Industrial Management Studies

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Publication Ethics

Indexing and Abstracting

Related Links

FAQ

Peer Review Process

Journal Metrics

News

Memorandum of Understanding between the Quarterly and the Iranian Management Sciences Association

Article Writing Guide

Files

Reviewer 1402

Reviewer 1401

Reviewer 1400

Consultants

Publons

Presentation of the Model of Measuring the Maturity Level of Business ‎Intelligence in Electronic Business‏ ‏‎(Case Study: Internet Service Provider Companies)

Document Type : Research Paper

Authors

1 PhD in Information Technology Management, Islamic Azad University, Isfahan Branch (Khorasgan), Isfahan, Iran

2 Associate Professor, Department of Management, Islamic Azad University, Isfahan Branch (Khorasgan), Isfahan, Iran

3 Assistant Professor, Department of Management, Islamic Azad University, Isfahan Branch (Khorasgan), Isfahan, Iran

Abstract

This study aims to design a model for measuring the level of maturity of business intelligence in electronic businesses, specifically for Internet Service Provider (ISP) companies. The study adopts a qualitative approach based on the phenomenological approach. A total of 10 specialists, experts, and managers from electronic businesses involved in providing Internet services are selected as participants using the maximal differentiation method. Data are collected through in-depth and semi-structured interviews and analyzed using Colaizzi's method. The findings are classified into five levels of business intelligence maturity (Level 1: Primary maturity, Level 2: Repeatable maturity, Level 3: Defined maturity, Level 4: Managed maturity, Level 5: Optimized maturity) using the Delphi technique. Subsequently, a model consisting of 33 dimensions and 232 indicators is designed based on the relevant literature and the researcher's viewpoint, with confirmation from experts. Finally, the model is validated using confirmatory factor analysis in Smart PLS software.
Introduction
Due to the fact that businesses face numerous challenges, such as the need for increased responsiveness and transparency towards customers, the growing number of tasks and organizational activities, and rapid technological changes, they require mechanisms capable of real-time data analysis and integration. Business intelligence serves as one of these mechanisms. Additionally, businesses need to assess and evaluate their current performance, compare their existing processes, tools, and methods with the best practices, and measure indicators of predictability, control, and effectiveness to effectively implement business intelligence. Therefore, they require a model to gauge the maturity level of business intelligence within their organization. Consequently, the objective of this research is to present a model for measuring the maturity level of business intelligence in electronic businesses.
Materials and Methods
Since the researcher aims to extract the components of business intelligence maturity based on people's mentalities and experiences, the phenomenological method, specifically Colaizzi's method, was employed. To achieve this, 10 experts from Internet service provider companies were interviewed and selected using the maximal differentiation sampling method. The analysis of these interviews resulted in the extraction of 277 significant codes. Given the research's focus on measuring the maturity level of business intelligence, 40 experts were then asked to classify the obtained concepts into five levels of business intelligence maturity (Level 1: Primary maturity, Level 2: Repeatable maturity, Level 3: Defined maturity, Level 4: Managed maturity, Level 5: Optimized maturity) using the Delphi technique and snowball sampling method. After three rounds of Delphi, 232 codes remained out of the initial total of 277 codes. These 232 indicators were then categorized into 33 dimensions based on the definitions, functions of business intelligence, and the perceived concepts of each indicator. Subsequently, the researcher designed a measurement model for the maturity level of business intelligence in electronic businesses specifically tailored for Internet service providers. Finally, the designed model was validated through confirmatory factor analysis using SmartPLS software.
Discussion and Results
This research has developed a model that enables companies, especially Internet service providers, to assess their current business state and their progress towards their goals. The model facilitates the decision-making process for e-business managers. With 5 levels, 33 dimensions, and 232 indicators encompassing technical, managerial, and human aspects, the model effectively enhances business capabilities and establishes a foundation for improving and advancing the level of maturity within the business. It is important to note that the model's Level 1 (Primary maturity) includes one dimension titled "reporting" with five indicators. Level 2 (Repeatable maturity) comprises five dimensions: advertising (eight indicators), management and performance evaluation (seven indicators), control (three indicators), documentation (five indicators), and automation (two indicators). Level 3 (Defined maturity) consists of six dimensions: access level (four indicators), customer orientation (16 indicators), process management (eight indicators), standardization of processes (10 indicators), improvement of information quality (five indicators), and improvement of service level (28 indicators). Level 4 (Managed maturity) encompasses 13 dimensions: assessment and analysis skills (14 indicators), business development and organizational processes (nine indicators), organizational management (12 indicators), organizational training (nine indicators), human resource management (16 indicators), organizational value (five indicators), security (two indicators), support (five indicators), business strategies (three indicators), management and development of essentials (11 indicators), business performance management (five indicators), policy making (four indicators), and cost-benefit (two indicators). Lastly, Level 5 (Optimized maturity) includes eight dimensions: predictive analysis (six indicators), dashboard (two indicators), knowledge management (six indicators), innovation (four indicators), competitive advantage (six indicators), technology development (four indicators), expansion of investment (three indicators), and data mining (three indicators).
Conclusions
This research has designed a model to facilitate the decision-making process of e-business managers, particularly those in Internet service providers. The model enables companies to assess their current business state and their progress towards their goals. The model encompasses 5 levels, 33 dimensions, and 232 different indicators, taking into account technical, managerial, and human aspects. With this comprehensive approach, the model has the potential to enhance business capabilities and establish a solid groundwork for improving and advancing the maturity level of the business. Internet service provider companies not only gain an understanding of their business intelligence maturity level and have the opportunity to elevate it through long-term planning, but they also empower themselves to navigate future changes and meet evolving customer expectations. The business intelligence maturity model introduced in this study serves as a framework for continuous improvement in their business activities. It provides a foundation and context for controlling processes and facilitates the ongoing enhancement of their operations.
This study aims to design a model for measuring the level of maturity of business intelligence in electronic businesses, specifically for Internet Service Provider (ISP) companies. The study adopts a qualitative approach based on the phenomenological approach. A total of 10 specialists, experts, and managers from electronic businesses involved in providing Internet services are selected as participants using the maximal differentiation method. Data are collected through in-depth and semi-structured interviews and analyzed using Colaizzi's method. The findings are classified into five levels of business intelligence maturity (Level 1: Primary maturity, Level 2: Repeatable maturity, Level 3: Defined maturity, Level 4: Managed maturity, Level 5: Optimized maturity) using the Delphi technique. Subsequently, a model consisting of 33 dimensions and 232 indicators is designed based on the relevant literature and the researcher's viewpoint, with confirmation from experts. Finally, the model is validated using confirmatory factor analysis in Smart PLS software.
Introduction
Due to the fact that businesses face numerous challenges, such as the need for increased responsiveness and transparency towards customers, the growing number of tasks and organizational activities, and rapid technological changes, they require mechanisms capable of real-time data analysis and integration. Business intelligence serves as one of these mechanisms. Additionally, businesses need to assess and evaluate their current performance, compare their existing processes, tools, and methods with the best practices, and measure indicators of predictability, control, and effectiveness to effectively implement business intelligence. Therefore, they require a model to gauge the maturity level of business intelligence within their organization. Consequently, the objective of this research is to present a model for measuring the maturity level of business intelligence in electronic businesses.
Materials and Methods
Since the researcher aims to extract the components of business intelligence maturity based on people's mentalities and experiences, the phenomenological method, specifically Colaizzi's method, was employed. To achieve this, 10 experts from Internet service provider companies were interviewed and selected using the maximal differentiation sampling method. The analysis of these interviews resulted in the extraction of 277 significant codes. Given the research's focus on measuring the maturity level of business intelligence, 40 experts were then asked to classify the obtained concepts into five levels of business intelligence maturity (Level 1: Primary maturity, Level 2: Repeatable maturity, Level 3: Defined maturity, Level 4: Managed maturity, Level 5: Optimized maturity) using the Delphi technique and snowball sampling method. After three rounds of Delphi, 232 codes remained out of the initial total of 277 codes. These 232 indicators were then categorized into 33 dimensions based on the definitions, functions of business intelligence, and the perceived concepts of each indicator. Subsequently, the researcher designed a measurement model for the maturity level of business intelligence in electronic businesses specifically tailored for Internet service providers. Finally, the designed model was validated through confirmatory factor analysis using SmartPLS software.
Discussion and Results
This research has developed a model that enables companies, especially Internet service providers, to assess their current business state and their progress towards their goals. The model facilitates the decision-making process for e-business managers. With 5 levels, 33 dimensions, and 232 indicators encompassing technical, managerial, and human aspects, the model effectively enhances business capabilities and establishes a foundation for improving and advancing the level of maturity within the business. It is important to note that the model's Level 1 (Primary maturity) includes one dimension titled "reporting" with five indicators. Level 2 (Repeatable maturity) comprises five dimensions: advertising (eight indicators), management and performance evaluation (seven indicators), control (three indicators), documentation (five indicators), and automation (two indicators). Level 3 (Defined maturity) consists of six dimensions: access level (four indicators), customer orientation (16 indicators), process management (eight indicators), standardization of processes (10 indicators), improvement of information quality (five indicators), and improvement of service level (28 indicators). Level 4 (Managed maturity) encompasses 13 dimensions: assessment and analysis skills (14 indicators), business development and organizational processes (nine indicators), organizational management (12 indicators), organizational training (nine indicators), human resource management (16 indicators), organizational value (five indicators), security (two indicators), support (five indicators), business strategies (three indicators), management and development of essentials (11 indicators), business performance management (five indicators), policy making (four indicators), and cost-benefit (two indicators). Lastly, Level 5 (Optimized maturity) includes eight dimensions: predictive analysis (six indicators), dashboard (two indicators), knowledge management (six indicators), innovation (four indicators), competitive advantage (six indicators), technology development (four indicators), expansion of investment (three indicators), and data mining (three indicators).
Conclusions
This research has designed a model to facilitate the decision-making process of e-business managers, particularly those in Internet service providers. The model enables companies to assess their current business state and their progress towards their goals. The model encompasses 5 levels, 33 dimensions, and 232 different indicators, taking into account technical, managerial, and human aspects. With this comprehensive approach, the model has the potential to enhance business capabilities and establish a solid groundwork for improving and advancing the maturity level of the business. Internet service provider companies not only gain an understanding of their business intelligence maturity level and have the opportunity to elevate it through long-term planning, but they also empower themselves to navigate future changes and meet evolving customer expectations. The business intelligence maturity model introduced in this study serves as a framework for continuous improvement in their business activities. It provides a foundation and context for controlling processes and facilitates the ongoing enhancement of their operations.

Keywords

Main Subjects

References
  1. اسماعیل­پور، مجید، حسینی، سید یعقوب و جعفر پور، یونس. (1397). چالش­های پذیرش تجارت الکترونیک در کسب‌وکارهای کوچک و متوسط (مطالعه موردی: شرکت­های فعال مستقر در شهرک صنعتی بوشهر). کاوش­های مدیریت بازرگانی، 10 (19)، 63-39.
  2. تقوا، محمدرضا و نوری، الهه. (1395). هوشمندی کسب‌وکار (مفاهیم و طراحی و توسعه سیستم). چاپ 1. تهران: مرکز چاپ و انتشارات دانشگاه علامه طباطبائی،180 صفحه.
  3. رونقی، محمدحسین و رونقی، مرضیه. (1393). ارائه مدل بلوغ هوشمندی کسب‌وکار در بین سازمان­های ایرانی. رشد فناوری، فصلنامه تخصصی پارک­ها و مراکز رشد، 10 (38)، 44-38.
  4. صفری، حسین و مرادی مقدم، محسن. (1393). بلوغ فرآیندهای کسب‌وکار. چاپ 1. تهران: موسسه کتاب مهربان نشر،362 صفحه.
  5. کزازی، ابوالفضل، بامداد صوفی، جهانیار و پهلوان نژاد، علی. (1396). ارائه یک مدل ترکیبی جدید برای شناسایی سطح بلوغ هوش تجاری سازمان­ها. کنفرانس سالانه پارادایم­های نوین مدیریت در حوزه هوشمندی. تهران، دبیرخانه دائمی کنفرانس، دانشگاه تهران.
  6. مهرابی، علی، محمودی، ادریس و حسینی، رضا علی. (1395). ارزیابی بلوغ هوشمندی کسب‌وکار با یک مدل ترکیبی جدید. مطالعات مدیریت فناوری اطلاعات، 4 (15)، 96-65.
  7. نیک روان شلمانی، علیرضا و احمدی، مجتبی. (1395). فناوری اطلاعات سازمانی و سیستم­های نرم­افزاری. چاپ 1. تهران: انتشارات ناقوس،224 صفحه.
  8. Ahmed, F., & Capretz. L.F. (2011). A business maturity model of software product line engineering. Information systems fronties, 13(4), 543-560.
  9. Al-Ayed, S. (2022). The impact of e-commerce drivers on e-customer loyalty: Evidence from KSA. International Journal of Data and Network Science, 6(1), 73-80.
  10. Basile, L. J., Carbonara, N., Pellegrino, R., & Panniello, U. (2022). Business intelligence in the healthcare industry: The utilization of a data-driven approach to support clinical decision making. Technovation, 102482.
  11. Becker, J., Knackstedt, R., & Pöppelbuß, J. (2009). Developing maturity models for IT management. Business & Information Systems Engineering, 1(3), 213-222.
  12. Bosilj Vuksic, V., Pejic Bach, M., Grubljesic, T., Jaklic, J., & Stjepic, A.M. (2017). The role of Alignment for the Impact of Business Intelligence Maturity on Business Process Performance in Croatian and Slovenian Companies. 40th International Convention on Information and Communication Technology, Electronics and Microelectronics Conference (MIPRO). Opatija, Croatia, 1587-1592.
  13. Brichni, M., Dupuy-Chessa, S., Gzara, L., Mandran, N., & Jeannet, C. (2017). BI4BI: A continuous evaluation system for Business Intelligence systems. Expert Systems with Applications, 76(2017), 97-112.
  14. Broekhuizen, T. L., Broekhuis, M., Gijsenberg, M. J., & Wieringa, J. E. (2021). Introduction to the special issue–digital business models: a multi-disciplinary and multi-stakeholder perspective. Journal of Business Research, 122, 847-852.
  15. Brzozowsk, A., & Bubel, D. (2015). E-business as a new trend in the economy. Procedia Computer Science, 65(2015), 1095-1104.
  16. Cardoso, E., & Su, X. (2022). Designing a Business Intelligence and Analytics Maturity Model for Higher Education: A Design Science Approach. Applied Sciences, 12(9), 4625.
  17. Cates, J.E., S.S. Gill, & Zeituny, N. (2005). The Ladder of Business Intelligence (LOBI): a framework for enterprise IT planning and architecture. International Journal of Business Information Systems, 1(1-2), 220-238.
  18. Chaffey, D. (2011). E-business and Ecommerce management: Strategy, Implementation and practice.5th Edition. Publisher: Pearson Education Limited.
  19. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.
  20. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed). Lawrence Erlbaum Associates, Publishers.
  21. Curtis, B., & Alden, J. (2007). Maturity Model du Jour: A Recipe for Side Dishes, BPTrends.
  22. Duque, J., Godinho, A., & Vasconcelos, J. (2022). Knowledge data extraction for business intelligence a design science research approach. Procedia Computer Science, 204, 131-139.
  23. Esmailpour, M., Hosseini, S.Y., & Jafarpour, Y. (2018). The challenges of accepting e-commerce in small and medium businesses (Case study: Active companies in Bushehr industrial town). Business Management Studies, 10(19), 39-63. [In Persian]
  24. Farshadi, R., Nazemi, E., & Abdolvand, N. (2022). A Framework For Ranking Critical Success Factors Of Business Intelligence Based On Enterprise Architecture And Maturity Model. Interdisciplinary Journal of Information, Knowledge & Management, 17.
  25. Fedouaki, F., Okar, C., & Almai, S.El. (2013). A maturity model for Business Intelligence System project in Small and Medium-sized Enterprises: an empirical investigation. IJCSI International Journal of Computer Science Issues, 10(6), 61-69.
  26. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
  27. Garcia, J. (2011). BI Maturity and Software Selection perspectives. Technology Evaluation Centure, 1-12, Available at: www.technologyevaluation.com.
  28. Gartner. (2008). Gartner Executive Programs CIO Survey, available at www.Gartner.com accessed June 2008.
  29. Harmon, P. (2004). Evaluating an Organization’s Business Process Maturity. Business Process Trends, 2(3), 1-11.
  30. Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing. Emerald Group Publishing Limited.
  31. Hewlett, P. (2007). The HP Business Intelligence Maturity Model. Viewed on 21 April 2009, <http://h71028.www7.hp.com/ERC/downloads/4AA1-5467ENW.pdf>.
  32. Kazazi, A., Bamdad Sufi, J., & Pahlavan Nejad, A. (2017). Presenting a new hybrid model to identify the maturity level of business intelligence of organizations. Annual conference of new management paradigms in the field of intelligence. Tehran, Permanent Conference Secretariat, University of Tehran. [In Persian]
  33. Kline, P. (1994). An Easy Guide to Factor Analysis. New York, NY: Routledge, 208P.
  34. Kumar, P., & Dhinesh Babu, L. D. (2019). Review on big data and its impact on business intelligence. In Information Systems Design and Intelligent Applications (pp. 93-109). Springer, Singapore.
  35. Lahrmann, G., Marx, F., Winter, R., & Wortmann, F. (2010). Business Intelligence Maturity Models: An Overview. Information Technology and Innovation Trends in Organizations: Conference Proceedings, VII Conference of the Italian Chapter of AIS (itAIS 2010).
  36. Lasi, H. (2013). Industrial Intelligence- A Business Intelligence-based Approach to Enhance Manufacturing Engineering in Industrial Companies. Procedia CIRP, 12(2013), 384-389.
  37. Lasrado, L. A., Vatrapu, R., & Andersen, K. N. (2015). Maturity models development in is research: a literature review. In IRIS Selected Papers of the Information Systems Research Seminar in Scandinavia, 6, 6. New York: IRIS.
  38. Lederer, M., & Schmid, P. (2021). Data Science for Business Analytics and Business Intelligence. In Encyclopedia of Organizational Knowledge, Administration, and Technology (pp. 495-508). IGI Global.
  39. Lessanibahri, S., Gastaldi, L., Pietrosi, A., & Corso, M. (2015). Co-developing a Roadmap towards Precision Medicine: Measuring the Maturity of BI in Healthcare. 10th International Forum on Knowledge Asset Dynamics Bari, Italy, 1671-1688.
  40. Mehrabi, A., Mahmoudi, E., & Hosseini, R.A. (2016). Assessing the maturity of business intelligence with a new hybrid model. Quarterly Journal of Information Technology Management Studies, 4(15), 65-96. [In Persian]
  41. Mkansi, M. (2022). E-business adoption costs and strategies for retail micro businesses. Electronic Commerce Research, 22(4), 1153-1193.
  42. Molina-Granja, F., Granda, W. B., Altamiran, J. D., & Ramos, P. L. (2022). Maturity Model for Data Analytics in Health Institutions. Journal of Positive School Psychology, 4585-4590.
  43. Moscoso-Zea, O., Castro, J., Paredes-Gualtor, J., & Luján-Mora, S. (2019). A hybrid infrastructure of enterprise architecture and business intelligence & analytics for knowledge management in education. IEEE access, 7, 38778-38788.
  44. Nikravan Shelmani, A.R. & Ahmadi, M. (2016). Organizational information technology and software systems. Ed. 1. Tehran: Naghus Publications, 224 P. [In Persian]
  45. Oghazi, P., Karlsson, S., Hellström, D., Mostaghel, R., & Sattari, S. (2021). From Mars to Venus: Alteration of trust and reputation in online shopping. Journal of Innovation & Knowledge, 6(4), 197-202.
  46. Omotosho, B. J. (2020). Small scale craft workers and the use of social media platforms for business performance in southwest Nigeria. Journal of Small Business & Entrepreneurship, 1-16.
  47. Palmié, M., Miehé, L., Oghazi, P., Parida, V., & Wincent, J. (2022). The evolution of the digital service ecosystem and digital business model innovation in retail: The emergence of meta-ecosystems and the value of physical interactions. Technological Forecasting and Social Change, 177, 121496.
  48. Patil, P. (2016). Survey of business intelligence system. International Journal of research in computer applications and robotics, 4(9), 13-17.
  49. Rajteric, I. H. (2010). Overview of Business Intelligence Maturity Models. Management, 15(1), 47-67.
  50. Ronaghi, M.H. & Ronaghi, M. (2014). Presenting the maturity model of business intelligence among Iranian organizations. Technology growth, specialized quarterly of parks and growth centers, 10(38), 38-44. [In Persian]
  51. Sacu, C., & Spruit, M. (2010). BIDM: The Business Intelligence Development Model. Proceedings of the 12th International Conference on Enterprise Information Systems, Volume 1, DISI, Funchal, Madeira-Portugal.
  52. Safari, H., & Moradi Moghadam, M. (2014). Maturity of business processes. Ed. 1. Tehran: Mehraban Publishing Institute, 362 P. [In Persian]
  53. SAS. (2011). Information Evaluation Model. http://www.sas.com/software/iem/ Retrieved September 2011.
  54. Sawadogo, P., & Darmont, J. (2021). On data lake architectures and metadata management. Journal of Intelligent Information Systems, 56(1), 97-120.
  55. Shaaban, E., Helmy, Y., Khedr, A., & Nasr, M. (2011). Business Intelligence Maturity Models: Toward New Integrated Model. Naif Arab University for Security Sciences, 12(11), 276-284.
  56. Soluk, J., Miroshnychenko, I., Kammerlander, N., & De Massis, A. (2021). Family influence and digital business model innovation: the enabling role of dynamic capabilities. Entrepreneurship Theory and Practice, 45(4), 867-905.
  57. Sun, Z., Sun, L., & Strang, K. (2018). Big data analytics services for enhancing business intelligence. Journal of Computer Information Systems, 58(2), 162-169.
  58. Taghwa, M.R., & Nouri, E. (2016). Business intelligence (concepts and system design and development). Ed. 1. Tehran: Allameh Tabatabaei University Printing and Publishing Center, 180 P. [In Persian]
  59. Tan, C.S., Sim, Y.W., & Yeoh, W. (2011). A Maturity Model of Enterprise Business Intelligence. Communication of the IBIMA, 2011(417812), 1-11.
  60. Trakadas, P., Simoens, P., Gkonis, P., Sarakis, L., Angelopoulos, A., Ramallo-González, A.P., Skarmeta, A., Trochoutsos, C., Calvο, D., Pariente, T., Chintamani, K., Fernandez, I., Irigaray, AA., Parreira, JX., Petrali, P., Leligou, N. & Karkazis, P. (2020). An artificial intelligence-based collaboration approach in industrial iot manufacturing: Key concepts, architectural extensions and potential applications. Sensors, 20(19), 5480.
  61. Tsai, H.H. (2015). The research trends forecasted by bibliometric methodology: a case study in e-commerce from 1996 to July 2015. Scientometrics, 105(2), 1079-1089.
  62. Ucakturk, A., Ucakturk, T., & Yavuz, H. (2015). Possibilities of Usage of Strategic Business Intelligence Systems Based on Databases in Agile Manufacturing. Procedia-Social and Behavioral Sciences, 207(2015), 234-241.
  63. Van looy, A. (2014). Business process maturity: a comparative study on a sample of business process maturity models. 1st Edition. Publisher: Springer.
  64. Watson, H., Ariyachandra, T., & Matyska, R. J. (2001). Data Warehousing Stages of Growth. Information Systems Management, 18(3), 42-50.
  65. Wetzels, M., Odekerken-Schröder, G., & Van Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS quarterly, 177-195.
  66. 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.
  67. Wirtz, J., Fritze, M. P., Jaakkola, E., Gelbrich, K., & Hartley, N. (2021). Service products and productization. Journal of Business Research, 137, 411-421            
Industrial Management Studies
Volume 21, Issue 68
May 2023
Pages 241-287
Files
Share
How to cite
Statistics