چهارچوب عوامل موثر بر بلوغ هوشمندی کسب‌ وکار

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 موثر 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 تاثیرگذارترین 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 تاثیر را بر بلوغ هوشمندی کسب‌وکار دارند.

the framework of factors affecting the maturity of business intelligence

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.introductionin 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 reviewthe 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).methodologythis 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