Abstract
Cost management and operational efficiencies play a critical part in both the financial institution’s ability to grow as well as their overall profit margins. For a financial institution to stay competitive in this era of fast-paced decision making, driven not only by local competition but by competitors at a global scale requires the ability to make rapid and accurate decisions based on all the available data. This can only be achieved through the effective use and adoption of BI and SSBI across all areas of the business. Through a thorough systematic literature review (SLR), this paper evaluated various adoption frameworks that have been used in past research relating to BI and SSBI. The synthesis process focused primarily on academic publications drawn via accepted databases and literature search engines for the period of 2000 to 2021. BI and SSBI were found to be primarily examined from an organisational stance while adoption from the humanistic stance of individuals was missing within the literature. Therefore, the Model of PC Utilisation (MPCU) has subsequently been proposed as a potential framework to examine the adoption of SSBI from a humanistic stance within a financial institution.
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References
Aničić, D., Aničić, J., Miletić, V.: Cost management efficiency factors of enterprises in Serbia. Ekonomika 66(1), 37–51 (2020)
Hartl, K., Jacob, O., Jacob, F.L., Budree, A., Fourie, L.: The impact of business intelligence on corporate performance management. In: Proceedings of the Annual Hawaii International Conference on System Science, pp. 5042–5051. IEEE Computer Society, Hawaii (2016)
Pal, T., Brar, S.: Business intelligence in banking: a study of bi technology implementation and challenges. CGC Int. J. Contemp. Technol. Res. 1(1) (2018)
Weiler, S., Matt, C., Hess, T.: Understanding user uncertainty during the implementation of self-service business intelligence: a thematic analysis. In: Proceedings of the Annual Hawaii International Conference on System Sciences, pp. 5878–5887. IEEE Computer Society, Hawaii (2019)
Lennerholt, C., van Laere, J.: Data access and data quality challenges of self-service business intelligence. In: 27th European Conference on Information Systems - Information Systems for a Sharing Society, pp. 1–13 (2019)
Maryska, M., Doucek, P.: Self-service business intelligence. Inf. Technol. Pract. 259–269 (2017)
Masouleh, M.F.: The impact of the adoption business intelligence among Iranian banks. J. Adv. Comput. Eng. Technol. 4(1), 13–20 (2018)
Olszak, C.M.: Toward better understanding and use of business intelligence in organizations. Inf. Syst. Manag. 33(2), 105–123 (2016)
Owusu, A.: Business intelligence systems and bank performance in Ghana: the balanced scorecard approach. Cogent Bus. Manag. 4(1), 1–22 (2017)
Immhoff, C., White., C.: Self-Service Empowering Users to Generate Insights. TWDI Research (2011)
Daradkeh, M., Al-Dwairi, R.M.: Self-service business intelligence adoption in business enterprises: the effects of information quality, system quality, and analysis quality. In: Operations and Service Management: Concepts, Methodologies, Tools, and Applications, pp. 1096–1118. IGI Global (2018)
Schuff, D., Corral, K., St. Louis, R.D., Schymik, G.: Enabling self-service BI: a methodology and a case study for a model management warehouse. Inf. Syst. Front. 20(2), 275–288 (2018)
Lennerholt, C., van Laere, J., Söderström, E.: Implementation challenges of self-service business intelligence: a literature review. In: 51st Hawaii International Conference on System Sciences, pp. 5055–5062. IEEE Computer Society (2018)
Alpar, P., Schulz, M.: Self-service business intelligence. Bus. Inf. Syst. Eng. 58(2), 151–155 (2016). https://doi.org/10.1007/s12599-016-0424-6
Maher, N.A., et al.: Passive data collection and use in healthcare: a systematic review of ethical issues. Int. J. Med. Inform. 129(1), 242–247 (2019)
Ul-ain, N., Giovanni V., Delone W.: Business intelligence system adoption, utilization and success - a systematic literature review. In: Proceedings of the 52nd Hawaii International Conference on System Sciences, pp. 5888–5897 (2019)
Aromataris, E., Pearson, A.: The systematic review: an overview. Am. J. Nurs. 114(3), 53–58 (2014)
Oosterwyk, G., Brown, I., Geeling, S.: A synthesis of literature review guidelines from information systems journals. In: Proceedings of 4th International Conference on the, pp. 250–260 (2019)
Tornatzky, L.G., Fleischer, M.: The Process of Technology Innovation. Lexington Books (1990)
Davis, F.D.: A technology acceptance model for empirically testing new end-user information systems: Theory and results (1985)
Rogers, E.M.: Diffusion of Innovations: modifications of a model for telecommunications. In: Die diffusion von innovationen in der telekommunikation, pp. 25–38 (1995)
Masha, H., Adeyelure, S., Jokonya, P.O.: Adoption of business intelligence in the south African public social sector department. In: Proceedings of 4th International Conference on the Internet, Cyber Security and Information Systems, pp. 157–168 (2019)
Indriasari, E., Wayan, S., Gaol, F.L., Trisetyarso, A., Saleh Abbas, B., Ho Kang, C.: Adoption of cloud business intelligence in Indonesia’s financial services sector. In: Nguyen, N.T., Gaol, F.L., Hong, T.-P., Trawiński, B. (eds.) ACIIDS 2019. LNCS (LNAI), vol. 11431, pp. 520–529. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14799-0_45
Chaveesuk, S., Horkonde, S.: An integrated model of business intelligence adoption in thailand logistics service firms. In: International Conference on Information Technology and Electrical Engineering, pp. 604–608 (2015)
Sujitparapitaya, S., Shirani, A., Roldan, M.: Business intelligence adoption in academic administration: an empirical investigation. Issues Inf. Syst. 13(2), 112–122 (2012)
Kester, Q., Preko, M.: Business intelligence adoption in developing economies: a case study of Ghana. Int. J. Comput. Appl. 127(1), 1–8 (2015)
Olexová, C.: Business intelligence adoption: a case study in the retail chain. Inf. Syst. Manag. 11(1), 95–106 (2014)
Rouhani, S., Ashrafi, A., Zareravasan, A., Afshari, S.: Business intelligence systems adoption model: an empirical investigation. J. Organ. End User Comput. 30(2), 43–67 (2018)
Owusu, A., Tijjani, D., Agbemabiese, G.C., Soladoye, A.: Determinants of business intelligence systems adoption in developing countries: an empirical analysis from Ghanaian Banks. J. Internet Bank. Commer. 8(6), 1–25 (2017)
Owusu, A.: Determinants of Cloud business intelligence adoption among Ghanaian SMEs. Int. J. Cloud Appl. Comput. 10(4), 48–69 (2020)
Bhatiasevi, V., Naglis, M.: Elucidating the determinants of business intelligence adoption and organizational performance. Inf. Dev. 36(1), 78–96 (2020)
Stjepić, A.M., Pejić Bach, M., Bosilj Vukšić, V.: Exploring risks in the adoption of business intelligence in SMEs using the TOE framework. J. Risk Financ. Manag. 14(2), 1–18 (2021)
Owusu, A., Ghanbari-Baghestan, A., Kalantari, A.: Investigating the factors affecting business intelligence systems adoption: a case study of private universities in Malaysia. Int. J. Technol. Diffus. 8(2), 1–25 (2017)
Ahmad, S., Miskon, S., Alabdan, R., Tlili, I.: Statistical assessment of business intelligence system adoption model for sustainable textile and apparel industry. IEEE Access, 9 pp. 106560–106574 (2021)
Stjepić, A.M.: Survey of the determinations of business intelligence systems adoption in SMEs. In: Proceedings of the Fourth Central European Conference of Information and Intelligent Systems, pp. 177–185 (2017)
Stjepić, A.M., Sušac, L., Vugec, D.S., Bis, A.: Technology, organizational and environmental determinants of business intelligence systems adoption in croatian SME: a case study of medium-sized enterprise. Int. J. Econ. Manag. Eng. 13(5), 737–742 (2019)
Puklavec, B., Oliveira, T., Popovič, A.: Understanding the determinants of business intelligence system adoption stages an empirical study of SMEs. Ind. Manag. Data Syst. 118(1), 236–261 (2018)
Oliveira, T., Martins, M.F.: Literature review of information technology adoption models at firm level. Electron. J. Inf. Syst. Eval. 14(1), 110–121 (2011)
Ilin, V., Ivetić, J., Simić, D.: Understanding the determinants of e-business adoption in ERP-enabled firms and non-ERP-enabled firms: A case study of the Western Balkan Peninsula. Technol. Forecast. Soc. Chang. 125(1), 206–223 (2017)
Koul, S., Eydgahi, A.: A systematic review of technology adoption frameworks and their applications. J. Technol. Manag. Innov. 12(4), 106–113 (2017)
Hatta, N.N.M., et al.: Business intelligence system adoption theories in SMEs: a literature review. ARPN J. Eng. Appl. Sci. 10(23), 18165–18174 (2015)
Thompson, R.L., Higgins, C.A., Howell, J.M.: Personal computing: toward a conceptual model of utilization. MIS Q. Manag. Inf. Syst. 15(1), 125–142 (1991)
Andreas, C.: UTAUT and UTAUT 2: a review and agenda for future research. Winners 13(2), 106–114 (2012)
Alkhwaldi, A., Kamala, M.: Why do users accept innovative technologies? a critical review of models and theories of technology acceptance in the information system literature. J. Multidiscipl. Eng. Sci. Technol. 4(8), 7962–7971 (2017)
Gunasinghe, A., Hamid, J.A., Khatibi, A., Azam, S.F.: Academicians’ acceptance of online learning environments: a review of information system theories and models. Glob. J. Comp. Sci. Technol. 19(1), 31–39 (2019)
Taherdoost, H.: A review of technology acceptance and adoption models and theories. Procedia Manufact. 22(1), 960–967 (2018)
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de Waal, S., Budree, A. (2022). A User-Driven Self-service Business Intelligence Adoption Framework. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2022 Posters. HCII 2022. Communications in Computer and Information Science, vol 1582. Springer, Cham. https://doi.org/10.1007/978-3-031-06391-6_47
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