Investigating a model of implementation and development business intelligence in organization with the purpose of improving decision-making
Subject Areas :Keywords: Business Intelligence Tools Network Processing Latency Decision Environment PLS ,
Abstract :
This article firstly tries to identify the various dimensions of successful implementation and development business intelligence as a set of technologies and processes that promote the procedure of the decision-making in the organization. Next, the effect of these dimensions on the process of decision-making in Eghtesad-e-Novin Bank, as a financial organization in which the speed of decision-making is of importance will be discussed. The criteria are the cultural, strategic and environmental factors, human resources and business intelligence tools the effects of which are discussed on the variable of "the latency in decision- making" in four divisions of organizational decision environment. The research methodology is mixed in this research and for this purpose in the qualitative part, the different dimensions of success in implementation and development business intelligence were extracted on the quality of decision-making with the Delphi technique by using the method of interviews and open questions in three stages and with the snowball method with the opinions of 18 experts. In the quantitative part, the priorities were determined with the help of the Analysis Network Process and Super Decision software. Finally, the main questionnaire has been finalized for the distribution among 90 personnel of different levels of the development and implement of business intelligence systems and the direct users of these systems in the organization. With the help of Smart Plus software and structural equations, as well as the effect of research variables on the reduction in the latency in decision-making, the relevant assumptions have been examined in a descriptive and survey manner with factor analysis. The proposed factors and models for evaluating business intelligence presented in this article help organizations, especially financial organizations in which speed in decision-making is of particular importance, to promote decision-making and minimize possible latencies in decision-making.
References:
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1) احمدی،ف.، و نصیرانی،خ و اباذری،پ. (1387) . تکنیک دلفی ، ابزاری در تحقیق، مجله ایرانی آموزش در علوم پزشکی. بهار و تابستان 1387 ص175 تا185
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3) اکبری، ز.، حقیقت منفرد، ج. و معین زاد، ح. (1392). بررسی رابطه کیفیت تصمیم گیری و قابلیت های هوش تجاری در بانک ملت، پایان نامه کارشناسی ارشد. دانشگاه آزاد اسلامی، واحد تهران مرکز، دانشکده مدیریت.
4) ایمانی جاجرمی، ح.، (1377). آشنایی با روش دلفی در تصمیم گیری و کاربردهای آن در تصمیم گیری.فصلنامه مدیریت شهری
5) داوری، ع.، رضازاده، الف.، (1392). مدل سازی معادلات ساختاری با نرم افزار PLS. سازمان انتشارات جهاد دانشگاهی.
6) شجاعی کیاسري، س.، قنبري، الف. و سلیمانی، ص. (1391). بررسی کاربرد هوش تجاري در فرایندهاي دولت الکترونیک به وسیله تحلیل مفهومی دولت الکترونیک، فصلنامه مدیریت، دانشگاه تهران، شماره13.
7) عالی زاده، ع،. (1385). اجرای تحقیق به روش دلفی، نشر یوسف، سال 1385،چاپ اول، ص 30
8) فلاح دوست، م. (1393). کاربرد رهیافتهاي هوش تجاري در بهبود تصمیمگیري مدیران بانکی، موسسه آموزش عالی غیردولتی و غیرانتفاعی پویندگان دانش. پایان نامه کارشناسی ارشد.
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11) موحدی، م. و سلطان زاده، ج. (1393). هوش تجاری و نقش آن درتصمیم گیری های سازمانی، اولین همایش ملی الکترونیکی دستاوردهای نوین در علوم مدیریت و حسابداری.
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. 13) Arnott, D., Lizama, F., & Song, Y. (2017). Patterns of business intelligence systems use in organizations, Decision Support Systems, 97, 58-68
. 14) Arnott, D., Pervan, G. (2008). Eight Key Issues for the Decision Support Systems Discipline. Decision Support Systems, vol. 44, no. 3, pp. 657–672
. 15) Aufaure, M.A., Raja, Ch., Olivier, C., Houda, KH. Gabriel, K. (2015). From Business Intelligence to semantic data stream management, Future Generation Computer Systems, Available online: http://dx.doi.org/10.1016/j.future.2015.11.015
. 16) Barclay, D., Higgins, C., & Thompson, R. (1995). The partial least squares (PLS) approach to causal modeling: personal computer adoption and use as an illustration. Technology studies, 2(2), 285-309
17) Barry, R., & Stair, R. (1992). Introduction to Management Science; Boston: Allyn and Bacon, 1992, Pp. 596-7
. 18) Brooks, P., El-Gayar, O., & Sarnikar, S. (2015). A framework for developing a domain specific Business Intelligence Maturity Model: Application to healthcare, International Journal of Information Management, 35(3), 337-345
. 19) Cheung C.F, Li, F.L. (2012). A quantitative correlation coefficient mining method for business intelligence in small and medium enterprises of trading business, Expert Systems with Applications, Vol. 39, no. 7, pp. 6279–6291
. 20) Chin, W. W. (1998) “Issues and Opinion on Structural Equation Modeling,'’ MIS Quarterly (22:1), pp. 7-16
. 21) Chugh, R. & Grandhi, S. (2013). Why Business Intelligence? Significance of Business Intelligence tools and integrating BI governance with corporate governance, International Journal of E-Entrepreneurship and Innovation, vol. 4, no.2, pp. 1-14
. 22) Constantiou, I., Shollo, A., Thanning, M. (2019). Mobilizing intuitive judgement during organizational Decision Making: When business intelligence is not the only thing that matters, Decision Support Systems, Volume 121, June 2019, Pages 51-61
. 23) Cronbach, L. J (1951).Coefficient alpha and the internal structure of tests.Psychometrika,16(3),297-334
24) Davenport, T. H. (2012). Business intelligence and organizational decisions, In Organizational Applications of Business Intelligence Management: Emerging Trends (pp. 1-12). IGI Global.
25)
Eachempati, P., & Srivastava, P. R. (2017, June). Systematic Literature Review of Big Data Analytics, 26)
Eisenfuhr, F. (2011). Decision Making. New York, NY: Springer. 27) Fischer, T. C. (2018). Technology in its context-a literature review of the macro and micro levels of business intelligence, International Journal of Business Intelligence and Data Mining, 13(1-3), 347-368
. 28) Fornell, C., & Larcker, D. F. (1981). Evaluation structural equation models with unobservable variables and measurement error. Journal of marketing research, 39-50
. 29)
Hackathorn, R. (2002).Minimizing Action Distance. DM Review, 12, 22-23. 30) Hackney, R. A., Dooley, P., Levvy, Y., & Parrish, J. (2015). Critical value factors in business intelligence systems implementation success: An empirical analysis of system and information quality
. 31) Harrison, R., Parker, A., Brosas, G. and Tian, C-X. (2015). The role of technology in the management and exploitation of internal business intelligence, Journal of Systems and Information Technology, Volume 17, Issue 3, pp. 247 – 262
. 32) Hulland, J. (1999). Use of partial least of squares (PLS) in strategic management research: a review of four recent studies, strategic management journal, volume 20, issue 2
. 33) Iqbal, R., Doctor, F., More, B., Mahmud, S., & Yousuf, U. (2018). Big data analytics: Computational intelligence techniques and application areas, Technological Forecasting and Social Change
. 34) Kao, H. Y., Yu, M. C., Masud, M., Wu, W. H., Chen, L. J., & Wu, Y. C. J. (2016). Design and evaluation of hospital-based business intelligence system (HBIS): A foundation for Design Science Research Methodology, Computers in Human Behavior, 62, 495-505
. 35) Karim, A., Siddiqa, A., Safdar, Z., Razzaq, M., Gillani, S. A., Tahir, H., & Imran, M. (2017). Big data management in participatory sensing: Issues, trends and future directions, Future Generation Computer Systems
. 36) Kasemsap, K. (2016). The Fundamentals of Business Intelligence, International Journal of Organizational and Collective Intelligence (IJOCI), 6(2), 12-25
. 37) Kowalczyk, M., Buxmann, P. (2015). An ambidextrous perspective on business intelligence and analytics support in decision processes: Insights from a multiple case study, Decision Support Systems, Volume 80, December 2015, Pages 1-13
. 38) Larson, D. (2019). A Review and Future Direction of Business Analytics Project Delivery, In Aligning Business Strategies and Analytics (pp. 95-114). Springer, Cham
. 39) 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
. 40) Lunenburg. (2010). The Decision Making Process, National forum of educational administration and supervision journal, Volume 27, Number 4
. 41) Muehlen, Z & Shapiro, M. (2009). Business Process Analytics.Handbook on Business Process Management Vol.2 Sprimnger Verlag, Berlin et al
. 42) Nandini, R., Rasheed, A., & Datta, D. (1993). Strategic Decision Process: Critical Review and Future Decisions, Journal of Management; No. 19 (1993). PP. 349-84
. 43) Negash, S. (2004). Business Intelligence. Communications of the Association for Information Systems, Vol. 13, No. 1, pp. 177–195
. 44)
Nunnally, J. (1978). Psychometric theory, 2nd Edn. New York: McGraw-Hill. 45) Olszak, C.M.: Toward better understanding and use of business intelligence in organizations. Inf. Syst. Manag. 33(2), 105–123 (2016)
. 46) Olszak, C.M & Ziemba, E. (2017). Approach to Building and Implementing Business Intelligence Systems, Interdisciplinary Journal of Information, Knowledge, and Management Volume 2
. 47) Popovič, A., Ray Hackney, R., Coelho, P., Jaklič, J. (2012). Towards business intelligence systems success: Effects of maturity and culture on analytical decision making, Decision Support Systems, Vol. 54, Issue 1, December 2012, Pages 739. 729
. 48) Ravasan, A. Z., & Savoji, S. R. (2019). Business Intelligence Implementation Critical Success Factors, In Applying Business Intelligence Initiatives in Healthcare and Organizational Settings (pp. 112-129). IGI Global
. 49) Richards, G., Yeoh, W., Chong, A. Y. L., & Popovič, A. (2017). Business intelligence effectiveness and corporate performance management: An empirical analysis, Journal of Computer Information Systems, 1-9
. 50) Rikhardsson, P., & Yigitbasioglu, O. (2018). Business intelligence & analytics in management accounting research: Status and future focus, International Journal of Accounting Information Systems,Volume 29, June 2018, Pages 37-58
. 51) Rouhani, S., & Lecic, D. M. (2018). Business Intelligence Impacts on Design of Enterprise Systems, In Encyclopedia of Information Science and Technology, Fourth Edition (pp. 2932-2942). IGI Global
. 52) Rouhani, S., Ashrafi, A., Zare Ravasan, A.and Afshari, S. (2016). The impact model of business intelligence on decision support and organizational benefits, Journal of Enterprise Information Management, Vol. 29 Iss: 1, pp.19 –50
. 53) Rubin, E., & Rubin, A. (2013). The impact of Business Intelligence systems on stock return volatility, Information & Management, Vol. 50; no. 1, pp. 67–75
. 54) Saltz, J. S., & Shamshurin, I. (2016, December). Big data team process methodologies: A literature review and the identification of key factors for a project's success, In Big Data (Big Data), 2016 IEEE International Conference on (pp. 2872-2879). IEEE
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