مطالعات مدیریت کسب و کار هوشمند

مطالعات مدیریت کسب و کار هوشمند

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چهارچوب عوامل موثر بر بلوغ هوشمندی کسب‌ وکار

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مدیریت، دانشگاه شیراز، شیراز، ایران

2 دانشیار بخش مدیریت دانشگاه شیراز شیراز ایران نویسنده مسئول: mh_ronaghi@shirazu.ac.ir

3 استاد بخش مدیریت، دانشگاه شیراز، شیراز، ایران

4 دانشیار بخش مدیریت، دانشگاه شیراز، شیراز، ایران

چکیده

بلوغ هوشمندی کسب‌وکار، نتیجه‌ای از تکامل و پیشرفت فناوری و رویکردهای مدیریتی است که با استفاده از تکنولوژی‌های پیشرفته مانند هوش مصنوعی و تحلیل داده‌ها، به ارائه اطلاعات دقیق، تحلیل‌های پیش‌بینی و بهبود تصمیمات در سازمان‌ها کمک می‌کند. علی‌رغم بلوغ فنی که کارایی و عملکرد سازمان‌ها را در طول زمان بهبود می‌بخشد، هوشمندی کسب‌وکار فاصله زیادی تا تبدیل‌شدن به روند اصلی در سازمان‌ها دارد. باتوجه‌به پژوهش‌های متعدد در حوزه هوشمندی کسب‌وکار، هدف این پژوهش ارائه چارچوب عوامل مؤثر بر بلوغ هوشمندی کسب‌وکار با استفاده از رویکرد فراترکیب بود. به‌منظور رسیدن به چارچوبی جامع که دربرگیرنده همه عوامل بلوغ هوشمندی کسب‌وکار باشد، 221 پژوهش علمی مرور شدند. کدهای مربوطه با استفاده از تحلیل محتوا در روش فراترکیب استخراج شدند. مقوله‌ها با استفاده از روش مدل‌سازی ساختاری تفسیری جامع، سطح‌بندی شدند و تأثیرگذارترین آنها مشخص شدند. یافته‌ها نشان می‌دهند که در مجموع 93 کد استخراج شده که در 6 مقوله تقسیم شدند. این مقوله‌ها شامل عوامل سازمان و مدیریت، محیط، زیرساخت فناوری، منابع انسانی – دانش، مدیریت داده و تحلیل داده هستند. مقوله‌های زیرساخت فناوری، مدیریت داده و تحلیل داده در سطح سه قرار گرفتند و بیشترین تأثیر را بر بلوغ هوشمندی کسب‌وکار دارند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

The framework of factors affecting the maturity of business intelligence

نویسندگان [English]

  • Javad Nazarian-Jashnabadi 1
  • MohammadHossein Ronaghi 2
  • moslem alimohammadlu 3
  • Abolghasem Ebrahimi 4

1 Ph.D. Student, Department of Management, Faculty of Economics, Management and Social Sciences, Shiraz University, Shiraz, Iran.

2 Department of Management, Shiraz University, Shiraz, Iran Corresponding Author: , mh_ronaghi@shirazu.ac.ir

3 Professor, Department of Management, Faculty of Economics, Management and Social Sciences, Shiraz University, Shiraz, Iran.

4 Associate Professor, Department of Management, Faculty of Economics, Management and Social Sciences, Shiraz University, Shiraz, Iran.

چکیده [English]

Abstract
The maturity of business intelligence is a result of the evolution and advancement of technology and management approaches that help to provide accurate information, predictive analyzes and improve decisions in organizations using advanced technologies such as artificial intelligence and data analysis. Despite technological maturity that improves the efficiency and performance of organizations over time, business intelligence is far from becoming a mainstream trend in organizations. According to numerous researches in the field of business intelligence, the aim of this research was to present the framework of factors affecting the maturity of business intelligence using a meta-composite approach. In order to reach a comprehensive framework that includes all the maturity factors of business intelligence, 221 scientific studies were reviewed. Relevant codes were extracted using content analysis in metacomposite method. The categories were leveled using the comprehensive interpretive structural modeling method and the most influential ones were determined. The findings show that a total of 93 codes were extracted and divided into 6 categories. These categories include organization and management factors, environment, technology infrastructure, human resources - knowledge, data management and data analysis. The categories of technology infrastructure, data management and data analysis were placed at level three and have the greatest impact on the maturity of business intelligence.

Introduction

In today's world, digital transformation has become one of the prominent and fundamental phenomena in the field of technology and business. This transformation has placed organizations in a process of change and evolution, significantly altering their approaches and operational methods (Hilbert, 2022). One of the concepts that has emerged as a result of these developments is business intelligence (Ragazou et al., 2023). The primary objective of business intelligence is to convert scattered, raw, and unstructured data into usable and valuable information. By integrating internal and external data and utilizing advanced analytics methods such as data mining and artificial intelligence, business intelligence facilitates more effective and precise decision-making for organizations (Sinarasri & Chariri, 2023). However, given the multifaceted nature of business intelligence, companies must operate more intelligently and strive for maturity by identifying critical factors in the successful implementation of business intelligence. This plays a crucial role in reducing the likelihood of business failures. In general, the shortage of appropriate knowledge resources for companies operating in this field, coupled with a lack of proper understanding among managers, has resulted in minimalist views on business intelligence, limiting its scope to basic services and reports.
Given the extensive use of business intelligence, addressing the topic of business intelligence and its influencing factors is crucial. On the other hand, the existence of numerous domestic and international research studies in various aspects of business intelligence necessitates the creation of a comprehensive and coherent framework to connect these research efforts. Considering the current concern, the main question of this research is to provide a comprehensive and coherent framework of the factors affecting business intelligence maturity. The results of this research play a role in advancing theoretical discussions on the maturity of business intelligence and provide suitable indicators for companies seeking to optimize their use of business intelligence. The use of quantitative approaches alongside systematic review can add significant value; therefore, the "Total Interpretive Structural Modeling" (TISM) approach is used to determine the levels of concepts. The research questions are as follows:
(1) What are the influential factors on business intelligence maturity?
(2) What is the classification of factors affecting the maturity of business intelligence?
(3) What are the most important concepts influencing business intelligence maturity?
(4) Among researchers, which factors influencing business intelligence maturity are most commonly used?

Literature Review

The concept of business intelligence maturity refers to an organizational growth stage in which organizations and businesses harness intelligent technologies and leverage their most powerful features. This stage signifies that achieving maturity in business intelligence is considered a strategic goal for organizations in the digital age. Business intelligence maturity offers several advantages, as highlighted in various studies: improved decision-making (Aparicio et al., 2023), enhanced customer satisfaction (Ramos, 2022), increased flexibility (Aparicio et al., 2023), and reduced costs and time required for work (Niazi, 2019).
The research conducted in the field of business intelligence across various domains has highlighted several advantages. These include data analytics and dashboards (Sinarasri & Chariri, 2023), security and privacy (Halper & Stodder, 2014), as well as forecasting and advanced analytics (Darwiesh et al., 2022). However, it's important to note that the topics and benefits mentioned here represent only a fraction of the research conducted in the field of business intelligence maturity. Most of these studies are domain-specific, focusing on industries such as banking (Rezaei et al., 2017; Monshy, 2021; Najmi et al., 2010), insurance, small businesses (Ragazou et al., 2023; Sinarasri & Chariri, 2023), e-commerce (Ramos, 2022), the manufacturing industry (Ahmad et al., 2020), and supply chain management (Arunachalam et al., 2018).
Some of these research studies have adopted a quantitative approach (Rangriz and Afshari, 2015). This type of research often focuses on the maturity of business intelligence using structural equations (Monshy, 2021; Poti et al., 2017; Khrisat et al., 2023; Golestanizadeh et al., 2023; Mbima & Tetteh, 2023) and examines the relationships between various latent variables and the maturity of business intelligence. However, these studies have not employed a systematic review approach to comprehensively explore the underlying concepts. Business intelligence encompasses diverse dimensions and extends beyond a few latent variables.
Another part of the researches has dealt with the modeling of business intelligence with a qualitative method; However, their investigation has reached limited variables and does not include all aspects of business intelligence (Fallah and Kazemi, 2019; Adineh et al., 2022). On the other hand, it should be clear what level of the organization the model is for (readiness, growth, maturity and decline). Because every organization with the conditions it lives in needs a certain level of business intelligence to progress and it is not possible to prescribe the advanced use of business intelligence to a newly established organization, which has not been observed in various researches (Ahmadizad et al., 2015; Srivastava & Venkataraman, 2022).

Methodology

This study is objective in nature and employs a qualitative approach. Its aim is to identify the factors that affect the maturity of business intelligence. To achieve this, a meta-synthesis approach is used to examine existing articles in the field and extract the relevant factors. The statistical population for this research includes credible and relevant articles published until 2023. Meta-synthesis entails reviewing prior studies and reinterpreting concepts by integrating previous results. In this research, the seven-stage Sandelowski & Barroso (2003) method is employed to conduct the meta-synthesis, as it is widely recognized as the most commonly used method for meta-synthesis in recent university research studies. The seventh and final step of the meta-synthesis method involves presenting the findings. In this phase, the TISM is utilized to categorize the meta-synthesis outputs into two categories: "impactful" or "influenced." Eventually, a comprehensive framework for understanding the factors that influence the maturity of business intelligence is established by employing TISM.

Results

The aim of this research was to provide a framework for understanding the factors that influence business intelligence maturity using a meta-synthesis approach. To develop a comprehensive framework encompassing all aspects of business intelligence maturity, 221 scientific studies were reviewed. Relevant codes were extracted through content analysis using the meta-synthesis method. The categories were stratified using the Total Interpretive Structural Modeling method, and the most influential ones were determined. The findings indicate that a total of 93 codes were extracted, which were categorized into 6 groups. These categories encompass organizational and managerial factors, the environment, technological infrastructure, Human resources - knowledge, data management, and data analysis. The categories of technological infrastructure, data management, and data analysis were placed at level three and exhibited the greatest impact on business intelligence maturity.

Discussion and Conclusion

This research investigates the factors influencing the maturity of business intelligence with the aim of establishing a comprehensive framework. The results obtained through the meta-synthesis method reveal six categories crucial to business intelligence maturity. These categories are categorized using the TISM method. Technology infrastructure, data management, and data analysis are placed at the third level and exhibit the most significant impact on other levels. Human resources - knowledge and organization and management factors were placed at the second level. This level is influenced by the third level and, in turn, influences the first level. The environment is categorized at the first level.
Among the factors affecting business intelligence maturity, the power of analysis, decision-making quality, and quick and easy access to data exhibit the highest recurrence rate in previous research. The ability to analyze data accurately and with a focus on data-centricity extracts comprehensive insights from the data (Lilly & Renjberfred, 2018), enabling precise predictions of trends, patterns, and behaviors both within and outside the organization (Hernández-Julio et al., 2021). The power of analysis empowers organizations to make strategic decisions based on accurate and reliable information and data (Batra, 2022). Most researchers assert that the quality of decision-making is one of the key advantages of implementing business intelligence in organizations (Fu et al., 2022). Regarding the aspect of fast and easy data access, scholars argue that it is a prerequisite for achieving business intelligence maturity (Sinarasri & Chariri, 2023).

کلیدواژه‌ها [English]

  • Business Intelligence
  • Maturity
  • Technology Infrastructure
  • Data Analytics
  • Metasynthesis
مراجع
  1. احمدی زاد، آ، ﻋﺒﺎﺳﯽ، ا، رحیم‌پور، م. (1395). الگوی سنجش هوشمندی کسب‌وکار در صنعت تلکام. فصلنامه مطالعات مدیریت راهبردی، 7 (28)، 281-267.
  2. آدینه، م.، حسن‌زاده، ع.، حسن‌زاده، م؛ و پور عزت، ع. (1401). واکاوی کارکرد هوشمندی کسب‌وکار در کتابخانه‌های دانشگاهی، تحقیقات کتابداری و اطلاع‌رسانی دانشگاهی، 56(2)؛ 14- 1.
  3. پوطی، ن، مرادی مخلص، ح، صالحی و حیدری، ج. (1397). مدلی برای انطباق سازمان‌های آموزشی با ملزومات هوشمندی کسب‌وکار. مطالعات مدیریت کسب‌وکار هوشمند، 6 (23)، 145-175.
  4. رضایی، ص، میرعابدینی، س‌ج و ابطحی، ع. (1397). عوامل مؤثر بر پیاده‌سازی هوشمندی کسب‌وکار در صنعت بانکداری ایران. مطالعات مدیریت کسب‌وکار هوشمند، 6(23)، 33-81.
  5. رنگ‌ریز، ح، افشاری، ن. (1395). مقایسه دیدگاه‌های مختلف درباره رابطه هوشمندی کسب‌وکار و مدیریت دانش. امواج دانش، 1(9)، 0-0.
  6. رونقی، م و دهقانی، م. (1399). ارائه چارچوب پذیرش گردشگری الکترونیک با استفاده از روش فراترکیب. گردشگری و توسعه، 9(4)، 49-61.
  7. رونقی، م و رونقی، م. (1393). ارائه مدل بلوغ هوشمندی کسب‌وکار در بین سازمان‌های ایرانی. رشد فناوری، 10(38)، 38-44.
  8. رئیسی‌وانانی، ا، گنجعلی‌خان‌حاکمی، ف. (1394). طراحی سیستم استنتاج فازی - عصبی انطباقی برای ارزیابی استقرار سیستم هوشمندی کسب‌وکار در صنعت تولید نرم‌افزار. مدیریت فناوری اطلاعات، 7(1)، 85-104.
  9. سعادتی، ز، تارخ، م. (1396). بررسی رویکرد تلفیقی در سیستم‌های هوش کسب‌وکار با تمرکز بر داده‌کاوی. سیاست‌نامه علم و فناوری، 07 (4)، 56-43.
  10. فلاح، م، کاظمی، ز. (1399). شناسایی پیشران‌های مؤثر بر موفقیت شرکت‌های دانش‌بنیان با تأکید بر نقش هوشمندی کسب‌وکار. نشریه علمی راهبردهای بازرگانی 16 (14)، 72-57.
  11. لیلی، آ و رنجبرفرد، م. (1398). مروری بر مدل‌های آمادگی هوش کسب‌وکار. رشد فناوری، 15(60)، 9-17.
  12. مانیان، ا، رونقی، م. (1394). ارائه چارچوب جامع پیاده‌سازی بازاریابی اینترنتی با استفاده از روش فراترکیب. مدیریت بازرگانی، 7(4)، 901-920.
  13. ملایی، ن و طهماسبی، ع. (1398). مدل پلتفرم داده بزرگ و نقش آن در کیفیت داده و هوشمندی کسب‌وکار. مدیریت بحران، 8 (ویژه‌نامه)، 61-72.
  14. منشی، م. (1400). تأثیر هوشمندی کسب‌وکار بر کارآفرینی سازمانی با نقش میانجی بلوغ مدیریت دانش (موردمطالعه: بانک تجارت استان تهران).مدیریت نوآفرینی، 1 (1)، 79-63.
  15. Adamik, A., Nowicki, M., & Puksas, A. (2022). Energy Oriented Concepts and Other SMART WORLD Trends as Game Changers of Co-Production—Reality or Future?. Energies, 15(11), 4112.
  16. Ahmad, S., Miskon, S., Alabdan, R., & Tlili, I. (2020). Towards sustainable textile and apparel industry: Exploring the role of business intelligence systems in the era of industry 4.0. Sustainability, 12(7), 2632.
  17. Alsibhawi, I. A. A., Yahaya, J. B., & Mohamed, H. B. (2023). Business Intelligence Adoption for Small and Medium Enterprises: Conceptual Framework. Applied Sciences, 13(7), 4121.
  18. Aparicio, G., Iturralde, T., & Rodríguez, A. V. (2023). Developments in the knowledge-based economy research field: a bibliometric literature review. Management Review Quarterly, 73(1), 317-352.
  19. Ariyarathna, K., & Peter, S. (2019). Business analytics maturity models: a systematic review of literature. Focus, 3(10), 4-20.
  20. Arunachalam, D., Kumar, N., & Kawalek, J. P. (2018). Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice. Transportation Research, 114, 416-436.
  21. Asimakou, T. (2017). Time for innovation: Concurrent and conflicting metaphors of time in a knowledge MNC. Time & Society, 26(1), 113-134.
  22. Badgujar, A. D., Kadam, S. S., Zambare, M. M., & Kulkarni, S. R. (2022). A comparative study: Business intelligence tools. International Journal of Research in Engineering, Science and Management, 5(1), 116-118.
  23. Batra, D. (2022). Antecedents of Organizational Agility During Business Uncertainty in Noninformation Technology Sectors. Journal of Database Management (JDM), 33(1), 1-22.
  24. Benkhider, N., & Meziani, M. (2021). Digital transformation process based-technology infrastructure and employee training evidence from World Bank. Recherchers economiques manageriales, 15(1), 537-552.
  25. Bhatiasevi, V., & Naglis, M. (2020). Elucidating the determinants of business intelligence adoption and organizational performance. Information development, 36(1), 78-96.
  26. 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.
  27. Calitz, A., Bosire, S., & Cullen, M. (2018). The role of business intelligence in sustainability reporting for South African higher education institutions. International Journal of Sustainability in Higher Education,19(7), 1185-1203.
  28. Canós‐Darós, L. (2013). An algorithm to identify the most motivated employees. Management Decision, 51(4), 813-823.
  29. Chanda, D. (2019). AI Based Data Architecture Impact Analysis. In Proceedings of 32nd International Conference on (Vol. 63, pp. 53-62).
  30. Chuah, M. H., & Wong, K. L. (2011). A review of business intelligence and its maturity models. African journal of business management, 5(9), 3424-3428.
  31. Darwiesh, A., El-Baz, A. H., Tarabia, A. M. K., & Elhoseny, M. (2022). Business Intelligence for Risk Management: A Review.  J. Bus. Oper. Res, 6 (1), 16-27.
  32. Fu, H. P., Chang, T. H., Teng, Y. H., Liu, C. H., & Chuang, H. C. (2022). Critical Factors Considered by Companies to Introduce Business Intelligence Systems. Axioms, 11(7), 338.
  33. Godlewska, M. (2018). Smart Document-Centric Processing of Human Oriented Information Flows. Computing and Informatics, 37(3), 673-692.
  34. Golestanizadeh, M., Sarvari, H., Cristofaro, M., & Chan, D. W. (2023). Effect of Applying Business Intelligence on Export Development and Brand Internationalization in Large Industrial Firms. Administrative Sciences, 13(2), 27.
  35. Gudas, S. (2009). Enterprise knowledge modelling: domains and aspects. Technological and economic development of economy, (2), 281-293.
  36. Gudfinnsson, K., Strand, M., & Berndtsson, M. (2015). Analyzing business intelligence maturity. Journal of Decision Systems, 24(1), 37-54.
  37. Halper, F., & Stodder, D. (2014). TDWI analytics maturity model guide. TDWI research, 1-20.
  38. Harison, E. (2012). Critical success factors of business intelligence system implementations: Evidence from the energy sector. International Journal of Enterprise Information Systems (IJEIS), 8(2), 1-13.
  39. Hausladen, I., & Schosser, M. (2020). Towards a maturity model for big data analytics in airline network planning. Journal of Air Transport Management,82, 101721.
  40. Hernández-Julio, Y. F., Hernández-Royett, J., Nieto-Bernal, W., & Romero-Prieto, J. E. (2021). Business intelligence maturity models: opportunities and recomendations for future reinvestigation-A systematic literature review-Part 2. Aglala, 12(1), 95-113.
  41. Hilbert, M. (2022). Digital technology and social change: the digital transformation of society from a historical perspective. Dialogues in clinical neuroscience, 22 (2) 189-194.
  42. Jahantigh, F. F., Habibi, A., & Sarafrazi, A. (2019). A conceptual framework for business intelligence critical success factors. International Journal of Business Information Systems, 30(1), 109-123.
  43. Khrisat, R. M., Khaddam, A. A., & Abusweilem, M. A. (2023). The role of using big data in predicting customer behaviour: the intermediary role of business intelligence in Jordanian telecommunications companies (a field study). International Journal of Business Information Systems, 42(1), 23-42.
  44. Koolivand, A., Salehi, M., Arabzadeh, M., & Ghodrati, H. (2023). The relationship between knowledge-based economy and fraudulent financial reporting. Journal of Facilities Management, 21(1), 16-29.
  45. Lasi, H. (2013). Industrial intelligence-a business intelligence-based approach to enhance manufacturing engineering in industrial companies. Procedia CIRP,12, 384-389.
  46. Lukman, T., Hackney, R., Popovič, A., Jaklič, J., & Irani, Z. (2011). Business intelligence maturity: the economic transitional context within Slovenia. Information Systems Management, 28(3), 211-222.
  47. Manikam, S., Sahibudin, S., & Kasinathan, V. (2019). Business intelligence addressing service quality for big data analytics in public sector. Indonesian Journal of Electrical Engineering and Computer Science,16(1), 491-499.
  48. Mbima, D. and Tetteh, F.K. (2023), "Effect of business intelligence on operational performance: the mediating role of supply chain ambidexterity", Modern Supply Chain Research and Applications,5(1)28-49.
  49. Najmi, M., Sepehri, M., & Hashemi, S. (2010). The evaluation of Business Intelligence maturity level in Iranian banking industry. Industrial Engineering and Engineering Management.466-470.
  50. Niazi, H. (2019). Strategy, Action Plan, and Approaches for Business Intelligence in Banking and Mining. In Applying Business Intelligence Initiatives.305-325.
  51. Olszak, C. M. (2016). Toward better understanding and use of business intelligence in organizations. Information systems management,33(2),105-123.
  52. Parra, X., Tort-Martorell, X., Alvarez-Gomez, F.,& Ruiz-Viñals,C. (2022). Chronological Evolution of the Information-Driven Decision-Making Process. Journal of the Knowledge Economy, 1-32.
  53. Passlick, J., Grützner, L., Schulz, M., & Breitner, M. H. (2023). Self-service business intelligence and analytics application scenarios: A taxonomy for differentiation. Information Systems and e-Business Management,21(1),159-191.
  54. Powell, A. (2017). CABI's innovative use of technology, data, and knowledge transfer to reduce crop losses in the developing world. Food and Energy Security,6(3),94-97.
  55. Prieto Morales, R., Meneses Villegas, C., & Vega Zepeda, V. (2015). Análisis comparativo de modelos de madurez en inteligencia de negocio.  Revista chilena de ingeniería,23(3),361-371.
  56. Ragazou, K., Passas, I., Garefalakis, A., & Zopounidis, C. (2023). Business intelligence model empowering SMEs to make better decisions and enhance their competitive advantage. Discover Analytics,1(1),2.
  57. Ramos, C. M. (2022). Business Intelligence Approach and Sentiment Analysis as a Management Strategy Applied to Study Customer Satisfaction in the Hospitality Sector. In Advances in Tourism, Technology and Systems,2(1), 537-547.
  58. Ronaghi, M. H., & Feizi, K. (2013). The relationship between work ethics and intelligence among employees of international organizations in Iran. Journal of Ethics in Science and Technology, 8(2), 1-11.
  59. Sadiq-Bamgbopa, Y. S., Hinmikaiye, A., Aladenika, B., & Adewale, A. (2022). The Connection between Manpower Development, Business Process Performance and Business Intelligence Maturity in Nigeria.
  60. Sandelowski, M., & Barroso, J. (2003). Toward a metasynthesis of qualitative findings on motherhood in HIV‐positive women. Research in nursing & health, 26(2),153-170.
  61. Sinarasri, A., & Chariri, A. (2023). Business intelligence, management control systems and startup performance: Empirical study from Indonesia. International Journal of Applied Economics, Finance and Accounting, 16(2), 234-247.
  62. Skyrius, R., & Skyrius, R. (2021). Business intelligence technologies. Business Intelligence: A Comprehensive Approach to Information Needs, Technologies and Culture, 145-185.
  63. Srivastava, G., Venkataraman, R., & V, K. (2022). A review of the state of the art in business intelligence software. Enterprise Information Systems, 16(1), 1-28.
  64. Staniewski, M. (2008). The elements of Human Resources Management supporting knowledge management. Amfiteatru Economic, Special, Noiembrie, 283-291.
  65. Sushil, S. (2012). Interpreting the interpretive structural model. Global Journal of Flexible Systems Management, 13(2), 87-106.
  66. Thamir, A., & Theodoulidis, B. (2013). Business intelligence maturity models: Information management perspective. In Information and Software Technologies,198-221.
  67. Zimmer, L. (2006). Qualitative meta‐synthesis: a question of dialoguing with texts. Journal of advanced nursing, 53(3), 311-318.
مطالعات مدیریت کسب و کار هوشمند
دوره 12، شماره 46
آبان 1402
صفحه 1-39
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  • تاریخ دریافت: 24 خرداد 1402
  • تاریخ بازنگری: 13 مرداد 1402
  • تاریخ پذیرش: 25 مهر 1402
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