Effect of Adopting Progressive Diversity and Inclusion Initiatives Based On Sexual Orientation on Employee Loyalty: A Study on Multinational Workforce in Egypt
Author(s)
Download Full PDF Pages: 10-42 | Views: 10 | Downloads: 4 | DOI: 10.5281/zenodo.15688552
Abstract
In recent decades, diversity and inclusion (D&I) have gained significant traction as vital components of organizational success, especially for multinational corporations operating in diverse global markets. The importance of D&I stems from its potential to foster a collaborative workplace culture where individuals feel valued, respected, and empowered regardless of their gender, ethnicity, race, or cultural background.
. In Western countries, progressive D&I practices have been widely embraced, leading to positive outcomes such as improved employee engagement, higher innovation rates, and greater customer satisfaction (Hofstede, 2011; Lloren & Parini, 2017). In contrast, the implementation of such initiatives in non-Western contexts, including Egypt, remains relatively underexplored.
Egypt presents a unique cultural landscape where traditional societal norms coexist with the modern aspirations of a growing corporate sector. For multinational corporations operating in Egypt, balancing global diversity standards with local cultural expectations poses significant challenges. While many organizations acknowledge the value of D&I in enhancing employee loyalty and organizational success, there is limited empirical evidence on how these initiatives impact key outcomes in the Egyptian context (Hofstede, 2011).
Keywords
Effect of Diversity and Inclusion on Employee loyalty in multinational companies in Egypt
References
Ahmad, A., & Hossain, M. A. (2018). Assimilation of business intelligence systems: The mediating role of organizational knowledge culture. In: Al-S. Sharhan et al. (Eds.). Lecture Notes in Computer Science: Vol. 11195. Challenges and Opportunities in the Digital Era (pp. 480-491).Springer. https://doi.org/10.1007/978-3-030-02131-3_43
Ain, N., Vaia, G., DeLone, W. H., & Waheed, M. (2019). Two decades of research on business intelligence system adoption, utilization and success – A systematic literature review. Decision Support Systems, 125, 113113. https://doi.org/10.1016/j.dss.2019.113113
Al-Fraihat, D., Joy, M., Masa’deh, R., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in Human Behavior, 102, 67–86.
Al-Hattami, H. M. (2021). Validation of the D&M IS success model in the context of accounting information system of the banking sector in the least developed countries. Journal of Management Control, 32(1), 127–153.
Al-Okaily, A., Al-Okaily, M., Teoh, A. P., & Al-Debei, M. M. (2022). An empirical study on data warehouse systems effectiveness: The case of Jordanian banks in the business intelligence era. EuroMed Journal of Business, 18(4), 489–510. https://doi.org/10.1108/EMJB-01-2022-0011
Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211
Al-Asfour, A., & Khan, S. (2014). Gender diversity in the workplace: A study on female employees in the Middle East. Journal of Business and Management, 5(2), 45-61
Ammari, A., Alkurdi, B., Alshurideh, M., & Alrowwad, A. (2017). Exploring the effect of diversity management practices and inclusion strategies on employee loyalty: Evidence from multinational organizations. Journal of Business Studies, 12(4), 85–95.
Ariyanto, R., Rohadi, E., & Lestari, V. A. (2020). The effect of information quality, system quality, service quality on intention to use and user satisfaction, and their effect on net benefits primary care application at primary health facilities in Malang. IOP Conference Series: Materials Science and Engineering, 732(1), 012084. https://doi.org/10.1088/1757-899X/732/1/012084
Asenahabi, B. M. (2019). Basics of research design: A guide to selecting appropriate research design. International Journal of Contemporary Applied Researches, 6(5), 76-89.
Asiamah, N., Mensah, H., & Oteng-Abayie, E. F. (2017). General, target, and accessible population: Demystifying the concepts for effective sampling. The Qualitative Report, 22(6), 1607-1621. https://doi.org/10.46743/2160-3715/2017.2674
Bagheri, S., & Zwering, M. (2023). Business intelligence and analytics success factors: A case study in the financial sector. Proceedings of the 56th Hawaii International Conference on System Sciences, 5410–5419. https://hdl.handle.net/10125/103294
Bashiri, A., Shirdeli, M., Niknam, F., Naderi, S., & Zare, S. (2023). Evaluating the success of Iran electronic health record system (SEPAS) based on the DeLone and McLean model: A cross-sectional descriptive study. BMC Medical Informatics and Decision Making, 23(1), 10. https://doi.org/10.1186/s12911-023-02100-y
Bell, E., Bryman, A., & Harley, B. (2022). Business Research Methods (6th ed.). Oxford University Press.
Berndt, A., & Petzer, D. (2011). Environmental concern of South African cohorts: An exploratory study. African Journal of Business Management, 5(19), 7899-7910. https://doi.org/10.5897/AJBM11.347
Bessadok, A. (2022). Analyzing student aspirations factors affecting e-learning system success using a structural equation model. Education and Information Technologies, 27(7), 9205–9230. https://doi.org/10.1007/s10639-022-11015-6
Bouaoula, W., Belgoum, F., Shaikh, A., Taleb-Berrouane, M., & Bazan, C. (2019). The impact of business intelligence through knowledge management. Business Information Review, 36(3), 130–140. https://doi.org/10.1177/0266382119868082
Bryman, A. (2012). Social research methods (4th ed.). Oxford University Press.
Bussolotto, D., Bagattini, L. D. C., & Camargo, M. E. (2021). Contribution of strategic information distribution to the decision making processes – An empirical. International Journal for Innovation Education and Research, 9(10), 131–144. . https://doi.org/10.31686/ijier.vol9.iss10.3422
Çelik, K., & Ayaz, A. (2022). Validation of the DeLone and McLean information systems success model: A study on student information system. Education and Information Technologies, 27(4), 4709-4727. https://doi.org/10.1007/s10639-021-10798-4
Cialdini, R. B., Kallgren, C. A., & Reno, R. R. (1991). A focus theory of normative conduct: A theoretical refinement and re-evaluation of the role of norms in human behavior. Advances in Experimental Social Psychology, 24, 201-234. https://doi.org/10.1016/S0065-2601(08)60330-5
Central Bank of Egypt (CBE). (n.d.). Number of Debit—Credit Cards and ATM POS Machines—Banking Sector [Financial Soundness Indicator Report]. Author. Retrieved March 24, 2024 from https://www.cbe.org.eg/en/news-publications/publications
Cennamo, L., & Gardner, D. (2008). Generational differences in work values, outcomes and person-organization values fit. Journal of Business and Psychology, 23(4), 311-324. https://doi.org/10.1007/s10869-008-9103-x
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Thousand Oaks, CA: Sage
Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). Thousand Oaks, CA: Sage
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60–95. https://doi.org/10.1287/isre.3.1.60
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748
Cooke, F. L., & Saini, D. S. (2010). Diversity Management and Organizational Performance: An Overview of Global Trends. International Journal of Human Resource Management, 21(12), 2027-2048
Elazzaoui, E., & Lamari, S. (2022). DeLone and McLean information systems success model in the public sector: A systematic review. Journal of Social Science and Organization Management, 3(1), 133-156.
Elbashir, M. Z., Collier, P. A., & Davern, M. J. (2008). Measuring the effects of business intelligence systems: The relationship between business process and organizational performance. International Journal of Accounting Information Systems, 9(3), 135–153. https://doi.org/10.1016/j.accinf.2008.03.001
Etikan, I., Musa, S. A., & Alkassim, R. S. A. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1-4. https://doi.org/10.11648/j.ajtas.20160501.11
Ely, R. J., & Meyerson, D. E. (2000). Theories of gender in organizations: A new approach to organizational analysis and change. Research in Organizational Behavior, 22, 103-151
Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics (5th ed.). Sage Publications.
Fink, L., Yogev, N., & Even, A. (2017). Business intelligence and organizational learning: An empirical investigation of value creation processes. Information & Management, 54(1), 38–56. https://doi.org/10.1016/j.im.2016.03.009
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Franke, G., & Sarstedt, M. (2019). Heuristics versus statistics in discriminant validity testing: A comparison of four procedures. Internet Research, 29(3), 430–447.
Gaardboe, R., & Jonasen, T. S. (2018). Business intelligence success factors: A Literature review. Journal of Information Technology Management, XXIX(1), 1–15.
Gravetter, F. J., & Wallnau, L. B. (2016). Statistics for the behavioral sciences (10th ed.). Cengage Learning
Greenhaus, J. H., & Powell, G. N. (2006). When work and family are allies: A theory of work-family enrichment. Academy of Management Review, 31(1), 72-92.
Gonzales, R., & Wareham, J. (2019). Analysing the impact of a business intelligence system and new conceptualizations of system use. Journal of Economics, Finance and Administrative Science, 24(48), 345–368. https://doi.org/10.1108/JEFAS-05-2018-0052
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Hočevar, B., & Jaklič, J. (2010). Assessing benefits of business intelligence systems—A case study. Management Journal of Contemporary Management Issues, 15(1), 87–119.
Hofstede, G. (2011). Dimensionalizing Cultures: The Hofstede Model in Context. Online Readings in Psychology and Culture, 2(1), 1-26
Huber, G. P. (1990). A theory of the effects of advanced information technologies on organizational design, intelligence, and decision making. Academy of Management Review, 15(1), 47–71. https://doi.org/10.5465/amr.1990.4308227
Ibrahim, B., Glood, S., Bahloos, S., & Abd, S. (2021). A meta-analysis review of DeLone and McLean information system success model. In S.-L. Peng, R.-X. Hao, & S. Pal (Eds.), Proceedings of First International Conference on Mathematical Modeling and Computational Science (pp. 195–208). Springer. https://doi.org/10.1007/978-981-33-4389-4_19
In, J. (2017). Introduction of a pilot study. Korean Journal of Anesthesiology, 70(6), 601–605.
Işık, Ö., Jones, M. C., & Sidorova, A. (2013). Business intelligence success: The roles of BI capabilities and decision environments. Information & Management, 50(1), 13-23.
Jakhar, R., & Krishna, C. (2020). Business intelligence: As a strategic tool for organization development (A literature review). ANWESH: International Journal of Management & Information Technology, 5(1), 44–46.
Kline, R. B. (2011). Convergence of structural equation modeling and multilevel modeling. In M. Williams & W. P. Vogt (Eds.), The SAGE Handbook of Innovation in Social Research Methods (pp. 562–589). SAGE Publications Ltd.
Kocakaya, S., & Kocakaya, F. (2014). A structural equation modeling on factors of how experienced teachers affect the students’ science and mathematics achievements. Education Research International, 2014, Article 490371. https://doi.org/10.1155/2014/490371
Long, L. (2010). A critical review of technology acceptance literature [Referred Research Paper, 4]. Southwest Decision Sciences Institute. http://www.swdsi.org/Swdsi2010/Sw2010_Preceedings/Papers/Pa104.Pdf
Lloren, M., & Parini, P. (2017). Diversity and Inclusion in the Global Workplace. Journal of Business Ethics, 142(1), 133-149
Lönnqvist, A., & Pirttimäki, V. (2006). The measurement of business intelligence. Information Systems Management, 23(1), 32–40.
Lutfi, A., Al-Okaily, M., Alsyouf, A., & Alrawad, M. (2022). Evaluating the D&M IS success model in the context of accounting information system and sustainable decision making. Sustainability, 14(13), Article 8120. https://doi.org/10.3390/su14138120
Mardiana, S., Tjakraatmadja, J. H., & Aprianingsih, A. (2015). DeLone–McLean information system success model revisited: The separation of intention to use-use and the integration of technology acceptance models. International Journal of Economics and Financial Issues, 5(1), 172–182.
Martins, J., Branco, F., Gonçalves, R., Au-Yong-Oliveira, M., Oliveira, T., Naranjo-Zolotov, M., & Cruz-Jesus, F. (2019). Assessing the success behind the use of education management information systems in higher education. Telematics and Informatics, 38, 182–193.
Meyer, J. P., & Allen, N. J. (1991). A three-component conceptualization of organizational commitment. Human Resource Management Review, 1(1), 61-89. https://doi.org/10.1016/1053-4822(91)90011-Z
Montero, J. N., & Lind, M. L. (2020). Determining Business Intelligence Usage Success. International Journal of Computer Science and Information Technology, 12(6), 45–67. https://doi.org/10.5121/ijcsit.2020.12604
Mudzana, T., & Maharaj, M. (2015). Measuring the success of business-intelligence systems in South Africa: An empirical investigation applying the DeLone and McLean model. South African Journal of Information Management, 17(1), 1-7. https://doi.org/10.4102/sajim.v17i1.646
Nnaji, C., Okpala, I., Awolusi, I., & Gambatese, J. (2023). A systematic review of technology acceptance models and theories in construction research. Journal of Information Technology in Construction, 28, 39–69. https://doi.org/10.36680/j.itcon.2023.003
Nugroho, Y., & Prasetyo, A. (2018). Assessing information systems success: A respecification of the DeLone and McLean model to integrating the perceived quality. Problems and Perspectives in Management, 16(1), 348–360. https://doi.org/10.21511/ppm.16(1).2018.34
Ng, E. S., & Rumens, N. (2017). Diversity and inclusion for LGBT workers: Current issues and new horizons for research. Human Resource Management Journal, 27(1), 1–8
Ojo, A. I. (2017). Validation of the DeLone and McLean information systems success model. Healthcare Informatics Research, 23(1), 60–66. https://doi.org/10.4258/hir.2017.23.1.60
Petter, S., DeLone, W., & McLean, E. (2008). Measuring information systems success: Models, dimensions, measures, and interrelationships. European Journal of Information Systems, 17(3), 236–263. https://doi.org/10.1057/ejis.2008.15
Phillips-Wren, G., Daly, M., & Burstein, F. (2021). Reconciling business intelligence, analytics and decision support systems: More data, deeper insight. Decision Support Systems, 146, Article 113560. https://doi.org/10.1016/j.dss.2021.113560
Pitts, D. W., Hicklin, A., Hawes, D. P., & Melton, E. (2010). What drives the implementation of diversity management programs? Evidence from public organizations. Journal of Public Administration Research and Theory, 20(4), 867–886
Pope, A. D. (2014). Business intelligence: Applying the unified theory of acceptance and use of technology (UMI No. 3616064) [Dissertation, Capella University]. ProQuest Dissertations Publishing.
Popovič, A., Hackney, R., Coelho, P. S., & Jaklič, J. (2012). Towards business intelligence systems success: Effects of maturity and culture on analytical decision making. Decision Support Systems, 54(1), 729–739. https://doi.org/10.1016/j.dss.2012.08.017
Pallant, J. (2020). SPSS Survival Manual: A Step by Step Guide to Data Analysis Using IBM SPSS (7th ed.). Routledge
Popovič, A., Hackney, R., Coelho, P. S., & Jaklič, J. (2014). How information-sharing values influence the use of information systems: An investigation in the business intelligence systems context. Journal of Strategic Information Systems, 23(4), 270–283. https://doi.org/10.1016/j.jsis.2014.08.003
Rai, A. (2021, October 26). Business intelligence and analytics software market: 2021 analysis, share, trends, and overview 2021-2027 (Orion Market Reports). Global Market Post. https://globalmarketpost.com/business-intelligence-and-analytics-software-market/
Razali, N. M., & Wah, Y. B. (2011). Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors, and Anderson-Darling tests. Journal of Statistical Modeling and Analytics, 2(1), 21-33.
Roberson, Q. M. (2006). Disentangling the meanings of diversity and inclusion in organizations. Group & Organization Management, 31(2), 212–236.
Richards, G., Yeoh, W., Chong, A. Y. L., & Popovič, A. (2019). Business intelligence effectiveness and corporate performance management: An empirical analysis. Journal of Computer Information Systems, 59(2), 188–196. https://doi.org/10.1080/08874417.2017.1334244
Roebianto, A., Savitri, S. I., Aulia, I., Suciyana, A., & Mubarokah, L. (2023). Content validity: Definition and procedure of content validation in psychological research. TPM - Testing, Psychometrics, Methodology in Applied Psychology, 30, 5–18. https://doi.org/10.4473/TPM30.1.1
Rollings, M. (2021, October 20). How to Make Better Business Decisions. Gartner. https://www.gartner.com/smarterwithgartner/how-to-make-better-business-decisions
Salim, M., Alfansi, L., Anggarawati, S., Saputra, F., & Afandy, C. (2021). The role of perceived usefulness in moderating the relationship between the DeLone and McLean model and user satisfaction. Uncertain Supply Chain Management, 9(3), 755–766.
Saunders, M. N. K., Lewis, P., & Thornhill, A. (2012). Research methods for business students (6th ed.). Pearson Education Limited.
Schieder, C., & Gluchowski, P. (2011, June 10). Towards a consolidated research model for understanding business intelligence success [Conference Proceedings]. 19th European Conference on Information Systems (ECIS), 205, Helsinki, Finland.. https://aisel.aisnet.org/ecis2011/205
Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Educational Research, 99(6), 323–338. https://doi.org/10.3200/JOER.99.6.323-338
Seddon, P. B., Staples, S., Patnayakuni, R., & Bowtell, M. (1999). Dimensions of information systems success. Communications of the Association for Information Systems, 2(1), Article 20. https://doi.org/10.17705/1CAIS.00220
Shin, B. (2003). An exploratory investigation of system success factors in data warehousing. Journal of the Association for Information Systems, 4(1), 141–170. https://doi.org/10.17705/1jais.00033
Shollo, A., & Galliers, R. D. (2016). Towards an understanding of the role of business intelligence systems in organisational knowing. Information Systems Journal, 26(4), 339–367. https://doi.org/10.1111/isj.12071
Siedlecki, S. L. (2020). Understanding descriptive research designs and methods. Clinical Nurse Specialist, 34(1), 8-12. https://doi.org/10.1097/NUR.0000000000000493
Silverman, D. (2013). What counts as qualitative research? Some cautionary comments. Qualitative Sociology Review, 9(2), 48–55.
Sparks, B. H., & McCann, J. T. (2015). Factors influencing business intelligence system use in decision making and organisational performance. International Journal of Sustainable Strategic Management, 5(1), 31-54. https://doi.org/10.1504/IJSSM.2015.074604
Statista Market Insights (n.d.). Business Intelligence Software – Egypt. Retrieved March 24, 2024 from https://www.statista.com/outlook/tmo/software/enterprise-software/business-intelligence-software/egypt
Subaeki, B., Rahman, A. A., Putra, S. J., & Alam, C. N. (2019). Success model for measuring information system implementation: Literature review. Journal of Physics: Conference Series, 1402(7), Article 077015. https://doi.org/10.1088/1742-6596/1402/7/077015
Sürücü, L., & Maslakçi, A. (2020). Validity and Reliability in Quantitative Research. Business & Management Studies: An International Journal, 8(3), 2694-2726. https://doi.org/10.15295/bmij.v8i3.1540
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53-55. https://doi.org/10.5116/ijme.4dfb.8dfd
Torres, R., Sidorova, A., & Jones, M. C. (2018). Enabling firm performance through business intelligence and analytics: A dynamic capabilities perspective. Information and Management, 55(7), 822–839. https://doi.org/10.1016/j.im.2018.03.010
Trieu, V.-H. (2017). Getting value from business intelligence systems: A review and research agenda. Decision Support Systems, 93, 111–124. https://doi.org/10.1016/j.dss.2016.09.019
Tunowski, R. (2020). Sustainability of commercial banks supported by business intelligence system. Sustainability, 12(11), Article 4754. https://doi.org/10.3390/su12114754
Vallurupalli, V., & Bose, I. (2018). Business intelligence for performance measurement: A case based analysis. Decision Support Systems, 111, 72–85.
Wang, Y.-S. (2008). Assessing e-commerce systems success: A respecification and validation of the DeLone and McLean model of IS success. Information Systems Journal, 18(5), 529–557.
Weston, R., & Gore, P. A., Jr. (2006). A Brief Guide to Structural Equation Modeling. The Counseling Psychologist, 34(5), 719–751. https://doi.org/10.1177/0011000006286345
Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): A literature review. Journal of Enterprise Information Management, 28(3), 443–488. https://doi.org/10.1108/JEIM-09-2014-0088
Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85–102. https://doi.org/10.1287/isre.1050.0042
Wu, J.-H., & Wang, Y.-M. (2006). Measuring KMS success: A respecification of the DeLone and McLean’s model. Information and Management, 43(6), 728–739. https://doi.org/10.1016/j.im.2006.05.002
Yadav, S. K., Singh, S., & Gupta, R. (2019). Sampling Methods. In S. K. Yadav, S. Singh, & R. Gupta (Eds.), Biomedical Statistics: A Beginner’s Guide (pp. 71–83). Springer. https://doi.org/10.1007/978-981-32-9294-9_9
Yeoh, W., & Koronios, A. (2010). Critical success factors for business intelligence systems. Journal of Computer Information Systems, 50(3), 23–32.
Yeoh, W., & Popovič, A. (2016). Extending the understanding of critical success factors for implementing business intelligence systems. Journal of the Association for Information Science and Technology, 67(1), 134–147. https://doi.org/10.1002/asi.23366
Yiu, L. M. D., Yeung, A. C. L., & Cheng, T. C. E. (2021). The Impact of Business Intelligence Systems on Profitability and Risks of Firms. International Journal of Production Research, 59(13), 3951–3974. https://doi.org/10.1080/00207543.2020.1756506
Cite this Article: