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Reconsidering individuals’ competencies in business intelligence and business analytics toward process effectiveness: mediation-moderation model

    Malek Al-edenat   Affiliation
    ; Nayel Alhawamdeh   Affiliation

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)

How to Cite
Al-edenat, M., & Alhawamdeh, N. (2022). Reconsidering individuals’ competencies in business intelligence and business analytics toward process effectiveness: mediation-moderation model. Business: Theory and Practice, 23(2), 239–251. https://doi.org/10.3846/btp.2022.16548
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References

Aamodt, M. G. (2015). Industrial organizational psychology: An applied approach (6th ed.). Cengage Learning.

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