SUPPLY CHAIN INTEGRATION PERFORMANCE SCALE IN ETHIOPIAN TEXTILE INDUSTRIES
Keywords:
Supply chain management, Performance measurement, Supply chain integration, Textile industry.Abstract
By combining supply network macro operations and decision-making levels, the study builds an integrated supply chain performance integration measurement scale (SCIPS) framework that offers a more thorough method for investigating supply chain performance measurement. Performance and supply chain-related theories serve as the foundation for the philosophical framework. In addition to looking at performance measures, the study looks into how well the Ethiopian textile industries' supply chains are integrated. Professionals in the textile sector self-reported the data, and bias analysis was carried out using the standard method. Professionals in the textile sector were mailed or administered the survey, and 385 data points were gathered through the properly filled-out study questionnaire. This study's objectives included developing an integrated supply chain performance assessment scale for Ethiopia's textile industries, confirming the factorial design of the measurement, and ultimately identifying the test's psychometric properties. Exploratory and confirmatory factor analyses were utilized to achieve these objectives. To better grasp the regional realities in this subject, a study was done to comprehend and assess the supply chain in the textile industry, along with any probable causes and practices.
References
African Development Bank (2008) Ethiopia: Country Assistance Evaluation 1996 – 2007, Operations Evaluation Department, (OPEV)
Barney, J.B., (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management, 17(1), 99-120.
Barrett, P. (2007). Structural equation modelling: Adjudging model fit. Personality and Individual Differences, 42(5). https://doi.org/10.1016/j.paid.2006. 09.018.
Bentler, P. M. (2007). On tests and indices for evaluating structural models. Personality and Individual Differences, 42(5). https://doi.org/10.1016/j.paid.2006. 09.024.
Boomsma, A. (2000). Reporting analyses of covariance structures. Structural Equation Modeling: A Multidisciplinary Journal, 7(3). https://doi.org/10. 1207/ S15328007SEM07036.
Cohen, R. J. (2018). Psychological Testing and Assessment (9th ed.). McGraw Hill.
Cohen, R. J., & Swerdlik, M. (2001). Psychological Testing and Assessment: An Introduction to Tests and Measurement (5th edition). McGraw-Hill Humanities/ Social Sciences/Languages.
Cohen. (2013). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Routledge.
Costello, A., & Osborne, J. (2019). Best practices in exploratory factor analysis: Four
recommendations for getting the most from your analysis. Practical Assessment, Research, and Evaluation, 10(1). https://doi.org/10.7275/jyj1-4868.
Cureton, E. E., & Mulaik, S. A. (1975). The weighted varimax rotation and the promax rotation. Psychometrika, 40(2). https://doi.org/10.1007/BF0229 1565.
De Toni, A. & Tonchia, S. (2001). “Performance Measurement Systems: Models, Characteristics and Measures,” International Journal of Operations and Production Management, 21(1-2), 46-70
DeVellis, R. F. (2017). Scale Development: Theory and Applications (4th ed.). Thousand Oaks, CA: Sage.
Fabrigar, & Wegener. (2012). Exploratory factor analysis. Oxford University Press.
Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. https://doi.org/ 10.1037/1082- 989X.4.3.272.
Field, A. P. (2009). Discovering statistics using SPSS: (and sex and drugs and rock “n” roll).
SAGE Publications.
Freeman, R. E., (1994). Ethical theory and business. Englewood Cliffs, New Jersey: Prentice-Hall.
Hair, J., Black, B., Babin, B. J. R. E., & Anderson, R. E. (2010). Multivariate Data Analysis (7th ed.). Prentice Hall, Upper Saddle River.
Howard, M. C. (2016). A Review of Exploratory Factor Analysis Decisions and Overview of Current Practices: What We Are Doing and How Can We Improve? International Journal of Human-Computer Interaction, 32(1). https://doi.org/10.1080/10447318. 2015.1087664.
Hoyle, R. H., & Panter, A. T. (1995). Writing about structural equation models. Structural Equation Modeling: Concepts, Issues, and Applications., 158–176.
Huang, Y.C., Yang, M.L., Wong, Y.J., (2016). Institutional pressures, resources commitment, and returns management. Supply Chain Management: An International Journal, 21(3), 398- 416.
Jones, T. M., (1995). Instrumental stakeholder theory: A synthesis of ethics and economics.
Academy of Management Review, 20, 404–43.
Koh, J., Venkatraman, N., 1991. Joint venture formations and stock market reactions: An assessment in the information technology sector. Academy of Management Journal, 34, 869–892.
MacCallum, R. C., & Austin, J. T. (2000). Applications of structural equation modeling in psychological research. Annual Review of Psychology, 51(1). https://doi.org/10.1146/annurev.psych.51.1.201.
Mata, F. J., Fuerst, W. L., Barney, J. B., (1995). Information technology and sustained competitive advantage: A resource-based analysis. MIS Quarterly, 19(4), 487.
McDonald, R. P., & Ho, M.-H. R. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7(1). https://doi.org/10.1037/ 1082 989X.7.1.64.
Medsker, G. J., Williams, L. J., & Holahan, P. J. (1994). A review of current practices for
causal models in organizational behavior and human resources management research. Journal of Management, 20(2), 439–464. https://doi.org/10.1016/ 0149-2063(94)90022-1.
Mentzer, J.T. (2001), Supply Chain Management, Sage, Thousand Oaks, CA.
Neely A, Gregory M & Platts K (1995) Performance measurement system design: A literature review and research agenda. International Journal of Operations & Production Management 15(4): 80–116.
Oliver, C., (1991). Strategic responses to institutional processes. Academy of Management Review, 16, 145–179.
Pagell, C. (2004). “Performance Measurement,” Work Study, 49(2), 63-66. Lapide, L. (2000). “What about Measuring Supply Chain Performance?,” AMR Research, ASCET - White Paper, 2(15), 287-297.
Rafnsson, F. D., Smari, J., Windle, M., Mears, S. A., & Endler, N. S. (2006). Factor structure and psychometric characteristics of the Icelandic version of the Coping Inventory for Stressful Situations (CISS). Personality and Individual Differences, 40(6). https://doi.org/10. 1016/j.paid.2005.11.011.
Raykov, T., Tomer, A., & Nesselroade, J. R. (1991). Reporting structural equation modeling results in Psychology and Aging: Some proposed guidelines. Psychology and Aging, 6(4). https://doi.org/10.1037/ 0882-7974.6.4.499
Sadler, I., & Hines, P. (2002). Strategic Operations Planning Process for manufacturers with a supply chain focus: Concepts and a meat processing application. Supply Chain Management: An International Journal, 7(4), 225–241. https://doi.org/10.1108/13598540210438962
Smith MF, Lancioni RA & Oliva TA (2005) The effects of management inertia onthe supply chain performance of produce-to-stock firms. Industrial Marketing Management 34(6): 614–628.
Stank T.P., Daugherty P.J., Ellinger. A.E. (1999), Marketing/Logistics Integration and Firm Performance, The International Journal of Logistics Management, Vol. 10 (No. 1), pp. 11-24
Tan, K C., S.B. Lyman, and J.D. Wisner (2002), “Supply Chain Management: a Strategic Perspective”. International Journal of Operations and Production management, 8 June 2002, Vol. 22, iss. 6, pp. 614-631.
Thompson, B. (2004). Exploratory and confirmatory factor analysis: understanding concepts and applications. Washington: American Psychological Association, 1–195.
Touboulic, A., Walker, H., (2015). Theories in sustainable supply chain management: a structured literature review. International Journal of Physical Distribution & Logistics Management, 45(1/2), 16-42.
Williamson, O. E. (1985). The economic institution of capitalism. New York: Free Press. Worthington, R. L., & Whittaker, T. A. (2006). Scale Development Research. The
Counseling Psychologist, 34(6). https://doi.org/10.1177/0011000006288127.
Yamin S, D.Y., Cheng, C.H. and Chi, M.L. (2007). A modified two-tuple FLC model for evaluating the performance of SCM: By the Six Sigma DMAIC process. Applied Soft Computing, 7, p.1027-1034.
Zailaini, H. and Rajagopal, W. (2004), “Supply chain practice and information sharing”,
Journal of Operation Management, Vol. 25, pp. 1348–1365
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
