Keywords:
Business Intelligence
Data-analytics
Decision-Making
DeLone and McLean
Task-Technology-Fit

  • Abstract

    While numerous studies have explored the field of Business Intelligence and Analytics Systems (BIAS), limited research has focused on understanding their actual value for decision-making quality (DMQ) and the processes by which it can be achieved. To address this gap, this study aims to explore the underlying mechanisms involved in managerial DMQ. The research model consists of five core dimensions derived from the Task-Technology-Fit (TTF) and DeLone and McLean (D&M) models, namely ‘TTF,’ ‘INTENTION TO USE,’ ‘USE,’ ‘SATISFACTION,’ and ‘NET BENEFITS,’ referred to as ‘DMQ’ for the purpose of this research. The paths assumed between these variables were built upon a unified conception between both models. Structural Equation Modeling under the Partial Least Squares approach was applied to data collected from 150 BIAS users for decision-making purposes from various industries. The hypothesized paths between the investigated variables were supported, except for the one linking ‘USE’ with ‘DMQ.’ Accordingly, the superiority of user satisfaction over system usage is confirmed through this investigation as a success measure. TTF also plays a crucial role in enhancing DMQ through direct and indirect effects. Overall, this study reports the first empirical evidence of the integration of the D&M and TTF models to deeply understand the role of BIAS in DMQ, which significantly enhanced the explanatory power of the findings. Considering the underlying mechanism uncovered, this research is also useful for both managers and developers, enabling them to directly enhance the level of fit between technology and user tasks, especially during the early stages of designing a BIAS tool, which in turn can also be enhanced through user satisfaction. Furthermore, when assessing the success or failure of a BIAS tool, managers should focus on users’ satisfaction (attitude) rather than use (behavior) itself as a success measure. The limitations of this research lie in the fact that the conceptual model does not consider all indirect factors from the TTF and D&M models. To remedy this limitation, future research may seek to integrate antecedent dimensions derived from the underpinning models, which may need to be validated with a larger sample size.

  • References

    1. Abu-AlSondos, I. (2023). The impact of business intelligence system (BIS) on quality of strategic decision-making. International Journal of Data and Network Science, 7(4), 1901-1912. https://doi.org/ 10.5267/j.ijdns.2023.7.003
    2. Alessandri, G., Borgogni, L., & Latham, G. P. (2017). A dynamic model of the longitudinal relationship between job satisfaction and supervisor‐rated job performance. Applied Psychology, 66(2), 207-232. https://doi.org/10.1111/apps.12091
    3. Al-Hattami, H. M., Senan, N. A. M., Al-Hakimi, M. A., & Azharuddin, S. (2024). An empirical examination of AIS success at the organizational level in the era of COVID-19 pandemic. Global Knowledge, Memory and Communication, 73(3), 312-330. https://doi.org/10.1108/GKMC-04-2022-0094
    4. Al-Hattami, H. M. (2021). Validation of the D&M IS success model in the context of accounting information system of the banking sector in the least developed countries. Journal of Management Control, 32(1), 127-153. https://doi.org/10.1007/s00187-020-00310-3
    5. Alkhwaldi, A. F. (2024). Understanding the acceptance of business intelligence from healthcare professionals’ perspective: An empirical study of healthcare organizations. International Journal of Organizational Analysis. Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJOA-10-2023-4063
    6. Almazmomi, N., Ilmudeen, A., & Qaffas, A. A. (2022). The impact of business analytics capability on data-driven culture and exploration: achieving a competitive advantage. Benchmarking: An International Journal, 29(4), 1264-1283. https://doi.org/10.1108/BIJ-01-2021-0021
    7. Al-Rahmi, W. M., Al-Adwan, A. S., Al-Maatouk, Q., Othman, M. S., Alsaud, A. R., Almogren, A. S., & Al-Rahmi, A. M. (2023). Integrating communication and task-technology fit theories: The adoption of digital media in learning. Sustainability, 15(10), 8144. https://doi.org/10.3390/su15108144
    8. Alyoussef, I. Y. (2023). Acceptance of e-learning in higher education: The role of task-technology fit with the information systems success model. Heliyon, 9(3). https://doi.org/10.1016/j.heliyon.2023.e13751
    9. Ariyanto, D., Dewi, A. A., Hasibuan, H. T., & Paramadani, R. B. (2022). The success of information systems and sustainable information society: measuring the implementation of a village financial system. Sustainability, 14(7), 3851. https://doi.org/10.3390/su14073851
    10. Bailey, J. E., & Pearson, S. W. (1983). Development of a tool for measuring and analyzing computer user satisfaction. Management science, 29(5), 530-545. https://doi.org/10.1287/mnsc.29.5.530
    11. Chadi, A., & Hetschko, C. (2018). The magic of the new: How job changes affect job satisfaction. Journal of Economics & Management Strategy, 27(1), 23-39. https://doi.org/10.1111/jems.12217
    12. Clark, T. D., Jones, M. C., and Armstrong, C. P. (2007). The Dynamic Structure of Management Support Systems: Theory Development, Research Focus, and Direction. MIS Quarterly, 579-615.https://doi.org/10.2307/25148808
    13. Cohen J. (1988) Statistical Power Analysis for the Behavioral Sciences: Lawrence Erlbaum Associates. https://doi.org/10.4324/9780203771587
    14. Cohen, M. D., March, J. G., & Olsen, J. P. (1972). A garbage can model of organizational choice. Administrative science quarterly, 1-25. https://doi.org/10.2307/2392088
    15. Côrte-Real, N., Oliveira, T., & Ruivo, P. (2017). Assessing business value of Big Data Analytics in European firms. Journal of Business Research, 70, 379-390. https://doi.org/10.1016/j.jbusres.2016.08.011
    16. Cyert, R., & March, J. (2015). Behavioral theory of the firm. In Organizational Behavior 2 (pp. 60-77). Routledge.
    17. Demirdöğen, G., Işık, Z., & Arayici, Y. (2022). Determination of business intelligence and analytics-based healthcare facility management key performance indicators. Applied Sciences, 12(2), 651. https://doi.org/10.3390/app12020651
    18. El Malki, A., & Touate, S. (2024). Bibliometric analysis of the literature on business Intelligence systems adoption and acceptance, trends and perspectives. Multidisciplinary Science Journal, 6. https://10.31893/multiscience.2024ss0517
    19. Elragal, A., & Elgendy, N. (2024). A data-driven decision-making readiness assessment model: The case of a Swedish food manufacturer. Decision Analytics Journal, 10, 100405. https://doi.org/10.1016/j.dajour.2024.100405
    20. 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
    21. Eybers, S., Gerber, A., Bork, D., & Karagiannis, D. (2019). Matching technology with enterprise architecture and enterprise architecture management tasks using task technology fit. In Enterprise, Business-Process and Information Systems Modeling: 20th International Conference, BPMDS 2019, 24th International Conference, EMMSAD 2019, Held at CAiSE 2019, Rome, Italy, June 3-4, 2019, Proceedings 20 (pp. 245-260). Springer International Publishing. https://doi.org/10.1007/978-3-030-20618-5_17
    22. Falk, R. F., & Miller, N. B. (1992). A primer for soft modeling. Ohio University of AkronPress.
    23. Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses. Behavior research methods, 41(4), 1149-1160. https://doi.org/10.3758/BRM.41.4.1149
    24. Fink, L., Yogev, N., & Even, A. (2017). Business intelligence and organizational learning: An empirical investigation of value creation processes. Information & management, 54(1), 38-56. https://doi.org/10.1016/j.im.2016.03.009
    25. 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/10.2307/3151312
    26. Ghobakhloo, M., & Tang, S. H. (2015). Information system success among manufacturing SMEs: case of developing countries. Information Technology for Development, 21(4), 573-600. https://doi.org/10.1080/02681102.2014.996201
    27. Gonzales, R., & Wareham, J. (2019). Analysing the impact of a business intelligence system and new conceptualizations of system use. Journal of Economics, Finance and Administrative Science, 24(48), 345-368. https://doi.org/10.1108/JEFAS-05-2018-0052
    28. Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS quarterly, 213-236.https://doi.org/10.2307/249689
    29. 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
    30. 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
    31. 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, 115-135. https://doi.org/10.1007/s11747-014-0403-8
    32. Hong, S., Katerattanakul, P., Hong, S.-K., & Cao, Q. (2006). Usage and perceived impact of data warehouses: A study in Korean financial companies. International Journal of Information Technology & Decision Making, 5(02), 297-315. https://doi.org/10.1142/S0219622006001927
    33. Hsu, P. F., Yen, H. R., & Chung, J. C. (2015). Assessing ERP post-implementation success at the individual level: Revisiting the role of service quality. Information & Management, 52(8), 925-942. https://doi.org/10.1016/j.im.2015.06.009
    34. Iivari, J. (2005). An empirical test of the DeLone-McLean model of information system success. ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 36(2), 8-27. https://doi.org/10.1145/1066149.1066152
    35. Jarrow, R. A., Maksimovic, V., & Ziemba, W. T. (1995). Handbooks in Operations Research and Management Science (Book 9). Amsterdam: North Holland.
    36. Jones, K. H., Werner, M. L., Terrell, K. P., Terrell, R. L., Irvine, W., & Allwright, D. (2003). Introduction to Financial Accounting: A User Perspective. Toronto, ON: Pearson Canada Inc.
    37. Kopeikina, L. (2005). The Right Decision Every Time. How to reach perfect clarity on tough decisions. New York: Prentice Hall.
    38. Lam, T., Cho, V., & Qu, H. (2007). A study of hotel employee behavioral intentions towards adoption of information technology. International Journal of Hospitality Management, 26(1), 49-65. https://doi.org/10.1016/j.ijhm.2005.09.002
    39. Langer, E. J. (1975). The illusion of control. Journal of personality and social psychology, 32(2), 311. https://doi.org/10.1037/0022-3514.32.2.311
    40. Lin, W. S., & Wang, C. H. (2012). Antecedences to continued intentions of adopting e-learning system in blended learning instruction: A contingency framework based on models of information system success and task-technology fit. Computers & education, 58(1), 88-99. https://doi.org/ 10.1016/j.compedu.2011.07.008
    41. Logeswaran, K., Savitha, S., Suresh, P., Prasanna Kumar, K. R., Gunasekar, M., Rajadevi, R., ... & Jayasurya, A. S. (2024). Unifying Technologies in Industry 4.0: Harnessing the Synergy of Internet of Things, Big Data, Augmented Reality/Virtual Reality, and Blockchain Technologies. Topics in Artificial Intelligence Applied to Industry 4.0, 127-147. https://doi.org/10.1002/9781394216147.ch7
    42. March, J. G. (1991). How decisions happen in organizations. Human-computer interaction, 6(2), 95-117. https://doi.org/10.1207/s15327051hci0602_1
    43. McGill, T. J., & Klobas, J. E. (2009). A task-technology fit view of learning management system impact. Computers & Education, 52(2), 496-508. https://doi.org/10.1016/j.compedu.2008.10.002
    44. Medeiros, M. M. D., & Maçada, A. C. G. (2022). Competitive advantage of data-driven analytical capabilities: the role of big data visualization and of organizational agility. Management Decision, 60(4), 953-975. https://doi.org/10.1108/MD-12-2020-1681
    45. Michie, S. G., Dooley, R. S., & Fryxell, G. E. (2006). Unified diversity in top‐level teams: Enhancing collaboration and quality in strategic decision‐making. International Journal of Organizational Analysis, 14(2), 130-149. https://doi.org/10.1108/10553180610742764
    46. Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics capabilities and innovation: the mediating role of dynamic capabilities and moderating effect of the environment. British journal of management, 30(2), 272-298.https://doi.org/10.1111/1467-8551.12343
    47. Paulino, E. P. (2022). Amplifying organizational performance from business intelligence: Business analytics implementation in the retail industry. Journal of Entrepreneurship, Management and Innovation, 18(2), 69-104. https://doi.org/10.7341/20221823
    48. Pendharkar, P. C., Khosrowpour, M., & Rodger, J. A. (2001). Development and testing of an instrument for measuring the user evaluations of information technology in health care. Journal of Computer Information Systems, 41(4), 84-89. https://doi.org/10.1080/08874417.2001.11647028
    49. Puklavec, B., Oliveira, T., & Popovič, A. (2018). Understanding the determinants of business intelligence system adoption stages: An empirical study of SMEs. Industrial Management & Data Systems, 118(1), 236-261. https://doi.org/10.1108/IMDS-05-2017-0170
    50. Raghunathan, S. (1999). Impact of information quality and decision-maker quality on decision quality: a theoretical model and simulation analysis. Decision support systems, 26(4), 275-286. https://doi.org/10.1016/S0167-9236(99)00060-3
    51. Rouhani, S., Ashrafi, A., Ravasan, A. Z., & Afshari, S. (2018). Business intelligence systems adoption model: An empirical investigation. Journal of Organizational and End User Computing, 30(2), 43-70. https://doi.org/10.4018/JOEUC.2018040103
    52. Rulinawaty, Samboteng, L., Purwanto, A. J., Kuncoro, S., Jasrial, Tahilili, M. H., ... & Karyana, A. (2024). Investigating the influence of the updated DeLone and McLean information system (IS) success model on the effectiveness of learning management system (LMS) implementation. Cogent Education, 11(1), 2365611. https://doi.org/10.1080/2331186X.2024.2365611
    53. Schweiger, D. M., Sandberg, W. R., & Ragan, J. W. (1986). Group approaches for improving strategic decision making: A comparative analysis of dialectical inquiry, devil's advocacy, and consensus. Academy of management Journal, 29(1), 51-71. https://doi.org/10.5465/255859
    54. Seddon, P. B. (1997). A respecification and extension of the DeLone and McLean model of IS success. Information Systems Research, 8(3), 240-253. https://doi.org/10.1287/isre.8.3.240
    55. 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
    56. Tam, C., & Oliveira, T. (2016). Understanding the impact of m-banking on individual performance: DeLone & McLean and TTF perspective. Computers in Human Behavior, 61, 233-244. https://doi.org/10.1016/j.chb.2016.03.016
    57. Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path modeling. Computational statistics & data analysis, 48(1), 159-205. https://doi.org/10.1016/j.csda.2004.03.005
    58. Tilles, S. (1963). How to evaluate corporate strategy (pp. 111-111). Harvard Business Review.
    59. Visinescu, L. L., Jones, M. C., & Sidorova, A. (2017). Improving decision quality: the role of business intelligence. Journal of Computer Information Systems, 57(1), 58-66. https://doi.org/10.1080/08874417.2016.1181494
    60. Wan, L., Xie, S., & Shu, A. (2020). Toward an understanding of university students’ continued intention to use MOOCs: When UTAUT model meets TTF model. Sage Open, 10(3). https://doi.org/10.1177/2158244020941858
    61. Wang, H., Tao, D., Yu, N., & Qu, X. (2020). Understanding consumer acceptance of healthcare wearable devices: An integrated model of UTAUT and TTF. International journal of medical informatics, 139, 104156. https://doi.org/10.1016/j.ijmedinf.2020.104156
    62. Wetzels, M., Odekerken-Schröder, G., & Van Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS quarterly, 177-195.https://doi.org/10.2307/20650284
    63. 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
    64. Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information systems research, 16(1), 85-102. https://doi.org/10.1287/isre.1050.0042
    65. Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in human behaviour, 67, 221-232. https://doi.org/10.1016/j.chb.2016.10.028
    66. Wu, J. H., & Wang, Y. M. (2006). Measuring KMS success: A respecification of the DeLone and McLean's model. Information & management, 43(6), 728-739. https://doi.org/10.1016/j.im.2006.05.002
    67. Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in human behaviour, 26(4), 760-767. https://doi.org/10.1016/j.chb.2010.01.013
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright (c) 2024 Mehdi Achhaiba, Mohamed Torra , Imane Asraoui, Ghizlane El-Guennouni

How to cite

Achhaiba, M., Torra , M., Asraoui, I., & El-Guennouni , G. (2025). Exploring the underlying mechanisms leading to decision-making quality in business intelligence-analytics-driven environments: Integrated task-technology-fit and DeLone & McLean perspective . Multidisciplinary Science Journal, (| Accepted Articles). https://doi.org/10.31893/multiscience.2025440
Crossref
Scopus
Google Scholar
Europe PMC
  • Article viewed - 715