Business Analytics Capabilities and Decision Quality: The Mediating Roles of Decision Speed and Comprehensiveness

References

• Adomako, S., Frimpong, K., Amankwah-Amoah, J., Donbesuur, F., & Opoku, R. A. (2021). Strategic decision speed and international performance: the roles of competitive intensity, resource flexibility, and structural organicity. Management International Review, 61, 27–55. https://doi.org/10.1007/s11575-021-00439-w
   Web of Science ®Google Scholar
• Afolayan, A. O., & de la Harpe, A. C. (2020). The role of evaluation in SMMEs’ strategic decision-making on new technology adoption. Technology Analysis & Strategic Management, 32(6), 697–710. https://doi.org/10.1080/09537325.2019.1702637
   Web of Science ®Google Scholar
• Akter, S., Bandara, R. J., & Sajib, S. (2021). How to empower analytics capability to tackle emergency situations? International Journal of Operations & Production Management, 41(9), 1469–1494. https://doi.org/10.1108/IJOPM-11-2020-0805
   Web of Science ®Google Scholar
• Alaskar, T. H., Mezghani, K., & Alsadi, A. K. (2021). Examining the adoption of Big data analytics in supply chain management under competitive pressure: Evidence from Saudi Arabia. Journal of Decision Systems, 30(2–3), 300–320. https://doi.org/10.1080/12460125.2020.1859714
   Web of Science ®Google Scholar
• Alkraiji, A. I. (2020). Weighting the challenges to the effectiveness of business intelligence systems in organizations: An empirical study of government organizations in Saudi Arabia. Journal of Decision Systems, 29(2), 102–127. https://doi.org/10.1080/12460125.2020.1770436
   Web of Science ®Google Scholar
• Alzghoul, A., Khaddam, A. A., Abousweilem, F., Irtaimeh, H. J., & Alshaar, Q. (2022). How business intelligence capability impacts decision-making speed, comprehensiveness, and firm performance. Information Development. https://doi.org/10.1177/02666669221108438
   Web of Science ®Google Scholar
• Ashrafi, A., Ravasan, A. Z., Trkman, P., & Afshari, S. (2019). The role of business analytics capabilities in bolstering firms’ agility and performance. International Journal of Information Management, 47, 1–15. https://doi.org/10.1016/j.ijinfomgt.2018.12.005
   Web of Science ®Google Scholar
• 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, 228–237. https://doi.org/10.1016/j.jbusres.2018.11.028
   Web of Science ®Google Scholar
• Bahrami, M., & Shokouhyar, S. (2021). The role of big data analytics capabilities in bolstering supply chain resilience and firm performance: A dynamic capability view. Ahead-Of-Print No Ahead-Of-printInformation Technology & People, 35(5), 1621–1651. https://doi.org/10.1108/ITP-01-2021-0048
   Web of Science ®Google Scholar
• Barney, J. B. (2001). Resource-based theories of competitive advantage: A ten-year retrospective on the resource-based view. Journal of Management, 27(6), 643–650. https://doi.org/10.1177/014920630102700602
   Web of Science ®Google Scholar
• Baum, J. 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
• Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1(3), 185–216. https://doi.org/10.1177/135910457000100301
   Web of Science ®Google Scholar
• Bromiley, P., & Rau, D. (2016). Operations management and the resource-based view: Another view. Journal of Operations Management, 41(1), 95–106. https://doi.org/10.1016/j.jom.2015.11.003
   Google Scholar
• Cao, G., Duan, Y., & Cadden, T. (2019). The link between information processing capability and competitive advantage mediated through decision-making effectiveness. International Journal of Information Management, 44, 121–131. https://doi.org/10.1016/j.ijinfomgt.2018.10.003
   Web of Science ®Google Scholar
• Carr, J. C., Vardaman, J. M., Marler, L. E., McLarty, B. D., & Blettner, D. (2021). Psychological antecedents of decision comprehensiveness and their relationship to decision quality and performance in family firms: An upper echelons perspective. Family Business Review, 34(1), 33–47. https://doi.org/10.1177/0894486520917774
   Web of Science ®Google Scholar
• Chatterjee, S., Rana, N. P., & Dwivedi, Y. K. (2021). How does business analytics contribute to organisational performance and business value? A resource-based view. Information Technology & People Volume Ahead-Of-Print Number. https://doi.org/10.1108/ITP-08-2020-0603
   Google Scholar
• Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188. https://doi.org/10.2307/41703503
   Web of Science ®Google Scholar
• Clark, T. D., Jr., Jones, M. C., & Armstrong, C. P. (2007). The dynamic structure of management support systems: Theory development, research focus, and direction. MIS Quarterly, 31(3), 579–615. https://doi.org/10.2307/25148808
   Web of Science ®Google Scholar
• Dane, E., & Pratt, M. G. (2007). Exploring intuition and its role in managerial decision Making. Academy of Management Review, 32(1), 33–54. https://doi.org/10.5465/amr.2007.23463682
   Web of Science ®Google Scholar
• Dane, E., Rockmann, K. W., & Pratt, M. G. (2012). When should I trust my gut? Linking domain expertise to intuitive decision-making effectiveness. Organizational Behavior and Human Decision Processes, 119(2), 187–194.
   Web of Science ®Google Scholar
• Davenport, T. H. (2014). How strategists use “big data” to support internal business decisions, discovery and production. Strategy & Leadership, 42(4), 45–50. https://doi.org/10.1108/SL-05-2014-0034
   Google Scholar
• Delen, D., & Ram, S. (2018). Research challenges and opportunities in business analytics. Journal of Business Analytics, 1(1), 2–12. https://doi.org/10.1080/2573234X.2018.1507324
   Google Scholar
• Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., Giannakis, M., Foropon, C., Roubaud, D., & Hazen, B. T. (2020). Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations. International Journal of Production Economics, 226, 107599. https://doi.org/10.1016/j.ijpe.2019.107599
   Web of Science ®Google Scholar
• Elbanna, S., Thanos, I. C., & Jansen, R. J. (2020). A literature review of the strategic decision-making context: A synthesis of previous mixed findings and an agenda for the way forward. M@n@gement, 23(2), 42–60. https://doi.org/10.37725/mgmt.v23i2.4621
   Google Scholar
• Elçi, A., & Abubakar, A. M. (2021). The configurational effects of task-technology fit, technology-induced engagement and motivation on learning performance during Covid-19 pandemic: An fsQCA approach. Education and Information Technologies, 26, 7259–7277. https://doi.org/10.1007/s10639-021-10580-6
   PubMed Web of Science ®Google Scholar
• Fiss, P. C. (2011). Building better causal theories: A fuzzy set approach to typologies in organization research. Academy of Management Journal, 54(2), 393–420. https://doi.org/10.5465/amj.2011.60263120
   Web of Science ®Google Scholar
• Forbes, D. P. (2005). The effects of strategic decision making on entrepreneurial self–efficacy. Entrepreneurship Theory & Practice, 29(5), 599–626. https://doi.org/10.1111/j.1540-6520.2005.00100.x
   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
• Fredrickson, J. W., & Mitchell, T. R. (1984). Strategic decision processes: Comprehensiveness and performance in an industry with an unstable environment. Academy of Management Journal, 27(2), 399–423. https://doi.org/10.2307/255932
   Web of Science ®Google Scholar
• General Authority for Statistics. (2020 December). Registered firms in kingdom of Saudi Arabia. Retrieved from https://www.stats.gov.sa/en.
   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. Journal of Strategic Information Systems, 27(1), 101–113. https://doi.org/10.1016/j.jsis.2017.10.001
   Web of Science ®Google Scholar
• Government of Saudi Arabia. (2016). Saudi vision 2030, government of Saudi Arabia, 1–85, available at: https://www.vision2030.gov.sa/media/rc0b5oy1/saudi_vision203.pdf (accessed 1 March 2021).
   Google Scholar
• Grant, R. M. (1996). Toward a knowledge‐based theory of the firm. Strategic Management Journal, 17(S2), 109–122. https://doi.org/10.1002/smj.4250171110
   Web of Science ®Google Scholar
• Gupta, S., Drave, V. A., Dwivedi, Y. K., Baabdullah, A. M., & Ismagilova, E. (2020). Achieving superior organizational performance via big data predictive analytics: A dynamic capability view. Industrial Marketing Management, 90, 581–592. https://doi.org/10.1016/j.indmarman.2019.11.009
   Web of Science ®Google Scholar
• Hair, J. F., Jr., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. https://doi.org/10.1016/j.jbusres.2019.11.069
   Web of Science ®Google Scholar
• Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
   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
• Harman, H. H. (1976). Modern factor analysis. University of Chicago Press. Chicago.
   Google Scholar
• Hayajneh, J. A. M., Elayan, M. B. H., Abdellatif, M. A. M., & Abubakar, A. M. (2022). Impact of business analytics and π-shaped skills on innovative performance: Findings from PLS-SEM and fsQCA. Technology in Society, 68, 101914.
   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
• Idrees, M. S. (1999). The bureaucratic decision-making quality and the use of new technology in Saudi Arabia. In The University of Mississippi (p. 9942353). The University of Mississippi ProQuest Dissertations Publishing.
   Google Scholar
• Ilmudeen, A., Bao, Y., & Zhang, P. (2022). Investigating the mediating effect of business-it alignment between management of it investment and firm performance. Information Systems Management, 1–21. https://doi.org/10.1080/10580530.2022.2107740
   Web of Science ®Google Scholar
• Janssen, M., van der Voort, H., & Wahyudi, A. (2017). Factors influencing big data decision-making quality. Journal of Business Research, 70, 338–345. https://doi.org/10.1016/j.jbusres.2016.08.007
   Web of Science ®Google Scholar
• Kaltoft, M., Cunich, M., Salkeld, G., & Dowie, J. (2014). Assessing decision quality in patient-centred care requires a preference-sensitive measure. Journal of Health Services Research & Policy, 19(2), 110–117. https://doi.org/10.1177/1355819613511076
   PubMed Web of Science ®Google Scholar
• Kanadlı, S. B., Bankewitz, M., & Zhang, P. (2018). Job-related diversity: The comprehensiveness and speed of board decision-making processes-an upper echelons approach. Journal of Management & Governance, 22(2), 427–456. https://doi.org/10.1007/s10997-017-9394-4
   Web of Science ®Google Scholar
• Kaya, B., Abubakar, A. M., Behravesh, E., Yildiz, H., & Mert, I. S. (2020). Antecedents of innovative performance: Findings from PLS-SEM and fuzzy sets (fsQCA). Journal of Business Research, 114, 278–289. https://doi.org/10.1016/j.jbusres.2020.04.016
   Web of Science ®Google Scholar
• Khaddam, A. A., Alzghoul, A., Abusweilem, M. A., & Abousweilem, F. (2021). Business intelligence and firm performance: A moderated-mediated model. The Service Industries Journal, 1–17. https://doi.org/10.1080/02642069.2021.1969367
   Web of Science ®Google Scholar
• Klee, S., Janson, A., & Leimeister, J. M. (2021). How data analytics competencies can foster business value–a systematic review and way forward. Information Systems Management, 38(3), 200–217. https://doi.org/10.1080/10580530.2021.1894515
   Web of Science ®Google Scholar
• Larson, D., & Chang, V. (2016). A review and future direction of agile, business intelligence, analytics, and data science. International Journal of Information Management, 36(5), 700–710. https://doi.org/10.1016/j.ijinfomgt.2016.04.013
   Web of Science ®Google Scholar
• LaValle, S., Hopkins, M. S., Lesser, E., Shockley, R., & Kruschwitz, N. (2010). Analytics: The new path to value. MIT Sloan Management Review, 52(1), 1–25.
   Google Scholar
• Lorentz, H., Aminoff, A., Kaipia, R., & Srai, J. S. (2021). Structuring the phenomenon of procurement digitalisation: Contexts, interventions, and mechanisms. International Journal of Operations & Production Management, 41(2), 157–192. https://doi.org/10.1108/IJOPM-03-2020-0150
   Web of Science ®Google Scholar
• Lowney, C. (2021). Make Better Decisions: Slow Down if the Lion Isn’t Coming. Forbes. Retrieved from https://www.forbes.com/sites/chrislowney/2021/01/21/make-better-decisions-slow-down-if-the-lion-isnt-coming/?sh=3f7a74de10f9. (accessed on December,)
   Google Scholar
• Lowry, P. B., & Gaskin, J. (2014). Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it. IEEE Transactions on Professional Communication, 57(2), 123–146. https://doi.org/10.1109/TPC.2014.2312452
   Web of Science ®Google Scholar
• Manlangit, A. T., Jabonete, F. G. V., & Ridulme, Q. R. (2022). Cultural competence and decision‐making of nurse leaders in a university hospital in Saudi Arabia: A descriptive correlational study. Journal of Nursing Management, 30(5), 1215–1224. https://doi.org/10.1111/jonm.13631
   PubMed Web of Science ®Google Scholar
• Meissner, P., & Wulf, T. (2014). Antecedents and effects of decision comprehensiveness: The role of decision quality and perceived uncertainty. European Management Journal, 32(4), 625–635. https://doi.org/10.1016/j.emj.2013.10.006
   Web of Science ®Google Scholar
• Merendino, A., Dibb, S., Meadows, M., Quinn, L., Wilson, D., Simkin, L., & Canhoto, A. (2018). Big data, big decisions: The impact of big data on board level decision-making. Journal of Business Research, 93, 67–78. https://doi.org/10.1016/j.jbusres.2018.08.029
   Web of Science ®Google Scholar
• Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98, 261–276. https://doi.org/10.1016/j.jbusres.2019.01.044
   Web of Science ®Google Scholar
• Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103434. https://doi.org/10.1016/j.im.2021.103434
   Web of Science ®Google Scholar
• Mikalef, P., & Krogstie, J. (2020). Examining the interplay between big data analytics and contextual factors in driving process innovation capabilities. European Journal of Information Systems, 29(3), 260–287. https://doi.org/10.1080/0960085X.2020.1740618
   Web of Science ®Google Scholar
• Mikalef, P., Pappas, I., Krogstie, J., & Pavlou, P. A., Eds. 2020. Big data and business analytics: A research agenda for realizing business value. Information & ManagementVol. 57(1)p. 103237. https://doi.org/10.1016/j.im.2019.103237.
   Google Scholar
• Mithas, S., Ramasubbu, N., & Sambamurthy, V. (2011). How information management capability influences firm performance. MIS Quarterly, 35(1), 237–256. https://doi.org/10.2307/23043496
   Web of Science ®Google Scholar
• Phillips-Wren, G., Daly, M., & Burstein, F. (2021). Reconciling business intelligence, analytics and decision support systems: More data, deeper insight. Decision Support Systems, 146, 113560. https://doi.org/10.1016/j.dss.2021.113560
   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, 539–569.
   PubMed Web of Science ®Google Scholar
• Priem, R. L., & Butler, J. E. (2001a). Is the resource-based ‘view’ a useful perspective for strategic management research? Academy of Management Review, 26(1), 22–40. https://doi.org/10.5465/amr.2001.4011928
   Web of Science ®Google Scholar
• Priem, R. L., & Butler, J. E. (2001b). Tautology in the resource-based view and the implications of externally determined resource value: Further comments. Academy of Management Review, 26(1), 57–66. https://doi.org/10.2307/259394
   Web of Science ®Google Scholar
• Ragin, C. C. (2009). Redesigning social inquiry: Fuzzy sets and beyond. University of Chicago Press. Chicago.
   Google Scholar
• Samba, C., Tabesh, P., Thanos, I. C., & Papadakis, V. M. (2021). 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., Smith, D., Reams, R., & Hair, J. F., Jr. (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
   Web of Science ®Google Scholar
• Schneider, C. Q., & Wagemann, C. (2012). Set-theoretic methods for the social sciences: A guide to qualitative comparative analysis. Cambridge University Press.
   Google Scholar
• Seddon, P. B., Constantinidis, D., Tamm, T., & Dod, H. (2017). How does business analytics contribute to business value? Information Systems Journal, 27(3), 237–269. https://doi.org/10.1111/isj.12101
   Web of Science ®Google Scholar
• Shamim, S., Zeng, J., Shariq, S. M., & Khan, Z. (2019). Role of big data management in enhancing big data decision-making capability and quality among Chinese firms: A dynamic capabilities view. Information & Management, 56(6), 103135. https://doi.org/10.1016/j.im.2018.12.003
   Web of Science ®Google Scholar
• Shamout, M. D. (2021). The nexus between supply chain analytic, innovation and robustness capability: Does firm age matter? VINE Journal of Information and Knowledge Management Systems, 51(1), 163–176. https://doi.org/10.1108/VJIKMS-03-2019-0045
   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
• Shirazi, F., Tseng, H. -T., Adegbite, O., Hajli, N., & Rouhani, S. (2022). New product success through big data analytics: Empirical evidence from Iran. Information Technology & People, 35(5), 1513–1539. https://doi.org/10.1108/ITP-03-2020-0105
   Web of Science ®Google Scholar
• Sun, Z., Strang, K., & Firmin, S. (2017). Business analytics-based enterprise information systems. Journal of Computer Information Systems, 57(2), 169–178. https://doi.org/10.1080/08874417.2016.1183977
   Web of Science ®Google Scholar
• Tabesh, P., & Jolly, P. M. (2022). A look inside the nonprofit boardroom: Influences on decision comprehensiveness and decision quality. Nonprofit Management & Leadership, 32(3), 365–386. https://doi.org/10.1002/nml.21491
   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
• Thiem, A., & Dusa, A. (2012). Qualitative comparative analysis with R: A user’s guide (Vol. 5). Springer Science & Business Media.
   Google Scholar
• 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
   Web of Science ®Google Scholar
• Tunc-Abubakar, T., Kalkan, A., & Abubakar, A. M. (2022). Impact of big data usage on product and process innovation: The role of data diagnosticity. Kybernetes ahead-Of-Print Number Ahead-Of-Print. https://doi.org/10.1108/K-11-2021-1138
   Google Scholar
• Visinescu, L. L., Jones, M. C., & Sidorova, A. (2017). Improving decision quality: The role of business intelligence. Journal of Computer Information Systems, 57(1), 8–66. https://doi.org/10.1080/08874417.2016.1181494
   Web of Science ®Google Scholar
• Wamba, S., & Akter, S. (2019). Understanding supply chain analytics capabilities and agility for data-rich environments. International Journal of Operations & Production Management, 39(6/7/8), 887–912. https://doi.org/10.1108/IJOPM-01-2019-0025
   Web of Science ®Google Scholar
• Wang, Y., & Byrd, T. A. (2017). 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
   Web of Science ®Google Scholar
• Woodside, A. G. (2013). Moving beyond multiple regression analysis to algorithms: Calling for adoption of a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory. Journal of Business Research, 64(4), 463–472. https://doi.org/10.1016/j.jbusres.2012.12.021
   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
• Zhao, X., Lynch, J. G., Jr., & Chen, Q. (2010). Reconsidering baron and Kenny: myths and truths about mediation analysis. The Journal of Consumer Research, 37(2), 197–206. https://doi.org/10.1086/651257
   Web of Science ®Google Scholar
• Zimmermann, A., Lioliou, E., & Oliveira, J. S. (2020). How do offshoring-related changes in job characteristics affect onshore managers’ affective organizational commitment? The moderating role of perceived organizational valence. Journal of Information Technology, 35(4), 316–336. https://doi.org/10.1177/0268396220942237
   Web of Science ®Google Scholar