Business Intelligence Technology in Research Organizations (Case Study of Academic Institutes in Tehran)
Subject Areas : Business Strategy
Mohammad Milad Ahmadi
1
,
Sasan Zare
2
1 - Department of Systems Management, Faculty of Management and Economics, Imam Hossein University, Tehran, Iran
2 - Department of Systems Management, Faculty of Management and Economics, Imam Hossein University, Tehran, Iran
Keywords: technology acceptance, Information Technology, Organizational Information Systems, Research Organizations, Business Intelligence,
Abstract :
Business Intelligence (BI) covers the tasks of collecting, processing, and analyzing large volumes of data. This includes internal systems and external resources, utilizing advanced high-speed analytics and forecasting tools enabling organizations to achieve organizational goals in a timely manner affording immediate decision-making. The main purpose of BI is to help companies improve their performance in the turbulent environment of business and enhance their competitive advantage in this immense data age. Research organizations need integrated information technologies such as business intelligence, perhaps more so than commercial companies need, given the highly competitive environment and increasing progress of various disciplines. The development of such a system, like other organizational information systems, requires the adoption of technology by its users. Various models, including behavioral models, have identified the acceptance factors of information technologies. The purpose of this study is the Interpretive Structural Modeling of factors affecting the adoption of business intelligence technology in research organizations. ISM is a systematic and interpretive process as it is formed based on group judgment and is structured and complemented by common relationships, and finally, depicts the overall structure of several complex elements in a graph model. The sample used in this study was experts of academic research institutes in Tehran. According to the findings, 20 main acceptance factors were modeled in four levels based on interactions between the categories of individual, organizational, and technological criteria.
Agrawal, N. (2019). Role of ETL in Business Intelligence. Mantra Labs global. Retrieved from: https://www.mantralabsglobal.com/blog/etl-in-business-intelligence/
Agarwal A., Shankar R., & Tiwari, M.K. (2006). Modeling agility of supply chain. Industrial Marketing Management, 36(4), 443-457.
Agi, M. A. N., & Nishant, R. (2017). Understanding influential factors on implementing green supply chain management practices: An interpretive structural modelling analysis. Journal of Environmental Management, 188, 351-363.
Ahmad, M., Tang, X.W., Qiu, J.N., & Ahmad, F. (2019). Interpretive Structural Modeling and MICMAC Analysis for Identifying and Benchmarking Significant Factors of Seismic Soil Liquefaction. Applied Sciences, 9(233), 1-21.
Ahmad, S., Miskon, S., Alkanhal, T. A., & Tlili, I. (2020). Modeling of business intelligence systems using the potential determinants and theories with the lens of individual, technological, organizational, and environmental contexts-a systematic literature review. Applied Sciences, 10(9), 3208.
Ain, N., Vaia, G., DeLonde, W. H., & Waheed, M. (2019). Two decades of research on business intelligence system adoption, utilization and success– A systematic literature review. Decision Support Systems, 125, 1- 13.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179- 211.
Akhmetov, B., Izbassova, N., & Akhmetov, B. (2012). Developing and customizing university business intelligence Cloud. Paper presented at 2012 International Conference on Cloud Computing Technologies, Applications and Management (ICCCTAM) Retrieved May 5, 2020 from https://ieeexplore.ieee.org/abstract/document/6488104.
Al-Zayyat, A.N., Alkhaldi, F.M., Tadros, I.H., & Al-Edwan, G. (2010). The Effect of Knowledge Management Processes on Project Management. Journal of IBIMA Business Review, 3, 1-6.
Altexsoft (2019). Complete Guide to Business Intelligence and Analytics: Strategy, Steps, Processes, and Tools. 23 Apr, 2019. Retrieved from: https://www.altexsoft.com/blog/business/complete-guide-to-business-intelligence-and-analytics-strategy-steps-processes-and-tools/
Attri, R., Dev, N., & Sharma, V. (2013). Interpretive Structural Modelling (ISM) approach: An Overview. Research Journal of Management Sciences, 2(2), 3- 8.
Azoff, M., & Charlesworth, I. (2004). The New Business Intelligence, A European Perspective. Atlanta: Butler Group, White Paper.
Arinze B., & Amobi, O. (2004). A Methodology for Developing Business Intelligence Systems. In M. Anandarajan, A. Anandarajan, & C.A. Srinivasan (Eds.), Business Intelligence Techniques (pp. 181-195). Berlin, Heidelberg: Springer.
Baars, H., & Kemper, H.G. (2008). Management Support with Structured and Unstructured Data- An Integrated Business Intelligence Framework. Information Systems Management, 25(2), 132- 148.
Bach, M.P., Čeljo, A., & Zoroja, J. (2016). Technology Acceptance Model for Business Intelligence Systems: Preliminary Research. Procedia Computer Science, 100: 995– 1001.
Bach, M.P., Zoroja, J., & Čeljo, A. (2017). An extension of the technology acceptance model for business intelligence systems: project management maturity perspective. International Journal of Information Systems and Project Management, 5(2), 5- 21.
Barbour, R. S., & Kitzinger, J. (1991). Developing Focus Group Research: Politics, Theory and practice, London: Sage.
Bazaee, A., & Karimian, H. (2018). The Impact of Business Intelligence on Marketing Performance with Moderating Role of Environmental Turbulence. Journal of System Management, 4(1), 27-44.
Bazargani, M., & Namazi, E. (2016). A Study Model in Business Intelligence for Improving Electronic Insurance. In Business Intelligence: Concepts, Methodologies, Tools, and Applications (pp. 994-998). Hershey PA, USA: IGI Global.
Baumgartner, T.A., Strong, C.H., and Hensley, L.D. (2002). Conducting and reading research in health and human performance (3rd ed.). New York: Mc Graw-Hill.
Bornayesh (2017). Designing and implementing a business intelligence solution. Bornayesh Management Consulting. Retrieved from: http://bornayesh.com/portfolio-item/bi/ (In Persian).
Bruce, D. (2019). 10 Key Steps for Business Intelligence Implementation. KnowledgeNile content marketing organization. Retrieved from: https://www.knowledgenile.com/blogs/business-intelligence-implementation-steps/
Boonsiritomachai, W., McGrath, G.M., & Burgess, S. (2016). Exploring business intelligence and its depth of maturity in Thai SMEs. Cogent Business & Management, 3(1), 1-17.
Bouchana, S., & Idrissi, M.A.J. (2015). Towards an assessment model of end user satisfaction and data quality in business intelligence systems. In 10th International Conference on Intelligent Systems: Theories and Applications (SITA), 20-21 October 2015 (pp. 1-6). Rabat, Morocco.
Burgess, J. (1996). Focusing on fear. The use of Focus Groups in a project for the Community Forest Unit, Countryside Commission, Area. 28.(2): 130- 135.
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.
D'Arconte, C. (2018). Business Intelligence applied in Small Size for Profit Companies. Procedia Computer Science, 131, 45-47.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3), 318-340.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982- 1003.
Devens, R. M. (2016). Cyclopaedia of Commercial and Business Anecdotes. Volume 1. Sydney: Wentworth Press.
Dewangana, D. K., Agrawal, R., & Sharma, V. (2015). Enablers for Competitiveness of Indian Manufacturing Sector: An ISM-Fuzzy MICMAC Analysis. Procedia - Social and Behavioral Sciences, 189, 416- 432.
Dumitru-Alexandru, B. (2016). Business Intelligence for Decision Making in Economics. In C. Dunis, P. Middleton, A. Karathanasopolous, & K. Theofilatos (Eds.), Artificial Intelligence in Financial Markets. New Developments in Quantitative Trading and Investment (pp. 125-158). London: Palgrave Macmillan.
Falakmasir, M. H., Moaven, S., Abolhassani, H., & Habibi, J. (2010). Business intelligence in e-learning: (case study on the Iran university of science and technology dataset). In The 2nd International Conference on Software Engineering and Data Mining, 23-25 June 2010 (pp. 473-477). Chengdu, China.
Fang, L. Y., Azmi, N. F. M., Yahya, Y., Sarkan, H., Sjarif, N. N. A., & Chuprat, S. (2018). Mobile Business Intelligence Acceptance Model for Organizational Decision Making. Bulletin of Electrical Engineering and Informatics, 7(4), 650- 656.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Boston, Massachusetts: Addison-Wesley.
Fitriana1, R., Djatna, T., & Eriyatno, T.D. (2011). Progress in Business Intelligence System research: A literature Review. International Journal of Basic and Applied Sciences. 11(3): 96- 105.
Gaol, F. L., Abdillah, L., & Matsuo, T. (2020). Adoption of Business Intelligence to Support Cost Accounting Based Financial Systems—Case Study of XYZ Company. Open Engineering, 11(1), 14-28.
Grublješič, T., & Jaklič, J. (2015). Business Intelligence Acceptance: The Prominence of Organizational Factors. Information Systems Management, 32(4), 299-315.
Gupta D. S. (2003). A Strategy for Intelligence. Network. Magazine India. Retrieved July 6, 2003, Retrieved from: http://www.networkmagazineindia.com/200307/cover2.
Hasan M.A., Shankar, R., & Sarkis, J. (2007). A study of barriers to agile manufacturing. International Journal of Agile System and Management, 2(1), 1-22.
Hindrayani, K. M., Maulana, F. T., Aji, R. P., & Maya, E. (2020). Business Intelligence for Educational Institution: A Literature Review. International Journal of Computer, Network Security and Information System, 2(1), 22-25.
Information Resources Management Association (2015). Business Intelligence: Concepts, Methodologies, Tools, and Applications. Hershey PA, USA: IGI Global.
Jackson, C.M., S. Chow, & R.A. Leitch (1997). Toward an Understanding of the Behavioral Intention to Use an Information System. Decision Sciences 28(2): 57-389.
Jaklič, J., Grublješič, T., & Popovič, A. (2018). The role of compatibility in predicting business intelligence and analytics use intentions. International Journal of Information Management, 43: 305–318.
Jayant, A., & Azhar, M. (2014). Analysis of the barriers for implementing green supply chain management (GSCM) Practices: An Interpretive Structural Modeling (ISM) Approach. Procedia Engineering, 97, 2157- 2166.
Kabakchieva, D. (2015). Business intelligence systems for analyzing university students' data. Cybernetics and Information Technologies, 15(1), 104–115.
Kohnke, O., Wolf, T. R., & Mueller, K. (2011). Managing user acceptance: an empirical investigation in the context of business intelligence standard software. International Journal of Information Systems and Change Management, 5(4), 269-290.
Khorashadi Zadeh, M.H., Karkon, A., & Golnari, H. (2017). The Effect of Information Technology on the Quality of Accounting Information. Journal of System Management, 3(3), 61-76.
Krueger, R.A., & Casey, M.A. (2000). Focus Groups: A practical guide for applied researchers (3rd ed.). Thousand Oaks, CA: Sage.
Lebied, M. (2018). 11 Steps on Your BI Roadmap to Implement A Successful Business Intelligence Strategy. The datapine Blog. Jul 20th 2018. Retrieved from: https://www.datapine.com/blog/roadmap-to-a-successful-business-intelligence-strategy/
Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003). The Technology Acceptance Model: Past, Present, and Future. Communications of the Association for Information Systems. 12(50): 752- 780.
Lönnqvist, A., & Pirttimäki, V. (2006). The Measurement of Business Intelligence, Information Systems Management, 23(1): 32- 40.
Mohaghar, A., Lucas, C., Hosseini., F., & Monshi., A. A. (2009). Use of Business Intelligence as A Strategic Information Technology in Banking: Fraud Discovery and Detection. Journal of Information Technology Management, University of Tehran 1(1), 105-120. (In Persian).
Nazari Farokhi, E., Poorebrahimi, A., & Nazari Farokhi, M. (2020). Designing an Intelligence Model for Auditing Professional Ethics in Knowledge Contents Production. Journal of System Management, 6(2), 155-168.
Niño, H. A. C., Niño, J. P. C., & Ortega, R. M. (2020). Business intelligence governance framework in a university: Universidad de la costa case study. International Journal of Information Management, 50, 405- 412.
Nyanga, C., Pansiri, J. & Chatibura, D. (2020). Enhancing competitiveness in the tourism industry through the use of business intelligence: a literature review. Journal of Tourism Futures, 6 (2), 139-151.
Owusu, A., Ghanbari-Baghestan, A., & Kalantari, A. (2017). Investigating the factors affecting business intelligence systems adoption: A case study of private universities in Malaysia. International Journal of Technology Diffusion (IJTD), 8(2), 1-25.
Puklavec, B., Oliveira, T., & Popovič, A. (2017). Understanding the determinants of business intelligence system adoption stages: an empirical study of SMEs. Industrial Management & Data Systems, 118(1), 236–261.
Rogers, E.M. (2003). Diffusion of innovations. (3rd edition). NY: The Free Press.
Roodposhti, F. R., & Mahmoodi, M. (2010). Explanation of Business Intelligence Model in Management Accounting Information System. Journal of Business Management. Islamic Azad University, 2(5): 31- 51. (In Persian).
Sabokro, M., Rahimi, E., & Abbasi Rostami, N. (2018). The effect of business intelligence on open innovation structure. Journal of Management Futures Research, Islamic Azad University, 29 (113), 21-32. (In Persian).
Safarzadeh, H., Mazandarani, N. B., & Javidihagh, M. (2010). The Role of Business Intelligence in Establishing of Effective Strategic Management in Organizations. Journal of Business Management, Islamic Azad University, 2(5), 53-83. (In Persian).
Sage, A.P. (1977). Interpretive structural modeling: Methodology for large scale systems. NY: McGraw-Hill.
Sohofi, S. M., & Kazemi, N. (2014). The Identification and Analysis of Causal and Effective Relationships of Required Infrastructures for the Deployment of an Electronic City based on Business Intelligence in Tehran. Urban Management Studies. Islamic Azad University, 6(17): 55- 65. (In Persian).
Skyrius, R., Katin, I., Kazimianec, M., Nemitko, S., Rumšas, G., & Žilinskas, R. (2016). Factors driving business intelligence culture. Issues in Informing Science and Information Technology. 13, 171–186.
Sönmez, F. (2018). Technology Acceptance of Business Intelligence and Customer Relationship Management Systems within Institutions Operating in Capital Markets. International Journal of Academic Research in Business and Social Sciences, 8(2), 400–422.
Sujitparapitaya, S., Shirani, A., & Roldan, M. (2012). Business intelligence adoption in academic administration: An empirical investigation. Issues in Information Systems, 13(2), 112-122.
Suša-Vugec, D., Bosilj-Vukšić, V., Pejić Bach, M., Jaklič, J., & Indihar Štemberger, M. (2020). Business intelligence and organizational performance: the role of alignment with business process management. Business process management journal, 26(6), 1709-1730.
Ta’a, A., Bakar, M. S. A., & Saleh, A. R. (2006). Academic business intelligence system development using SAS® tools. In Workshop on Data Collection System for PHLI-MOHE (Vol. 13, p. 14).
Tavallaei, R., & Ahmadi, M. M. (2018). Factors Influencing Acceptance of E-health: an Interpretive Structural Modeling. Journal of Information Technology Management, 10(3), 106-126.
Thakkar, J., Deshmukh, S., Gupta, A. & Shankar, R. (2007). Development of a balanced scorecard: An integrated approach of Interpretive Structural Modeling (ISM) and Analytic Network Process (ANP). International Journal of Productivity and Performance Management, 56(1), 25-59.
Tornatzky, L., & Fleischer, M. (1990). The process of technology innovation. Lexington: Lexington Books.
Turban, E., Sharda, R., & Delen, D. (2010). Decision support and business intelligence systems. NJ: Prentice Hall.
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sciences, 39(2), 273-315.
Wilkinson, S. (2004). Focus Group research. In D. Silverman (Ed.), Qualitative research: Theory, method, and practice (pp. 177-199). Thousand oaks, CA: Sage.
Yiu, L.M.D., Yeung, A.C.L. & Jong, A.P.L. (2020). Business intelligence systems and operational capability: an empirical analysis of high-tech sectors. Industrial Management & Data Systems. 120(6), 1195-1215.
Yoon, T. E., Ghosh, B., & Jeong, B.K. (2014). User Acceptance of Business Intelligence (BI) Application: Technology, Individual Difference, Social Influence, and Situational Constraints. In 2014 47th Hawaii International Conference on System Science, 6-9 January 2014(pp. 3758-3766). Waikoloa, Hawaii.
Yusof, M.M., Kuljis, J., Papazafeiropoulou, A., & Stergioulas, L.K. (2008). An evaluation framework for Health Information Systems: human, organization and technology-fit factors (HOT-fit). International Journal of Medical Informatics, 77(6), 386-398.
Zhao, Z., Navarrete, C., & Iriberri, A. (2012). Open Source Alternatives for Business Intelligence: Critical Success Factors for Adoption. AMCIS 2012 Proceedings, 29, 1-15.
