Integrating Work Environment Perceptions: Explorations ... Classes/Survey... · ally detrimental (damaging or painful) to the self and therefore to one's well-being (Lazarus, 1982,

Journal of Applied Psychology1989, Vol. 74, No. 5,739-751

Copyright 1989 by the American Psychological Association, Inc0021-9010/89/$00.75

Integrating Work Environment Perceptions:Explorations into the Measurement of Meaning

Lois A. James and Lawrence R. JamesGeorgia Institute of Technology

Many of the perceptual variables used in industrial/organizational psychology assess the meaningthat work environment attributes have for individuals (e.g., the ambiguity of role prescriptions).This study represents an initial attempt to test the hypothesis that a unifying theme exists for inte-grating diverse measures of meaning. The unifying theme is based on a hierarchical cognitive model

wherein each assessment of meaning reflects a general appraisal of the degree to which the overallwork environment is personally beneficial versus personally detrimental to the organizational well-

being of the individual. Results of confirmatory factor analyses on multiple samples supported ahierarchical cognitive model with a single, general factor underlying measures of meaning. Theseresults are used to explain the substantive impact of work environment perceptions on individual

outcomes.

Industrial/organizational (I/O) psychologists use many vari-

ables to assess perceptions of work environments. Examples in-

clude perceptual indicators of job attributes (e.g., job challenge,

job autonomy), characteristics of leaders and leadership pro-

cesses (e.g., leader consideration and support, leader work facili-

tation), workgroup characteristics and processes (workgroup

cooperation, workgroup esprit), and interfaces between individ-

uals and subsystems or organizations (e.g., role ambiguity, fair-

ness and equity of reward system). The following two principles

have guided many applied psychologists' efforts to measure

work environment perceptions: (a) Individuals respond to envi-

ronments in terms of how they perceive them and (b) the most

important component of perception is the meaning or mean-

ings imputed to the environment by the individual (Ekeham-

mer, 1974; Endler & Magnusson, 1976; Lewin, 1938, 1951;

Mischel, 1968).

The Concept of Meaning

Our intention is to explore the concept of meaning in the

context of work environments. We begin with basics by noting

that the attribution of meaning, or a meaning analysis, refers to

the use of stored mental representations or schemas (i.e., beliefs

that are products of learning and experience) to interpret (i.e.,

to make sense of) stimuli; in this case, work environment attri-

butes (e.g., events, objects, processes, structures; cf. Jones &

Gerard, 1967; Mandler, 1982; Stotland & Canon, 1972). The

objective of a meaning analysis is often to describe what is "out

there" in the environment (Mandler, 1982, p. 8). This form of

cognition focuses on perceptions of the presence or absence of

features and structures of environmental attributes. The em-

phasis on description is reflected in the use of designations such

Correspondence concerning this article should be addressed to LoisA. James, School of Psychology, Georgia Institute of Technology, At-lanta, Georgia 30332.

as descriptive cognition and descriptive meaning (Mandler,

1982), cold cognition (Zajonc, 1980), descriptive schemas

(Stotland & Canon, 1972), and denotative meaning (Osgood,

Suci, & Tannenbaum, 1957). Examples of variables from the

I/O literature that reflect primarily descriptive meaning include

technological complexity, span of control, centralization of de-

cision making, functional specialization, physical space charac-

teristics (temperature, lighting, sound), formal communication

networks, and formal rules, regulations, and reward structures.

Information processing may proceed beyond describing what

is "out there" to a valuation of environmental attributes

(Mandler, 1982). Valuation refers to cognitive appraisals of en-

vironmental attributes in terms of schemas derived from values

such as equity, freedom from threat, and opportunity for gain

(Mandler, 1982). In comparison with descriptive meaning, val-

uation is more internally oriented and requires additional infor-

mation processing to judge how much of a value is represented

in or by (perceived) environmental attributes (e.g., how much

equity is represented by a pay raise, how much challenge is pres-

ent in a set of job tasks, how much friendliness is represented in

interactions with coworkers; cf. Jones & Gerard, 1967; Mandler,

1982; Stotland & Canon, 1972). The subjective, value-based

meanings furnished by the valuation process are variously re-

ferred to as evaluative meaning (Mandler, 1982), affective (con-

notative) meaning (Osgood, May, & Miron, 1975; Osgood et

al., 1957), emotionally relevant cognitions or simply emotional

cognitions (Reisenzein, 1983; Schacter & Singer, 1962), and

cognitive appraisals (Lazarus & Folkman, 1984). Each of these

designations points to the central role played by valuation in

causal models of emotion and well-being.

To review this role briefly, Lazarus and his colleagues (Laza-

rus, 1982, 1984; Lazarus & Folkman, 1984) viewed emotional

responses to environmental attributes as functions of cognitive

appraisals of the significance of these attributes for one's well-

being. Valuation appears to be the key to such cognitive apprais-

als inasmuch as (a) values serve as standards for assessing wel-

fare (Locke, 1976), where welfare is defined in terms of a sense

739

740 LOIS A. JAMES AND LAWRENCE R. JAMES

of well-being (Mirriam-Webster, 1975); and (b) valuation pro-

vides appraisals of the degree to which these standards are rep-

resented in environmental attributes. The cognitive appraisals

thus described are viewed as "emotional cognitions" (Reisen-

zein, 1983; Schacter & Singer, 1962) because they reflect the

subjective meanings that, in combination with perceived physi-

ological arousal, help to label the emotion and to determine the

direction and intensity of the experienced emotion. Finally, and

most importantly, a single latent component is believed to be

shared by all emotionally relevant cognitions. It is this single

latent component, or g-factor, that most directly furnishes the

emotional cognitions with the facility to assess significance for

well-being. As proposed by Lazarus (1982, 1984), this g-factor

comprises a higher order schema for appraising the degree to

which the environment is personally beneficial versus person-

ally detrimental (damaging or painful) to the self and therefore

to one's well-being (Lazarus, 1982, 1984).

It is noteworthy that perceptions may vary between those

conveying primarily descriptive meaning and those conveying

primarily valuations (Mandler, 1982). We are interested in the

latter because we wish to understand the psychological pro-

cesses linking cognitions of work environments to affect and,

ultimately, to behavior. In pursuit of this objective, we shall now

apply the theoretical perspective to measures reflecting valua-

tions of work environment attributes, with special attention

given to the hypothesis that a general factor underlies emotional

cognitions.

A Hierarchical Model of Emotional Cognitions

in Work Environments

Examples of emotional cognitions are easily found in I/O

psychology. These are constructs that provide cognitive apprais-

als of work environment attributes in terms of schemas engen-

dered by work-relevant values. Based on Locke (1976), a work-

relevant value is denned as that which one desires or seeks to

attain because it is that which one regards as conducive to one's

sense of organizational well-being. Sense of organizational well-

being is defined as the degree to which positive affect exceeds

negative affect in regard to one's overall work experiences (this

is a focused definition based on overall well-being; see Brad-

burn, 1969; Diener, 1984). Values serve as standards for assess-

ing organizational well-being and may be expressed in quite spe-

cific terms, such as one desires a $ 10,000 raise. However, a po-

tentially large number of specific expressions are believed to be

manifestations of a much smaller number of latent or psycho-

logical values (Locke, 1976). A nonexhaustive but important

set of latent values appears to include desires for (a) clarity, har-

mony, and justice; (b) challenge, independence, and responsibil-

ity; (c) work facilitation, support, and recognition; and (d)

warm and friendly social relations (Locke, 1976, p. 1329).

Psychological climate (PC) furnishes perhaps the most

readily identifiable set of variables in I/O psychology for ap-

praising work environments in terms of schemas based on these

latent values (see PC variables in Table 1). This is not a chance

occurrence; the PC variables were designed to assess work envi-

ronments as they are "cognitively represented in terms of their

psychological meaning and significance to the individual"

(James, 1982, p. 219). Indeed, items and scales for PC variables

were developed, using interviews, observations, and literature

reviews, to obtain empirical indicators of what we now refer to

as valuations or emotional cognitions (see James & Sells, 1981,

and Jones & James, 1979, for reviews of the development of the

PC measures). It follows that we would expect a congruence

between the four latent values described previously and the em-

pirically derived factors underlying the PC variables in Table 1.

Such a congruence is evident. Four PC factors that have demon-

strated factorial invariance over diverse work environments are

(a) Role Stress and Lack of Harmony, (b) Job Challenge and

Autonomy, (c) Leadership Facilitation and Support, and (d)

Workgroup Cooperation, Friendliness, and Warmth (see Table

2 and James & Sells, 1981).

The four PC factors above were derived by exploratory factor

analyses, which included orthogonal rotations. As shown later

in Table 2, the PC variables tended to cluster within a priori

domains denned by an environmental referent (e.g., all leader-

ship PC variables loaded on a single factor). These results sug-

gest the somewhat obvious notion that specific environmental

attributes (e.g., workgroup members) are related most directly

to specific values (e.g., friendliness and warmth). At a more psy-

chological level of explanation, the results also suggest that indi-

viduals tend to separate emotional cognitions pertaining to

jobs, leaders, workgroups, and individual/organizational inter-

faces into separate internal compartments.

However, compartmentalization need not imply the lack of

relations among the factors indicated by orthogonal rotation.

The preceding discussion furnishes two related bases for pro-

posing that the PC factors are related. First, each PC factor, and

thus each PC variable, has in common the judgmental process

of a cognitive appraisal of an environmental attribute or attri-

butes in terms of that attribute's significance to the well-being

of the individual. To be specific, each PC variable furnishes in-

formation pertaining to the efficacy of the perceived environ-

ment to provide for the achievement of a desired standard or

standards and thereby to promote or to detract from the welfare

of the individual in the work environment. By efficacywe mean

facility to produce an intended effect (Mirriam-Webster, 1975).

For example, perceptions that a job is challenging are believed

to be efficacious to personal welfare because increasing levels of

perceived job challenge have been shown to produce higher lev-

els of job satisfaction (James & Jones, 1980; James & Tetrick,

1986). Second, judgments of significance to well-being—that

is, efficacy to promote or detract from personal welfare—are

hypothesized to be based substantively on a single, higher order

schema or general factor, which is the degree to which the indi-

vidual believes that membership in this work environment is

personally beneficial versus personally detrimental to his or her

organizational well-being. This hypothesized general factor not

only implies that the four PC factors are correlated but also

provides a basis for integrating the PC factors and explaining,

at least in part, the substantive impact of perceived work envi-

ronments on individuals.

Our objective is to test the hypothesis that a general factor

underlies PC perceptions. We propose that each of the four PC

factors reflects a cognitive appraisal of the degree to which the

overall work environment is believed to be personally beneficial

versus personally detrimental to the organizational well-being

of the individual. This prediction presupposes a hierarchical,

MEASUREMENT OF MEANING 741

workgroup Cooperation,Warmth and Friendliness

Figure 1. A hierarchical model of meaning.

integrative model of PC perceptions wherein "personal benefit

versus personal detriment to organizational well-being" serves

as a single, higher order, general factor. Such a model is graphi-

cally displayed in Figure 1, where the general factor is desig-

nated PCg (i.e., general factor of psychological climate). The

causal arrows extending from PQ to each of the PC factors de-

note that PC8 is a latent, psychological common denominator

for the factors. The substantive interpretation of these causal

arrows is that perceptions reflecting valuations of individual/

organizational interfaces (Role Stress and Lack of Harmony),

job characteristics (Job Challenge and Autonomy), leaders

(Leadership Facilitation and Support), and workgroups (Work-

group Cooperation and Friendliness) have in common the la-

tent, cognitive appraisal of the degree to which the overall work

environment is personally beneficial versus personally detri-

mental to the organizational well-being of the individual.

Empirical Tests of the Model

Confirmatory factor analysis was used to test the goodness of

fit of the model in Figure 1 on data from four different work

environments. The key assumptions subjected to empirical

tests were as follows:

1. The 4 first-order PC factors are correlated.

2. The correlations among the 4 first-order PC factors may

be explained by a single general factor, which we have desig-

nated PCg.

3. PCB explains reasonable amounts of variance in the initial,

manifest variables used to measure PC (i.e., the PC variables in

Table 1).

Issues pertaining to covariation between PCg and affect, and

potential contamination due to systematic bias in the method

of measurement, are also addressed.

Method

Sample

Sampling was designed to include individuals and work environments

representative of varying levels of technology and of civilian versus mili-

tary settings. The samples were drawn from the data sets used by James

and Jones (1980) and James, Hater, and Jones (1981). The four samples

of nonsupervisory personnel were as follows: (a) aircraft maintenance

personnel from two Navy Air Training Commands located in the south-

ern portion of the United States (N = 422). Jobs varied across a fairly

wide range of technologies, from the very routine (e.g., fueling aircraft)

to the very complex (e.g., repairing sophisticated equipment). Average

age was 22.2 years, average tenure was 3.1 years, and average education

was 12 years (12 years of education represents completion of high

school), (b) Systems analysts and programmers from an information-

systems department of a large, western, private health care program.

Average age was 36.5 years, average tenure was 1.5 years, and average


education was 15 years (N = 128). (c) Front-line firefighters from a large

metropolitan fire department. Average age was 37.5 years, average ten-

ure was 7 years, and average education was 12.6 years (N = 288). (d)

Production-line personnel from four plants of a paper-product manu-

facturing organization. Average age was 34.1 years, average tenure was

3.5 years, and average education was 11.2 years (Ar = 208).

Instruments

All data included in this study were collected by means of question-

naires administered to the nonsupervisory personnel on a confidential

basis. All items were measured on 5-point Likert scales or Likert-type

scales. The data sets involved measures of the perceived work environ-

ment and variables used for additional construct validation analyses. A

brief description of each data set follows.

Psychological climate. The latest version of the psychological climate

(PC) inventory developed by James, Jones, and colleagues was used to

measure perceived work environment variables (cf. James & Sells,

1981). Seventeen PC variables and accompanying items were common

to the four studies. One additional PC variable, esprit de corps, was

included for Navy personnel. Each of these variables represents a com-

posite of 3-11 items designed to measure a perceptual construct. Co-

efficient alpha for each PC variable in each of the four samples is re-

ported in Table 1. Scores on the variables are means taken over items.

In general, the coefficient alphas are acceptable for research purposes.

It is more important, as we shall see, that the modest values in some

cases do not appear to influence overall results of the analyses.

Variables used in additional construct validation analyses. As ex-

plained in the next section of this article, the variables added to the

analysis were overall job satisfaction and personality indicators of

achievement motivation, rigidity, and self-esteem. The overall job satis-

faction variable was based on the Minnesota Satisfaction Questionnaire

(Weiss, Dawis, England, & Lofquist, 1967), with minor revisions to in-

sure that (a) the items were applicable for all samples, (b) all a priori

factors of PC were represented in the item domains (i.e., satisfaction

with aspects of jobs, individual/organizational interfaces, groups, and

leaders). The same 20 items were used in all studies. An assessment of

general satisfaction was used as a manifest indicator of overall organiza-

tional well-being. This variable was based on a composite of the 20

items, for which coefficient alpha was .90 for Navy personnel, .91 for

systems analysts, .91 for production-line personnel, and .89 for fire-

fighters.

In regard to the personality variables, the item-composites used to

measure achievement motivation (13 items, alpha = .77 on the total

sample) and rigidity (12 items, alpha = .74 on the total sample) were

the same as those described in James, Gent, Hater, and Coray (1979).

The achievement motivation item-composite was designed to assess ori-

entation toward success and included items that measured need for

achievement, aspiration level, persistence, and preference for achieve-

ment-related activities. The rigidity item-composite was based on a

need for certainty and included items such as "I don't like things to be

uncertain or unpredictable" and "I like to have everything organized

before I start a task." The measure for self-esteem was a general manifes-

tation of self-esteem and general self-confidence in the work setting (11

items, alpha = .68 on the total sample) and included items such as "I

take a positive attitude toward myself and "My ability gives me an

advantage in my current job" (cf. James & Jones, 1980).

Analytic Procedure

The goodness of fit of the proposed hierarchical model in Figure 1

was tested by the confirmatory factor-analytic program for higher order

latent variables contained in LISREL vi (Joreskog & Sorbom, 1986). The

17 PC variables (18 PC variables for Navy personnel) served as manifest

Table 1

Classification of the Item Composites by the Four

Psychological Climate (PC) Domains and Their Respective

Coefficient Alphas for Each Sample

Coefficient alphas

PC variables NP SA PL FF

Role stress and lack of harmony

Role ambiguityRole conflictRole overloadSubunit conflict

.70 .83 .68 .76

.81 .63 .66 .71

.80 .36 .64 .52

.60 .82 .66 .63.71Lack of organizational identification .79 .87 .81

Lack of management concern andawareness .76 .82 .80 .82

Job challenge and autonomy

Challenge and varietyAutonomyJob importance

.83 .82 .67 .72

.71 .73 .60 .69

.73 .66 .62 .65

Leadership facilitation and support

Leader trust and supportLeader goal facilitationLeader interaction facilitationPsychological influenceHierarchial influence

.85

.86

.80

.82

.84

.85

.88

.77

.76

.70

.87

.81

.61

.75

.63

.85

.89

.73

.73

.61

Workgroup cooperation, friendliness, and warmth

Workgroup cooperationWorkgroup friendliness and warmthReputation for effectivenessEsprit de corps

.75

.73

.59

.61

.76

.82

.64

.74

.67

.41

.74

.77

.61

Note. NP = Navy personnel, SA = systems analysts, PL = productionline, FF = firefighters.

indicators, the four PC factors from Table 1 were treated as first-order

latent variables or factors, and PCg was the predicted higher order latent

variable or factor. In the first-order analysis—that is, the test of relations

between the 4 first-order factors (FOFs) and the 17 (or 18) manifest PC

variables—each manifest variable was allowed to be an indicator of only

one FOF (i.e., a free parameter to be estimated, although one such pa-

rameter, randomly selected, had to assume a fixed value of 1.0 for each

FOF; Joreskog & Sorbom, 1986). Relations between each such indica-

tor and the other three factors were fixed at zero.

On the basis of prior exploratory factor-analytic work by Jones and

James (1979), we expected that the 17 (or 18) manifest PC variables

would factor into the domains indicated in Figure 1. Two differences

between this study and the prior research by Jones and James were that

(a) the PC questionnaire was revised and shortened by collapsing the

role and subsystem/organizational domains to obtain the individual/

organizational interface domain (a product being a single factor to rep-

resent the interface) and (b) the first-order factors were left free to be

correlated. The second-order analysis ascertained whether a single

higher order factor could account for the covariation among the four

FOFs. With four FOFs, this model is overidentified and thus the model

is disconfirmable by tests of fit (cf. James, Mulaik, & Brett, 1982). All

coefficients in the vector relating PCg to the FOFs were free to be esti-

mated, and the variance of PCg was fixed at unity.

It is noteworthy that, in each of the four samples, the determinant of

the observed variance-covariance matrix for the 17 (or 18) PC manifest


variables was very small relative to the magnitude of the diagonal ele-ments (e.g., .000006 for the Navy sample). These results indicated ahigh degree of collinearity. In such conditions, Joreskog and Sorbom(1986) recommended the use of unweighted least squares (ULS) to esti-

mate the values of the free parameters.The statistical distribution for ULS is unknown. One ramification is

that chi-square (x2) cannot be used to test the goodness of fit of themodel. Even though LISREL does provide other indices to assess the over-

all fit of the model to observed data, we did attempt to obtain a maxi-mum likelihood (ML) solution and included a chi-square test to assessthe fit of the estimated models. The models were therefore tested byusing both ULS and ML. Comparisons are provided between the ULS

and ML (a) parameter estimates; (b) correlations among the first-orderfactors; (c) the goodness-of-fit index (GFI), a measure of the relativeamount of variance and covariance jointly accounted for by the model;(d) the root-mean-square residual (RMSR), the average residual vari-ance and covariance, respectively; and (e) the squared multiple correla-tion (J?2), a measure of how well the indicators jointly accounted for thelatent variables. Standardized data were used in these analyses to dealwith the scale dependency associated with ULS estimation (cf. Long,

1983).The observed covariance matrix was then used to determine a chi-

square for the first-order factor model (x2) and the second-order factor

model (x!), based on the ML solution. The x? was used as a stand-aloneindex (cf. Marsh, Balla, & McDonald, 1988) to assess the fit of the first-order model. The fit of the hierarchical model was assessed with (a) adifference chi-square test (\l - x? with dfc- df, degrees of freedom), a

measure of the adequacy of PC, to explain the covariation among thefirst-order factors; and (b) the Tucker-Lewis (1973) index (TLI) [(XJ/4/i — Xi/dK)/(xtJdfn - 1.0), where n represents the null model], a mea-sure of the covariance accounted for by the hierarchical model relativeto the total covariance among the PC composites.

Results

Results of the confirmatory factory analyses (CFAs) are re-

ported in an order that corresponds to the empirically testable

assumptions presented earlier. Additional construct validation

efforts are then addressed.

Tests for PCg

The ULS standardized solution provided by the first-order

CFA on all four samples (i.e., Navy personnel, systems analysts,

production line, and firefighters) are presented in Table 2. The

data in Table 2 are consistent with prior research (cf. James &

Sells, 1981) regarding relations between manifest (PC) variables

and the four FOFs (see Figure 1). The goodness-of-fit indices

based on the ULS solution for all four samples generally indi-

cated that the fit of the first-order model to the data was good

(e.g., the GFI was greater than .95 for all samples). Nonetheless,

inspection of the normalized residual matrix revealed a large

number of normalized residuals with absolute values greater

than two. This finding is indicative of a possible specification

error in the first-order model (cf. Hayduk, 1987; Joreskog &

Sorbom, 1986).

A host of potential misspecifications exist, some of which are

indigenous to the hierarchical mode) and are not substantively

damaging to the theoretical fit of the model. For example, when

using item-composites as manifest indicators, it is plausible that

the items from different composites within the same FOF co-

vary beyond the covariation accounted for by that FOF (Hert-

zog, 1989). Closer inspection of the normalized residual matrix

suggested that this was a viable explanation for the large number

of inflated residuals. This was because the majority of the large

residuals were associated with PC variables that loaded on a

common FOF. Second, it was possible that some of the PC com-

posites loaded on more than one factor. We tested both of these

assertions with a ML solution as follows.

Covariation among composites unaccounted for by a com-

mon FOF was assessed by allowing several of the unique vari-

ances for the PC composites (i.e., the off-diagonal elements of

the theta epsilon [TE] matrix) to covary. Specifically, the off-

diagonal TE within each factor with the highest modification

index was set free to be estimated. We then allowed a PC com-

posite to load on more than one factor if it was theoretically

reasonable and if the modification index indicated that the freed

parameter would result in a substantial gain in the fit of the

model. Overall, these analyses were conducted following the

guidelines recommended by MacCallum (1986) for follow-up

analyses.

The ML standardized solutions provided by the first-order

CFAs for all four samples are presented in Table 3. These results

are highly similar to the ULS solution. Differences between the

solutions included the correlated TEs and the additional factor

loadings obtained by composites loading on more than one

FOF. There were 3-5 correlated TEs over samples, all but one

of which were within a common factor. The one exception oc-

curred in the production sample, in which it was indicated that

identification with an organization was conditional on the repu-

tation of the individual's workgroup. This relationship could

not be accounted for by the four FOFs, even when the PC com-

posites for identification and group reputation were allowed to

cross-load on the factors. In regard to PC composites loading

on more than one factor, this occurred rarely and primarily for

the psychological influence composite. In general, however, re-

sults reported in Tables 2 and 3 are highly consistent.

The chi-squares provided by the ML analysis for the four

samples were as follows: Navy, x2(123, AT = 422) = 446.51; sys-

tems analysts, x2(109, jV = 128) = 196.28; production line,

X2(107,JV= 208) = 248.23; and firefighters, x2(109,JV = 288) =

323.35. The chi-squares appear to be acceptable given the sam-

ple sizes and the possible violation of multivariate normality.

Although lack of multivariate normality does not affect param-

eter estimates, it might inflate the chi-square (Bentler & Chou,

1987). We thus checked the joint normality assumption for all

four samples. Specifically, squared generalized distances were

computed for each observation in each sample (cf. Johnson &

Wichern, 1988, p. 154). Because the squared generalized dis-

tances behave like chi-square random variables with sample

sizes greater than 30, a chi-square test of significance could be

used to test the multivariate normality assumption. All four chi-

square tests were significant (p < .001), which indicates that the

data sets from which the four samples were drawn were not

distributed according to multivariate normal distributions.

Additional support for the model was provided by the TLI,

which was greater than .90 for all samples. More importantly,

for each of the four samples, there were only five normalized

residuals slightly greater than 2 (the range was 2.1 to 2.7), all of

which were associated with role overload. These results suggest


Table 2

Standardized Estimates of Relations of Manifest Psychological Climate (PC) Variables on 4

First-Order Factors, Usingan Unweighted Least Squares Solution

Navy personnel Systems analysts Production line Firefighters

PC variables 1 1

1. Role ambiguity .86"2. Role conflict .763. Role overload .484. Subunit conflict .485. Lack of organization identification .696. Lack of management concern and

awareness .68


.87* .76"

.65 .65

.38 .53

.62 .67

.90 .82

.80 .88

.80"

.67

.39

.62

.68

.82


7. Challenge and variety8. Autonomy9. Job importance

.74

.79"

.72

.76

.78"

.67

.61

.77"

.54

.69

.66"

.39


10. Leader trust and support1 1 . Leader goal facilitation1 2. Leader interaction facilitation13. Psychological influence14. Hierarchical influence

.93"

.88

.84

.83

.87

.84'

.85

.75

.72

.75

.88"

.75

.63

.83

.78

.90"

.85

.83

.79

.78


15. Workgroup cooperation16. Workgroup friendliness and warmth17. Reputation for effectiveness18. Esprit de corps

.71"

.69

.67

.85

.76"

.77

.90

.76"

.71

.76

.91"

.85

.76

" Fixed parameter (not equal to 1.0 because results are presented in terms of a standardized solution such that the Psi matrix has 1 .Os in diagonal).

the need for additional psychometric work on this PC com-

posite.

Of particular interest are the results shown in Table 4, which

demonstrate high correlations among the four FOFs for all four

samples. The similarity of the correlations among the FOFs for

ULS (below the diagonal) and ML solutions (above the diago-

nal) are noteworthy.

In sum, the key parameter estimates—factor loadings and

correlations among the first-order factors—were essentially the

same for the ULS solution and the ML solution. The ML solu-

tion was more complex in that parameters were added to the

model to address correlated errors and factorial complexity. Al-

though this added complexity enhanced the statistical fit of the

ML model, it had no meaningful effect on the estimates of key

parameters. We thus proceeded to use the simple structure ULS

first-order factor model as well as the more complex ML first-

order factor model to test for a single, higher order factor. Spe-

cifically, a second-order factor structure was created in which

each first-order factor was accounted for by a common compo-

nent due to PC, and a residual first-order factor.

Results of analyses designed to test the goodness of fit of a

single, higher-order factor model are reported in Tables 5

through 7. Tests of overall fit, presented in Table 5, generally

indicate that the single, higher-order factor model was con-

firmed in all samples. The difference chi-square was nonsig-

nificant in three of the samples. (Even though the difference chi-

square was significant in the production sample, the difference

was small.) The TLI was greater than .90 in all samples. The

ULS and ML goodness-of-fit indices were also generally compa-

rable. The RMSR values were uniformly low, and the multiple

correlations were high across all samples. The GFI values were

all .97 or .98 for the ULS solution but dropped to the middle to

upper .80s for the ML solution. This indication of a possible

discrepancy in the ULS and ML solutions may reflect the effects

of skewed distributions or collinearity, or both, on the ML esti-

mates. However, these effects do not appear to have been sub-

stantial given that the GFI values based on the ML solution are

still reasonably high and not seriously inconsistent with the GFI

values based on ULS estimates.

Estimated loadings for the FOFs on PC, are presented in Ta-

ble 6. The generally high magnitude of these estimates suggests

that PCg explained substantial portions of variance in each of

the FOFs. The estimated loadings between the ULS and ML

solutions were again similar. The mean loading for absolute val-

ues for both ULS and ML was .82, which denotes that an aver-

age of 67% of the variance in the FOFs was attributable to PCg.

These results, combined with those of the goodness-of-fit tests,

furnish strong support for the prediction that the FOFs share a

common element of meaning.

It should be mentioned that the patterns of loadings varied


Table3Standardized Estimates of Relations of Manifest Psychological Climate (PC) Variables on 4

First-Order Factors, Using a Maximum Likelihood Solution

Navy personnel Systems analysts Production line Firefighters

PC variables

1. Role ambiguity2. Role conflict3. Role overload4. Subunit conflict5. Lack of organization identification6. Lack of management concern and


.79* .87' .75"

.76 .63 .67

.48 .35 .57

.51 .70 .68

.62 .85 .79

.66 .82 .56 -.33

.87"

.72

.42

.48

.59

.84


7. Challenge and variety8. Autonomy9. Job importance

.69

.82'

.61

.76

.78'

.60

.51

.74'

.45

.70

.66'

.40


10. Leader trust and support11. Leader goal facilitation12. Leader interaction facilitation13. Psychological influence14. Hierarchical influence

.92'

.87

.87.37 .55

.88

.81*

.88

.77.56 .28

.76

.87*

.79

.65.37 .55

.78

.90'

.85

.83

.81

.76


15. Workgroup cooperation16. Workgroup friendliness and warmth17. Reputation for effectiveness18. Esprit de corps

.63"

.60

.69

.84

.68"

.65

.87

.69-

.63

.71

.93'

.87

.71

Theta epsilon

TEI6TE 5TEHTE 3TE 7

15 =6 =

12 =2 =9 =

.38

.25

.08

.25.25

TE16 15 =TE1013 =TE 7 9 =

.42

.17

.13

TE15TE 17TE 7TE 3

16592

====

.35

.17

.37

.17

TE 6TE13TEH

11112

= .18= -.11= .10

• Fixed parameter.

somewhat among samples. Nevertheless, the general pattern of

high loadings suggests a general factor in all samples.

Indirect Effects ofPCs

The indirect effects of PC, on the 17 (or 18) manifest vari-

ables are presented in Table 7. The indirect effects are a product

of the direct effects of PC, on the FOFs and the direct effects of

the FOFs on the manifest variables. For example, the total effect

of PCg on perceived role ambiguity equals the direct effect of

PCg on the first-order factor Role Stress and Lack of Harmony

times the direct effect of Role Stress and Lack of Harmony on

the PC composite ambiguity (i.e., for ULS, the solution [•>•] i =

-.77] [X,i = .86] = indirect effects of-.662).

The range was from .29 to .84 over samples, and the means

of the indirect effects were essentially identical over samples.

These results indicate that reasonable amounts of variance in

the initial measures of meaning were explained by PCg (an aver-

age of 37% of the variance was explained) and thus that PQ

was an important cause of the initial perceptions of meaning.

Consequently, the third and final assumption tested by CFA was

supported.

Additional Construct Validation Issues

The results reported previously furnish empirical support for

the hierarchical model of PCg and, within the context of the

CFAs, one may say that the model was confirmed. However,

confirmation of a model suggests that the model furnishes a use-

ful basis for explaining how and why events occurred. It does

not connote that the confirmed model is unique or has been

proven to be true (James et al., 1982), which is to say that alter-

native models may explain the data equally well. An alternative

model of particular concern here is based on systematic or non-

random measurement errors. Such a model predicts that the

higher order PCg factor is actually a measure of common re-


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sponse biases such as acquiescence or social desirability. In cur-

rent jargon, one might propose that this general factor is a prod-

uct of a pervasive method factor and thus lacks the substantive

meaning that we have attributed to it (i.e., an appraisal of per-

sonal benefit vs. personal detriment).

To test the hypothesis that the general factor represents a per-

vasive method factor, we estimated the correlations between the

general factor and overall job satisfaction (OJS) and three per-

sonality variables (achievement motivation, self-esteem, and ri-

gidity) known to be predictors of PC perceptions or moderators,

or both, of relations between PC perceptions and individual

outcomes (James etal., 1979, 1981; James & Jones, 1980). The

rationale underlying this test is that if the general factor is inter-

preted as PC8, then PCg should be positively, and probably

highly, correlated with OJS. This prediction follows from the

theory that an appraisal of the degree to which one is personally

benefited versus personally hindered or harmed is the basis on

which an overall evaluation is made of one's organizational

well-being (as measured by OJS).

However, one might argue that a strong, positive correlation

between the general factor and OJS is a mere extension of the

pervasive method factor that engendered the general factor in

the first place. If this is a valid inference, then it would also

follow that the pervasive method factor would influence re-

sponses to other measures in the questionnaire used to assess

meaning. In particular, this method factor should also influence

responses to items measuring achievement motivation, self-es-

teem, and rigidity. These personality variables (a) were mea-

sured in precisely the same manner and by the same method as

the PC variables and OJS, (b) had psychometric characteristics

(e.g., reliabilities) that were similar to the PC variables and OJS,

and (c) could be assumed to be subject to the same types of

response biases as the PC variables and OJS. It follows that a

pervasive method factor that produces spuriously high corre-

lations between the general factor and OJS should also promote

spuriously high correlations between this same general factor

and the personality variables.

The squared correlations provided by ULS between the gen-

eral factor and OJS, and between the general factor and each of

the personality variables, are reported in Table 8. A differential

pattern of relations is clear; the general factor correlated much

more highly with OJS than with the personality variables. Even

though these results are, again, only indicative of construct va-

lidity, they question an alternative model that poses a pervasive

method factor as an alternative explanation of PCg.

Discussion

This study was based on the theory that factors underlying

emotionally relevant cognitions (valuations) of work environ-

ments reflect appraisals of the degree to which the overall work

environment is believed to be personally beneficial versus per-

sonally detrimental to the organizational well-being of the indi-

vidual. Personal benefit versus personal detriment to organiza-

tional well-being thus served as a proposed general factor, desig-

nated PCg, in a hierarchical model of value-based meanings for

work environments. Results of confirmatory factor analyses on

multiple and diverse samples supported most empirical predic-

tions suggested by this model. Specifically, (a) first-order factors


Table 5

Goodness-of-Fit Indices for a Hierarchical Model of Meaning

Measures NP SA PL FF

Chi-square for hierarchical modelChi-square difference*Tucker-Lewis index

<VN

447.30.79.91

125422

200.193.91.92

111128

256.408.17*

.92109206

327.894.54

.91111288

Comparison of relevant goodness-of-nt indices

ULS ML ULS ML ULS ML ULS ML

GFIRMSRR2, first-order factorsJJ!, second-order factors

.98

.06

.99

.89

.89

.06

.99

.90

.98

.07

.98

.90

.85

.06

.99

.90

.97

.07

.98

.93

.86

.06

.97

.96

.97

.06

.99

.92

.88

.06

.99

.93

Note. NP = Navy personnel, SA = systems analysts, PL = production line, FF = firefighters. ULS = un-weighted least squares; ML = maximum likelihood; GFI = goodness-of-fit index; RMSR - root-mean-square residual.* Chi-square difference is between first-order model and higher order model with 2 df.

(FOFs) underlying manifest perceptual variables were corre- an endogenous variable, it is likely that Person X Situation in-

lated, (b) a general factor (PC,) explained much of the covaria- teraction models will be required to explain PC, (cf. Endler &

tion among the FOFs, and (c) the general factor explained rea- Magnusson, 1976; James et al., 1 979, 1981; James, Hater, Gent,

sonable portions of variance in the manifest perceptual vari- & Bruni, 1978). Discussions of value development are also

ables. Additionally, a model that attempted to attribute results likely to be productive (cf. Locke, 1976), as is recent cognitive

to a pervasive method factor was disconflrmed, and PC, was research on top-down versus bottom-up processing models (cf.

highly correlated with an indicator of overall organizational Diener, 1984). As a causal variable, models may be developed

well-being. that relate PC, to affect (see later discussion), behavioral dispo-

Such empirical support for PCg denotes only that this is an sitions, and behaviors themselves. Finally, measurement issues

area potentially worthy of future research. It does not suggest require further attention. The rudimentary construct valida-

that the PC, model is true, unique, or proven. The tenuous na- tion effort reported here suggests only that a pervasive method

ture of this model is evident when one considers the large num- factor could not explain the data. More intense efforts that use

ber of assumptions on which the model was based but which multiple measures of each construct, combined with more so-

were not tested in the reported analyses. Future research is phisticated analyses (cf. Schmitt & Stults, 1986; Widaman,

needed to assess if indeed values, and perhaps other compo- 1985), are required before one may feel confident that models

nents of belief systems (e.g., self-concepts, self-regulatory sys- based on nonrandom measurement errors are clearly discon-

tents), engender the cognitive constructs used to impute mean- firmable.

ing to work environments. Research is also needed that ad- In sum, our effort represents a first step in what may prove

dresses other causes as well as effects of PC,. When viewed as to be a fruitful area of research. It has many limitations, but it

Table 6

Standardized Estimates of Relations of the 4 First-Order Latent Variables on a Single, Higher

Order Latent Variable for ULS and ML Solutions

PC, for each sample

NP SA PL FF

First-order factors ULS ML ULS ML ULS ML ULS ML

Role Stress and Lack of Harmony -.77 -.84 -.81 -.83 -.95 -.97 -.80 -.78Job Challenge and Autonomy .80 .79 .71 .69 .67 .70 .92 .90Leadership Facilitation and Support .80 .77 .90 .88 .87 .82 .93 .94Workgroup Cooperation, Friendliness,

and Warmth .87 .88 .78 .84 .75 .82 .72 .69

Note. ULS = unweighted least squares; ML = maximum likelihood. NP = Navy, SA = systems analysts,PL = production line, FF = firefighters.


Table 7

Indirect Effects ofPCs on Manifest Indicators for VLS and ML Solutions

NP SA PL FF

PC variables ULS ML ULS ML ULS ML ULS ML

Role ambiguityRole conflictRole overloadSubunit conflictLack of organization identificationLack of management concern and

awareness


-.66 -.66 -.70 -.72-.59-.37-.37-.53

-.64-.40-.43-.52

-.52-.31-.50-.73

-.52-.29-.58-.71

-.72 -.73 -.64 -.67-.62-.50-.64-.78

-.65-.56-.66-.77

-.54-.31-.50-.54

-.52 -.55 -.65

-.56-.33-.53-.46

-.82 -.66 -.65


Challenge and varietyAutonomyJob importance

.69

.63

.58

.55

.65

.48

.54

.56

.48

.53

.54

.42

.41

.52

.36

.36

.52

.32

.63

.61

.36

.63

.59

.35


Leader trust and supportLeader goal facilitationLeader interaction facilitationPsychological influenceHierarchial influence

.74

.70

.67

.66

.69

.71

.67

.67

.71

.68

.75

.77

.68

.65

.68

.71

.77

.68

.64

.67

.76

.65

.55

.72

.68

.71

.65

.53

.71

.65

.84

.79

.77

.73

.73

.85

.80

.78

.77

.72


Workgroup cooperationWorkgroup friendliness and warmthReputation for effectivenessEsprit de corps

.62 .56

.60 .53

.58 .61

.74 .75

.59 .57

.60 .55

.70 .73

.57 .57

.53 .52

.57 .59

.66 .64

.61 .60

.55 .49

Summary statistics

RangeMean1

ULSML

.37-.7S

.60 .61

.60 .61

.29-.77

.61 .62

.61 .61

.32-.S2 .33-.S4

Note. NA = Navy personnel, SA = systems analysts, PL = production line, FF = firefighters. ULS = un-weighted least squares; ML = maximum likelihood.a Mean was computed on absolute values.

also has some interesting implications. For instance, the PC8

mode] suggests that underlying cognitive structures pertaining

to emotionally relevant cognitions of work environments are

Table 8

Squared Correlations ofPCs with Overall Job Satisfaction and

Personality Variables Provided by Unweighted Least Sqttares

PC8

Variables NP SA PL FF

Overall job satisfactionAchievement motivationRigiditySelf-esteem

.79

.04

.00

.06

.88

.06

.10

.01

.75

.11

.04

.06

.73

.05

.00

.03

Note. NA = Navy personnel, SA - systems analysts, PL = productionline, FF = firefighters.

more integrated and parsimonious than has been realized. The

existing tendencies to compartmentalize environmental per-

ception research by environmental domains such as jobs and

leaders have resulted in a large, diffuse set of perceptual con-

structs that not only lacks a unifying theme but also lacks an

integrated theory regarding the substantive impact of the per-

ceived work environment on individuals. We propose that such

an integrated theory may exist and that it is possible, in part, to

explain the substantive impact of the perceived work environ-

ment on individuals. Stated simply, people respond to work en-

vironments in terms of how they perceive these environments,

and a key substantive concern in perception is the degree to

which individuals perceive themselves as being personally bene-

fited as opposed to being personally harmed (hindered) by their

environment.

This article concludes with discussions of two models that

furnish alternative explanations for the correlations among the

PC variables. These models do not exhaust potentially viable


alternatives to PCg theory, but we consider them to be among

the more salient competitors of such theory.

Situational Process Model

The logic here is that perceptions on the PC variables repre-

sent, primarily, the influences of situational processes and thus

covariation among the PC variables reflects, primarily, covaria-

tion among workgroup and other contextual processes. Partial

support for a situational process model is furnished by prior

studies that have reported significant correlations between mea-

sures of situational variables and one or more of the PC vari-

ables (cf. James et al., 1979, 1981; James & Jones, 1980; James

& Sells, 1981; Jones & James, 1979). It is also true, however,

that many of these studies indicated that perceptions on PC

variables were also related uniquely to person variables (e.g.,

job involvement) or to Person X Situation interactions, or both.

Thus, it cannot be said that the PC variables are primarily func-

tions of workgroup and other contextual processes.

An additional point is that to confirm a situational process

model with our data would require that the correlations among

situational processes not only be quite high but also that re-

lations involving differing sets of processes be similar. To illus-

trate, consider the rather flat profile of correlations among the

first-order PC factors, reported in Table 4, that had an average

absolute value of approximately .66. One might build a case

that the meanings associated with jobs, leaders, workgroups,

and individual/organizational interfaces should be correlated

simply because the environmental attributes that are the ob-

jects of perception are correlated (e.g., open-systems theory).

However, it is rather unlikely that such a case would predict a

flat profile of correlations over diverse environmental attri-

butes, particularly for an average correlation as high as .66. A

more cogent explanation of these findings is that each first-order

factor was generated at least in part by a common, latent, cogni-

tive variable. Of course, a meaningful proportion of the vari-

ance in the first-order factors is unique and, by definition, reli-

able, so we must be careful not to overstate our position. In

addition, it is likely that covariation among situational pro-

cesses carried over into the meanings attributed to these pro-

cesses. Nevertheless, a flat profile of high correlations is pre-

cisely what one would expect if a strong, general, cognitive fac-

tor were present in the data. Thus, these findings are consistent

with a cognitive "g-factor theory" and suggest that PCg is useful

for explaining salient portions of variance in work environment

perceptions.

Job Satisfaction Model

The average correlation (over samples) of .89 between PCe

and OJS (see Table 8) stimulated yet again the question, What is

the difference between climate and job satisfaction (JS; Guion,

1973; Johannesson, 1973)? Indeed, one reviewer suggested that

the second-order factor that we designated PCg may instead be

OJS. This same reviewer noted the need to conduct empirical

tests to ascertain whether second-order factors for PC and JS

are empirically distinguishable. We agree, but we could not

conduct such a test with these data because a brief, short-form

instrument was used to assess OJS. Such an instrument does

not provide the range of data needed to meaningfully examine

the factor structure of JS or to compare OJS with PCg. Thus, a

test to ascertain whether PCg and OJS are distinguishable awaits

future research.

As part of this future research we propose a test of an addi-

tional model. The theory underlying this model is that PCg and

an indicator of organizational well-being such as OJS should be

highly correlated because emotionally relevant cognitions and

the emotions (or feeling states) are components of reciprocally

interacting, interdependent, nonrecursive, fused processes (cf.

Ittelson, Proshansky, Rivlin, & Winkel, 1974; James & Jones,

1974, 1980; James &Tetrick, 1986; Lazarus, 1982, 1984; Park,

Sims, & Motowidlo, 1986). In regard to a PCe -»• OJS relation,

a cognitive appraisal of the degree to which an event, agent, or

object is personally beneficial versus detrimental to one's organ-

izational well-being is believed to determine the type and qual-

ity of emotion experienced as well as the direction and intensity

of the emotion (Lazarus, 1982, 1984). Moreover, the global ap-

praisal of whether the work environment as a whole is person-

ally beneficial versus detrimental is hypothesized to be the pri-

mary cause of organizational well-being, which may be charac-

terized by a general feeling state (Isen, 1984) reflecting

primarily positive affect (e.g., happiness, contentment, satisfac-

tion) or primarily negative affect (e.g., depression, melancholy,

dissatisfaction; cf. Diener, 1984). The reciprocal OJS -»• PQ

causal relation is based on the hypothesis that existing or de-

sired levels of overall organizational well-being may cause indi-

viduals to restructure or redefine cognitions to make them con-

sistent with the experienced (or desired) level of affect (cf. James

&Tetrick, 1986).

Although the reciprocal relationship between PQ and OJS

has not been tested, a possible implication of the reciprocal in-

fluence of OJS on PCg is noteworthy. That is, the reciprocal

OJS -»• PCB causal relation suggests an additional source of co-

variation among the first-order PC factors. This possibility is

predicated on the assumption that overall job satisfaction is, in

part, a function of individual dispositions such that individuals

have a generalized predisposition toward a given level or degree

of satisfaction (Pulakos & Schmitt, 1983). If such a predisposi-

tion exists, then an individual's global cognitive appraisal of

benefit versus harm engendered by the work environment may

be a function of that individual's predisposition to be satisfied

or dissatisfied. It follows that the covariation among the first-

order PC factors attributable to PC, may be, in part, a function

of the reciprocal influence of OJS on PQ. In short, a key (albeit

indirect) cause of covariation among the first-order PC factors

may be an affective predisposition.

To illustrate, negative affectivity (NA) is viewed as a mood-

dispositional variable associated with negative emotions, such

as dissatisfaction. Individuals that measure high on NA tend

to interpret situations in a manner that is consistent with their

experienced negative emotions (e.g., dissatisfaction). Such indi-

viduals accentuate the negative aspects of most situations (Wat-

son & Clark, 1984). If these same individuals tend to interpret

the work environment in a manner that is consistent with exist-

ing levels of negative affect, then a predisposition toward dissat-

isfaction may cause them to view their work environment as

being detrimental to their overall well-being. The implied low

score on PCe would in turn stimulate perceptions that jobs lack


challenge, leaders are unsupportive, roles are stressful, and so

on. In this manner, covariation among the perceptions of chal-

lenge, support, stress, and so forth, would reflect the indirect

influences of a predisposition toward NA.

Research pertaining to the possibility of a dispositional

source of job satisfaction has been sparse. Nonetheless, the few

studies that have been done have suggested that job satisfaction

may in part be a function of stable individual characteristics

(Arvey, Bouchard, Segal, & Abraham, 1989; Schmitt & Pu-

lakos, 1985; Staw, Bell, & Clausen, 1986; Staw & Ross, 1985).

Thus, the possibility that predispositions in affect influence

both PCg and the first-order PC factors is a viable question for

future research. On the other hand, this is but one of several

hypotheses, other hypotheses being that situational factors are

ultimately responsible for covariations among first-order PC

factors or between repeated measurements on OJS, or both (cf.

Gerhart, 1987) and that stable individual values are responsible

for the valuations underlying interpretations of environments.

Of course, it is possible, if not likely, that PC perceptions and

PCBare multiply determined, that is, a function not only of situ-

ations, both actual and perceived, and stable individual predis-

positions (e.g., affect, values), but also of potentially complex

interactions among these various sources (cf. James, James, &

Ashe, in press).

In sum, the presence of alternative models points to the need

(and direction) for future research. A key hypothesis to be

tested is that a general perceptual factor of personal benefit ver-

sus personal detriment furnishes a theme for unifying percep-

tions of work environments and an integrated theory for ex-

plaining, in part, the substantive impact of work environment

perceptions on individual outcomes. A series of hypotheses then

pertain to the causes of this general perceptual factor, which

includes stable individual characteristics (values) and predispo-

sitions (affect), situational factors, and various interactions

among these potential causes.

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Received July 26,1988

Revision received January 30, 1989

Accepted February 1,1989 •

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Integrating Work Environment Perceptions: Explorations ... Classes/Survey... · ally detrimental (damaging or painful) to the self and therefore to one's well-being (Lazarus, 1982,