Dr. Binshan Lin

BellSouth Professor

Binshan.Lin@lsus.edu

May 2012

Kasetsart University PhD Workshop, Thailand

1 May 2012 Dr. Lin

Dr. Binshan Lin is the BellSouth Corporation Professor at Louisiana State University in Shreveport (LSUS). He received his Ph.D. from the Louisiana State University in 1988. He is an nine-time recipient of the Outstanding Faculty Award at LSUS. Professor Lin receives the Computer Educator of the Year by the International Association for Computer Information Systems (IACIS) in 2005, Ben Bauman Award for Excellence in IACIS 2003, Distinguished Service Award at the Southwest Decision Sciences Institute (SWDSI) in 2007, Outstanding Educator Award at the SWDSI in 2004, and Emerald Literati Club Awards for Excellence in 2003.

Dr. Lin has published over 260 articles in refereed journals, and currently serves as Editor-in-Chief of Industrial Management & Data Systems.

Professor Lin serves as President of SWDSI (2004-2005), Program Chair of IACIS Pacific 2005 Conference, Program Chair of Management International Conference (MIC) 2006, General Chair of MIC Conference (2007 and 2008). In addition, Dr. Lin serves as Program Chair of Technology Innovation and Industrial Management (TIIM) International Conference 2009, Conference Director of TIIM Conference (2010-present), and Conference Director of MakeLearn International Conference (2012-present). Dr. Lin also serves as a vice president (2007-2009; 2010-2012) of Decision Sciences Institute (DSI).

2 May 2012 Dr. Lin

Dr. Sewall Wright

1889-1988

1st paper in 1920

May 2012 Dr. Lin 3

Y1 = α1 + β1X + ε1i

Y2 = α2 + β2X + β3Y1 + ε2i

X Y1 ε1i

Y2 ε2i

May 2012 Dr. Lin 4

Structural equation modeling (SEM) is

a statistical technique for testing and estimating

causal relations using a combination of statistical

data and qualitative causal assumptions.

May 2012 Dr. Lin 5

Starfield and Bleloch (1991)

Understanding of Processes

univariate

descriptive statistics

exploration,

methodology and

theory

development

realistic

predictive

models

abstract

models

multivariate

descriptive statistics

more detailed

theoretical

models

univariate

data

modeling

multivariate

data

modeling

Data

SEM

May 2012 Dr. Lin 6

J.C. Westland, ECRA, 2010.

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Do the

conventional

methods

meet your

needs?

All your great scientific ideas! ANOVA result you

hope to get!

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Awareness Considerati

on Purchase

Media

May 2012 Dr. Lin 10

There is no consensus on a single

definition for TQM.

We see TQM as a business-level strategy

or management process.

Its components of process and content are

necessary but not sufficient conditions for success.

11 May 2012 Dr. Lin

TQM is defined as a holistic management

philosophy that strives to satisfy customer

needs and expectations through

continuous improvement efforts in every

function and process within an organization

12 May 2012 Dr. Lin

13 May 2012 Dr. Lin

Occurs when different expectations

impinge concurrently, resulting in

“dissonance” for the individual who aims to

perform the incompatible roles (Lynch, 2007)

Higher Quantity vs. Higher Quality

As a mediator variable in a causal model

of employee behaviour

14 May 2012 Dr. Lin

Effect of a Cause (Description) ◦ What follows a cause?

Cause of an Effect (Explanation) ◦ Why did the effect happen?

Do bacteria “cause” disease? ◦ Actually toxins cause disease ◦ Actually certain chemical reactions are cause

Holland, P. W. (1988). “Causal inference, path analysis, and recursive structural equations

models” Sociological Methodology, 18, 449-484.

May 2012 Dr. Lin 15

Multiple Regression Causal Modeling X1

X2

X3

X4

X5

Y

How well do predictors predict in Y? What are independent effects when effects of other variables are controlled?

X1

X3 X4

X2 X5

Y

How well do predictors relate with regard to ultimate prediction of Y?

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Latent variables (as opposed to

observable variables), are variables that

are not directly observed but are rather

inferred from other variables that are

observed (directly measured).

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J.C. Westland, ECRA, 2010.

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J.C. Westland, ECRA, 2010.

May 2012 Dr. Lin 20

Latent variables: representation of the variance shared among the variables

Total

Variance

Common

Variance

Unique

Variance

Specific

Variance

Random

Error

A mediation model is one that seeks to

identify the mechanism that underlies the

relationship between an IV and a DV via

the inclusion of a 3rd explanatory variable,

known as a mediator variable.

21 May 2012 Dr. Lin

The perception that an individual lacks

information required to perform a job or task,

leading one to feel deserted (Onyemah, 2008)

Job description

Operating manual

IS managers dealing with unclear and varying

expectations from end users

Positive relationship between role conflict and

role ambiguity experienced by employees

22 May 2012 Dr. Lin

Six dimensions of TQM practices are assessed using an

adapted version of scales developed by Prajogo et al.

(2007), Prajogo and Sohal (2006), Samson and

Terziovski (1999), Sohail and Teo (2003) and Zhang et

al. (2000).

42 items are grouped into six segments to measure the

different dimensions of TQM practices: leadership,

strategic planning, customer focus, human resource

focus, process management and information analysis.

The response format is a 5-point Likert type scale

ranging from “strongly disagree” to “strongly agree”.

23 May 2012 Dr. Lin

Role conflict and role ambiguity are measured using

scales developed by Rizzo et al. (1970).

The scales developed have been extensively validated

and have established records for its psychometric

properties.

A 5-point Likert type scale is utilized ranging from

“strongly disagree” to “strongly agree”.

24 May 2012 Dr. Lin

Step #1: Determine the individual constructs

Theory identifies the items to be used as

measurement variables

Theoretical constructs should be operationalized from

scales of prior research or through new scales

May 2012 Dr. Lin 25

Step #2: Develop & specify the measurement model

A path diagram should be drawn

Representation of the entire set of relationships that

constitutes a SEM

Step #3: Designing a Study to Produce Empirical

Results

Step #4: Assessing the measurement model validity

Step #5: Specify structural model

Step #6: Assess structural model validity

May 2012 Dr. Lin 26

An assessment of the degree of consistency

between multiple measurements of the same

variable

Concerned with whether alternative

measurements at different times would reveal

similar information

Internal consistency reliability: Cronbach’s

alpha coefficient α > 0.5 or 0.6

May 2012 Dr. Lin 27

The extent to which measure(s) correctly

represent the constructs of study

Concerned with how well the construct is

defined by the measure(s)

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Leadership

Strategic

Planning

Role Conflict

Information

Analysis

Process

Management

Human

Resource

Focus

Customer

Focus

Role Ambiguity

TQ

M P

ra

ctic

es

H6c

H6b

H6a

H2a

H2c H3a

H3b

H3c

H4a

H4b

H4c

H5a

H5c

H2b

H5b

H1

H7a

H7b

H7c

29 May 2012 Dr. Lin

The unit of analysis for this research is individual - the full-time salaried employees of ISO 9001:2000 certified organizations in Malaysia.

ISO 9000 standard is a base for organizations to apply and certify a management system in relation to quality management.

ISO 9000 certification is granted to the firms after they demonstrate that they have mapped operating processes associated with the quality of their products, and that they have complied with these repeatable, standardized and documented processes.

In 2011 the questionnaires were distributed to 100 ISO certified firms listed in the Federation of Malaysian Manufacturers (FMM) Directory.

30 May 2012 Dr. Lin

98 organizations (35 manufacturing firms + 63

service firms).

A total of 650 questionnaires are distributed and

453 are completed and returned.

31 questionnaires have to be excluded as

outliers. The outliers are detected using the

graphical method, that is, residuals scatter plot

(±3 std dev).

422 returns are used for analysis, with net

response rate of 65%.

31 May 2012 Dr. Lin

32

Large Sample Size

SEM researchers suggest a sample size

of at least ten times the number of

parameters we will be estimating.

May 2012 Dr. Lin

Profile Percentage (%) Profile Percentage (%)

Age Length of Service

< 21 years old 0.71% More than 6 months but

less than 1 year 20.62%

21-25 years old 23.46% 1–2 years 24.41%

26-30 years old 35.55% 3-5 years 20.61%

31-35 years old 16.11% 6-10 years 15.40%

36-40 years old 11.14% 11-20 years 14.69%

41 or above 13.03% Above 20 years 4.27%

Qualifications Type of Work

No college degree 10.19% Administration 37.44%

Diploma 15.40% Production 20.62%

Bachelor degree/

Professional

qualification

59.01%

Computer and

IT

26.54%

Master degree 13.74% Sales and marketing 15.40%

PhD degree 1.66%

33 May 2012 Dr. Lin

Measurement Model involves the development of

measurement models using confirmatory factor analysis

(CFA) to achieve the best fitting group of items to represent

each measurement scale.

The 2nd model (Structural Model 1) examines the

relationships between TQM practices and role conflict.

The 3rd model (Structural Model 2) examines the

relationship between TQM practices and role ambiguity.

The 4th model (Structural Model 3) examines the relations

among TQM practices, role conflict and role ambiguity as well

as the mediating effect of role conflict between TQM

practices and role ambiguity simultaneously.

34 May 2012 Dr. Lin

LD SP CF HR PM IA RC RA

LD 0.864

SP 0.729**

(0.069)

0.861

CF 0.502**

(0.037)

0.711**

(0.061)

0.839

HR 0.646**

(0.081)

0.699**

(0.078)

0.594**

(0.064)

0.894

PM 0.640**

(0.056)

0.735**

(0.060)

0.651**

(0.054)

0.754**

(0.095)

0.852

IA 0.588**

(0.051)

0.699**

(0.059)

0.649**

(0.058)

0.671**

(0.082)

0.734**

(0.069)

0.875

RC -0.293**

(0.005)

-0.322**

(0.005)

-0.294**

(0.005)

-0.263**

(0.005)

-0.361**

(0.007)

-0.373**

(0.008)

0.668

RA -0.377**

(0.009)

-0.442**

(0.010)

-0.343**

(0.007)

-0.366**

(0.010)

-0.456**

(0.011)

-0.428**

(0.010)

0.591**

(0.008)

0.761

* p < 0.05; ** p < 0.01; *** p < 0.001; LD=Leadership; SP=Strategic planning; CF=Customer focus; HR=Human resource focus; PM=Process management; IA=Information analysis; RC=Role conflict; RA=Role ambiguity.

35 May 2012 Dr. Lin

Model Fit Indices

χ² / df GFI AGFI RMSEA NFI CFI TLI

≤ 3 a ≥ 0.80 b ≥ 0.80 b ≤ 0.05 c ≥ 0.80 b ≥ 0.90 d ≥ 0.90 e

Measurement Model 1.655 0.882 0.861 0.039 0.887 0.952 0.946

Structural Model 1 1.578 0.874 0.854 0.037 0.870 0.948 0.942

Structural Model 2 1.598 0.866 0.845 0.038 0.862 0.943 0.937

Structural Model 3 1.538 0.858 0.838 0.036 0.847 0.940 0.934

36 May 2012 Dr. Lin

* p < 0.05; ** p < 0.01; *** p < 0.001; LD=Leadership; SP=Strategic planning; CF=Customer focus; HR=Human resource focus; PM=Process management; IA=Information analysis; RC=Role conflict; RA=Role ambiguity.

Hypotheses Causal

Path

Path Coefficients Critical Ratios p-value

H1 RC RA 0.752 6.070 0.000***

H2a LD RC -0.140 -1.270 0.204

H2b LD RA 0.102 1.072 0.284

H3a SP RC 0.154 0.685 0.493

H3b SP RA -0.351 -1.784 0.074†

H4a CF RC -0.022 -0.175 0.861

H4b CF RA 0.220 1.974 0.048*

H5a HR RC 0.242 2.949 0.003**

H5b HR RA 0.045 0.647 0.518

H6a PM RC -0.356 -2.572 0.010*

H6b PM RA -0.166 -1.410 0.159

H7a IA RC -0.282 -3.049 0.002**

H7b IA RA 0.028 0.362 0.717

37 May 2012 Dr. Lin

The hypotheses H1, H3b, H4b, H5a, H6a and H7a are

empirically supported.

However, the findings do not support hypotheses

H2a, H2b, H3a, H4a, H5b, H6b and H7b because the

respective path coefficients are not significant in

the predicted directions.

38 May 2012 Dr. Lin

* p < 0.05; ** p < 0.01; *** p < 0.001; Mediator = Role conflict; DV=Role

ambiguity; IV=Independent variables

Constructs

(Hypotheses)

Baron

&

Kenny

Test

Coefficients of

Structured

Model 1

(IV Mediator)

Coefficients of

Structured

Model #2 (IV

DV)

Coefficients of Structured Model

#3

(IV DV, mediator controlled)

Leadership (H2c) -1.230 -0.134 -0.004 0.102

Strategic Planning (H3c) 0.671 0.151 -0.233 -0.351

Customer Focus (H4c) -0.170 -0.018 0.198 0.220*

Human Resource

Focus (H5c)

2.625** 0.232** 0.216** 0.045

Process Management

(H6c)

-2.347** -0.352* -0.416*** -0.166

Information Analysis

(H7c)

-2.683** -0.276** -0.169* 0.028

Role Conflict (H1) - - - 0.752***

39 May 2012 Dr. Lin

The Baron and Kenny (1986) statistic is used to test for the

significance of the mediating effect.

Three regression equations are used to test for the mediation

model and the following three conditions must hold to

establish the mediation.

First, the independent variables must be shown to be

significantly related to the mediator in structural model 1.

Second, the independent variables must be shown to be

significantly related to the dependent variable in structural

model 2.

Third, the mediator must affect the dependent variable in

structural model 3.

40 May 2012 Dr. Lin

The mediator (role conflict) is significantly related to the

dependent variable (role ambiguity) in Structural Model

3, while human resource focus (β = 0.045, p > 0.05),

process management (β = -0.166, p > 0.05), and

information analysis (β = 0.028, p > 0.05) are found to

have no significant relationship with role ambiguity.

Role conflict is found to be a full mediator between the

following: human resource focus and role ambiguity;

process management and role ambiguity; information

analysis and role ambiguity.

Thus, H5c, H6c and H7c are statistically supported.

41 May 2012 Dr. Lin

The negative relationships between two TQM practices

(i.e., process management and information analysis)

and role conflict provide incentives for industrial

practitioners.

In order to reduce the levels of role conflict among

employees, the organizational administrators and

managers are incentivised to develop appropriate

implementation procedures to enhance the process

management as well as to improve efficient use of

information analysis.

42 May 2012 Dr. Lin

The industrial practitioners must be attentive to the

pressures of customer focus which increase

employees’ role ambiguity.

Using behaviour-based evaluation gives employees

more control over their evaluations, thereby reducing

employees’ role ambiguity.

43 May 2012 Dr. Lin

The organizational administrators and managers

must be aware that the presence of role conflict

inevitably leads to higher levels of role ambiguity.

On the other hand, role conflict appears to be a

full mediator influencing several TQM practice.

One effective way to alleviate role ambiguity is to

eliminate, if not reduce, the conflicting roles and

expectations communicated to an individual.

44 May 2012 Dr. Lin