References

• Alexiev, A., Volberda, H., Jansen, J., & Van Den Bosch, F. (2020). Contextualizing senior executive advice seeking: The role of decision process comprehensiveness and empowerment climate. Organization Studies, 41(4), 471–497. https://doi.org/10.1177/0170840619830128
   Web of Science ®Google Scholar
• Amason, A. C., & Schweiger, D. M. (1997). The effects of conflict on strategic decision-making effectiveness and organizational. In C. K. W. De Dreu & E. Van de Vliert (Eds.), Using conflict in organizations (pp. 101–115). Sage Publications. https://doi.org/10.4135/9781446217016.n8
   Google Scholar
• Baum, R., & Wally, S. (2003). Strategic decision speed and firm performance. Strategic Management Journal, 24(11), 1107–1129. https://doi.org/10.1002/smj.343
   Web of Science ®Google Scholar
• Belanche, D., Casaló, L. V., Flavián, C., & Schepers, J. (2020). Service robot implementation: A theoretical framework and research agenda. The Service Industries Journal, 40(3-4), 203–225. https://doi.org/10.1080/02642069.2019.1672666
   Web of Science ®Google Scholar
• Božič, K., & Dimovski, V. (2019). Business intelligence and analytics for value creation: The role of absorptive capacity. International Journal of Information Management, 46, 93–103. https://doi.org/10.1016/j.ijinfomgt.2018.11.020
   Web of Science ®Google Scholar
• Caseiro, N., & Coelho, A. (2019). The influence of Business Intelligence capacity, network learning and innovativeness on startups performance. Journal of Innovation & Knowledge, 4(3), 139–145. https://doi.org/10.1016/j.jik.2018.03.009
   Web of Science ®Google Scholar
• Chen, Y., Wang, Y., Nevo, S., Jin, J., Wang, L., & Chow, W. S. (2014). IT capability and organizational performance: The roles of business process agility and environmental factors. European Journal of Information Systems, 23(3), 326–342. https://doi.org/10.1057/ejis.2013.4
   Web of Science ®Google Scholar
• Cheng, C., Zhong, H., & Cao, L. (2020). Facilitating speed of internationalization: The roles of business intelligence and organizational agility. Journal of Business Research, 110, 95–103. https://doi.org/10.1016/j.jbusres.2020.01.003
   Web of Science ®Google Scholar
• Cheung, G. W., & Lau, R. S. (2008). Testing mediation and suppression effects of latent variables: Bootstrapping with structural equation models. Organizational Research Methods, 11(2), 296–325. https://doi.org/10.1177/1094428107300343
   Web of Science ®Google Scholar
• Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Academic Press.
   Google Scholar
• Dubey, R., Gunasekaran, A., & Childe, S. J. (2019). Big data analytics capability in supply chain agility. Management Decision, 57(8), 2092–2112. https://doi.org/10.1108/MD-01-2018-0119
   Web of Science ®Google Scholar
• Dykes, B. J., Hughes-Morgan, M., Kolev, K. D., & Ferrier, W. J. (2019). Organizational speed as a dynamic capability: Toward a holistic perspective. Strategic Organization, 17(2), 266–278. https://doi.org/10.1177/1476127018804249
   Web of Science ®Google Scholar
• Ebrahimi, S., Ghasemaghaei, M., & Hassanein, K. (2016). Understanding the role of data analytics in driving discriminatory managerial decisions. IS in organizations and society, ICIS 2016 PROCEEDINGS from http://aisel.aisnet.org/icis2016/ISOrganizations/Presentations/6/
   Google Scholar
• Edwards, J. R. (1991). Person-job fit: A conceptual integration, literature review, and methodological critique. John Wiley & Sons. http://psycnet.apa.org/psycinfo/1991-98004-008
   Google Scholar
• Eisenhardt, K. M. (1989). Making fast strategic decisions in high-velocity environments. Academy of Management Journal, 32(3), 543–576. https://doi.org/10.5465/256434.
   Web of Science ®Google Scholar
• Ferraris, A., Mazzoleni, A., Devalle, A., & Couturier, J. (2019). Big data analytics capabilities and knowledge management: Impact on firm performance. Management Decision, 57(8), 1923–1936. https://doi.org/10.1108/MD-07-2018-0825
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Francioni, B., & Clark, K. D. (2020). The mediating role of speed in the global sourcing decision process. Journal of Purchasing and Supply Management, 26, 100609. https://doi.org/10.1016/j.pursup.2020.100609
   Web of Science ®Google Scholar
• Frisk, J. E., & Bannister, F. (2017). Improving the use of analytics and big data by changing the decision-making culture. Management Decision, 55(10), 2074–2088. https://doi.org/10.1108/MD-07-2016-0460
   Web of Science ®Google Scholar
• Ghasemaghaei, M. (2019). Does data analytics use improve firm decision-making quality? The role of knowledge sharing and data analytics competency. Decision Support Systems, 120, 14–24. https://doi.org/10.1016/j.dss.2019.03.004
   Web of Science ®Google Scholar
• Ghasemaghaei, M., Ebrahimi, S., & Hassanein, K. (2018). Data analytics competency for improving firm decision-making performance. The Journal of Strategic Information Systems, 27(1), 101–113. https://doi.org/10.1016/j.jsis.2017.10.001
   Web of Science ®Google Scholar
• Ghasemaghaei, M., Hassanein, K., & Turel, O. (2017). Increasing firm agility through the use of data analytics: The role of fit. Decision Support Systems, 101, 95–105. https://doi.org/10.1016/j.dss.2017.06.004
   Web of Science ®Google Scholar
• Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19, 213–236. https://doi.org/10.2307/249689
   Web of Science ®Google Scholar
• Gürlek, M., & Koseoglu, M. A. (2021). Green innovation research in the field of hospitality and tourism: The construct, antecedents, consequences, and future outlook. The Service Industries Journal, 41(11-12), 734–766. https://doi.org/10.1080/02642069.2021.1929930.
   Web of Science ®Google Scholar
• Hagen, B., & Zucchella, A. (2014). Born global or born to run? The long-term growth of born global firms. Management International Review, 54(4), 497–525. https://doi.org/10.1007/s11575-014-0214-7
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In R. R. Sinkovics & P. N. Ghauri (Eds.), New challenges to international marketing (Advances in International Marketing, Vol. 20) (pp. 277–319). Emerald Group Publishing Limited. https://doi.org/10.1108/S1474-7979(2009)0000020014
   Google Scholar
• Huang, C. C., Liang, W. Y., Wen, D. W., Ting, P. H., & Shen, M. Y. (2018). Qualitative analysis of big data in the service sectors. The Service Industries Journal, 1–19. https://doi.org/10.1080/02642069.2018.1509957
   Web of Science ®Google Scholar
• Hurt, K. J., & Welbourne, J. (2018). Conflict and decision-making: Attributional and emotional influences. Negotiation and Conflict Management Research, 11(3), 225–251. https://doi.org/10.1111/ncmr.12133
   Web of Science ®Google Scholar
• Judge, W. Q., & Miller, A. (1991). Antecedents and outcomes of decision speed in different environmental context. Academy of Management Journal, 34(2), 449–463. https://doi.org/10.5465/256451
   PubMed Web of Science ®Google Scholar
• Kaufmann, L., & Gaeckler, J. (2015). On the relationship between purchasing integration and purchasing decision-making speed. International Journal of Physical Distribution & Logistics Management, 45(3), 214–236. https://doi.org/10.1108/IJPDLM-05-2013-0150
   Web of Science ®Google Scholar
• Kristof-Brown, A. L., Zimmerman, R. D., & Johnson, E. C. (2005). Consequences of individuals ‘fit at work: A meta-analysis of person–job, person–organization, person–group, and person–supervisor fit. Personnel Psychology, 58(2), 281–342. https://doi.org/10.1111/j.1744-6570.2005.00672.x
   Web of Science ®Google Scholar
• Kulkarni, U., 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://aisel.aisnet.org/jais/vol18/iss7/1 https://doi.org/10.17705/1jais.00462
   Web of Science ®Google Scholar
• Lee, S. C., Liang, H., & Liu, C. Y. (2010). The effects of absorptive capacity, knowledge sourcing strategy, and alliance forms on firm performance. The Service Industries Journal, 30(14), 2421–2440. https://doi.org/10.1080/02642060802635551
   Web of Science ®Google Scholar
• McCarthy, I. P., & Ruckman, K. (2017). Licensing speed: Its determinants and payoffs. Journal of Engineering and Technology Management, 46, 52–66. https://doi.org/10.1016/j.jengtecman.2017.11.002
   Web of Science ®Google Scholar
• Mert, İ. S., Sen, C., & Alzghoul, A. (2021). Organizational justice, life satisfaction, and happiness: the mediating role of workplace social courage. Kybernetes, ahead-of-print, 273. http://doi.org/10.1108/K-02-2021-0116
   Web of Science ®Google Scholar
• Perlow, L. A., Okhuysen, G. A., & Repenning, N. P. (2002). The speed trap: Exploring the relationship between decision making and temporal context. Academy of Management Journal, 45(5), 931–955. https://doi.org/10.5465/3069323
   Web of Science ®Google Scholar
• Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63(1), 539–569. https://doi.org/10.1146/annurev-psych-120710-100452
   PubMed Web of Science ®Google Scholar
• Rahimnia, F., & Molavi, H. (2020). A model for examining the effects of communication on innovation performance: Emphasis on the intermediary role of strategic decision-making speed. European Journal of Innovation Management, 24, 1035–1056, https://doi.org/10.1108/EJIM-10-2019-0293
   Web of Science ®Google Scholar
• Rigdon, E. E. (2012). Rethinking partial least squares path modeling: In praise of simple methods. Long Range Planning, 45(5-6), 341–358. https://doi.org/10.1016/j.lrp.2012.09.010
   Web of Science ®Google Scholar
• Rikhardsson, P., & Yigitbasioglu, O. (2018). Business intelligence & analytics in management accounting research: Status and future focus. International Journal of Accounting Information Systems, 29, 37–58. https://doi.org/10.1016/j.accinf.2018.03.001
   Web of Science ®Google Scholar
• Rouhani, S., Ashrafi, A., Ravasan, A. Z., & Afshari, S. (2016). The impact model of business intelligence on decision support and organizational benefits. Journal of Enterprise Information Management, 29(1), 19–50. https://doi.org/10.1108/JEIM-12-2014-0126
   Web of Science ®Google Scholar
• Samba, C., Tabesh, P., Thanos, I. C., & Papadakis, V. M. (2020). Method in the madness? A meta-analysis on the strategic implications of decision comprehensiveness. Strategic Organization, 19(3), 414–440. https://doi.org/10.1177/1476127020904973
   Web of Science ®Google Scholar
• Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017). Partial least squares structural equation modeling. In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of market research. Springer. https://doi.org/10.1007/978-3-319-05542-8_15-1
   Google Scholar
• She, Z., Li, Q., London, M., Yang, B., & Yang, B. (2019). Effects of CEO narcissism on decision making comprehensiveness and speed. Journal of Managerial Psychology, 35(1), 42–55. https://doi.org/10.1108/JMP-01-2019-0042
   Web of Science ®Google Scholar
• Shepherd, N. G., Mooi, E. A., Elbanna, S., & Rudd, J. M. (2020). Deciding fast: Examining the relationship between strategic decision speed and decision quality across multiple environmental contexts. European Management Review, 18(2), 119–140. https://doi.org/10.1111/emre.12430
   Web of Science ®Google Scholar
• Talaulicar, T., Grundei, J., & Werder, A. V. (2005). Strategic decision making in start-ups: The effect of top management team organization and processes on speed and comprehensiveness. Journal of Business Venturing, 20(4), 519–541. https://doi.org/10.1016/j.jbusvent.2004.02.001
   Web of Science ®Google Scholar
• Van Rijmenam, M., Erekhinskaya, T., Schweitzer, J., & Williams, M. A. (2019). Avoid being the Turkey: How big data analytics changes the game of strategy in times of ambiguity and uncertainty. Long Range Planning, 52(5), 101841. https://doi.org/10.1016/j.lrp.2018.05.007
   Web of Science ®Google Scholar
• Venkatraman, N. (1989). The concept of fit in strategy research: Toward verbal and statistical correspondence. Academy of Management Review, 14(3), 423–444. https://doi.org/10.5465/amr.1989.4279078
   Web of Science ®Google Scholar
• Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246. https://doi.org/10.1016/j.ijpe.2014.12.031
   Web of Science ®Google Scholar
• Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J.-F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009
   Web of Science ®Google Scholar
• Wen, Q., Qiang, M., & Gloor, P. (2018). Speeding up decision-making in project environment: The effects of decision makers’ collaboration network dynamics. International Journal of Project Management, 36(5), 819–831. https://doi.org/10.1016/j.ijproman.2018.02.006
   Web of Science ®Google Scholar
• 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
   Google Scholar
• Yakubu, M. N., Dasuki, S. I., Abubakar, A. M., & Kah, M. M. (2020). Determinants of learning management systems adoption in Nigeria: A hybrid SEM and artificial neural network approach. Education and Information Technologies, 25, 3515–3539. https://doi.org/10.1007/s10639-020-10110-w
   Web of Science ®Google Scholar
• Yuce, A., Abubakar, A. M., & Ilkan, M. (2019). Intelligent tutoring systems and learning performance. Online Information Review. Applying task-technology fit and IS success model. Online Information Review, 43(4), 600–616. https://doi.org/10.1108/OIR-11-2017-0340
   Web of Science ®Google Scholar
• Zhao, X., Lynch Jr, J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197–206. doi:10.1086/651257
   Web of Science ®Google Scholar