behavior and management: A contingency approach. Chicago: St. Clair Press.

Hamner, W. C. 1991. Reinforcement theory and contingency management in organizational settings. In R. M. Steers & L. W. Porter (Eds.) Motivation and work behavior, fifth edition. New York: McGraw-Hill.

Harackiewicz, J. M. & Larson, J. R. 1986. Managing motivation: The impact of supervisor feedback on subordinate task interest. Journal of Personality and Social Psychology, 51: 547-556.

Harackiewicz, J. M., Manderlink, G. & Sansone, C. 1984. Rewarding pinball wizardry: Effects of evaluation and cue value on intrinsic interest. Journal of Personality and Social Psychology, 47: 287-300.

Henderson, J. C. & Lee, S. 1992. Managing I/S design teams: A control theories perspective. Management Science, 38: 757-777.

204

Hergenhahn, B. R. & Olson, M. H. 1993. An introduction to theories of learning, fourth edition. Englewood Cliffs, NJ: Prentice Hall.

Herzberg, F. 1966. Work and the nature of man. Cleveland: World.

Herzberg, F. 1976. The motivation-hygiene concept and problems of manpower. In R. A. Zawachi & D. D. Warrick (Eds.). Organization development: Managing change in the public sector, 178-182. Chicago: IPMA.

Hirschman, A. O. 1984. Against parsimony: Three easy ways of complicating some categories of economic discourse. American Economic Review, 74: 89-96.

Hofstede, G. H. 1967. The game of budget control, second edition. Assen, The Netherlands: Van Gorcum & Comp.

Holmes, J. G. & Rempel, J. K. 1989. Trust in close relationships. In C. Hendrick (Ed.), Close relationships: Review of personality and social psychology, 10: 187-220. Newbury Park, CA: Sage.

Hunter, J. E. & Gerbing, D. W. 1982. Unidimensional measurement, second order factor analysis, and causal models. In B. M. Staw & L. L. Cummings (Eds.) Research in Organizational Behavior, 4: 267-320. Greenwich, CN: JAI Press.

Huston, T. L. 1983. Power. In H. H. Kelley, E. Berscheid, A. Christensen, J. H. Harvey, T. L. Huston, G. Levinger, E. McClintock, L. A. Peplau & D. R. Peterson (Eds.). Close relationships, 169-219. New York: W. H. Freeman.

Idaszak, J. R. & Drasgow, F. 1987. A revision of the Job Diagnostic Survey: Elimination of a measurement artifact. Journal of Applied Psychology, 72: 69-74.

Ivancevich, J. M. & McMahon, J. T. 1982. The effects of goal setting, external feedback, and self-generated feedback on outcome variables: A field experiment. Academy of Management Journal, 25: 359-372.

Ives, B. & Chervany, N. L. 1983. Alternative data entry technologies and settings: Impact on worker attitudes and behavior. Information & Management, 6: 37-47.

205

Ives, B., Hamilton, S. & Davis, G. B. 1980. A framework for research in computer-based management information systems. Management Science. 26: 910-934.

Jackson, M. 1997. Employers strive to cultivate a loyal workforce. Minneapolis Star Tribune business archives, October 13.

Jayaaratne, S, Vinokur-Kaplan, D. & Chess, W. A. 1995. The importance of personal control: A comparison of social workers in private practice and public agency settings. Journal of Applied Social Sciences, 19: 47-59.

Johnson-George, C. & Swap, W. C. 1982. Measurement of specific interpersonal trust: Construction and validation of a scale to assess trust in a specific other. Journal of Personality and Social Psychology, 43: 1306-1317.

Jones, D. N. 1997. Is workplace morale on decline? Deseret News Archives, September 7: 1.

Kakabadse, A. 1986. Organizational alienation and job climate: A comparative study of structural conditions and psychological adjustment. Small Group Behavior, 17: 458-471.

Kanfer, R. 1990. Motivation theory and industrial and organizational psychology. In Dunnette, M. D. (Ed.) Handbook of industrial & organizational psychology, 1: 75-170. Palo Alto, CA: Consulting Psychologist Press.

Kaplan, A. 1964. The conduct of inquiry. New York: Chandler.

Kaplan, B. & Duchon, D. 1988. Combining qualitative and quantitative methods in information systems research: A case study. MIS Quarterly, 12:571-586

Kearney, M. H., Murphy, S. & Rosenbaum, M. 1994. Mothering on crack cocaine: A grounded theory analysis. Social Science and Medicine, 38: 351-361.

Keller, G., Warrack, B. & Bartel, H. 1988. Statistics for management and economics: A systematic approach. Belmont, CA: Wadsworth.

Kirk, J. & Miller, M. L. 1986. Reliability and validity in qualitative research. Beverly Hills, CA: Sage.

206

Kirsch, L. J. 1992. Formal, informal, and self controls: An analysis of working relationships during systems development projects. Unpublished doctoral dissertation, Information and Decision Sciences Department, University of Minnesota, Minneapolis.

Kohn, A. 1993a. Why incentive plans cannot work. Harvard Business Review, 71: 54-60.

Kohn, A. 1993b. Punished by rewards: The trouble with gold stars, incentive plans, A’s, praise, and other bribes. Boston: Houghton Mifflin.

Kramer, R. M. 1996. Divergent realities and convergent disappointments in the hierarchic relation: Trust and the intuitive auditor at work. In R. M. Kramer & T. R. Tyler (Eds.), Trust in organizations: Frontiers of theory and research: 216-245. Thousand Oaks, CA: Sage.

Kruglanski, A. W., Freedman, I. & Zeevi, G. 1971. The effects of extrinsic incentives on some qualitative aspects of task performance. Journal of Personality, 39: 606-617.

Kumar, A. & Dillon, W. R. 1990. On the use of confirmatory measurement models in the analysis of multiple-informant reports. Journal of Marketing Research, 27: 102-111.

Laudon, K. C. & Laudon, J. P. 1995. Essentials of management information systems: Organization and technology. Upper Saddle River, NJ: Prentice-Hall.

Lawler, E. E. 1971. Pay and organizational effectiveness: A psychological view. New York: McGraw-Hill.

Lawler, E. E. & Hall, D. T. 1970. Relationship of job characteristics to job involvement, satisfaction, and intrinsic motivation. Journal of Applied Psychology, 54: 305-312.

Lawler, E. E. & Rhode, J. G. 1976. Information and control in organizations. Pacific Palisades, CA: Goodyear Publishing Company.

Lee, A. S. 1991a. Integrating positivist and interpretive approaches to organizational research. Organization Science, 2: 342-365.

207

Lee, L. 1991b. The day the phones stopped. New York: Donald I. Fine.

Lending, D. 1996. CASE Technology and Systems Development Job Characteristics. Unpublished Doctoral Dissertation, Information and Decision Sciences department, University of Minnesota, Minneapolis.

Lepper, M. R., Greene, D. & Nisbett, R. E. 1973. Undermining Children’s intrinsic interest with extrinsic rewards: A test of the ‘overjustification’ hypothesis. Journal of Personality and Social Psychology, 28: 129-137.

Lewis, J. D. & Weigert, A. J. 1985. Trust as a social reality. Social Forces, 63: 967-965.

Lientz, B. P. & Swanson, E. B. 1980. Software maintenance management. Reading, MA: Addison-Wesley.

Lincoln, Y. S. & Guba, E. G. 1985. Naturalistic inquiry. Beverly Hills, CA: Sage.

Locke, E. A. 1968. Toward a theory of task motivation and incentives. Organizational Behavior and Human Performance, 3: 157-189.

Locke, E. A., Latham, G. P. & Erez, M. 1988. The determinants of goal commitment. Academy of Management Review, 13: 23-39.

Lofland, J. 1971. Analyzing social settings: A guide to qualitative observation and analysis. Belmont, CA: Wadsworth.

Lopez, E. M. 1981. Increasing intrinsic motivation with performance-contingent reward. Journal of Psychology, 108: 59-65.

Lucas, H. C. 1975. Why information systems fail. New York: Columbia University Press.

Luhmann, N. 1991. Trust and Power. Ann Arbor, MI: University Microfilms International.

Lyytinen, K. & Hirschheim, R. 1987. Information systems failures--A survey and classification of the empirical literature. Oxford Surveys in Information Technology, 257-309. Oxford: Oxford University Press.

208

Maslow, A. H. 1943. A theory of human motivation. Psychological Review, 50: 370-396.

Maslow, A. H. 1954. Motivation and personality. New York: Harper & Row.

Mayer, R. C., Davis, J. H. & Schoorman, F. D. 1995. An integrative model of organizational trust. Academy of Management Review, 20, 709-734.

Mayo, E. 1949. The social problems of an industrial civilization. London: Routledge & Kegan Paul.

McClelland, G. H. & Judd, C. M. 1993. Statistical difficulties of detecting interactions and moderator effects. Psychological Bulletin, 114: 376-390.

McIntosh, N. J. 1990. Leader support and responses to work in US nurses: A test of alternative theoretical perspectives. Work & Stress, 4: 139-154.

McKnight, D. H. 1996. Management strategies for operating critical transaction processing systems, In J. M. Carey (Ed.) Proceedings of the Americas conference on information systems, 194-196. Association for Information Systems, Phoenix, Arizona, August 16-18.

McKnight, D. H. & Chervany, N. L. 1996. The meanings of trust. MIS research center, working paper series, WP 96-04, Information and Decision Sciences department, Carlson School of Management, University of Minnesota.

McKnight, D. H., Cummings, L. L. & Chervany, N. L. 1996. Trust formation in new organizational relationships. MIS research center, working paper series, WP 96-01, Information and Decision Sciences department, Carlson School of Management, University of Minnesota.

Meehl, P. E. 1978. Theoretical risks and tabular asterisks: Sir Karl, Sir Ronald, and the slow progress of soft psychology. Journal of Consulting and Clinical Psychology, 46: 806-834.

Mintzberg, H. 1979. An emerging strategy of “direct” research. Administrative Science Quarterly, 24: 582-589.

209

Mohrman, A. M., Jr., Resnick-West, S. M. & Lawler, E. E. 1989. Designing performance appraisal systems: Aligning appraisals and organizational realities. San Francisco: Jossey-Bass.

Mowday, R. T., Steers, R. M. & Porter, L. W. 1979. The measurement of organizational commitment. Journal of Vocational Behavior, 14: 224-247.

Mowday, R. T. & Sutton, R. I. 1993. Organizational behavior: Linking individuals and groups to organizational contexts. Annual Review of Psychology, 44: 195-229.

Neter, J., Wasserman, W. & Kutner, M. H. 1990. Applied linear statistical models: Regression, analysis of variance, and experimental designs, third edition. Homewood, IL: Irwin.

Norman, D. A. 1983. Design rules based on analyses of human error. Communications of the ACM, 26: 254-258.

Nunnally, J. C. 1978. Psychometric theory, second edition. New York: McGraw-Hill.

O’Reilly, C. & Roberts, K. 1974. Information filtration in organizations: Three experiments. Organizational Behavior and Human Performance, 11: 253-265.

Orlikowski, W. J. 1992. The duality of technology: Rethinking the concept of technology in organizations. Organization Science, 3: 398-427.

Orlikowski, W. J. 1993. CASE tools as organizational change: Investigating incremental and radical changes in systems development. MIS Quarterly, 17: 309-340.

Ouchi, W. G. 1979. A conceptual framework for the design of organizational control mechanisms, Management Science, 25: 833-848.

Ouchi, W. G. & Maguire, M. A. 1975. Organizational control: Two functions. Administrative Science Quarterly, 20: 559-569.

Pearce, J. L. & Porter, L. W. 1986. Employee responses to formal performance appraisal feedback. Journal of Applied Psychology, 71: 211-218.

210

Pentland, B. T. 1992. Organizing moves in software support lines. Administrative Science Quarterly, 37: 527-548.

Perrow, C. 1984. Normal accidents: Living with high-risk technologies. New York: Basic Books.

Peters, T. 1992. Liberation management: Necessary disorganization for the nanosecond nineties. New York: Alfred A. Knopf.

Pfeffer, J. 1981. Power in organizations. HarperBusiness.

Pinder, C. D. 1991. Valence-instrumentality-expectancy theory. In R. M. Steers & L. W. Porter, (Eds.) Motivation and work behavior, 144-164, fifth edition. New York: McGraw-Hill.

Pittman, T. S., Davey, M. E., Alafat, K. A., Wetherill, K. V. & Kramer, N. A. 1980. Informational versus controlling verbal rewards. Personality and Social Psychology Bulletin, 6: 228-233.

Poole, M. S. & Van de Ven, A. H. 1989. Using paradox to build management and organization theories. Academy of Management Review, 14: 562-578.

Powers, R. F. & Dickson, G. W. 1973. MIS project management: Myths, opinions, and reality. California Management Review, Spring: 147-156.

Pritchard, R. D. & Montagno, R. V. 1978. Effects of specific vs. nonspecific and absolute vs. comparative feedback on performance and satisfaction. US AFHRL Technical Report, 78-12.

Quick, R. 1997. Surge in e-mail volume causes a five-hour blackout at AOL. Wall Street Journal interactive edition, November 25.

Quintanilla, S. A. R. 1987. New technologies and human error: Social and organizational factors. In J. Rasmussen, K. Duncan & J. Leplat (Eds.) New technology and human error, 125-128. Chichester, U. K.: John Wiley & Sons.

Rasmussen, J. 1986. Information processing and human-machine interaction: An approach to cognitive engineering. New York: North-Holland.

211

Rasmussen, J., Duncan, K. & Leplat, J. (Eds.). 1987. New technology and human error. Chichester, U. K.: John Wiley & Sons.

Reuters News Service. 1996. “AOL Outage Takes Toll,” and “America Online Says it Has Resumed Service,” August 8.

Ring, P. S. & Van de Ven, A. H. 1994. Developmental processes of cooperative interorganizational relationships. Academy of Management Review. 19: 90-118.

Roberts, K. H. & Glick, W. 1981. The job characteristics approach to task design: A critical review. Journal of Applied Psychology, 66: 193-217.

Rosin, H. M & Korabik, K. 1991. Workplace variables, affective responses, and intention to leave among women managers. Journal of Occupational Psychology, 64: 317-330.

Ross, M. 1975. Salience of reward and intrinsic motivation. Journal of Personality and Social Psychology, 32: 245-254.

Rubin, Z. 1973. Liking and loving: An invitation to social psychology. New York: Holt, Rinehart and Winston.

Ryan, R. 1982. Control and information in the intrapersonal sphere: An extension of cognitive evaluation theory. Journal of Personality and Social Psychology, 43: 450-461.

Sagasti, F. R. & Mitroff, I. I. 1973. Operations research from the viewpoint of general systems theory, OMEGA, 1: 695-709.

Salancik, G. & Pfeffer, J. 1978. A social information processing approach to job attitudes and task design. Administrative Science Quarterly, 23: 224-253.

Sanjek, R. 1990. The secret life of fieldnotes. In Sanjek, R. (Ed.). Fieldnotes: The makings of anthropology, 187-270. Ithaca, NY: Cornell University Press.

Sarata, B. P, & Jeppesen, J. C. 1977. Job design and staff satisfaction in human service settings. American Journal of Community Psychology, 5: 229-236.

212

Schlesinger, L. A. & Heskett, J. L. 1991. The service-driven service company. Harvard Business Review, September-October, 69: 71-81.

Schwab, D. P. 1980. Construct validity in organizational behavior. In B. M. Staw & L. L. Cummings (Eds.). Research in Organizational Behavior, 2: 3-43. Greenwich, CN: JAI Press.

Shanab, M. E., Peterson, D., Dargahi, S. & Deroian, P. 1981. The effects of positive and negative verbal feedback on the intrinsic motivation of male and female subjects. Journal of Social Psychology, 115: 195-205.

Shapiro, S. P. 1987. The social control of impersonal trust. American Journal of Sociology, 93: 623-658.

Shneiderman, B. 1980. Software psychology: Human factors in computer and information systems, Cambridge, MA: Winthrop.

Simon, H. A. 1981. The sciences of the artificial (second edition). Cambridge, MA: MIT Press.

Simons, R. 1995. Levers of control: How managers use innovative control systems to drive strategic renewal. Boston: Harvard Business School Press.

Sitkin, S. B. & Pablo, A. L. 1992. Reconceptualizing the determinants of risk behavior. Academy of Management Review, 17: 9-38.

Skinner, B. F. 1953. Science and human behavior. New York: Macmillan.

Slaughter, S. 1995. Software development practices and software maintenance performance: A field study. Unpublished dissertation, Information and Decision Sciences Department, University of Minnesota, Minneapolis.

Smits, S. J., McLean, E. R. & Tanner, J. R. 1997. A longitudinal study of I/S careers: Synthesis, conclusions, and recommendations. In F. Niederman (Ed.), Proceedings of the 1997 ACM SIGCPR conference, 36-48, April 3-5, San Francisco.

Staw, B. M. 1976. Intrinsic and extrinsic motivation. Morristown, NJ: General Learning Press.

213

Staw, B. M., Calder, B. J. & Hess, R. 1976. Intrinsic motivation and norms about payment. Working paper, Northwestern University.

Steers, R. M. & Porter, L. W. 1977. The role of achievement motivation in job design. Journal of Applied Psychology, 62: 472-479.

Steers, R. M. & Porter, L. W. 1979. Motivation and Work Behavior. New York: McGraw-Hill.

Strauss, A. 1987. Qualitative analysis for social scientists. New York: Cambridge University Press.

Strauss, A. & Corbin, J. 1990. Basics of qualitative research. Newbury Park, CA: Sage.

Strickland, L. H. 1958. Surveillance and trust. Journal of Personality, 26: 200-215.

Swanson, E. B. 1984. Organizational designs for software maintenance. In L. Maggi, J. L. King & K. L. Kraemer (Eds.). Proceedings of the 5th international conference on information systems, 73-82, Tucson, November 28-30.

Swanson, E. B. & Beath, C. M. 1989. Maintaining information systems in organizations, New York: John Wiley and Sons.

Swanson, E. B. & Ramiller, N. C. 1993. Information Systems Research Thematics: Submissions to a New Journal, 1987-1992, Information Systems Research, 14: 299-330.

Taber, T. D. & Taylor, E. 1990. A review and evaluation of the psychometric properties of the Job Diagnostic Survey. Personnel Psychology, 43: 467-500.

Tannenbaum, A. S. 1968. Control in organizations. New York: McGraw-Hill.

Tansky, J. W. 1993. Justice and organizational citizenship behavior: What is the relationship? Employee Responsibilities and Rights Journal, 6: 195-207.

Taylor, F. W. 1911. Scientific management. New York: Harper and Brothers.

Tetlock, P. 1985. Accountability: The neglected social context of judgment and choice. In B. M. Staw & L. L. Cummings, Research in Organizational Behavior, 7: 297-332.

214

Thagard, P. 1992. Conceptual Revolutions. Princeton, NJ: Princeton University Press.

Thompson, J. D. 1967. Organizations in action. New York: McGraw-Hill.

Tolman, E. C. 1932. Purposive behavior in animals and men. New York: Appleton-Century-Crofts.

Tripathi, K. N. 1991. Effect of contingency and timing of reward on intrinsic motication. Journal of General Psychology, 118: 97-105.

Tyler, T. R. & Kramer, R. M. 1996. Whither trust? In R. M. Kramer & T. R. Tyler (Eds.), Trust in organizations: Frontiers of theory and research: 1-15. Thousand Oaks, CA: Sage.

Van de Ven, A. H. & Ferry, D. L. 1980. Measuring and assessing organizations. New York: Wiley.

Van Maanen, J. 1979a. Reclaiming qualitative methods for organizational research. Administrative Science Quarterly, 24:520-526.

Van Maanen, J. 1979b. The fact of fiction in organizational ethnography. Administrative Science Quarterly, 24:539-550.

Vroom, V. H. 1964. Work and motivation. New York: Wiley.

Wall Street Journal. 1995. “Amid record profits, companies continue to lay off,” May 4.

Wall Street Journal. 1996. “AOL Fights to Keep its Customers On-Line,” August 8.

Wall Street Journal. 1996. “Re-Engineering gurus take steps to remodel their stalling vehicles,” November 26.

Walton, R. E. 1968. Social and psychological aspects of verification, inspection, and international assurance, Technical Report #1. Lafayette: Purdue University.

Weick, K. E. 1990. Technology as equivoque: Sensemaking in new technologies. In P. S. Goodman, L. S. Sproull and Associates (Eds.) Technology and organizations, 1-44. San Francisco: Jossey-Bass.

215

Weick, K. E. & Roberts, K. H. 1993. Collective mind in organizations: Heedful interrelating on flight decks. Administrative Science Quarterly, 38: 357-381.

Weiner, B. 1974. An attributional interpretation of expectancy-value theory. In B. Weiner (Ed.). Cognitive views of human motivation, 51-69. New York: Academic Press.

Weisberg, S. 1985. Applied linear regression. Second edition. New York: Wiley.

Williamson, O. E. 1975. Markets and hierarchies: Analysis and antitrust implications. New York: Free Press.

Wrightsman, L. S. 1991. Interpersonal trust and attitudes toward human nature. In J. P. Robinson, P. R. Shaver & L. S. Wrightsman (Eds.) Measures of personality and social psychological attitudes: Vol. 1: Measures of social psychological attitudes, 373-412. San Diego, CA: Academic Press.

Yin, R. K. 1984. Case study research: Design and methods. Newbury Park, CA: Sage.

Zuboff, S. 1985. Automate/Informate: The two faces of intelligent technology. Organizational Dynamics, 14: 5-18.

Zuboff, S. 1988. In the age of the smart machine. New York: Basic Books.

Zucker, L. G. 1986. Production of trust: Institutional sources of economic structure, 1840-1920. In B. M. Staw & L. L. Cummings (Eds.), Research in Organizational Behavior, 8: 53-111. Greenwich, CN: JAI Press.

216

APPENDICES

Appendix A Example of Open and Axial Coding

The following example illustrates the construct creation and linking process.I. OPEN CODING (to establish constructs and their descriptors--see Figure 12)

A. Interview text: "I think [manager #1 (Mgr1) and manager #2 (Mgr2)]...they cared about people."

A. Construct creation: In general, this has to do with people caring for other people; a construct was created called "Felt Caring." By comparing this construct with other constructs, "Felt Caring" was found to be similar, but not identical, to those constructs (e.g., liking, trust) that also expressed something about the personal relationships between workers. Hence, the construct "Felt Caring" was made a subset of the existing higher level conceptual category called "Relationships between people." The detail conceptual descriptors of the construct "Felt Caring" were examined, based on this item. These include: a) Who cares b) about Whom.

B. Interview text: "Mgr1 and Mgr2 like whenever we had outages, even at night, would always show up....I think there was more interaction between us and Mgr1 and Mgr2....with Mgr1 or Mgr2 you could yak or kid...they'd be apt to show up [to an outage] in their sweats."

B. Construct creation: A construct called "Outages--Attendance" was created, with Who and When descriptors. Another construct called "Interaction between people" was created. Conceptual descriptors for this construct were: a) who (Mgr1/Mgr2); b) when--frequency ("whenever"); c) when--occasion (during outages); d) when--time of day (night); e) how--mode or medium (in person); f) how--style (formal/informal). Informality was indicated by "yak or kid," and by the informal attire ("sweats") worn during the interaction.

C. Interview text: "Mgr1 and Mgr2 used to always come down at holiday time, say, you know, 'Happy New Year,' 'Merry Christmas,'...it's just little things sometimes people do for you that make you know that they appreciate you."

C. Construct creation: First, a new construct was easily formed: "felt appreciation." It was given who and about whom descriptors. However, the first half of this text was more problematic. At first, the researcher coded the part before "Merry Christmas..." as an indicator of the construct "Felt Caring." But the second half of the quote shows that the interviewee interpreted the holiday greetings as "felt appreciation." By comparing it to other constructs, he decided to place it in the "Interaction between people" construct, with how--style (formal/informal) mode and when-occasion descriptors.

D. Interview text: "If Mgr1 or Mgr2 came down [to a computer outage], I probably would have felt easy."

D. Construct creation: The first phrase reflects "Outages--Attendance." The second phrase has to do with how comfortable or "easy" a person feels with the superior. The researcher placed this in a category called "Nervous around others," which was also in the larger relationships between people category. Detail descriptors: a) Who; b) about Whom; c) when [during an outage]; d) Where [in the computer center]; e) Certainty of easiness [probably]

217

Figure 12 Model of Construct Creation Relationships

K Between People K K K

Interaction Felt Felt Outages-- Nervous Between People Caring Appreciation Attendance Around Others

I I I I I I

I Text Text Text Text A B C D

K = Kind link (one construct is a subset or “kind” of another)I = Instance link (object in box is an instance of construct in ellipse)

Notation source: Thagard (1992)

II. AXIAL CODING (to establish relationships between constructs)

A. B. C. D. Interview text: [see above]

A. B. C. D. Relationship creation: These four pieces of text are connected by reference to Mgr1 and Mgr2 (and to the interviewee). One causal link is made obvious by the interviewee's comments (Figure 13): informal holiday interaction led the interviewee to feel appreciated ("it's just little things sometimes people do for you that make you know that they appreciate you"). The links between interaction and felt caring don't appear to be causal, but they do seem to be positively associated. The links between interaction/felt caring/felt appreciation and nervousness were then explored. The evidence in this text only indicates that interaction/caring/felt appreciation are negatively associated with nervousness around Mgr1 and Mgr2. No causal link can be formed here.

Figure 13 Model of Construct LinkagesLEGEND:

+Ca Ca = CausalA = Associative

Interaction Felt Felt Nervous + = Positive link Between People Caring Appreciation Around Others - = Negative link

+A +A -A -A

-A

218

APPENDIX B QUESTIONNAIRE ITEMS BY CONSTRUCT

A. QUESTIONS ASKED TECHNICIANSNote: Question number indicates the order in which questions were asked

JOB CHARACTERISTICSSkill Variety: The extent to which the job requires the worker to use a diverse set of talents.1. My job requires me to do many different things at work, using a variety of my skills and talents.2. This job requires me to use a number of complex or high-level skills.3. Overall, my tasks are not simple and repetitive.

Job Significance: The extent to which the worker perceives the job as crucial or important to their own, or the general, workplace.4. This job is one where a lot of other people, in this organization and other organizations, can be affected by how well my work gets done.5. This job is important in that the results of my work can significantly affect other peoples’ ability to do their work.6. This job itself is very significant and important in that it facilitates or enables other peoples’ work.7. My job is very important in the broader scheme of things, that is, in the general workplace.

Task Identity: The extent to which the worker sees the job as a whole or complete set of work, as opposed to just a component piece of an overall set of work.8. This job is arranged so that I can usually do an entire piece of work from beginning to end, not just a small part of an overall piece of work.9. This job generally provides me the chance to completely finish the pieces of work I begin.10. My job usually involves a complete piece of work that has an obvious beginning and end.

Job Feedback: The extent to which the job itself provides workers knowledge about how well they have done a task.11. This job itself provides me information about my work performance. That is, the actual work itself provides clues about how well I am doing--aside from any feedback co-workers or supervisors may provide.12. After I finish a task, I know whether I performed it well.13. Just doing the work required by this job provides many chances for me to figure out how well I am doing.

Growth Need Strength: The extent to which a worker desires a job that is challenging or growth-producing.14. I would like to have stimulating and challenging work.15. I would like to exercise independent thought and action in my work.16. I would like to have opportunities for personal growth and development at my work.

Knowledge of Results: The extent to which workers understand how well they are doing on the job.17. I usually know whether or not my work is satisfactory on this job.18. I have a pretty good idea of how I am performing my work19. I can generally tell whether I am doing well or poorly in this job.

MOTIVATIONIntrinsic Motivation—Enjoyment: The degree to which the person perceives that their job gives them enjoyment or pleasure. 20. I get a lot of enjoyment out of doing my job.21. When it comes right down to it, I really enjoy my work.22. Just doing my job gives me a sense of keen satisfaction.23. Doing my job gives me a very satisfying feeling.

219

Intrinsic Motivation--Self-Esteem: The degree to which the person perceives that their job gives them a feeling of self-esteem (i.e., when they do the job well).24. When I do my job well, it gives me a feeling of accomplishment.25. When I perform my work well, it contributes to my personal growth and development.26. My opinion of myself goes up when I do this job well.27. Performing this job well reinforces my feelings of self-esteem.

Job Satisfaction: The extent to which one feels pleased or satisfied with one’s job.32. Generally speaking, I feel satisfied with this job.33. Overall, I feel satisfied with the kind of work I do in this job.34. In general, I feel satisfied with my job.35. I seldom think of finding another job.

Experienced Work Meaningfulness: The degree to which the person experiences the job as one that is generally meaningful, valuable, and worthwhile.39. To me, most of the work I do is valuable and important.40. My work is worthwhile and valuable.41. In general, the work I do in this job is important. 42. Only a few of the things I do on this job seem useless or trivial.

Organizational Commitment: Willingness of the worker to exert considerable effort/sacrifice on behalf of the organization. (Note: this is the work/effort component of the overall Organizational Commitment construct.)44. I am willing to put in a great deal of effort beyond that normally expected in order to help this organization be successful.45. This organization inspires the very best in me in the way of job performance.46. I show by my actions that I really care about the fate of this organization.47. I am willing to sacrifice to help this organization meet its goals.

Felt Responsibility: The degree to which one feels personally obligated or responsible for one’s work.36. I feel a high degree of personal responsibility for the work I do on this job.37. Whether or not this job gets done--and done properly--is clearly my responsibility.38. I feel I should personally take responsibility for the results of my work on this job.

RELATIONSHIPSLiking: The extent to which a subordinate has positive affect toward the boss. 60. My lead/supv, _____________, is one person I really like.61. I have a lot of respect for my lead/supv.62. Overall, I react very favorably to my lead/supv.63. I admire my lead/supv.

Trusting Intention: A subordinate’s willingness to depend on the supervisor on an issue important to the subordinate’s career.64. When an issue that is critical to my career arises, I feel I can depend on my lead/supv.65. I can always rely on my lead/supv in a career-related issue.66. My lead/supv is a person on whom I feel I can rely when the issue is important to my career.67. I feel I can depend on my lead/supv on a career-sensitive issue.

Trusting Belief-Benevolence: The extent to which a subordinate believes that the boss is benevolent (cares for the welfare of the subordinate and is motivated to act in the subordinate’s interest).68. When it comes to my well-being, my lead/supv really cares.69. If I required help, my lead/supv would care enough to help me.70. I believe that my lead/supv cares enough to act in my personal best interest.

220

71. When you get right down to it, my lead/supv cares about what happens to me.

IF HI: You have a good working R w/your lead. Briefly, what is the basis for that good R?

Trusting Belief-Competence: The extent to which a subordinate believes that the boss is capable, skillful, and/or proficient at work.76. My lead/supv is skillful and effective in her/his work.77. My lead/supv performs his/her job very well.78. Overall, I have a capable and proficient lead/supv.79. Overall, my lead/supv is competent technically.

Felt Gratitude: The extent to which a subordinate perceives that their boss appreciates the subordinate’s work.80. My lead/supv often shows appreciation for me when I do a good job.81. When I do my task well, my lead/supv often expresses appreciation to me.

TRUST--OTHERSystem Trust: The belief that impersonal structures (e.g., regulations, procedures) exist that support or encourage fairness in one’s work environment.53. Our workplace has processes that assure that we will be treated fairly and equitably.54. I work in an environment in which good procedures make things fair and impartial.55. Fairness to employees is built into how issues are handled in our work environment. 56. In this workplace, sound practices exist that help ensure fair and unbiased treatment of employees.

Dispositional Trust: General tendency of one to believe in the benevolence of other people across most situations.57. In general, people really do care about the well-being of others.58. The typical person is sincerely concerned about the problems of others.59. Most of the time, people care enough to try to be helpful, rather than just looking out for themselves.

INCENTIVESChallenge Content of Incentive: The degree to which reaching the contingent performance standard required substantial effort.43. Achieving my [incentive plan name] goals during last year’s incentive period was very challenging for me, based on when the goals were given.

Motivational Effect: The degree to which the incentive awards have positive motivational impact on the individual and the team.82. The goal-oriented [plan name] bonuses have a positive motivational effect on the CODWRD team. 83. The goal-oriented [plan name] bonuses have a positive motivational effect on me.84. The CODWRD team is more conscientious now because of the [plan name] bonuses.85. I am more conscientious now because of the [plan name] bonuses.86. The CODWRD team works harder because of the [plan name] bonuses.87. I work harder now because of the [plan name] bonuses.

Satisfaction with Incentive: The degree to which the person is pleased or content with the incentive award. 88. Most of my co-workers feel satisfied with the [plan name] bonus they received.89. I feel satisfied with the [plan name] bonus I received.

Aside from our normal questions, tell me, very briefly, does the [plan name] have any other effects on you or the team?

CONTROLS

221

Autonomy: The extent to which a boss allows a subordinate to make decisions about their work on their own.72. In my work, I usually do not have to refer matters to my lead/supv for a final decision.73. Usually, my lead/supv does not have to approve my decisions before I can take action.74. Rather than asking my lead/supv, I usually make my own decisions about what to do on my job.75. I can usually do what I want on this job without consulting my lead/supv.

Feedback (from Supervisor): The frequency with which a boss gives a subordinate work-related feedback.90. My lead/supv gives me a lot of feedback about how I am doing on this job.91. My lead/supv frequently tells me how I am doing on my job functions.92. Our lead/supv gives me frequent feedback about my performance.93. Our lead/supv often let’s me know the extent to which I did a task satisfactorily.

Micromanagement: The extent to which a boss becomes so involved in a subordinate’s task that the boss does the task for the subordinate.94. My lead/supv rarely gets so involved that s/he does my task for me.95. Our lead/supv rarely gets too involved in the activities of my job.96. I hardly ever see our lead/supv take a larger role in work assigned to me than s/he should.97. Our lead/supv rarely performs a part of my job for me.

Accountability: The extent to which job holders are held responsible for their work.101. How much are you held personally responsible for achieving your performance goals or standards?102. How much are you personally given credit for successes you have on the job?103. How much are you held personally accountable for the work decisions you make in your job?104. How much are you held personally responsible for mistakes you make on the job?

In genl, who in mgmt are you held accountable by?______________________________________

Pressure: The extent to which a subordinate feels under stress from the boss when performing their job.105. I seldom feel significant pressure from my lead/supv to perform at a consistently high level on this job.106. I seldom feel significant pressure from my manager to perform at a consistently high level on this job.

PERCEIVED TEAM EFFECTIVENESSOverall Team Effectiveness: The perception that the team performs its function proficiently.107. I feel that this team effectively performs its overall task.108. Overall, this team performs its functions effectively.109. In general, the CODWRD team is effective in doing its job.

Team Coordination Effectiveness: The perception that the members of the team function in a cooperative and helpful way so as to accomplish the team’s function.110. The people who work together on the CODWRD team do their job properly and efficiently without getting in each other’s way.111. The people who work together on this team perform their tasks without interfering with each other’s duties.112. When it comes to jointly fixing system problems (*or * making sched changes), my activities are well-coordinated with activities of other CODWRD team members.113. Team members are willing to assist each other when needed.114. I feel the various CODWRD team members work together very well.

222

Information Sharing Accuracy: The perception that the information shared among team members is factual.115. The information I receive from other team members is seldom inaccurate.116. I can feel confident that information I receive from other CDWRD team members is correct.117. I rarely have to go back and check the information I have received from team members.118. I never have to worry about getting false information from members of the CODWRD team.

Information Sharing Openness: The perception that team members openly share information with each other.119. It is easy to talk openly about work-related issues to all members of this team.120. The members of this team freely discuss various topics important to the CODWRD team.121. It is easy to ask for information from any member of this team.122. I feel free to discuss almost any work-related issue in the CODWRD team.

Conflict Resolution: The perception that the group deals with and resolves its internal problems productively and positively.125. Conflicts among team members are usually resolved effectively and positively.126. When disagreements occur, the CODWRD team is good at bringing the issues into the open and working them out peacefully.127. When problems between team members do arise, they are handled satisfactorily.Do you Agree or Disagree, or are you Neutral?128. Conflict is typically dealt with and resolved constructively by the CODWRD team.

EXPLANATORY/PLAUSIBLE ALTERNATIVESInteraction with Team Members--98. In general, how much do you interact with other CODWRD team members. Again, the CODWRD team consists of all those who keep central site CODWRD up and running:Interaction with Supervisor--99. In general, how much do you interact with your lead/supv:

Interaction with Manager--100. In general, how much do you interact with your manager:Effect of Supervisor Interaction on Worker Self-esteem (SEspv)--28. My work-related interactions with my lead/supv usually have a positive effect on my self-esteem.29. Interacting with my lead/supv on the job generally reinforces my feelings of self-esteem.Feelings today versus Earlier--30. I get a greater feeling of accomplishment from my job today than I did 3 years ago.31. I get more enjoyment from doing my job today than I did 3 years ago.Reasons?________________________________Feelings Today versus Earlier--48. When I compare my current level of work commitment to the company’s success versus three years ago, I am more committed today.49. I am more committed to work hard for this company today than I was three years ago.(any reasons you are more/less committed today?)Communication Today versus Earlier--123. Compared to the CODWRD team 3 years ago, current CODWRD team members share information more openly today.124. Compared to the CODWRD team 3 years ago, team information shared with me by current CODWRD team members is more accurate today.members. Intrinsic Motivation Orientation--50. From the following list, please select the one reason that best represents why you try to work hard and do a good job: (I’ll read the list again if you would like) (1st List)

1. Opportunities for a promotion2. The challenge of the task3. Merit pay increases4. A feeling of accomplishment5. Something else (specify) (would you like me to read the list again?)

223

51. From the following list, please select the one reason that best represents why you try to work hard and do a good job: (2nd List)

1. [incentive plan name] bonuses2. Solving the incident, outage, or potential problem3. Achievement award programs4. Enjoyment of the job5. Something else (specify) (shall I read them again?)

52. From the following list, please select the one reason that best represents why you try to work hard and do a good job: (3rd List)

1. Opportunities for a Promotion2. Appreciation from your boss3. Merit pay increases4. [incentive plan name] bonuses5. Something else (specify) (shall I read them again?)

Relationship with Team Members--129. In general, I have a good relationship with those CODWRD team members I interact with.Percent of Time Directly Keeping System Available--130. What percentage of your job (in terms of % of hours spent) relates directly to keeping CODWRD up and running? ____ By ‘directly related,’ I mean either fixing CODWRD when it goes down, or working to prevent it from crashing in the first place.Extent of Time Worked with Supervisor--131. How many years and months have you worked with your current lead/supv, both now and in past jobs? _____yrs. ____Mths.

DEMOGRAPHICAge--132. How old did you turn on your last birthday? ____Number of recent Promotions--133. How many promotions have you had over the past five years, if any? ____Number of recent Merit Pay Increases--134. How many base pay increases have you had over the past five years, if any? ____Grade Level--135. What is your current grade level? ____Educational Attainment--136. How many years of academic, vocational, or professional education have you obtained beyond high school? _____

224

B. QUESTIONS ASKED SUPERVISORS

(Same questions [107-128] as above for: Overall Team Effectiveness, Team Coordination Effectiveness, Information Sharing Accuracy, Information Sharing Openness, Communication Today versus Earlier, Conflict Resolution)

Questions regarding each person they supervise: (each question was prefaced by a definition)12

Organizational Commitment (defined above)1. _______________________ typically displays a commitment and desire to work hard and exert2. Please rate the following employees 1-N, with 1=Most; N=Least, on the following attributes: (again, use all N numbers, if at all possible)Amount of Commitment to Work Hard they display(If at all possible, please use all N numbers instead of showing “ties” between people.)

#(1-N) ____ ______________

Autonomy (defined above)3. I usually give a lot of decision-making autonomy to _______________________ on matters related to her/his job.4. Please rate the following employees 1-N, with 1=Most; N=Least, on the following attributes: (again, use all N numbers, if at all possible)Amount of Decision-makingAutonomy I give them in their work

#(1-N) ____ ______________

Contribution to Team Coordination Effectiveness: The degree to which a worker enhances the willing and effective cooperation and helpfulness among team members.5. ____________typically makes a good contribution to effective team cooperation, both by his/her own example and by his/her positive influence on other team members.6. Please rate the following employees 1-N, with:

1=Best at coordinating their activities with others and helping other members of the team when needed.

N=Worst at coordinating their activities with others and helping other members of the team when needed.

Contribution to Team Communication Effectiveness: The degree to which a person enhances the communication effectiveness of the team through example and influence on others.7. ____________typically makes a good contribution to effective team communication, both by his/her own example and by his/her positive influence on other team members.8. Please rate the following employees 1-N, with:

1=Best at communicating openly and accurately with team members and influencing other team members to do the same.

N=Worst at communicating openly and accurately with team members and influencing other team members to do the same.

Contribution to Team Conflict Resolution: The degree to which a person enhances the team’s ability to resolve internal problems through example and influence on others.

12 Question order for supervisor instrument was: 1, 3, 5, 7, 9, 11, 4, 2, 10, 6, 8, 12, 13225

9. ____________usually enhances the team’s ability to constructively resolve large or small disagreements or conflicts that arise, both by his/her own example and by his/her positive influence on other team members.10. Please rate the following employees 1-N, with:

1=Best at helping the CODWRD team effectively resolve its internal conflicts or disagreements, large or even small; and

N=Worst at helping the CODWRD team effectively resolve its internal conflicts or disagreements, large or even small.

Contribution to Overall Team Effectiveness: The degree to which a person enhances the team’s ability to effectively accomplish its overall goals.11. ____________typically makes a good contribution to overall team effectiveness, both by his/her own example and by his/her positive influence on other team members.12. Please rate the following employees 1-N, with:

1=Best contributor towards overall CODWRD team effectiveness.N=Worst contributor towards overall CODWRD team effectiveness.

Employee Performance: The extent to which the worker does his/her job functions in a capable manner.13. Please rate the following employees 1-N, with 1=Best Overall Performer; N=Worst Overall Performer. Base the ratings on the most recent official ratings you (and/or others) have done for each person, recent merit pay evaluations, or ‘write-ups’ for bonuses or special awards given.(If at all possible, please use all N numbers instead of showing “ties” between people.)#(1-N) ____ ______________

226

APPENDIX C OPERATOR QUESTIONNAIRE

INTRODUCTION

Hi, ________; this is (researcher). Are you having a good day today?_______________________ (Good. I was hoping to find you in a good mood!) I appreciate the opportunity to talk with you. Just to remind you, I’m a Management Information Systems researcher from the University of Minnesota. The purpose of the questionnaire is to study how teams like yours keep systems running, in terms of social and interpersonal issues.

For the next forty to forty-five minutes, I’ll be asking you some questions about your job and workplace. By the way, if you get called to do something, we can be interrupted and just resume where we left off. I want to assure you of a couple of things before we get started. First, your answers are being captured over here with a pen and piece of paper--not with a tape recorder.Second, your answers and this conversation will be kept completely confidential. That’s one reason we’re doing this by phone. The other reason is that it is less expensive for me to conduct research over the phone than in person. Your specific answers will not be shared with anyone else either within or outside your organization. I’m not an agent of management. So your answers will not be made available to your management. Any results of the study will be presented only at a summary level. So you can share your thoughts freely. All right?Third, the questions I’m going to ask you do not have ‘right’ or ‘wrong’ answers. In fact, I have no preconceived notions of the right answers myself. Your opinion is the correct answer, and that’s what I’m interested in hearing. Your first impressions are usually going to be the best answer. Hence, we will go through the questions fairly quickly. Let me know if we go too fast, though. Also, if you feel uncomfortable answering a question, or if you don’t hear or fully understand something I say, please let me know as we go along. Okay?

The questionnaire has two parts. Part I addresses aspects of the job. Part II addresses people and team issues. By ‘team,’ I mean the CODWRD team. I’m defining the CODWRD team as those people who keep the CODWRD system up and running. Based on that definition, you consider yourself part of the CODWRD team, right? _____ That would also include people in your work group and several other work groups, right? If the CODWRD team consists of those who keep the central site part of the CODWRD system up and running, which other groups do you think belong to the CODWRD team? ____________________________________________________________________________________

**1ST TIME/GP: By the way, who do you report to? I mean, do you have a lead or someone who acts as a kind of supervisor over you?___________Do you call him/her a lead orwhat?______________________For each of the following statements, I want you to react first by telling me whether you agree or disagree with the statement. Then I will ask you whether you strongly, moderately, or slightly agree or disagree with the statement. Slightly means you agree a little. Strongly means you agree a lot. Moderately is in-between; it means you agree, but not a large or a small amount. You may also tell me if you neither agree nor disagree, but are completely neutral. However, even if you only slightly agree, you should say that you agree rather than saying that you are neutral. You may also say “I don’t know” if that’s the appropriate response.Okay?************************************************************************************Part I-- the nature of your job. These first three questions address the skill variety involved in your job.

227

Question: 1. My job requires me to do many different things at work, using a variety of my skills and talents. Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?Next question.

2. This job requires me to use a number of complex or high-level skills.Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?Next question.

3. Overall, my tasks are not simple and repetitive.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next, we address how important your job is to others in the general workplace--at CODWRD and elsewhere.

Question: 4. This job is one where a lot of other people, in this organization and other organizations, can be affected by how well my work gets done.Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next.

5. This job is important in that the results of my work can significantly affect other peoples’ ability to do their work.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

6. This job itself is very significant and important in that it facilitates or enables other peoples’ work.Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

228

7. My job is very important in the broader scheme of things, that is, in the general workplace.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

The next three questions assess the extent to which you do a whole piece of work, as opposed to just doing part of a larger piece of work.

8. This job is arranged so that I can usually do an entire piece of work from beginning to end, not just a small part of an overall piece of work.Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?Next question.

9. This job generally provides me the chance to completely finish the pieces of work I begin.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

10. My job usually involves a complete piece of work that has an obvious beginning and end.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

These next questions are about how the job itself informs you about your work performance.

Question 11. This job itself provides me information about my work performance. That is, the actual work itself provides clues about how well I am doing--aside from any feedback co-workers or supervisors may provide.Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next question.

229

12. After I finish a task, I know whether I performed it well.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next.13. Just doing the work required by this job provides many chances for me to figure out how

well I am doing.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

These next questions should be answered without consideration of what your job is like today. Rather, they cover what you want your ideal job to be like.

14. I would like to have stimulating and challenging work.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?Next question.

15. I would like to exercise independent thought and action in my work.Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next question.

16. I would like to have opportunities for personal growth and development at my work.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

These next 3 questions address how well you typically know your task results.

17. I usually know whether or not my work is satisfactory on this job.Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next question.

230

18. I have a pretty good idea of how I am performing my work.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next question.19. I can generally tell whether I am doing well or poorly in this job.

Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Okay, the next topic relates to the enjoyment or pleasure you get from your job.

20. I get a lot of enjoyment out of doing my job.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?Next.

21. When it comes right down to it, I really enjoy my work.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

22. Just doing my job gives me a sense of keen satisfaction.Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next question.

23. Doing my job gives me a very satisfying feeling.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

The next questions deal with how your job affects your self-concept.

231

24. When I do my job well, it gives me a feeling of accomplishment.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next.25. When I perform my work well, it contributes to my personal growth and development.

Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

26. My opinion of myself goes up when I do this job well.Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next question.27. Performing this job well reinforces my feelings of self-esteem.

Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Here are two similar questions:

28. My work-related interactions with my lead/supv usually have a positive effect on my self-esteem.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

29. Interacting with my lead/supv on the job generally reinforces my feelings of self-esteem.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

The next question compares today with 3 years ago. To prepare to answer this, take just a moment and think back to what you were doing 3 years ago--that would be March of 1994, who you were working with, and how you felt about your job and so forth.....Do you recall your workgroup? your manager? your vice president? Okay, are you ready?

30. I get a greater feeling of accomplishment from my job today than I did 3 years ago.

232

Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next.31. I get more enjoyment from doing my job today than I did 3 years ago.

Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?Reasons?_____________________________________________________________________Next we ask about your current level of job satisfaction.

32. Generally speaking, I feel satisfied with this job.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

33. Overall, I feel satisfied with the kind of work I do in this job.Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next question.

34. In general, I feel satisfied with my job.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next question.35. I seldom think of finding another job.

Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next, I’ll ask about the sense of personal obligation or responsibility you feel in doing your job.

36. I feel a high degree of personal responsibility for the work I do on this job.

233

Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next question.

37. Whether or not this job gets done--and done properly--is clearly my responsibility.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

38. I feel I should personally take responsibility for the results of my work on this job.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

This next set is about how significant and important you feel your work is.

39. To me, most of the work I do is valuable and important.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next question.

40. My work is worthwhile and valuable.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?Next.

41. In general, the work I do in this job is important. Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

42. Only a few of the things I do on this job seem useless or trivial.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

234

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Okay, we’ll shift gears for this question:

43. Achieving my [incentive plan name] goals during last year’s incentive period was very challenging for me, based on when the goals were given.Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

The next questions address your relationship with the company in terms of work commitment.

44. I am willing to put in a great deal of effort beyond that normally expected in order to help this organization be successful.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next question.

45. This organization inspires the very best in me in the way of job performance.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next.46. I show by my actions that I really care about the fate of this organization.

Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

47. I am willing to sacrifice to help this organization meet its goals. Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Okay, the next 2 questions compare today to 3 years ago--so think back again for a moment....Ready?

48. When I compare my current level of work commitment to the company’s success versus three years ago, I am more committed today.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

235

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next question.49. I am more committed to work hard for this company today than I was three years ago.

Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

(any reasons you are more/less committed today? ____________________________________________)That completes Part I. To give you a little break before we proceed to Part II, I have three questions of a different type.

50. From the following list, please select the one reason that best represents why you try to work hard and do a good job: (I’ll read the list again if you would like)

(1st List) 1. Opportunities for a promotion2. The challenge of the task3. Merit pay increases4. A feeling of accomplishment5. Something else (specify) (would you like me to read the list again?)

51. From the following list, please select the one reason that best represents why you try to work hard and do a good job: (2nd List)

1. [incentive plan name] bonuses2. Solving the incident, outage, or potential problem3. Achievement award programs4. Enjoyment of the job5. Something else (specify) (shall I read them again?)

52. From the following list, please select the one reason that best represents why you try to work hard and do a good job: (3rd List)

1. Opportunities for a Promotion2. Appreciation from your boss3. Merit pay increases4. [incentive plan name] bonuses5. Something else (specify) (shall I read them again?)

Part II covers people and team issues.

First, we’ll talk about the nature of your work environment in terms of structures that encourage fairness to workers.

53. Our workplace has processes that assure that we will be treated fairly and equitably.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

236

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next.

54. I work in an environment in which good procedures make things fair and impartial.Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next question.

55. Fairness to employees is built into how issues are handled in our work environment. Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

56. In this workplace, sound practices exist that help ensure fair and unbiased treatment of employees.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next, we’ll ask 3 questions about what you believe about other people in the world generally; not people at work, but people in general. Okay?

57. In general, people really do care about the well-being of others.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

237

58. The typical person is sincerely concerned about the problems of others.Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

59. Most of the time, people care enough to try to be helpful, rather than just looking out for themselves.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

How you feel towards your lead/supv is the next topic. You said your lead/supv’s name was _____. Right?

62. My lead/supv, _____________, is one person I really like.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?Next.

63. I have a lot of respect for my lead/supv.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

64. Overall, I react very favorably to my lead/supv.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next question

65. I admire my lead/supv.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Now we go to additional feelings about your lead/supv.

66. When an issue that is critical to my career arises, I feel I can depend on my lead/supv.

238

Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next.67. I can always rely on my lead/supv in a career-related issue.

Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

68. My lead/supv is a person on whom I feel I can rely when the issue is important to my career.Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?Next question.

69. I feel I can depend on my lead/supv on a career-sensitive issue.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

These next questions are similar, but relate to issues of caring and concern.

70. When it comes to my well-being, my lead/supv really cares.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

71. If I required help, my lead/supv would care enough to help me.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

72. I believe that my lead/supv cares enough to act in my personal best interest.Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

73. When you get right down to it, my lead/supv cares about what happens to me.Agree, Neutral, or Disagree?

239

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?IF HI: You have a good working R w/your lead. Briefly, what is the basis for that good R?

These next questions address the amount of decision-making autonomy you have.

74. In my work, I usually do not have to refer matters to my lead/supv for a final decision.Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next.

75. Usually, my lead/supv does not have to approve my decisions before I can take action.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

76. Rather than asking my lead/supv, I usually make my own decisions about what to do on my job.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next.77. I can usually do what I want on this job without consulting my lead/supv.

Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next we look at other perceptions you have about your lead/supv.

78. My lead/supv is skillful and effective in her/his work.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

79. My lead/supv performs his/her job very well.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

240

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

80. Overall, I have a capable and proficient lead/supv.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

81. Overall, my lead/supv is competent technically.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

82. My lead/supv often shows appreciation for me when I do a good job.Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

83. When I do my task well, my lead/supv often expresses appreciation to me.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next a question about the [incentive plan name] bonus’s current affects on the CODWRD team. The CODWRD team consists of all those (in several groups) who keep central site CODWRD up and running.

84. The goal-oriented [plan name] bonuses have a positive motivational effect on the CODWRD team. Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

85. The goal-oriented [plan name] bonuses have a positive motivational effect on me.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

86. The CODWRD team is more conscientious now because of the [plan name] bonuses.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

241

87. I am more conscientious now because of the [plan name] bonuses.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

88. The CODWRD team works harder because of the [plan name] bonuses.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

89. I work harder now because of the [plan name] bonuses.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

89a. Most of my co-workers feel satisfied with the [plan name] bonus they received.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

89b. I feel satisfied with the [plan name] bonus I received.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

90. Aside from our normal questions, tell me, very briefly, does the [plan name] have any other effects on you or the team?_______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

The next few questions relate to supervisory feedback.

95. My lead/supv gives me a lot of feedback about how I am doing on this job.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next.96. My lead/supv frequently tells me how I am doing on my job functions.

242

Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

97. Our lead/supv gives me frequent feedback about my performance.Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?Next question.

98. Our lead/supv often let’s me know the extent to which I did a task satisfactorily.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

The next 4 questions relate to supervisory involvement.

99. My lead/supv rarely gets so involved that s/he does my task for me.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

100. Our lead/supv rarely gets too involved in the activities of my job.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

101. I hardly ever see our lead/supv take a larger role in work assigned to me than s/he should.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

102. Our lead/supv rarely performs a part of my job for me.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

These next 4 questions are on a different scale, and address how much you interact with others on the job. In increasing order, the scale choices are “Not at All,” “A Little,” “Some,” “Quite a Bit,” and “Very Much.” And I will repeat the scale as we go along.

243

103. In general, how much do you interact with other CODWRD team members. Again, the CODWRD team consists of all those who keep central site CODWRD up and running:

Not at All, A Little, Some, Quite a Bit, or Very Much?

104. In general, how much do you interact with your lead/supv:

Not at All, A Little, Some, Quite a Bit, or Very Much?

105. In general, how much do you interact with your manager:

Not at All, A Little, Some, Quite a Bit, or Very Much?

On the same scale, I’ll be asking you about the amount of accountability you feel is present in your job.

107. How much are you held personally responsible for achieving your performance goals or standards?

Not at All, A Little, Some, Quite a Bit, or Very Much?

108. How much are you personally given credit for successes you have on the job?

Not at All, A Little, Some, Quite a Bit, or Very Much?

109. How much are you held personally accountable for the work decisions you make in your job?

Not at All, A Little, Some, Quite a Bit, or Very Much?

110. How much are you held personally responsible for mistakes you make on the job?

Not at All, A Little, Some, Quite a Bit, or Very Much?

In genl, who in mgmt are you held accountable by?______________________________________

Now two questions on the level of pressure you feel on the job.

244

111. I seldom feel significant pressure from my lead/supv to perform at a consistently high level on this job.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next question112. I seldom feel significant pressure from my manager to perform at a consistently high level

on this job.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next we’ll talk about team effectiveness. By ‘team,’ I mean the CODWRD team, those who keep central site CODWRD up and running.

118. I feel that this team effectively performs its overall task.Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?Next.

119. Overall, this team performs its functions effectively.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

120. In general, the CODWRD team is effective in doing its job.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next we address internal team coordination effectiveness.

121. The people who work together on the CODWRD team do their job properly and efficiently without getting in each other’s way.Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?122. The people who work together on this team perform their tasks without interfering with

each other’s duties.

245

Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?Next question.

123. When it comes to jointly fixing system problems (*or * making sched changes), my activities are well-coordinated with activities of other CODWRD team members.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

124. Team members are willing to assist each other when needed.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

125. I feel the various CODWRD team members work together very well.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Now I’ll ask about aspects of information sharing among CODWRD team members.

126. The information I receive from other team members is seldom inaccurate.Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?Next question.

127. I can feel confident that information I receive from other CDWRD team members is correct.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?Next.

246

128. I rarely have to go back and check the information I have received from team members.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

129. I never have to worry about getting false information from members of the CODWRD team.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Okay, this next set is a bit different from the prior set, but is still about CODWRD team communication effectiveness.

130. It is easy to talk openly about work-related issues to all members of this team.Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?Next question.

131. The members of this team freely discuss various topics important to the CODWRD team.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next question.

132. It is easy to ask for information from any member of this team.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

133. I feel free to discuss almost any work-related issue in the CODWRD team.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Okay, this next set is a bit different, but is still about CODWRD team communication. Think back again to your work group and CODWRD team of 3 years ago.

134. Compared to the CODWRD team 3 years ago, current CODWRD team members share information more openly today.

247

Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?Next question.

135. Compared to the CODWRD team 3 years ago, team information shared with me by current CODWRD team members is more accurate today.members. Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Next we talk about CODWRD team conflict resolution. Once more, the CODWRD team consists of those who keep CODWRD up and running

136. Conflicts among team members are usually resolved effectively and positively.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?Next.

137. When disagreements occur, the CODWRD team is good at bringing the issues into the open and working them out peacefully.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

138. When problems between team members do arise, they are handled satisfactorily.Do you Agree or Disagree, or are you Neutral?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?Next question.

139. Conflict is typically dealt with and resolved constructively by the CODWRD team.Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

This is a different kind of question.

140. In general, I have a good relationship with those CODWRD team members I interact with.Agree, Neutral, or Disagree?

248

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

Finally, a few background questions.

145. What percentage of your job (in terms of % of hours spent) relates directly to keeping CODWRD up and running? ____ By ‘directly related,’ I mean either fixing CODWRD when it goes down, or working to prevent it from crashing in the first place.

146. How many years and months have you worked with your current lead/supv, both now and in past jobs? _____yrs. ____mths.

147. How old did you turn on your last birthday? ____

148. How many promotions have you had over the past five years, if any? ____

151. How many base pay increases have you had over the past five years, if any? ____

152. What is your current grade level? ____

153. How many years of academic, vocational, or professional education have you obtained beyond high school? _____

THAT COMPLETES THE QUESTIONNAIRE, _____. THANKS VERY MUCH FOR YOUR HELP! Again, I want to compliment you on being a part of the CODWRD team! I have one request for you. So that I can obtain consistent results from all team members, I would request that you do not discuss this questionnaire with other members of the team or those in other departments. Okay?____________________ Also, to follow up on what I said at the beginning, were there any questions I asked that made you feel uncomfortable or that I should not have asked? ________________

THANKS AGAIN, AND BEST OF WISHES TO YOU!

249

APPENDIX D SUPERVISOR QUESTIONNAIRE

First, I’ll ask you to evaluate each of your team members in terms of several attributes. Is that okay? If my list is correct, I have the following people reporting to you: ________ _______ _______ _______________ _______ _______ ________ _______ _______ _______ _______ (Reconcile the list)The first attribute I want you to give me your opinion with respect to your team members is the individual’s contribution to effective team cooperation. By this, I mean the degree to which an individual enhances the willing and effective cooperation among members of the CODWRD team. By cooperation, I mean the person willingly coordinates activities with others and helps them out when needed. In other words, the degree to which an individual both willingly cooperates with CODWRD team members her- or himself, and also influences other team members to do the same.I’ll ask about your folks in alphabetical order, so think ahead a little about some reasonable range of best to worst workers, such that we can cover a wide range in terms of your best (which will be strongly agree answers) and your worst (which will be strongly disagree answers). Okay?

1. ____________typically makes a good contribution to effective team cooperation, both by his/her own example and by his/her positive influence on other team members: (circle)Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

(Repeat 1. For each employee)

Now we’ll talk about the individual’s contribution to effective team communication. By this, I mean the degree to which an individual enhances communication effectiveness among members of the CODWRD team. By communication effectiveness, I mean the extent to which a team member talks openly and accurately to others on work related issues. In other words, these questions address the degree to which an individual both communicates with CODWRD team members openly and accurately her- or himself, and also influences other team members to do the same.

2. ____________typically makes a good contribution to effective team communication, both by his/her own example and by his/her positive influence on other team members: (circle)Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

(Repeat 2. For each employee)

Now we’ll talk about the individual’s contribution to team conflict resolution. By this, I mean the degree to which an individual enhances the team’s ability to effectively resolve disagreements or conflicts that arise, large or small. So these questions address the degree to which an individual facilitates the CODWRD team’s ability to bring issues into the open and work them out peacefully.

250

3. ____________usually enhances the team’s ability to constructively resolve large or small disagreements or conflicts that arise, both by his/her own example and by his/her positive influence on other team members: (circle)Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

(Repeat 3. For each employee)

Now we’ll talk about the individual’s contribution to overall team effectiveness. By this, I mean the degree to which an individual enhances the team’s ability to do its work effectively. By team effectiveness, I mean the team does its job in a way that allows it to meets its objectives. So these questions address the degree to which an individual facilitates the effectiveness of the overall CODWRD team.

4. ____________typically makes a good contribution to overall team effectiveness, both by his/her own example and by his/her positive influence on other team members: (circle)Agree, Neutral, or Disagree?

AGREE Do you agree Strongly, Moderately, or Slightly?

DISAGREE Do you disagree Strongly, Moderately, or Slightly?

(Repeat 4. For each employee)

Please rate the following employees 1-N, with 1=Most; N=Least, on the following attributes: (please use all N numbers, if at all possible)

1. Individual contribution to 2. Individual contribution to 3. Individual contribution toeffective team cooperation effective team communication effective conflict resolution#(1-N) #(1-N) #(1-N)____ ______________ ____ _____________ ____ _________________ _____________ ____ ______________ ____ _________________ ______________ ____ ______________ ____ _____________...

4. Individual contribution tooverall team effectiveness #(1-N) ____ __________________ _________________ ______________ ...

251

Please rate the following employees 1-N, with 1=Best Overall Performer; N=Worst Overall Performer. Base the ratings on the most recent official ratings you (and/or others) have done for each person, recent merit pay evaluations, ‘write-ups’ for bonuses or special awards given, or other information you have about them.(If at all possible, please use all N numbers instead of showing “ties” between people.)

#(1-N) ____ __________________ __________________ ______________...

252

APPENDIX E PRETEST INSTRUMENT A--MATCHING

Instructions: First, carefully read all three construct definitions. Match the Item in the left column with the appropriate Construct in the right column by drawing a straight line from the Item number to the Construct letter. The ___ in most items refers to a specific person.

ITEMS: CONSTRUCTS: Definitions 1. If I were faced with a question related to my professional future, I feel I could depend on ___.

2. When you get right down to it, ___cares about what happens to me.

3. I can count on ___ to act in my A. Dispositional Trust: The general personal best interest. tendency of one to believe in the

benevolence of other people in 4. The typical person is sincerely most situations. [Benevolence concerned about the problems of others. means one cares for the welfare

of the other person and is5. I feel that I could depend on ___, even on motivated to act in the other a crucial issue that could affect my career. person’s interests.]

6. ___ is more inclined to help me out than to look out for him/herself.

7. In general, people really do care about the well-being of others.

8. ___ is a person on whom I can rely when the issue is important to my career. B. Trusting Belief--Benevolence:

One’s belief that a specific other9. If I really needed help with something, I could person will act with benevolence always count on ___ to come to my aid. towards one. [See benevolence

definition above]10. Most of the time, people care enough to help, rather than just looking out for themselves.

11. When a career-critical issue arises, I would want to be dependent on ___.

12. Most people do not hesitate to go out of C. Trusting Intention: One’s willingness their way to help someone in trouble. to depend on a specific person on an

issue that is critical to one’s career.13. Human nature is fundamentally cooperative.

14. When it comes to things important to me, ___ really cares.

15. I can always rely on ___ in a career-related issue.

253

APPENDIX F PRETEST INSTRUMENT B--CATEGORIZATION

Differentiating Types of Trust

Instructions: Below are statements that related to different types of Trust. Please place them into three to five categories (Category A, B, C...) such that statements within a category are most similar in meaning to each other and are dissimilar in meaning from statements in other categories. As you proceed, briefly describe the meanings of your categories at the bottom of the page. After your initial round of categorizing, read all the statements by category to verify that they ‘fit’ where you placed them.

CATEGORY STATEMENT (A, B, C,...)_____ If I were faced with an issue related to my professional future, I feel I could depend on

___.

_____ Because of the way employee issues are handled here, I believe we are safe from unfairor unjust treatment.

_____ When you get right down to it, ___ cares about what happens to me.

_____ Fairness to employees is built into the way issues are handled in our work environment.

_____ I can count on ___ to act in my personal best interest.

_____ I feel that I could depend on ___, even on an issue that could affect my career.

_____ If I required help, ___ would care enough to help me.

_____ In this workplace, safeguards exist that protect us from unfair treatment.

_____ ___ is a person on whom I can rely when the issue is important to my career.

_____ I work in an environment in which good procedures make things fair and impartial forthe employees.

_____ If I really needed help with something. I could always count on ___ to come to my aid.

_____ Our workplace has processes that assure that we will be treated fairly and equitably.

_____ When a career-critical issue arises, I would want to be dependent on ___.

_____ When it comes to things important to me, ___ really cares.

_____ I can always rely on ___ in a career-related issue.

_____ This organization treats its employees in a fair, impartial manner.

Category A means:_____________________________________________________________________Category B means: _____________________________________________________________________Category C means:_____________________________________________________________________Category D means:_____________________________________________________________________Category E means: _____________________________________________________________________

254

APPENDIX G PRETEST INSTRUMENT C--SORTING

Differentiating Types of Trust

Instructions: Attached are sixteen statements that relate to three to five different types of Trust. Please sort them into three to five categories (Category A, B, C, D, E) such that statements within a category are most similar in meaning to each other and are dissimilar in meaning from statements in other categories. (Sort by conceptual meaning, not by the degree of trust the statement implies). After your initial round of categorizing, read all the statements by category to verify that they ‘fit’ where you placed them. Adjust accordingly. Then fill in PART I. Next, briefly describe the meanings of your categories (PART II).

PART I: SORTING SUMMARY

Items placed in Category A (#s): _____ _____ _____ _____ _____

Items placed in Category B (#s): _____ _____ _____ _____ _____

Items placed in Category C (#s): _____ _____ _____ _____ _____

Items placed in Category D (#s): _____ _____ _____ _____ _____

Items placed in Category E (#s): _____ _____ _____ _____ _____

PART II: CATEGORY DESCRIPTION

Category A means: ___________________________________________________________________

Category B means: ___________________________________________________________________

Category C means: ___________________________________________________________________

Category D means: ___________________________________________________________________

Category E means: ___________________________________________________________________

List the numbers of any statements you found difficult to categorize: (#s)___ ___ ___

255

APPENDIX H PAIRWISE INTERCORRELATION MATRICES(see Appendix I for key to construct abbreviations)

**not shown**

APPENDIX I DESCRIPTIVE STATISTICS(ordered by increasing Mean)

Abbrev. Construct Min Max Mean Mode StdDevMicr Micromanagement 1.00 6.75 1.79 1.00 1.22Acct Accountability 2.50 5.00 4.27 5.00 0.65Perf Individual Performance 1.00 7.00 4.31 4.50 1.96FB Feedback 1.00 7.00 4.63 6.00 1.94SysTr System Trust 1.00 7.00 4.67 6.00 1.68Comm Contribution to Communication

Effectiveness1.50 7.25 4.70 3.15 1.50

Coord Contribution to Coordination Effectiveness

1.50 7.35 4.73 2.45 1.52

ConfRes Contribution to Conflict Resolution 1.50 7.25 4.74 2.95 1.40CTE Contribution to Team Effectiveness 1.81 7.00 4.74 6.35 1.40TeamEff Contribution to Overall Team

Effectiveness1.50 7.35 4.80 2.00 1.49

JobID Job Identity 1.00 7.00 4.83 6.00 1.66DispTr Dispositional Trust 1.00 7.00 5.55 6.00 1.20TrInt Trusting Intention 1.00 7.00 5.67 7.00 1.72JobFB Job Feedback 1.67 7.00 5.95 7.00 1.25Auton Autonomy 2.00 7.00 5.97 7.00 1.00TrBBn Trusting Belief-Benevolence 1.25 7.00 6.08 7.00 1.40Rs Relationships 1.21 7.00 6.08 7.00 1.27OCW Organizational Commitment 1.50 7.00 6.13 7.00 1.05Like Liking 1.00 7.00 6.24 7.00 1.21SkVar Skill Variety 2.00 7.00 6.28 6.67 0.88IMEnj Intrinsic Motivation-Enjoyment 2.50 7.00 6.28 7.00 0.88JobSat Job Satisfaction 2.00 7.00 6.29 7.00 0.88TrBCp Trusting Belief-Competence 1.33 7.00 6.32 7.00 1.14Mots Motivation 4.62 7.00 6.36 7.00 0.58KnRes Knowledge of Results 2.33 7.00 6.38 7.00 0.94IMSE Intrinsic Motivation-Self-Esteem 3.50 7.00 6.46 7.00 0.71ExMng Experienced Meaningfulness 4.00 7.00 6.62 7.00 0.60JobSig Job Significance 4.00 7.00 6.77 7.00 0.45GNS Growth Need Strength 5.33 7.00 6.82 7.00 0.36FR Felt Responsibility 5.00 7.00 6.88 7.00 0.32

256

APPENDIX J PAIRWISE INTERCORRELATION MATRICES-- HIGH LEVEL CONCEPTS**not shown**

257