Reconsidering individuals’ competencies in business intelligence and business analytics toward process effectiveness: mediation-moderation model
Abstract
The purpose of this study is to investigate the impact of individuals’ competencies in business intelligence (BI) and analytics (BA) on process effectiveness (PE). Moreover, to investigate the mediating role of user participation (UP) and the moderating role of gender in this relationship. An empirical analysis based on survey data was conducted. A sample of 215 middle and upper management levels from SMEs located in Jordan was surveyed to collect the data. Structural equation modelling through partial least squares-multi group analysis (PLS-MGA) is used to analyze the data. The results support the direct positive impact of individuals’ competencies in business intelligence (BA) and business analytics (BA). Moreover, user participation has been found to mediate this relationship. Additionally, the results showed that gender moderates the relationship between individuals’ competencies in business intelligence (BI) and analytics (BA) on process effectiveness (PE). The findings improve the understanding of the needed individuals’ competencies in business intelligence (BI) and analytics (BA) that affect process effectiveness (PE). This will help develop and arrange strategies that increase individuals’ competencies in business intelligence (BI) and analytics (BA) among employees. Furthermore, managers and owners should put plans for strategies to augment confidence amongst female employees.
Keyword : business intelligence (BI), business analytics (BA), process effectiveness (PE), user participation (UP)
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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
Ali, F., Rasoolimanesh, S. M., Sarstedt, M., Ringle, C. M., & Ryu, K. (2018). An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research. International Journal of Contemporary Hospitality Management, 30(1), 514–538. https://doi.org/10.1108/IJCHM-10-2016-0568
Amin, H., Hamid, M. R. A., Tanakinjal, G. H., & Lada, S. (2006). Undergraduate attitudes andexpectations for mobile banking. Journal of Internet Banking and Commerce, 11(3), 1–10.
Andersson, L. M., & Bateman, T. S. (1997). Cynicism in the workplace: Some causes and effects. Journal of Organizational Behavior, 18(5), 449–469. https://doi.org/10.1002/(SICI)1099-1379(199709)18:5<449::AID-JOB808>3.0.CO;2-O
Appelbaum, D., Kogan, A., Vasarhelyi, M., & Yan, Z. (2017). Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems, 25, 29–44. https://doi.org/10.1016/j.accinf.2017.03.003
Arnott, D., Lizama, F., & Song, Y. (2017). Patterns of business intelligence systems use in organizations. Decision Support Systems, 97, 58–68. https://doi.org/10.1016/j.dss.2017.03.005
Austin, J., Stevenson, H., & Wei-Skillern, J. (2006). Social and commercial entrepreneurship: Same, different, or both? Entrepreneurship Theory and Practice, 30(1), 1–22. https://doi.org/10.1111/j.1540-6520.2006.00107.x
Aydiner, A. S., Tatoglu, E., Bayraktar, E., Zaim, S., & Delen, D. (2019). Business analytics and firm performance: The mediating role of business process performance. Journal of Business Research, 96(November 2018), 228–237. https://doi.org/10.1016/j.jbusres.2018.11.028
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. https://doi.org/10.1007/BF02723327
Barki, H., & Hartwick, J. (1994). Measuring user participation, user involvement, and user attitude. MIS Quarterly, 18(1), 59–82. https://doi.org/10.2307/249610
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
Barney, J., Wright, M., & Ketchen, D. J. (2001). The resource-based view of the firm: Ten years after 1991. Journal of Management, 27(6), 625–641. https://doi.org/10.1177/014920630102700601
Bedeley, R. T., Ghoshal, T., Iyer, L. S., & Bhadury, J. (2018). Business analytics and organizational value chains: A relational mapping. Journal of Computer Information Systems, 58(2), 151–161. https://doi.org/10.1080/08874417.2016.1220238
Borissova, D., Cvetkova, P., Garvanov, I., & Garvanova, M. (2020). A framework of business intelligence system for decision making in efficiency management. In K. Saeed & J. Dvirsky (Eds), Computer Information Systems and Industrial Management. CISIM 2020. Lecture Notes in Computer Science (Vol. 12133). Springer. https://doi.org/10.1007/978-3-030-47679-3_10
Brill, C. (2019). The influence of management support on the drivers of business intelligence success [Doctoral dissertation, University of Pretoria, March].
Bronzo, M., de Resende, P. T. V., de Oliveira, M. P. V., McCormack, K. P., de Sousa, P. R., & Ferreira, R. L. (2013). Improving performance aligning business analytics with process orientation. International Journal of Information Management, 33(2), 300–307. https://doi.org/10.1016/j.ijinfomgt.2012.11.011
Burke, R. R. (2002). Technology and the customer interface: What consumers want in the physical and virtual store. Journal of the Academy of Marketing Science, 30(4), 411–432. https://doi.org/10.1177/009207002236914
Cao, G., Duan, Y., & Li, G. (2015). Linking business analytics to decision making effectiveness: A Path model analysis. IEEE Transactions on Engineering Management, 62(3), 384–395. https://doi.org/10.1109/TEM.2015.2441875
Carranza, R., Díaz, E., Martín-Consuegra, D., & Fernández-Ferrín, P. (2020). PLS–SEM in business promotion strategies. A multigroup analysis of mobile coupon users using MICOM. Industrial Management and Data Systems, 120(12), 2349–2374. https://doi.org/10.1108/IMDS-12-2019-0726
Cavaye, A. L. M. (1995). User participation in system development revisited. Information and Management, 28(5), 311–323. https://doi.org/10.1016/0378-7206(94)00053-L
Cepeda-Carrion, G., Cegarra-Navarro, J. G., & Cillo, V. (2019). Tips to use partial least squares structural equation modelling (PLS-SEM) in knowledge management. Journal of Knowledge Management, 23(1), 67–89. https://doi.org/10.1108/JKM-05-2018-0322
Chawla, D., & Joshi, H. (2020). The moderating role of gender and age in the adoption of the mobile wallet. Foresight, 22(4), 483–504. https://doi.org/10.1108/FS-11-2019-0094
Chen, H., Chiang, R. H. L., Storey, V. C., & Robinson, J. M. (2012). Business intelligence research business intelligence and analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165–1188. https://doi.org/10.2307/41703503
Cheng, X., Su, L., Luo, X., Benitez, J., & Cai, S. (2021). The good, the bad, and the ugly: Impact of analytics and artificial intelligence-enabled personal information collection on privacy and participation in ridesharing. European Journal of Information Systems, 31(3), 339–363. https://doi.org/10.1080/0960085X.2020.1869508
Cheung, C. M. K., & Lee, M. K. O. (2011). Exploring the gender differences in student acceptance of an internet-based learning medium. In Technology Acceptance in Education (pp. 183–199). Sense Publishers. https://doi.org/10.1007/978-94-6091-487-4_10
Chowdhury, S. (2005). Demographic diversity for building an effective entrepreneurial team: Is it important? Journal of Business Venturing, 20(6), 727–746. https://doi.org/10.1016/j.jbusvent.2004.07.001
Clulow, V., Barry, C., & Gerstman, J. (2007). The resource-based view and value: The customer-based view of the firm. Journal of European Industrial Training, 31(1), 19–35. https://doi.org/10.1108/03090590710721718
Cosic, R., Shanks, G., & Maynard, S. (2012, 3–5 December). Towards a business analytics capability maturity model. In ACIS 2012: Proceedings of the 23rd Australasian Conference on Information Systems (pp. 1–11).
Cosic, R., Shanks, G., & Maynard, S. (2015). A business analytics capability framework. Australasian Journal of Information Systems, 19, S5–S19. https://doi.org/10.3127/ajis.v19i0.1150
Davenport, T. H. (2006). Competing on analytics. Harvard Business Review, 84(1), 98–108.
Davenport, T., & Harris, J. (2017). Competing on analytics: The new science of winning (1st ed.). Harvard Business Press.
Department of Statistics. (2020). Jordan in figures 2017. http://dosweb.dos.gov.jo/ar/
Diochon, M., & Anderson, A. R. (2009). Social enterprise and effectiveness: A process typology. Social Enterprise Journal, 5(1), 7–29. https://doi.org/10.1108/17508610910956381
Duan, Y., Cao, G., & Edwards, J. S. (2020). Understanding the impact of business analytics on innovation. European Journal of Operational Research, 281(3). https://doi.org/10.1016/j.ejor.2018.06.021
El-Adaileh, N. A., & Foster, S. (2019). Successful business intelligence implementation: A systematic literature review. Journal of Work-Applied Management, 11(2), 121–132. https://doi.org/10.1108/JWAM-09-2019-0027
Etikan, I., Musa, S. A., & Alkassim, R. S. (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
Fornell, C., & Larcker, D. F. (2016). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Foshay, N., & Kuziemsky, C. (2014). Towards an implementation framework for business intelligence in healthcare. International Journal of Information Management, 34(1), 20–27. https://doi.org/10.1016/j.ijinfomgt.2013.09.003
Galbraith, J. R. (1965). Organization design: An information processing view. Interfaces, 4(3), 28–36. https://doi.org/10.1287/inte.4.3.28
Gbosbal, S., & Kim, S. K. (1986). Building effective intelligence systems for competitive advantage. Sloan Management Review, 28(1), 49–58.
Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61(1), 1–7. https://doi.org/10.1093/biomet/61.1.101
Gessner, G., & Scott, R. A. (2009). Using business intelligence tools to help manage costs and effectiveness of business-to-business inside-sales programs. Information Systems Management, 26(2), 199–208. https://doi.org/10.1080/10580530902797623
Ghatasheh, N., Faris, H., AlTaharwa, I., Harb, Y., & Harb, A. (2020). Business analytics in telemarketing: Cost-sensitive analysis of bank campaigns using artificial neural networks. Applied Sciences (Switzerland), 10(7), 8–13. https://doi.org/10.3390/app10072581
Goswami, A., & Dutta, S. (2015). Gender differences in technology usage: A literature review. Open Journal of Business and Management, 4(1), 51–59. https://doi.org/10.4236/ojbm.2016.41006
Guimaraes, T., & Igbaria, M. (1997). Client/server system success: Exploring the human side. Decision Sciences, 28(4), 851–876. https://doi.org/10.1111/j.1540-5915.1997.tb01334.x
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414–433. https://doi.org/10.1007/s11747-011-0261-6
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Hair Jr., J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107. https://doi.org/10.1504/IJMDA.2017.10008574
Hamad, F., Al-Aamr, R., Jabbar, S. A., & Fakhuri, H. (2021). Business intelligence in academic libraries in Jordan: Opportunities and challenges. IFLA Journal, 47(1). https://doi.org/10.1177/0340035220931882
Hartwick, J., & Barki, H. (1994). Explaining the role of user participation in information system use. Management Science, 40(4), 440–465. https://doi.org/10.1287/mnsc.40.4.440
Hawking, P., & Sellitto, C. (2010). Business Intelligence (BI) critical success factors. In ACIS 2010 Proceedings – 21st Australasian Conference on Information Systems. AIS Electronic Library (AISeL).
Howson, C., Sallam, R. L., Richardson, J. L., Tapadinhas, J., Idoine, C. J., & Woodward, A. (2018). Magic quadrant for analytics and business intelligence platforms. Gartner (Issue Tech Rep).
Hostmann, B., Rayner, N., & Herschel, G. (2009). Gartner’s business intelligence, analytics and performance management framework. Gartner (Issue October). https://www.gartner.com/imagesrv/summits/docs/apac/business-intelligence/BI-Analytics-PM-Framework-166512.pdf
Hu, L., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424–453. https://doi.org/10.1037//1082-989X.3.4.424
Hunton, J. E., & Price, K. H. (1997). Effects of the user participation process and task meaningfulness on key information system outcomes. Management Science, 43(6), 797–812. https://doi.org/10.1287/mnsc.43.6.797
Hunton, J. E., & Beeler, J. D. (1997). Effects of user participation in systems development: A longitudinal field experiment. MIS Quarterly, 21(4), 359–383. https://doi.org/10.2307/249719
Işik, Ö., Jones, M. C., & Sidorova, A. (2013). Business intelligence success: The roles of BI capabilities and decision environments. Information and Management, 50(1), 13–23. https://doi.org/10.1016/j.im.2012.12.001
Işik, Ö., Sidorova, A., & Jones, M. C. (2012). Business intelligence success and the role of BI capabilities. Intelligent Systems in Accounting, Finance and Management, 18(January), 161–176. https://doi.org/10.1002/isaf.329
Jordanian Young Economists Society. (2017). Challenges facing SMEs and what is needed to empower SMEs sector in Jordan. https://www.kas.de/documents/%20252038/253252/7_dokument_dok_pdf_41279_2.pdf/571a302c-7e84-7fdd-fa5b-72d2ecc44e85?version=1.0&t=1539652585795
Kohtamäki, M., & Farmer, D. (2017). Strategic agility – integrating business intelligence with strategy. In M. Kohtamäki (Ed.), Real-time strategy and business intelligence (pp. 11–36). Palgrave Macmillan. https://doi.org/10.1007/978-3-319-54846-3
Krishnamoorthi, S., & Mathew, S. K. (2018). Business analytics and business value: A comparative case study. Information and Management, 55(5), 643–666. https://doi.org/10.1016/j.im.2018.01.005
Kristoffersen, E., Mikalef, P., Blomsma, F., & Li, J. (2021). Towards a business analytics capability for the circular economy. Technological Forecasting and Social Change, 171, 120957. https://doi.org/10.1016/j.techfore.2021.120957
Kulkarni, U. R., Robles-Flores, J. A., & Popovič, A. (2017). Business intelligence capability: The effect of top management and the mediating roles of user participation and analytical decision making orientation. Journal of the Association for Information Systems, 18(7), 516–541. https://doi.org/10.17705/1jais.00462
Lahrmann, G., Marx, F., Winter, R., & Wortmann, F. (2011). Business intelligence maturity: Development and evaluation of a theoretical model. In The Proceedings of the Annual Hawaii International Conference on System Sciences, February. IEEE. https://doi.org/10.1109/HICSS.2011.90
Lin, W. T., & Shao, B. B. M. (2000). The relationship between user participation and system success: A simultaneous contingency approach. Information and Management, 37(6), 283–295. https://doi.org/10.1016/S0378-7206(99)00055-5
Liu, F., Zhao, X., Chau, P. Y. K., & Tang, Q. (2015). Roles of perceived value and individual differences in the acceptance of mobile coupon applications. Internet Research, 25(3), 471–495. https://doi.org/10.1108/IntR-02-2014-0053
Lonnqvist, A., & Puhakka, V. (2006). The measurement of business intelligence. Information Systems Management, 23(1), 32–40. https://doi.org/10.1080/07366980903446611
Masa’Deh, R., Obeidat, Z., Maqableh, M., & Shah, M. (2021). The impact of business intelligence systems on an organization’s effectiveness: The role of metadata quality from a developing country’s view. International Journal of Hospitality and Tourism Administration, 22(1), 64–84. https://doi.org/10.1080/15256480.2018.1547239
McKeen, J. D., & Guimaraes, T. (1997). Successful strategies for user participation in systems development. Journal of Management Information Systems, 14(2), 133–150. https://doi.org/10.1080/07421222.1997.11518168
Michalewicz, Z., Schmidt, M., Michalewicz, M., & Chiriac, C. (2006). Adaptive business intelligence. Springer. https://doi.org/10.1007/978-3-540-32929-9
Mikalef, P., Pappas, I. O., Krogstie, J., & Giannakos, M. (2018). Big data analytics capabilities: A systematic literature review and research agenda. Information Systems and E-Business Management, 16(3), 547–578. https://doi.org/10.1007/s10257-017-0362-y
Naala, M., Nordin, N., Omar, W. A. B. W. (2017). Innovation capability and firm performance relationship: A study of PLS-structural equation modeling (PLS-SEM). International Journal of Organization & Business Excellence, 2(1), 39–50.
Nadler, D. A., & Tushman, M. L. (1980). A congruence model for organizational assessment. Organizational Dynamics, 9(2), 35–51. https://doi.org/10.1016/0090-2616(80)90039-X
Nandi, M. L., Nandi, S., Moya, H., & Kaynak, H. (2020). Blockchain technology-enabled supply chain systems and supply chain performance: A resource-based view. Supply Chain Management, 25(6), 841–862. https://doi.org/10.1108/SCM-12-2019-0444
Niu, Y., Ying, L., Yang, J., Bao, M., & Sivaparthipan, C. B. (2021). Organizational business intelligence and decision making using big data analytics. Information Processing and Management, 58(6), 102725. https://doi.org/10.1016/j.ipm.2021.102725
Okkonen, J., Pirttimäki, V., Hannula, M., & Lonnqvist, A. (2002, May 9–11). Triangle of business intelligence, performance measurement and knowledge management. In Proceedings of the 2nd Annual Conference on Innovative Research in Management, EURAM 2002. Stockholm, Sweden.
Orlikowski, W. J. (1992). The duality of technology: Rethinking the concept of technology in organizations. Organization Science, 3(3), 398–427. https://doi.org/10.1287/orsc.3.3.398
Orlikowski, W. J. (2000). Using technology and constituting structures: A practice lens for studying technology in organizations. Organization Science, 11(4), 404–428. https://doi.org/10.1007/978-1-84628-901-9_10
Otoo, F. N. K. (2019). Human resource development (HRD) practices and banking industry effectiveness: The mediating role of employee competencies. European Journal of Training and Development, 43(3–4), 250–271. https://doi.org/10.1108/EJTD-07-2018-0068
Pandya, V. M. (2012, 6–7 September). Comparative analysis of development of SMEs in developed and developing countries. International Conference on Business and Management, (pp. 426–433). Phuket-Thailand.
Pee, L. G., & Kankanhalli, A. (2016). Interactions among factors influencing knowledge management in public-sector organizations: A resource-based view. Government Information Quarterly, 33(1), 188–199. https://doi.org/10.1016/j.giq.2015.06.002
Peng, D. X., & Lai, F. (2012). Using partial least squares in operations management research: A practical guideline and summary of past research. Journal of Operations Management, 30(6), 467–480. https://doi.org/10.1016/j.jom.2012.06.002
Petrini, M., & Pozzebon, M. (2009). Managing sustainability with the support of business intelligence: Integrating socio-environmental indicators and organisational context. Journal of Strategic Information Systems, 18(4), 178–191. https://doi.org/10.1016/j.jsis.2009.06.001
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
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
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. The Journal of Strategic Information Systems, 23(4), 270–283. https://doi.org/10.1016/j.jsis.2014.08.003
Popovič, A., Puklavec, B., & Oliveira, T. (2019). Justifying business intelligence systems adoption in SMEs: Impact of systems use on firm performance. Industrial Management and Data Systems, 119(1), 210–228. https://doi.org/10.1108/IMDS-02-2018-0085
Popovič, A., Turk, T., & Jaklič, J. (2010). Conceptual model of business value of business intelligence systems. Management: Journal of Contemporary Management Issues, 15(1), 5–30.
Potnuru, R. K. G., & Sahoo, C. K. (2016). HRD interventions, employee competencies and organizational effectiveness: An empirical study. European Journal of Training and Development, 40(5), 345–365. https://doi.org/10.1108/EJTD-02-2016-0008
Premkumar, G., Ramamurthy, K., & Saunders, C. S. (2005). Information processing view of organizations: An exploratory examination of fit in the context of interorganizational relationships. Journal of Management Information Systems, 22(1), 257–294. https://doi.org/10.1080/07421222.2003.11045841
Presbitero, A. (2021). Communication accommodation within global virtual team: The influence of cultural intelligence and the impact on interpersonal process effectiveness. Journal of International Management, 27(1), 100809. https://doi.org/10.1016/j.intman.2020.100809
Ramakrishnan, T., Khuntia, J., Kathuria, A., & Saldanha, T. (2016, March). Business intelligence capabilities and effectiveness: An integrative model. In 2016 49th Hawaii International Conference on System Sciences (HICSS) (pp. 5022–5031). IEEE. https://doi.org/10.1109/HICSS.2016.623
Ramakrishnan, T., Khuntia, J., Kathuria, A., & Saldanha, T. J. V. (2020). An integrated model of business intelligence & analytics capabilities and organizational performance. Communications of the Association for Information Systems, 46, 722–750. https://doi.org/10.17705/1CAIS.04631
Ransbotham, S., Kiron, D., & Prentice, P. (2016). Beyond the hype: The hard work behind analytics success. MIT Sloan Management Review, 57(3), 6–6.
Richter, N. F., Sinkovics, R. R., Ringle, C. M., & Schlägel, C. (2016). A critical look at the use of SEM in international business research. International Marketing Review, 33(3), 376–404. https://doi.org/10.1108/IMR-04-2014-0148
Riggio, R. E. (2017). Introduction to industrial/organizational psychology. Routledge. https://doi.org/10.4324/9781315620589
Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2020). Partial least squares structural equation modeling in HRM research. International Journal of Human Resource Management, 31(12), 1617–1643. https://doi.org/10.1080/09585192.2017.1416655
Sahay, B. S., & Ranjan, J. (2008). Real time business intelligence in supply chain analytics. Information Management and Computer Security, 16(1), 28–48. https://doi.org/10.1108/09685220810862733
Salman, M., & Ganie, S. A. (2020). Employee competencies as predictors of organizational performance: A study of public and private sector banks. Management and Labour Studies, 45(4), 416–432. https://doi.org/10.1177/0258042X20939014
Sangari, M. S., & Razmi, J. (2015). Business intelligence competence, agile capabilities, and agile performance in supply chain an empirical study. International Journal of Logistics Management, 26(2), 356–380. https://doi.org/10.1108/IJLM-01-2013-0012
Santiago Rivera, D., & Shanks, G. (2015). A dashboard to support management of business analytics capabilities. Journal of Decision Systems, 24(1), 73–86. https://doi.org/10.1080/12460125.2015.994335
Sarstedt, M., Ringle, C. M., Smith, D., Reams, R., & Hair, J. F. (2014). Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers. Journal of Family Business Strategy, 5(1), 105–115. https://doi.org/10.1016/j.jfbs.2014.01.002
Saunders, M., Lewis, P., & Thornhill, A. (2009). Research methods for business students (6th ed.). Pearson Education Limited.
Savlovschi, L. I., & Robu, N. R. (2011). The role of SMEs in modern economy. Economia, Seria Management, 14(1), 277–281.
Schuberth, F. (2021). Confirmatory composite analysis using partial least squares: Setting the record straight. Review of Managerial Science, 15(5), 1311–1345. https://doi.org/10.1007/s11846-020-00405-0
Shin, D. H. (2009). Towards an understanding of the consumer acceptance of mobile wallet. Computers in Human Behavior, 25(6), 1343–1354. https://doi.org/10.1016/j.chb.2009.06.001
Sosik, J. J., Kahai, S. S., & Piovoso, M. J. (2009). Silver bullet or voodoo statistics? A primer for using the partial least squares data analytic technique in group and organization research. Group and Organization Management, 34(1), 5–36. https://doi.org/10.1177/1059601108329198
Spears, J. L., & Barki, H. (2010). User participation in information systems security risk management. MIS Quarterly, 34(3), 503–522. https://doi.org/10.2307/25750689
Steers, R. M. (1976). When is an organization effective? A process approach to understanding effectiveness. Organizational Dynamics, 5(2), 50–63. https://doi.org/10.1016/0090-2616(76)90054-1
Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111–133. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
Sun, H., & Zhang, P. (2006). The role of moderating factors in user technology acceptance. International Journal of Human Computer Studies, 64(2), 53–78. https://doi.org/10.1016/j.ijhcs.2005.04.013
Sun, Z., Sun, L., & Strang, K. (2018). Big Data analytics services for enhancing business intelligence. Journal of Computer Information Systems, 58(2), 162–169. https://doi.org/10.1080/08874417.2016.1220239
Taylor, M., Reilly, D., & Wren, Ch. (2020). Internet of things support for marketing activities. Journal of Strategic Marketing, 28(2), 149–160. https://doi.org/10.1080/0965254X.2018.1493523
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1093/0199248540.003.0013
Tian, H., Iqbal, S., Anwar, F., Akhtar, S., Khan, M. A. S., & Wang, W. (2021). Network embeddedness and innovation performance: A mediation moderation analysis using PLS-SEM. Business Process Management Journal, 27(5), 1590–1609. https://doi.org/10.1108/BPMJ-08-2020-0377
Toepoel, V., & Schonlau, M. (2017). Dealing with nonresponse: Strategies to increase participation and methods for postsurvey adjustments. Mathematical Population Studies, 24(2), 79–83. https://doi.org/10.1080/08898480.2017.1299988
Trauth, E. M., Quesenberry, J. L., & Morgan, A. J. (2004). Understanding the under representation of women in IT. In SIGMIS Conference on Computer Personnel Research: Careers, Culture, and Ethics in a Networked Environment (pp. 114–119). ACM Digital Library. https://doi.org/10.1145/982372.982400
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
Tripathi, A., Bagga, T., & Aggarwal, R. K. (2020). Strategic impact of business intelligence: A review of literature. Prabandhan: Indian Journal of Management, 13(3), 35–48. https://doi.org/10.17010/pijom/2020/v13i3/151175
Trkman, P., McCormack, K., De Oliveira, M. P. V., & Ladeira, M. B. (2010). The impact of business analytics on supply chain performance. Decision Support Systems, 49(3), 318–327. https://doi.org/10.1016/j.dss.2010.03.007
Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application (JITTA), 11(2), 5–40. https://aisel.aisnet.org/jitta/vol11/iss2/2
Venkatesh, V., & Morris, M. G. (2000). Why don’t men stop asking for directions? Gender, social influence and their role in society. MIS Quarterly, 24(1), 115–139. https://doi.org/10.2307/3250981
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3). https://doi.org/10.2307/30036540
Verona, G. (1999). A resource-based view of product development. The Academy of Management Review, 24(1), 132–142. https://doi.org/10.2307/259041
Viaene, S., & Van den Bunder, A. (2011). The secrets to managing business analytics projects. MIT Sloan Management Review, 53(1), 65–69.
Vidgen, R., Shaw, S., & Grant, D. B. (2017). Management challenges in creating value from business analytics. European Journal of Operational Research, 261(2), 626–639. https://doi.org/10.1016/j.ejor.2017.02.023
Wan, W. W. N., Luk, C. L., & Chow, C. W. C. (2005). Customers’ adoption of banking channels in Hong Kong. International Journal of Bank Marketing, 23(3), 255–272. https://doi.org/10.1108/02652320510591711
Wang, Y., & Byrd, T. A. (2019). Business analytics-enabled decision making effectiveness through knowledge absorptive capacity in health care. Journal of Knowledge Management, 21(3), 517–539. https://doi.org/10.1108/JKM-08-2015-0301
Wang, Y., Kung, L. A., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3–13. https://doi.org/10.1016/j.techfore.2015.12.019
Watson, W. E., Ponthieu, L. D., & Critelli, J. W. (1995). Team interpersonal process effectiveness in venture partnerships and its connection to perceived success. Journal of Business Venturing, 10(5), 393–411. https://doi.org/10.1016/0883-9026(95)00036-8
Watson, W., Stewart, W. H., & BarNir, A. (2003). The effects of human capital, organizational demography, and interpersonal processes on venture partner perceptions of firm profit and growth. Journal of Business Venturing, 18(2), 145–164. https://doi.org/10.1016/S0883-9026(01)00082-9
Wieder, B., & Ossimitz, M. L. (2015). The impact of business intelligence on the quality of decision making – a mediation model. Procedia Computer Science, 64, 1163–1171. https://doi.org/10.1016/j.procs.2015.08.599
Williams, N., Williams, S., & Planning, S. (2010). The profit impact of business intelligence. In The Profit Impact of Business Intelligence (1st ed.). Elsevier Inc. https://doi.org/10.1016/B978-012372499-1/50002-8
Wixom, B. H., & Watson, H. J. (2001). An empirical investigation of the factors affecting data warehousing success. MIS Quarterly, 25(1), 17–41. https://doi.org/10.2307/3250957
Wright, P. M., Dunford, B. B., & Snell, S. A. (2001). Human resources and the resource based view of the firm and the resource based view of the firm. Journal of Management, 27(6), 701–721. https://doi.org/10.1177/014920630102700607
Wright, P. M., McMahan, G. C., McCormick, B., & Sherman, W. S. (1998). Strategy, core competence, and HR involvement as determinants of HR effectiveness and refinery performance. Human Resource Management, 37(1), 17–29. https://doi.org/10.1002/(SICI)1099-050X(199821)37:1<17::AID-HRM3>3.0.CO;2-Y
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
Zhang, K. Z. K., Cheung, C. M. K., & Lee, M. K. O. (2014). Examining the moderating effect of inconsistent reviews and its gender differences on consumers’ online shopping decision. International Journal of Information Management, 34(2), 89–98. https://doi.org/10.1016/j.ijinfomgt.2013.12.001