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Structural Equation Modeling

Structural Equation Modeling. Dr. Binshan Lin BellSouth Professor Binshan.Lin@lsus.edu May 2012 Kasetsart University PhD Workshop, Thailand. Instructor Profile.

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Structural Equation Modeling

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  1. Structural Equation Modeling Dr. Binshan Lin BellSouth Professor Binshan.Lin@lsus.edu May 2012 Kasetsart University PhD Workshop, Thailand May 2012 Dr. Lin

  2. Instructor Profile • 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). May 2012 Dr. Lin

  3. Dr. Sewall Wright 1889-1988 1st paper in 1920 May 2012 Dr. Lin

  4. X Y1 ε1i Y2 ε2i The Wright Idea Y1 = α1 + β1X + ε1i Y2 = α2 + β2X + β3Y1 + ε2i May 2012 Dr. Lin

  5. Definition • Structural equation modeling (SEM) is a statistical technique for testing and estimating causal relationsusing a combination of statistical data and qualitative causal assumptions.  May 2012 Dr. Lin

  6. multivariate descriptive statistics multivariate data modeling realistic predictive models SEM univariate descriptive statistics univariate data modeling Data exploration, methodology and theory development abstract models more detailed theoretical models Understanding of Processes May 2012 Dr. Lin Starfieldand Bleloch (1991)

  7. J.C. Westland, ECRA, 2010. May 2012 Dr. Lin

  8. All your great scientific ideas! ANOVA result you hope to get! Do the conventional methods meet your needs? May 2012 Dr. Lin

  9. May 2012 Dr. Lin

  10. Testing the Purchase Funnel Awareness Consideration Purchase Media May 2012 Dr. Lin

  11. TQM • 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. May 2012 Dr. Lin

  12. TQM • TQM is defined as a holistic management philosophy that strives to satisfycustomer needs and expectations through continuous improvement efforts in everyfunction and process within an organization May 2012 Dr. Lin

  13. Role Conflict May 2012 Dr. Lin

  14. Role Conflict • 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 May 2012 Dr. Lin

  15. Cause vs. Effect • 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

  16. Multiple Regression • Causal Modeling X1 X1 X3 X4 X2 Y Y X3 X2 X4 X5 X5 How well do predictors predict in Y? What are independent effects when effects of other variables are controlled? How well do predictors relate with regard to ultimate prediction of Y? May 2012 Dr. Lin

  17. Latent Variables • 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).  May 2012 Dr. Lin

  18. J.C. Westland, ECRA, 2010. May 2012 Dr. Lin

  19. J.C. Westland, ECRA, 2010. May 2012 Dr. Lin

  20. Conceptualizing Latent Variables • Latent variables: representation of the variance shared among the variables May 2012 Dr. Lin

  21. Mediator Variable • 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 3rdexplanatory variable, known as a mediator variable. May 2012 Dr. Lin

  22. Role Ambiguity • 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 May 2012 Dr. Lin

  23. TQM Measurement • Six dimensions of TQM practices are assessed using an adapted version of scales developed by Prajogoet 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”. May 2012 Dr. Lin

  24. Role Conflict & Role Ambiguity • 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-pointLikert type scale is utilized ranging from “strongly disagree” to “strongly agree”. May 2012 Dr. Lin

  25. Six Steps of SEM Process • 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

  26. Six Steps of SEM Process • 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

  27. Reliability • 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

  28. Validity • The extent to which measure(s) correctly represent the constructs of study • Concerned with how well the construct is defined by the measure(s) May 2012 Dr. Lin

  29. May 2012 Dr. Lin

  30. Samples & Procedures • 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. May 2012 Dr. Lin

  31. Sampling • 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%. May 2012 Dr. Lin

  32. Lower Bound of Sample Size 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

  33. Profiles of the Survey Respondents May 2012 Dr. Lin

  34. Four Models • 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. May 2012 Dr. Lin

  35. Correlations and Composite Reliabilities for All Variables • * 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. May 2012 Dr. Lin

  36. Model Fit Indices for the Measurement & Structural Models►(Chau & Hu, 2011)►Goodness-of-Fit Indices (Forza & Filippini, 1998)►Adjusted Goodness-of-Fit Indices (Forza & Filippini, 1998) ►Root Mean Square Error Approximation (Browne & Cudeck, 1993)►Normal Fit Index (Forza & Filippini, 1998)►Comparative Fit Index (Hair et al, 2010)►Tucker-Lewis Index: (Vanderberg & Scarpello, 1994) May 2012 Dr. Lin

  37. Path Coefficients for Structural Model 3 * 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. May 2012 Dr. Lin

  38. Hypotheses Testing • 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. May 2012 Dr. Lin

  39. Tests of Mediating Effects of Role Conflict on the TQM - Role Ambiguity Relation * p < 0.05; ** p < 0.01; *** p < 0.001; Mediator = Role conflict; DV=Role ambiguity; IV=Independent variables May 2012 Dr. Lin

  40. Barton & Kenny Test • 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. May 2012 Dr. Lin

  41. Result • 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 H7care statistically supported. May 2012 Dr. Lin

  42. Implication #1 • 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. May 2012 Dr. Lin

  43. Implication #2 • 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. May 2012 Dr. Lin

  44. Implication #3 • 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. May 2012 Dr. Lin

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