مدل ارزیابی موفقیت ابزارهای هوش کسب‌وکار

نوع مقاله: مقاله پژوهشی

نویسندگان

1 استادیار، دانشکده مدیریت، دانشگاه تهران، تهران، ایران.

2 کارشناس ارشد مهندسی فناوری اطلاعات، گرایش مدیریت سیستم‌های اطلاعاتی، دانشگاه الکترونیکی مهرالبرز، تهران،

چکیده

 
در محیط تجاری سریعا در حال تغییر امروز، نیاز به اطلاعات تجاری سودمند برای سازمان­ها نه تنها به منظور کسب موفقیت بلکه برای بقاء حیاتی می­باشد. با توجه به ناتوانایی سیستم­های اطلاعاتی مدیریت در برآورده کردن انتظارات تصمیم­گیرندگان سازمانی در عرصه رقابت در سال­های اخیر، فناوری­های هنرگونه­ای نظیر هوش کسب­وکار به یکی از مفاهیم مهم در مدیریت سیستم­های اطلاعاتی تبدیل شده و در خط مقدم فناوری اطلاعات برای پشتیبانی تصمیم­گیری مدیریت قرار گرفته است. در همین راستا ارزیابی موفقیت این سیستم­ها در سازمان­ها ضرورت یافته و نیاز به ارائه مدلی برای انجام این ارزیابی احساس می­شود. از این­رو، در این پژوهش سعی شده است با شناسایی و معرفی مهم‌ترین فاکتورها و عوامل مؤثر در ارزیابی موفقیت این سیستم­ها، یک مدل ارزیابی موفقیت هوش کسب­وکار(در قالب یک مطالعه موردی) ارائه گردد. پژوهش حاضر از نظر هدف کاربردی و از نظر روش، توصیفی پیمایشی و تحقیق میدانی محسوب می­شود. نتایج حاصل از تحقیق نشان می­دهد که از میان 50 عامل استخراج شده از ادبیات موضوع جهت سنجش موفقیت ابزارهای هوش کسب­وکار در صنعت بانکداری کشور، بر حسب نظر اعضای نمونه آماری 24 عامل تاثیرگذار در هر یک از حوزه­های نمایش، خلق دانش، یکپارچگی اطلاعات و حافظه سازمانی در مدل تحقیق شناسایی شدند.v

کلیدواژه‌ها


عنوان مقاله [English]

An Assessment Model for the Success of Business Intelligence Tools

نویسندگان [English]

  • Saeed Rouhani 1
  • Sogol Rabiee Savoji 2
1  Assistant Professor, College of Management, University of Tehran
2 MA in Information Technology Management, Mehr Alborz Electronic University
چکیده [English]

 
In today's fast-changing business environment, the need for useful business information for organizations is vital, not only for success, but for survival. Due to the disability of management information systems to meet the expectations of organizational decision makers in the competition area in recent years, technologies such as Business Intelligence (BI) has become one of the most important concepts in the management of information systems and has the priority importance to support the management decision making. In this regard, it has become necessary to assess the success of such systems in organizations and there is a need to provide an assessment model in this regard. Hence, in this study, it has been tried by identifying and introducing the most important and effective factors a model for assessing the success of business intelligence (in the form of a case study) is presented. This study in terms of purpose is an applied research and in terms of method it is a descriptive survey and it is a field research. The success of business intelligent tools in the banking industry was evaluated and the results show that out of 50 factors extracted from the literature, 24 factors were identified effective for the assessment of BI tools in each area of knowledge presentation, knowledge creation, information integration and organizational memory.
 

 

تارخ، محمدجعفر و مهاجری، حسین. هوش تجاری؛ نگرشی پویا در عرصه کسب­وکار. انتشارات دانشگاه صنعتی خواجه نصیرالدین طوسی. چاپ اول. دی ماه 1391.

رهنمای رودپشتی، ف. و عاضدی تهرانی، ش.، هوش تجاری مالی. اولین همایش ملی هوش تجاری/کسب و کار. 1389.

Candal-Vicente, I. (2009). Factors That Affect the Successful Implementation of a Data Warehouse. Paper presented at the Computing in the Global Information Technology, 2009. ICCGI'09. Fourth International Multi-Conference on.

Chenoweth, T., Corral, K., & Demirkan, H. (2006). Seven key interventions for data warehouse success. Communications of the ACM, 49(1), 114-119.

Cutter Consortium Report (2003) “Cutter Consortium Report on Corporate Use of BI and Data Warehousing Technologies”, at : http://www.dmreview.com.

Dekoulou, P., & Trivellas, P. (2014). Learning Organization in Greek Advertising and Media Industry: A way to face crisis and gain sustainable competitive advantage. Procedia-Social and Behavioral Sciences, 148, 338-347.

Dresner, H., Linden, A., Buytendijk, F., Friedman, T., Strange, K., Knox, M., & Camm, M. (2002). The business intelligence competency center: An essential business strategy. Gartner Strategic Analysis Report.

Elbashir, M. Z., Collier, P. A., & Davern, M. J. (2008). Measuring the effects of business intelligence systems: The relationship between business process and organizational performance. International Journal of Accounting Information Systems, 9(3), 135-153.

Gartner. (2009) . Gartner EXP Worldwide Survey of More than 1,500 CIOs Shows IT Spending to Be Flat in 2009. STAMFORD, Conn.

Ghazanfari, M., Jafari, M., & Rouhani, S. (2011). A tool to evaluate the business intelligence of enterprise systems. Scientia Iranica, 18(6), 1579-1590.

Gomes, C. F. S., & Ribeiro, P. (2014). Gestao da cadeia de suprimentos integrada a tecnologia da informacao (Vol. 2nd ed): Sao Paulo: Cengage Learning Editores.

HashemiTabatabaei, S. (2010). Evaluation of business intelligence maturity level in Iranian banking industry. master thesis,tarbiat modares university .fuculity of engineering.

Hawking, P., & Sellitto, C. (2010). Business Intelligence (BI) Critical Success Factors.

Holsapple, C. W., & Sena, M. P. (2005). ERP plans and decision-support benefits. Decision Support Systems, 38(4), 575-590.

Hung, S.-Y., Ku, Y.-C., Liang, T.-P., & Lee, C.-J. (2007). Regret avoidance as a measure of DSS success: An exploratory study. Decision Support Systems, 42(4), 2093-2106.

Hwang, M. I., & Xu, H. (2005). A survey of data warehousing success issues. Business Intelligence Journal, 10(4), 7-14.

Hwang, M. I., & Xu, H. (2008). A structural model of data warehousing success. Journal of Computer Information Systems, 49(1), 48-56.

IDC. (1996). Financial Impact of Data Warehousing, International Data Corporation.

Ishikiriyama, C. S., Miro, D., & Gomes, C. F. S. (2015). Text Mining Business Intelligence: a small sample of what words can say. Procedia Computer Science, 55, 261-267.

Işık, Ö., Jones, M. C., & Sidorova, A. (2013).Business intelligence success: The roles of BI capabilities and decision environments. Information & Management, 50(1), 13-23.

Jagielska, I., Darke, P., & Zagari, G. (2003). Business Intelligence systems for decision support: Concepts, processes and practice.

Johnson, L. K. (2004). Strategies for Data Warehousing, MIT Sloan Management Review, (Spring). 45(3), 9.

Liautaud, B., & Hammond, M. (2001). e-Business intelligence: turning information into knowledge into profit: McGraw-Hill, Inc.

Oana, V.-L., & Ogan, M. Y. (2012). The Use of Dashboards in Performance Management: Evidence from Sales Managers. The International Journal of Digital Accounting Research, 12, 39-58.

Olszak, C. M., & Ziemba, E. (2007). Approach to building and implementing business intelligence systems. Interdisciplinary Journal of Information, Knowledge, and Management, 2, 134-148.

Ong, I. L., Siew, P. H., & Wong, S. F. (2011). Assessing organizational business intelligence maturity. Paper presented at the Information Technology and Multimedia (ICIM), 2011 International Conference on.

Pirttimäki, V., Lönnqvist, A., & Karjaluoto, A. (2006). Measurement of business intelligence in a Finnish telecommunications company. The Electronic Journal of Knowledge Management, 4(1), 83-90.

Popovic, A., Hackney, R., Coelho, P. S., & Jaklič, J. (2012). Towards business intelligence systems success: Effects of maturity and culture on analytical decision making. Decision Support Systems, 54(1), 729-739.

Popovic, A., Turk, T., & Jaklic, J. (2010). Conceptual model of business value of business intelligence systems. Management: Journal of Contemporary Management Issues, 15(1), 5-30.

Rezaie, K., Ansarinejad, A., Haeri, A., Nazari-Shirkouhi, A., & Nazari-Shirkouhi, S. (2011). Evaluating the Business Intelligence Systems Performance Criteria Using Group Fuzzy AHP Approach. Paper presented at the Computer Modelling and Simulation (UKSim), 2011 UkSim 13th International Conference on. Cambridge.

Rouhani, S., Ashrafi, A., Zare, A., & Afshari, S. (2016). The impact model of business intelligence on decision support and organizational benefits. Journal of Enterprise Information Management, 29(1).

Sabherwal, R., & Becerra-Fernandez, I. (2011). Business Intelligence: Practices, technologies, management: John Wiley & Sons.1st Edition. ISBN-13: 978-0470461709.

Sharma, R. S., & Djiaw, V. (2011). Realising the strategic impact of business intelligence tools. VINE, 41(2), 113-131.

Skyrius, R., Kazakevièiene, G., & Bujauskas, V. (2013). From management information systems to business intelligence: the development of management information needs. IJIMAI, 2(3), 31-37.

Stephens, P. (2002). BI: The business case.(www. bi-solutions.co.uk).

Tutunea, M. F. (2015). Business Intelligence Solutions for Mobile Devices–An Overview. Procedia Economics and Finance, 27, 160-169.

Uçaktürk, A., Uçaktürk, T., & Yavuz, H. (2015). Possibilities of Usage of Strategic Business Intelligence Systems Based on Databases in Agile Manufacturing. Procedia-Social and Behavioral Sciences, 207, 234-241.

Visual.ly. (2014). Business Intelligence Tools.(https://visual.ly/m/business-intelligence-tools/).

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.

Wixom, B. H., & Watson, H. J. (2001). An empirical investigation of the factors affecting data warehousing success. MIS quarterly, 17-41

 

 

 

 

 

 

 

تارخ، محمدجعفر و مهاجری، حسین. هوش تجاری؛ نگرشی پویا در عرصه کسب­وکار. انتشارات دانشگاه صنعتی خواجه نصیرالدین طوسی. چاپ اول. دی ماه 1391.

رهنمای رودپشتی، ف. و عاضدی تهرانی، ش.، هوش تجاری مالی. اولین همایش ملی هوش تجاری/کسب و کار. 1389.

Candal-Vicente, I. (2009). Factors That Affect the Successful Implementation of a Data Warehouse. Paper presented at the Computing in the Global Information Technology, 2009. ICCGI'09. Fourth International Multi-Conference on.

Chenoweth, T., Corral, K., & Demirkan, H. (2006). Seven key interventions for data warehouse success. Communications of the ACM, 49(1), 114-119.

Cutter Consortium Report (2003) “Cutter Consortium Report on Corporate Use of BI and Data Warehousing Technologies”, at : http://www.dmreview.com.

Dekoulou, P., & Trivellas, P. (2014). Learning Organization in Greek Advertising and Media Industry: A way to face crisis and gain sustainable competitive advantage. Procedia-Social and Behavioral Sciences, 148, 338-347.

Dresner, H., Linden, A., Buytendijk, F., Friedman, T., Strange, K., Knox, M., & Camm, M. (2002). The business intelligence competency center: An essential business strategy. Gartner Strategic Analysis Report.

Elbashir, M. Z., Collier, P. A., & Davern, M. J. (2008). Measuring the effects of business intelligence systems: The relationship between business process and organizational performance. International Journal of Accounting Information Systems, 9(3), 135-153.

Gartner. (2009) . Gartner EXP Worldwide Survey of More than 1,500 CIOs Shows IT Spending to Be Flat in 2009. STAMFORD, Conn.

Ghazanfari, M., Jafari, M., & Rouhani, S. (2011). A tool to evaluate the business intelligence of enterprise systems. Scientia Iranica, 18(6), 1579-1590.

Gomes, C. F. S., & Ribeiro, P. (2014). Gestao da cadeia de suprimentos integrada a tecnologia da informacao (Vol. 2nd ed): Sao Paulo: Cengage Learning Editores.

HashemiTabatabaei, S. (2010). Evaluation of business intelligence maturity level in Iranian banking industry. master thesis,tarbiat modares university .fuculity of engineering.

Hawking, P., & Sellitto, C. (2010). Business Intelligence (BI) Critical Success Factors.

Holsapple, C. W., & Sena, M. P. (2005). ERP plans and decision-support benefits. Decision Support Systems, 38(4), 575-590.

Hung, S.-Y., Ku, Y.-C., Liang, T.-P., & Lee, C.-J. (2007). Regret avoidance as a measure of DSS success: An exploratory study. Decision Support Systems, 42(4), 2093-2106.

Hwang, M. I., & Xu, H. (2005). A survey of data warehousing success issues. Business Intelligence Journal, 10(4), 7-14.

Hwang, M. I., & Xu, H. (2008). A structural model of data warehousing success. Journal of Computer Information Systems, 49(1), 48-56.

IDC. (1996). Financial Impact of Data Warehousing, International Data Corporation.

Ishikiriyama, C. S., Miro, D., & Gomes, C. F. S. (2015). Text Mining Business Intelligence: a small sample of what words can say. Procedia Computer Science, 55, 261-267.

Işık, Ö., Jones, M. C., & Sidorova, A. (2013).Business intelligence success: The roles of BI capabilities and decision environments. Information & Management, 50(1), 13-23.

Jagielska, I., Darke, P., & Zagari, G. (2003). Business Intelligence systems for decision support: Concepts, processes and practice.

Johnson, L. K. (2004). Strategies for Data Warehousing, MIT Sloan Management Review, (Spring). 45(3), 9.

Liautaud, B., & Hammond, M. (2001). e-Business intelligence: turning information into knowledge into profit: McGraw-Hill, Inc.

Oana, V.-L., & Ogan, M. Y. (2012). The Use of Dashboards in Performance Management: Evidence from Sales Managers. The International Journal of Digital Accounting Research, 12, 39-58.

Olszak, C. M., & Ziemba, E. (2007). Approach to building and implementing business intelligence systems. Interdisciplinary Journal of Information, Knowledge, and Management, 2, 134-148.

Ong, I. L., Siew, P. H., & Wong, S. F. (2011). Assessing organizational business intelligence maturity. Paper presented at the Information Technology and Multimedia (ICIM), 2011 International Conference on.

Pirttimäki, V., Lönnqvist, A., & Karjaluoto, A. (2006). Measurement of business intelligence in a Finnish telecommunications company. The Electronic Journal of Knowledge Management, 4(1), 83-90.

Popovic, A., Hackney, R., Coelho, P. S., & Jaklič, J. (2012). Towards business intelligence systems success: Effects of maturity and culture on analytical decision making. Decision Support Systems, 54(1), 729-739.

Popovic, A., Turk, T., & Jaklic, J. (2010). Conceptual model of business value of business intelligence systems. Management: Journal of Contemporary Management Issues, 15(1), 5-30.

Rezaie, K., Ansarinejad, A., Haeri, A., Nazari-Shirkouhi, A., & Nazari-Shirkouhi, S. (2011). Evaluating the Business Intelligence Systems Performance Criteria Using Group Fuzzy AHP Approach. Paper presented at the Computer Modelling and Simulation (UKSim), 2011 UkSim 13th International Conference on. Cambridge.

Rouhani, S., Ashrafi, A., Zare, A., & Afshari, S. (2016). The impact model of business intelligence on decision support and organizational benefits. Journal of Enterprise Information Management, 29(1).

Sabherwal, R., & Becerra-Fernandez, I. (2011). Business Intelligence: Practices, technologies, management: John Wiley & Sons.1st Edition. ISBN-13: 978-0470461709.

Sharma, R. S., & Djiaw, V. (2011). Realising the strategic impact of business intelligence tools. VINE, 41(2), 113-131.

Skyrius, R., Kazakevièiene, G., & Bujauskas, V. (2013). From management information systems to business intelligence: the development of management information needs. IJIMAI, 2(3), 31-37.

Stephens, P. (2002). BI: The business case.(www. bi-solutions.co.uk).

Tutunea, M. F. (2015). Business Intelligence Solutions for Mobile Devices–An Overview. Procedia Economics and Finance, 27, 160-169.

Uçaktürk, A., Uçaktürk, T., & Yavuz, H. (2015). Possibilities of Usage of Strategic Business Intelligence Systems Based on Databases in Agile Manufacturing. Procedia-Social and Behavioral Sciences, 207, 234-241.

Visual.ly. (2014). Business Intelligence Tools.(https://visual.ly/m/business-intelligence-tools/).

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.

Wixom, B. H., & Watson, H. J. (2001). An empirical investigation of the factors affecting data warehousing success. MIS quarterly, 17-41