Conceptualizing the Antecedents and Individual Impact of Business Intelligence in the Public Sector: The Technology-Organisation-Authoritative Framework
Keywords:
Business Intelligence, Public Sector, Effective Use, Individual Impact
Abstract
Business intelligence (BI) refers to a technological tool and process that transforms large and fragmented data into well-informed graphical insights to aid managerial decision-making. Recognizing the significant benefits of BI in leveraging extensive amounts of data, governments have been progressively investing in BI tools to improve efficiency in public offices. Nevertheless, despite a significant body of knowledge related to BI adoptions and their advantages, there is a dearth of theoretical understanding of how the effective use of the system affects employees' work performance, particularly among civil servants who have distinctive work natures compared to businesses. Most studies have also ignored external and institutional factors that influence individual usage, whilst studies on the effective use of BI tools remain limited. The paper thus proposed a new conceptual framework for examining the factors that influence the effective use of BI and its impact on individual job performance. The suggested propositions could provide a theoretical contribution by integrating technological, organizational and authoritative dimensions that are novel and unique to the public sector. It would also contribute to practical understandings for public managers and policymakers in ensuring the investment made on BI is worthwhile. Ultimately, the paper seeks to bridge the gap in BI studies related to public organizations.
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References
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Elazzaoui, E., & Lamari, S. (2022). Delone and McLean information systems success model in the public sector: A systematic review. Journal Of Social Science and Organization Management, Vol. 3, 133-156 Pages. https://doi.org/10.48434/IMIST.PRSM/JOSSOM-V3I1.30393
Gonzales, R., & Wareham, J. (2019). Analyzing 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
Hmoud, H., Al-Adwan, A. S., Horani, O., Yaseen, H., & Zoubi, J. Z. A. (2023). Factors influencing business intelligence adoption by higher education institutions. Journal of Open Innovation: Technology, Market, and Complexity, 9(3), 100111. https://doi.org/10.1016/j.joitmc.2023.100111
Jeyaraj, A. (2020a). DeLone & McLean Models of Information System Success: Critical Meta-Review and Research Directions. International Journal of Information Management, p. 54, 102139. https://doi.org/10.1016/j.ijinfomgt.2020.102139
Jeyaraj, A. (2020b). Variation in the effect of system usage and individual impact: A meta-regression of empirical findings. Information & Management, 57(6), 103242. https://doi.org/10.1016/j.im.2019.103242
Jha, S. K., & Jha, B. (2022). An Introduction to Business Intelligence. In D. Singh, A. Singh, A. Omar, & S. Goyal, Business Intelligence and Human Resource Management (1st ed., pp. 1–30). Productivity Press. https://doi.org/10.4324/9781003184928-1
Kaasalainen, V. (2020). Business Intelligence System Implementation and Design Framework: A Public Sector Case Study [Master Degree]. University of Jyväskylä.
Kapo, A., Turulja, L., Zaimovi?, T., & Mehi?, S. (2021). Examining the effect of user satisfaction and business intelligence system usage on individual job performance. Management: Journal of Contemporary Management Issues, 26(2), 43–62. https://doi.org/10.30924/mjcmi.26.2.3
Kašparová, P. (2023). Intention to use business intelligence tools in decision-making processes: Applying a UTAUT 2 model. Central European Journal of Operations Research, 31(3), 991–1008. https://doi.org/10.1007/s10100-022-00827-z
Kiu, C. T. T., & Chan, J. H. (2023). Firm characteristics and the adoption of data analytics in performance management: A critical analysis of EU enterprises. Industrial Management & Data Systems. https://doi.org/10.1108/IMDS-07-2023-0430
Medeiros, M. M. D., Hoppen, N., & Maçada, A. C. G. (2020). Data science for business: Benefits, challenges and opportunities. The Bottom Line, 33(2), 149–163. https://doi.org/10.1108/BL-12-2019-0132
Merhi, M. I. (2021). Evaluating the critical success factors of data intelligence implementation in the public sector using analytical hierarchy process. Technological Forecasting and Social Change, p. 173, 121180. https://doi.org/10.1016/j.techfore.2021.121180
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Montero, J. N., & Lind, M. (2020). Determining Business Intelligence Usage Success. International Journal of Computer Science and Information Technology, 12(6), 45–67. https://doi.org/10.5121/ijcsit.2020.12604
Mudau, T. N., Cohen, J., & Papageorgiou, E. (2024). Determinants and consequences of routine and advanced use of business intelligence (BI) systems by management accountants. Information & Management, 61(1), 103888. https://doi.org/10.1016/j.im.2023.103888
Paradza, D., & Daramola, O. (2021). Business Intelligence and Business Value in Organisations: A Systematic Literature Review. Sustainability, 13(20), 11382. https://doi.org/10.3390/su132011382
Petter, S., DeLone, W., & McLean, E. (2008). Measuring information systems success: Models, dimensions, measures, and interrelationships. European Journal of Information Systems, 17(3), 236–263. https://doi.org/10.1057/ejis.2008.15
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
Purnomo, A., Firdaus, M., Sutiksno, D. U., Putra, R. S., & Hasanah, U. (2021). Mapping of Business Intelligence Research Themes: Four Decade Review. 2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), 32–37. https://doi.org/10.1109/COMNETSAT53002.2021.9530790
Ragu-Nathan, B. S., Apigian, C. H., Ragu-Nathan, T. S., & Tu, Q. (2004). A path analytic study of the effect of top management support for information systems performance. Omega, 32(6), 459–471. https://doi.org/10.1016/j.omega.2004.03.001
Sabeh, H. N., Husin, M. H., Kee, D. M. H., Baharudin, A. S., & Abdullah, R. (2021). A Systematic Review of the DeLone and McLean Model of Information Systems Success in an E-Learning Context (2010–2020). IEEE Access, 9, 81210–81235. https://doi.org/10.1109/ACCESS.2021.3084815
Talaoui, Y., & Kohtamäki, M. (2021). 35 years of research on business intelligence process: A synthesis of a fragmented literature. Management Research Review, 44(5), 677–717. https://doi.org/10.1108/MRR-07-2020-0386
Torres, R., & Sidorova, A. (2019). Reconceptualizing information quality as effective use in the context of business intelligence and analytics. International Journal of Information Management, 49, 316–329. https://doi.org/10.1016/j.ijinfomgt.2019.05.028
Trieu, V.-H. (2023). Towards an understanding of actual business intelligence technology use: An individual user perspective. Information Technology & People, 36(1), 409–432. https://doi.org/10.1108/ITP-11-2020-0786
Trieu, V.-H., Burton-Jones, A., Green, P., & Cockcroft, S. (2022). Applying and Extending the Theory of Effective Use in a Business Intelligence Context. MIS Quarterly, 46(1), 645–678. https://doi.org/10.25300/MISQ/2022/14880
Triono, S. P. H., Alamsyah, A., & Dudija, N. (2023). Driving factors for the use of business intelligence and analytics among Indonesian startups. International Journal of Technoentrepreneurship, 4(4), 277–296. https://doi.org/10.1504/IJTE.2023.134928
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), 425. https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2016). Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead. Journal of the Association for Information Systems, 17(5), 328–376. https://doi.org/10.17705/1jais.00428
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
Wixom, B. H., & Watson, H. J. (2001). An Empirical Investigation of the Factors Affecting Data Warehousing Success. MIS Quarterly, 25(1), 17. https://doi.org/10.2307/3250957
Zha, H., Liu, K., Tang, T., Yin, Y.-H., Dou, B., Jiang, L., Yan, H., Tian, X., Wang, R., & Xie, W. (2022). Acceptance of clinical decision support system to prevent venous thromboembolism among nurses: An extension of the UTAUT model. BMC Medical Informatics and Decision Making, 22(1), 221. https://doi.org/10.1186/s12911-022-01958-8
Zitha, A., & Ajigini, O. A. (2023). A Model for the Business Intelligence System Acceptance in the South African Banking Sector. Journal of Theoretical and Applied Information Technology, 101(8), 3027–3043. Scopus.
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, p. 125, 113113. https://doi.org/10.1016/j.dss.2019.113113
Ali, M. S., & Miah, S. J. (2018). Identifying Organisational Factors for Successful Business Intelligence Implementation: International Journal of Business Intelligence Research, 9(2), 47–63. https://doi.org/10.4018/IJBIR.2018070103
Alkaabi, W., & Kassim, N. M. (2023). Examining the Psychological Factors Influencing Intention to Use Business Intelligence Dashboards in the UAE. Journal for ReAttach Therapy and Developmental Diversities, 6(8s), 164–179.
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
Al-Okaily, A., Teoh, A. P., & Al-Okaily, M. (2023). Evaluation of data analytics-oriented business intelligence technology effectiveness: An enterprise-level analysis. Business Process Management Journal, 29(3), 777–800. https://doi.org/10.1108/BPMJ-10-2022-0546
Alturas, B. (2021). Models of Acceptance and Use of Technology Research Trends: Literature Review and Exploratory Bibliometric Study. In M. Al-Emran & K. Shaalan (Eds.), Recent Advances in Technology Acceptance Models and Theories (Vol. 335, pp. 13–28). Springer International Publishing. https://doi.org/10.1007/978-3-030-64987-6_2
Ayaz, A., & Yanarta?, M. (2020). An analysis of the unified theory of acceptance and use of technology theory (UTAUT): Acceptance of electronic document management system (EDMS). Computers in Human Behavior Reports, 2, 100032. https://doi.org/10.1016/j.chbr.2020.100032
Baig, M. I., Shuib, L., & Yadegaridehkordi, E. (2021). A Model for Decision-Makers’ Adoption of Big Data in the Education Sector. Sustainability, 13(24), 13995. https://doi.org/10.3390/su132413995
Blut, M., Chong, A. Y. L., Tsigna, Z., & Venkatesh, V. (2022). Meta-Analysis of the Unified Theory of Acceptance and Use of Technology (UTAUT): Challenging its Validity and Charting a Research Agenda in the Red Ocean. Journal of the Association for Information Systems, 23(1), 13–95. https://doi.org/10.17705/1jais.00719
Bordeleau, F.-E., Mosconi, E., & De Santa-Eulalia, L. A. (2020). Business intelligence and analytics value creation in Industry 4.0: A multiple case study in manufacturing medium enterprises. Production Planning & Control, 31(2–3), 173–185. https://doi.org/10.1080/09537287.2019.1631458
Brown, S. A., Massey, A. P., Montoya-Weiss, M. M., & Burkman, J. R. (2002). Do I have to? User acceptance of mandated technology. European Journal of Information Systems, 11(4), 283–295. https://doi.org/10.1057/palgrave.ejis.3000438
Burton-Jones, A., & Grange, C. (2013). From Use to Effective Use: A Representation Theory Perspective. Information Systems Research, 24(3), 632–658. https://doi.org/10.1287/isre.1120.0444
Candra, S., & Nainggolan, A. (2022). Understanding Business Intelligence and Analytics System Success from Various Business Sectors in Indonesia. CommIT (Communication and Information Technology) Journal, 16(1), 37–52. https://doi.org/10.21512/commit.v16i1.7849
Chhabra, V., Rajan, P., & Chopra, S. (2020). User acceptance of new technology in mandatory adoption scenario for food distribution in India. International Journal on Food System Dynamics, 11(2), 153–170. https://doi.org/10.18461/ijfsd.v11i2.47
Choudhury, A., Asan, O., & Medow, J. E. (2022). Clinicians' Perceptions of an Artificial Intelligence–Based Blood Utilisation Calculator: Qualitative Exploratory Study. JMIR Human Factors, 9(4), e38411. https://doi.org/10.2196/38411
Daud, N. M., Rokhman, F., Mohamed, I. S., Masyhar, A., Syaifudin, A., & Farida, L. A. (2022). Examining the Connection Between Mandatory Technology Usage and Technology Withdrawal in The Maritime Industry. Journal of Maritime Research.
Delone, W. H., & McLean, E. R. (2003). The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748
DeLone, W. H., & McLean, E. R. (2016). Information Systems Success Measurement. Foundations and Trends in Information Systems, 2(1), 1–116. https://doi.org/10.1561/2900000005
Di Vaio, A., Hassan, R., & Alavoine, C. (2022). Data intelligence and analytics: A bibliometric analysis of human–Artificial intelligence in public sector decision-making effectiveness. Technological Forecasting and Social Change, 174, 121201. https://doi.org/10.1016/j.techfore.2021.121201
Elazzaoui, E., & Lamari, S. (2022). Delone and McLean information systems success model in the public sector: A systematic review. Journal Of Social Science and Organization Management, Vol. 3, 133-156 Pages. https://doi.org/10.48434/IMIST.PRSM/JOSSOM-V3I1.30393
Gonzales, R., & Wareham, J. (2019). Analyzing 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
Hmoud, H., Al-Adwan, A. S., Horani, O., Yaseen, H., & Zoubi, J. Z. A. (2023). Factors influencing business intelligence adoption by higher education institutions. Journal of Open Innovation: Technology, Market, and Complexity, 9(3), 100111. https://doi.org/10.1016/j.joitmc.2023.100111
Jeyaraj, A. (2020a). DeLone & McLean Models of Information System Success: Critical Meta-Review and Research Directions. International Journal of Information Management, p. 54, 102139. https://doi.org/10.1016/j.ijinfomgt.2020.102139
Jeyaraj, A. (2020b). Variation in the effect of system usage and individual impact: A meta-regression of empirical findings. Information & Management, 57(6), 103242. https://doi.org/10.1016/j.im.2019.103242
Jha, S. K., & Jha, B. (2022). An Introduction to Business Intelligence. In D. Singh, A. Singh, A. Omar, & S. Goyal, Business Intelligence and Human Resource Management (1st ed., pp. 1–30). Productivity Press. https://doi.org/10.4324/9781003184928-1
Kaasalainen, V. (2020). Business Intelligence System Implementation and Design Framework: A Public Sector Case Study [Master Degree]. University of Jyväskylä.
Kapo, A., Turulja, L., Zaimovi?, T., & Mehi?, S. (2021). Examining the effect of user satisfaction and business intelligence system usage on individual job performance. Management: Journal of Contemporary Management Issues, 26(2), 43–62. https://doi.org/10.30924/mjcmi.26.2.3
Kašparová, P. (2023). Intention to use business intelligence tools in decision-making processes: Applying a UTAUT 2 model. Central European Journal of Operations Research, 31(3), 991–1008. https://doi.org/10.1007/s10100-022-00827-z
Kiu, C. T. T., & Chan, J. H. (2023). Firm characteristics and the adoption of data analytics in performance management: A critical analysis of EU enterprises. Industrial Management & Data Systems. https://doi.org/10.1108/IMDS-07-2023-0430
Medeiros, M. M. D., Hoppen, N., & Maçada, A. C. G. (2020). Data science for business: Benefits, challenges and opportunities. The Bottom Line, 33(2), 149–163. https://doi.org/10.1108/BL-12-2019-0132
Merhi, M. I. (2021). Evaluating the critical success factors of data intelligence implementation in the public sector using analytical hierarchy process. Technological Forecasting and Social Change, p. 173, 121180. https://doi.org/10.1016/j.techfore.2021.121180
Merhi, M. I., & Bregu, K. (2020). Effective and efficient usage of big data analytics in the public sector. Transforming Government: People, Process and Policy, 14(4), 605–622. https://doi.org/10.1108/TG-08-2019-0083
Montero, J. N., & Lind, M. (2020). Determining Business Intelligence Usage Success. International Journal of Computer Science and Information Technology, 12(6), 45–67. https://doi.org/10.5121/ijcsit.2020.12604
Mudau, T. N., Cohen, J., & Papageorgiou, E. (2024). Determinants and consequences of routine and advanced use of business intelligence (BI) systems by management accountants. Information & Management, 61(1), 103888. https://doi.org/10.1016/j.im.2023.103888
Paradza, D., & Daramola, O. (2021). Business Intelligence and Business Value in Organisations: A Systematic Literature Review. Sustainability, 13(20), 11382. https://doi.org/10.3390/su132011382
Petter, S., DeLone, W., & McLean, E. (2008). Measuring information systems success: Models, dimensions, measures, and interrelationships. European Journal of Information Systems, 17(3), 236–263. https://doi.org/10.1057/ejis.2008.15
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
Purnomo, A., Firdaus, M., Sutiksno, D. U., Putra, R. S., & Hasanah, U. (2021). Mapping of Business Intelligence Research Themes: Four Decade Review. 2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), 32–37. https://doi.org/10.1109/COMNETSAT53002.2021.9530790
Ragu-Nathan, B. S., Apigian, C. H., Ragu-Nathan, T. S., & Tu, Q. (2004). A path analytic study of the effect of top management support for information systems performance. Omega, 32(6), 459–471. https://doi.org/10.1016/j.omega.2004.03.001
Sabeh, H. N., Husin, M. H., Kee, D. M. H., Baharudin, A. S., & Abdullah, R. (2021). A Systematic Review of the DeLone and McLean Model of Information Systems Success in an E-Learning Context (2010–2020). IEEE Access, 9, 81210–81235. https://doi.org/10.1109/ACCESS.2021.3084815
Talaoui, Y., & Kohtamäki, M. (2021). 35 years of research on business intelligence process: A synthesis of a fragmented literature. Management Research Review, 44(5), 677–717. https://doi.org/10.1108/MRR-07-2020-0386
Torres, R., & Sidorova, A. (2019). Reconceptualizing information quality as effective use in the context of business intelligence and analytics. International Journal of Information Management, 49, 316–329. https://doi.org/10.1016/j.ijinfomgt.2019.05.028
Trieu, V.-H. (2023). Towards an understanding of actual business intelligence technology use: An individual user perspective. Information Technology & People, 36(1), 409–432. https://doi.org/10.1108/ITP-11-2020-0786
Trieu, V.-H., Burton-Jones, A., Green, P., & Cockcroft, S. (2022). Applying and Extending the Theory of Effective Use in a Business Intelligence Context. MIS Quarterly, 46(1), 645–678. https://doi.org/10.25300/MISQ/2022/14880
Triono, S. P. H., Alamsyah, A., & Dudija, N. (2023). Driving factors for the use of business intelligence and analytics among Indonesian startups. International Journal of Technoentrepreneurship, 4(4), 277–296. https://doi.org/10.1504/IJTE.2023.134928
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), 425. https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2016). Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead. Journal of the Association for Information Systems, 17(5), 328–376. https://doi.org/10.17705/1jais.00428
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
Wixom, B. H., & Watson, H. J. (2001). An Empirical Investigation of the Factors Affecting Data Warehousing Success. MIS Quarterly, 25(1), 17. https://doi.org/10.2307/3250957
Zha, H., Liu, K., Tang, T., Yin, Y.-H., Dou, B., Jiang, L., Yan, H., Tian, X., Wang, R., & Xie, W. (2022). Acceptance of clinical decision support system to prevent venous thromboembolism among nurses: An extension of the UTAUT model. BMC Medical Informatics and Decision Making, 22(1), 221. https://doi.org/10.1186/s12911-022-01958-8
Zitha, A., & Ajigini, O. A. (2023). A Model for the Business Intelligence System Acceptance in the South African Banking Sector. Journal of Theoretical and Applied Information Technology, 101(8), 3027–3043. Scopus.
Published
2024-10-01
How to Cite
Alfariz, M. M., Malik, A. M. A., & Lokman, A. M. (2024). Conceptualizing the Antecedents and Individual Impact of Business Intelligence in the Public Sector: The Technology-Organisation-Authoritative Framework. Information Management and Business Review, 16(3(I)S), 319-327. https://doi.org/10.22610/imbr.v16i3(I)S.4037
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Section
Research Paper
Copyright (c) 2024 Mohd Mustafa Alfariz, Ariff Md Ab Malik, Anitawati Mohd Lokman
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