Management approaches in the field of smart
Javad Nazarian-Jashnabadi; MohammadHossein Ronaghi; moslem alimohammadlu; Abolghasem Ebrahimi
Abstract
AbstractThe 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 ...
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AbstractThe 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 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.ResultsThe 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 ConclusionThis 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).
Management approaches in the field of smart
maryam mirsharif; akbar alemtabriz; alireza motameni
Abstract
The evolution of information technology, artificial intelligence, and large volumes of data in web2, led to the formation of a new approach from the convergence of two scientific fields of business intelligence (BI) and social media analysis (SMA), which is called social business intelligence (SBI) ...
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The evolution of information technology, artificial intelligence, and large volumes of data in web2, led to the formation of a new approach from the convergence of two scientific fields of business intelligence (BI) and social media analysis (SMA), which is called social business intelligence (SBI) with some researchers. Growing the number of studies in BI and SMA and the explosion of information, required coherence, integration and summary to knowledge extraction. The purpose of this paper is to recognize the capabilities that are the result of the two scientific field convergence. The bibliometric methods have been used to analyze publications tile 2022 and map the topics trend, historical graph, co-occurrence network and knowledge map of social business intelligence capabilities. The results indicate that the nature of business intelligence studies changes toward the analysis of big social media data and integration of analytical and managerial capabilities in BI with the power of marketing, communication and networking in SM. Also, five clusters of social marketing capability, data analytic capability, knowledge capability, communication capability, and transformational capability have been identified for SBI. About the role of SBI in empowering organizations in the digital era, especially in business related to marketing and innovation goals, it is recommended to equip organizations with this technology and its capabilities.IntroductionThe evolution of information technology, artificial intelligence, and large volumes of data in web2, led to the formation of a new approach from the convergence of two scientific fields of business intelligence (BI) and social media analysis (SMA), which is called social business intelligence (SBI) with some researchers. Growing the number of studies in BI and SMA and the explosion of information, required coherence, integration and summary to knowledge extraction. One of the main topics of interest for business intelligence researchers is big social media data analysis, which brings many capabilities for organizations in the information age. This research has been used the bibliometric analysis method to recognize the capabilities of social business intelligence. Therefore, the social academic network of social business intelligence capabilities has been analyzed in order to gain knowledge about the research field, main topics, evolution path of concepts and a comprehensive view in the expansion of the current limited knowledges.Research Question(s)RQ: What are the capabilities of social business intelligence (SBI)?To answer this question, the following points are followed:1) How are the growth and development of studies in social business intelligence capabilities?2) In what scientific groups have these abilities been used?3) What are the most productive countries, publications, and most cited articles?4) Who are the influential authors in the research field?5) What is the evolution of citations and time trends of concepts in social business intelligence capabilities?6) What are the most important concepts in social business intelligence capabilities?Literature ReviewAlthough business intelligence has developed and grown over the years, the concept of social media-based business intelligence has gained a lot of attention in recent years. First, Studies focus on business intelligence capabilities and dynamic capabilities and the resource base view has been discussed a lot. In some studies, the organizational, technological, and innovational capabilities of business intelligence and the impact of the environment on the success of business intelligence have been explained (Işık et al., 2013), (Ramakrishnan et al., 2016), in group of studies, the positive relationship between dynamic capabilities, managerial capabilities in business intelligence and analysis (BI&A) has been investigated (Torres et al., 2018), in other group of studies, innovative infrastructure capabilities, process capabilities have been addressed to help decision making (Ramakrishnan et al., 2018).In recent years, the nature of studies in business intelligence capabilities has changed towards emerging technologies such as big data analysis, digital businesses, and social media big data. This group of studies focuses on the ability of social media analysis, the impact of social media capabilities in achieving knowledge management; sharing information, communication, facilitating business marketing, achieving competitive intelligence, and the strategic capability of social media in the organization's achievement of innovation. Various researchers have described the analytical aspect of SBI in knowledge extraction, decision making and marketing capabilities of social media base BI that can influence market intelligence, customer needs, and satisfaction (Ghofrani et al., 2018; Hameed et al., 2022; Pourkhani et al., 2019). Nevertheless, Social media data is recognized as the best source of data for business intelligence research (Choi et al., 2020; Tunowski, 2020) that can be used to achieve various goals such as data collection and perception, analytical results, and market goals. However, this research area is still in the early stages of development and needs more studies to mature.MethodologyIn this research, the five-step bibliographic analysis method (Zupic & Čater, 2015) has been developed to achieve the research objectives and extract knowledge about SBI capabilities. despite various studies on social media in business intelligence, there is little understanding of the synergy power of business intelligence and social media and SBI capabilities. thus, to achieve a comprehensive view of the convergence of two scientific fields and their capabilities, the bibliographic analysis has been used to extract the most cited articles, influential authors, most important publications, growth trends, and Thematic evolution. the co-occurrence network analysis of keywords has been used to extract topics' trends. to collect the required metadata, the Web of Science (WOS), the most comprehensive scientific database has been used, and approved by the Scientific Information Society (ISI). Also, the research chain explained by Chio (2020), related to business intelligence and social media analysis, has been used to extract and collect the required data and summarize part of the research literature.ConclusionThe results of the research indicate that in line with the growth of studies in the convergence of the two fields of business intelligence and social media analysis, the upward growth of studies in the capabilities of business intelligence centered on social media analysis is also evident and the increase in the number of studies with the expansion of the use of social media in businesses and big data analysis. The most important clusters identified in the word co-occurrence network are the concepts of social marketing capability, data analysis capability, communication capability, knowledge capability, and strategic capability. In other words, Business intelligence based on social media analysis or social business intelligence includes both capabilities and positive points of using business intelligence inside social media analysis capabilities, in other words, business intelligence capabilities in strategic fields, management, and analysis, are combined with the ability of marketing, expansion of communications and networking in social media. As a result, social business intelligence improves company performance by using artificial intelligence algorithms and big social media data analysis.Acknowledgmentshave been very grateful for the spiritual support of Dr. Eslam Nazemi.Keywords: Business Intelligence, social media, Social Business Intelligence, Bibliometrics, Capabilities.
Management approaches in the field of smart
Mahnaz Saeedi Mamaghani; Mohammad Javad Ershadi; Arman Sajedinejad
Abstract
The maturity of business intelligence, which is the main goal of this research, plays an important role in intelligent decision-making, planning, control and monitoring in the field of health care. In order to identify the effective factors, the Delphi method was used and experts' opinions were, and ...
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The maturity of business intelligence, which is the main goal of this research, plays an important role in intelligent decision-making, planning, control and monitoring in the field of health care. In order to identify the effective factors, the Delphi method was used and experts' opinions were, and in order to determine the effectiveness and effectiveness of the indicators and finally to prioritize them, used the DANP method. The statistical sample includes 20 targeted academic experts and health care experts. According to the results of the Delphi section, 26 main indicators finalized in the research were identified, which are divided into three main categories including organizational, process and judgment criteria. According to the results of the DANP process, flexible and expandable technical infrastructure criteria, data and system quality and the correct definition of business intelligence problems and processes were prioritized as the three criteria with the highest ranking in the maturity of business intelligence. The business intelligence maturity model proposed by the research can be a road map for the successful implementation of business intelligence in the field of health care.IntroductionBusiness intelligence is one of the most important issues in recent decades as a decision-making system for managers of organizations in order to plan, control and intelligently monitor companies and their subordinate units and measure the achievement of organizational goals. Business intelligence includes a comprehensive set of tools, technologies and products designed to collect, aggregate, analyze and present usable data (Reinschmidt J. & Francoise A., 2000) Introduction of new and complex medical technologies, the global trend of increasing length Longevity, the unexpected development of chronic diseases and emerging diseases (such as Covid-19) can lead to an increase in health care costs to unsustainable levels (Janssen & Moors, 2013; Qaseem et al., 2012). Public or private medical care organizations have focused their efforts on achieving new, cost-effective and efficient levels of care (Romanow et al., 2012). For this purpose, information technologies play a fundamental role by transforming data into knowledge that can improve patient care, medical care facilities, and process management (Behkami & U. Daim, 2012; Li & Mao, 2015; Pai & Huang, 2011). Considering the very important role of data in supporting the improvement of the organizational level, business intelligence is one of the important areas of research for researchers and activists in the treated field (Chen et al., 2012).The field of business intelligence has improved significantly over the past decade and has promising applications in the health field (El-Gayar & Timsina, 2014; Gandomi & Haider, 2015). Indeed, business intelligence can not only improve outcomes in healthcare organizations but also help them achieve continuous improvement and precision in medicine (Christensen et al., 2008; Gastaldi et al., 2015; Tremblay et al., 2012).Investigating the process of information production and transmission in the field of healthcare is of great importance. Today, organizations active in this field need correct information at the right time, in order to make the best decision by the right person. But many of the systems used by users do not have appropriate and expected performance, and health care organizations need to act smarter, but despite the potential, business intelligence has not been widespread in the field of medical care (Hanson, 2011) and research There are limited studies on how to successfully implement a sample business intelligence solution in the field of medical care (Foshay & Kuziemsky, 2014). This research tries to fill this gap by developing a model that provides maturity levels for evaluating and improving business intelligence solutions in healthcare. Therefore, considering the explanations and issues raised in the field of business intelligence, the present research seeks to answer a main question, what is the maturity model of business intelligence in the field of health care providers? In this regard, two sub-questions are also raised, which are: What are the indicators affecting the maturity model of business intelligence in the field of health care providers, and what is the prioritization of these factors?Literature ReviewAccording to the studies of Foshay and Kuziemsky, healthcare organizations are under constant pressure to not only achieve more results with fewer resources, but also to gradually transform into information-based systems (Foshay & Kuziemsky, 2014). Considering that the amount of information recorded by electronic health records and medical record centers is growing rapidly, healthcare organizations are trying to use tools such as business intelligence to improve the efficiency and effectiveness of their operations (Kuiler, 2014; Wang et al., 2018).According to the research conducted by Naqash, business intelligence solutions help decision-makers by providing practical information in the right format, at the right time and in the right place (Negash, 2004). The business intelligence market has grown significantly and has become the first investment priority for CIOs (Gartner, 2015). Also, the awareness of the potential benefits of business intelligence is increasing (Chuah & Wong, 2011), however, the implementation of business intelligence in health and treatment organizations is progressing relatively slowly and in a case-by-case manner (Foshay & Kuziemsky, 2014).Some studies show the benefits of business intelligence to improve patient care, treatment outcomes, effective use of human resources, lower costs (Borzekowski, 2009), higher revenue (Ayal & Seidman, 2009) and improved productivity (Lucas et al., 2010). have reported As reported in other studies, the successful implementation of business intelligence in healthcare depends on understanding and analyzing the characteristics of this field (Avison & Young, 2007; Mettler & Vimarlund, 2009). Therefore, one of the most important goals of this research is to provide a maturity model for the continuous development and improvement of business intelligence solutions to healthcare professionals. In Table (1), some business intelligence maturity models in the field of health care that have been implemented in the past are reviewed. Table 1. Some business intelligence maturity models in healthcareResearch resultsResearch researchersA framework for defining and prioritizing decision support information needs in the context of specific health care processes is presented.Foshay & Kuziemsky (2014)In this study, the subject of comprehensive business intelligence in special care and understanding the basic concepts of business intelligence solutions with comprehensive features have been discussed.Pereira et al. (2016)A way to identify the capabilities and weaknesses of the intelligent information system in the hospital has been presented.Carvalho et al. (2018)The methodology of implementing the model of hospital information systems is presented.Carvalho, Rocha, & Abreu (2019)This article identifies a wide range of maturity models in the health sector and its characteristics and strengthens the belief that the maturity of the hospital information system can contribute to the quality of information and knowledge management in this field.Gomes & Romão (2018)The result of this research is the maturity model of the hospital information system based on 6 stages of maturity. The hospital information system maturity model has the feature of collecting a set of key and effective factors of maturity and related characteristics and not only enables the evaluation of the overall maturity of a hospital information system, but also the individual maturity of its different dimensions.Carvalho, Rocha, Vasconcelos, et al. (2019)The purpose of this research is to determine how the existing business continuity maturity models conform to the ISO 22301 standard and to map the existing health care model with the business continuity maturity model.Haidzir et al. (2018)In this research, while determining organizational maturity levels, effective factors in improving maturity have been identified and prioritized, and a road map for applying business intelligence in this field has been presented.(Gastaldi et al. (2018)The result of this research is to present a maturity model including six stages of the growth and maturity sequence of the hospital information system.Carvalho, Rocha, Vasconcelos, et al. (2019)The importance of scientific research on business intelligence with a focus on patients has been investigated.Zheng et al. (2018)In this research, by providing a maturity assessment framework and infrastructure development based on results, information and digital transformation in health care has been encouraged and guided.Williams et al. (2019)In this research, it has been determined that organizational business intelligence application screens at all management levels have a positive and significant effect on measurable performance indicators. In this context, when businesses monitor their operational activities through business intelligence, they have come to the conclusion that performance indicators provide less time wastage, high reliability, integrated data, quality and accurate valuation benefits in the evaluation process.Işık et al. (2021)The relevant factors for the adoption of business intelligence system have been established using a systematic literature review and a theoretical structure based on technology, organization, environment and determinants and theories of CEOs. This research deepens the literature of business intelligence system and promotes the understanding of the important decision-making elements of business intelligence system.Salisu et al. (2021)The co-creation approach will optimize the currency, accuracy and appropriateness of information in the digital health profile, understanding and use of the digital health profile and the maturity assessment tool to facilitate informed iterative discussions by Pacific Island countries on digital health maturity in order to use digital tools to strengthen use the country's health systems. Digital health profile and maturity assessment tool can rationalize the selection and use of existing tools and reduce cognitive overload.Liaw et al. (2021)In this research, an alternative solution with the benefits and possible costs of its implementation in the hospital has been shown, and the proposed initial evaluation method can be used in different health and treatment units after confirming the weight of the criteria based on the adopted strategy.Wielki & Jurczyk (2019)The results of the study enrich the recent literature of business intelligence system and improve the understanding of the decision-making processes of practitioners to obtain the maximum value from the adoption of business intelligence system.Ahmad et al., (2020)The findings support the argument that the organizational learning culture plays an important role in the business intelligence system and also affects the business performance.Arefin et al., (2021)MethodologyTo implement business intelligence in the field of health care, the characteristics of this field must be understood and analyzed; This task has been carried out in three stages. First, the subject literature was analyzed with the "systematic review" method, and in addition to the field of health care, all sectors in which the maturity of business intelligence was evaluated were also considered. In the following, a series of key success factors of business intelligence and maturity components were extracted by examining more than 23 articles in the fields related to business intelligence and further, the steps of implementing the proposed method are also described.3.1. First stage - knowledge acquisitionAt this stage, previous studies in the field of business intelligence maturity model, evaluating the value of key success factors in business intelligence and identifying maturity components were reviewed. In this study, the structured search strategy method was used as data sources from Emerald, Sage, Elsevier, IEEE, Taylor & Francis, and Springer databases in the period from 2000 to the beginning of 2022. At first, this study used the following keywords and search terms, combined and separate: "business intelligence", "factors affecting business intelligence system", "business intelligence maturity", "maturity measurement" and "business intelligence system in health care". The collection of articles presented in this research was consistent with the topic of this research in terms of questions, objectives, adopted frameworks and findings. The definitions used and their alignment with the measurement adopted were evaluated, to ensure that the factors of business intelligence investigated by different researchers are largely similar. Finally, by advancing the previous steps, an initial version of the business intelligence maturity model was adopted, which is significantly different from the final business intelligence maturity model.3.2. The second stage - identifying and categorizing the criteriaAfter extracting the main criteria influencing the success of business intelligence in three areas of organization, process and technology, using the Delphi decision-making technique, the key factors and important criteria of business intelligence maturity in health care organizations were determined and categorized, and finally the maturity model of business intelligence in the field of health care, it was confirmed by a survey of experts.3.3. The third step - determining the criteriaAfter finalizing the dimensions and criteria of the research with the Delphi method, using pairwise comparisons and the Dimtel method based on the network analysis process method, the internal and external connections of the factors were determined and each of the factors were weighted and prioritized. In this step, a committee evaluation method was used to evaluate the validity of the questionnaire (Harkness & Schoua-Glusberg, 1998). In addition, ANP-DEMATEL combined method was used to evaluate how and how much the components affect each other. Various researchers such as (Đurek et al., 2019; Rasouli et al., 2021) have used this approach in the field of maturity model.ResultsIn this research, business intelligence was investigated in three basic areas of organization, process and technology, and each of these areas has criteria. First, articles were comprehensively reviewed in the field of business intelligence maturity in order to determine the dimensions of the goal and criteria. The criteria of the designed research maturity model were finalized using the Delphi method and with the opinion of experts, and then decision-making methods with multiple criteria were used to measure the optimality. The effects of goals, dimensions, and criteria on each other were investigated with the Dimtel method, then the dimensions and criteria were weighted in terms of importance with the network analysis process method. According to the results of Dimtel, the two dimensions of technology and process are effective, and the organizational dimension is effective. The organizational field has a higher relative importance than process and technology and has more interaction with other factors of the system and is affected by two dimensions of technology and process. The results of the analysis of the questionnaires of the network analysis process method answered by the experts show that the organizational factor is the most preferred and heaviest factor in the maturity of business intelligence, and then the process factor has a higher weight and the technology factor has a lower weight than the other two areas. In line with the results of William et al. (2019) and Gastaldi et al. (2018), who have encouraged and guided information and digital transformation in health care by providing a maturity assessment framework and infrastructure development based on results, respectively, two technical infrastructure criteria Flexible and expandable (hardware and software) and data and system quality were obtained from the highest importance compared to other criteria. And in the same way, the criterion of the correct definition of business intelligence problems and processes was prioritized with the third rank compared to other criteria in the maturity of business intelligence, and the rest of the criteria were also ranked in the article. Jayanthi Ranjan (2008) has also achieved this. In this way, a comprehensive and complete business intelligence maturity model was obtained in the field of health care, which can make the path of business intelligence maturity smoother in health care and be a road map for the successful implementation of business intelligence maturity in health care. It is suggested that in future researches, the proposed maturity model should be practically implemented in health care organizations and the maturity level of business intelligence should be evaluated. Figure 1. The final research model (source: researcher's findings)Organizational field• Cooperation between the employees of the organization and the information technology department• Alignment of business strategies with business intelligence strategies• Senior management support for the business intelligence project• Clear goals and vision for business intelligence• Development of business intelligence strategy• The ability of the organization to provide sufficient resources and funds needed for business intelligence projects• Risk-taking of senior managers in investing in new information technologies• Capabilities of the team/employees/managers• Monitor information through the Business Intelligence Assessment Center• Continuous improvement of organizational processes (improvement of competence)Technology field· Flexible and expandable technical infrastructure (hardware and software)· Data and system quality· Appropriate technology/tools or the use of appropriate technology and tools for hospital conditions· Business intelligence system architecture· Integration of business intelligence systems with other systems· Quality of data analysis· ConnectorBusiness intelligence in the field of health careProcess area• Correct definition of business intelligence problems and processes• Using patterns and repeatable methods in designing business intelligence projects• Aligning business intelligence solutions with user expectations• User training and support• Effective change management• Balanced and strong composition of the business intelligence project group• Project planning and management in the implementation of business intelligence• Measuring business intelligence• Decision makingKeywords: Business Intelligence, Healthcare, DEMATEL, ANP. The maturity of business intelligence, which is the main goal of this research, plays an important role in intelligent decision-making, planning, control and monitoring in the field of health care. In order to identify the effective factors, the Delphi method was used and experts' opinions were, and in order to determine the effectiveness and effectiveness of the indicators and finally to prioritize them, used the DANP method. The statistical sample includes 20 targeted academic experts and health care experts. According to the results of the Delphi section, 26 main indicators finalized in the research were identified, which are divided into three main categories including organizational, process and judgment criteria. According to the results of the DANP process, flexible and expandable technical infrastructure criteria, data and system quality and the correct definition of business intelligence problems and processes were prioritized as the three criteria with the highest ranking in the maturity of business intelligence. The business intelligence maturity model proposed by the research can be a road map for the successful implementation of business intelligence in the field of health care.IntroductionBusiness intelligence is one of the most important issues in recent decades as a decision-making system for managers of organizations in order to plan, control and intelligently monitor companies and their subordinate units and measure the achievement of organizational goals. Business intelligence includes a comprehensive set of tools, technologies and products designed to collect, aggregate, analyze and present usable data (Reinschmidt J. & Francoise A., 2000) Introduction of new and complex medical technologies, the global trend of increasing length Longevity, the unexpected development of chronic diseases and emerging diseases (such as Covid-19) can lead to an increase in health care costs to unsustainable levels (Janssen & Moors, 2013; Qaseem et al., 2012). Public or private medical care organizations have focused their efforts on achieving new, cost-effective and efficient levels of care (Romanow et al., 2012). For this purpose, information technologies play a fundamental role by transforming data into knowledge that can improve patient care, medical care facilities, and process management (Behkami & U. Daim, 2012; Li & Mao, 2015; Pai & Huang, 2011). Considering the very important role of data in supporting the improvement of the organizational level, business intelligence is one of the important areas of research for researchers and activists in the treated field (Chen et al., 2012).The field of business intelligence has improved significantly over the past decade and has promising applications in the health field (El-Gayar & Timsina, 2014; Gandomi & Haider, 2015). Indeed, business intelligence can not only improve outcomes in healthcare organizations but also help them achieve continuous improvement and precision in medicine (Christensen et al., 2008; Gastaldi et al., 2015; Tremblay et al., 2012).Investigating the process of information production and transmission in the field of healthcare is of great importance. Today, organizations active in this field need correct information at the right time, in order to make the best decision by the right person. But many of the systems used by users do not have appropriate and expected performance, and health care organizations need to act smarter, but despite the potential, business intelligence has not been widespread in the field of medical care (Hanson, 2011) and research There are limited studies on how to successfully implement a sample business intelligence solution in the field of medical care (Foshay & Kuziemsky, 2014). This research tries to fill this gap by developing a model that provides maturity levels for evaluating and improving business intelligence solutions in healthcare. Therefore, considering the explanations and issues raised in the field of business intelligence, the present research seeks to answer a main question, what is the maturity model of business intelligence in the field of health care providers? In this regard, two sub-questions are also raised, which are: What are the indicators affecting the maturity model of business intelligence in the field of health care providers, and what is the prioritization of these factors?Literature ReviewAccording to the studies of Foshay and Kuziemsky, healthcare organizations are under constant pressure to not only achieve more results with fewer resources, but also to gradually transform into information-based systems (Foshay & Kuziemsky, 2014). Considering that the amount of information recorded by electronic health records and medical record centers is growing rapidly, healthcare organizations are trying to use tools such as business intelligence to improve the efficiency and effectiveness of their operations (Kuiler, 2014; Wang et al., 2018).According to the research conducted by Naqash, business intelligence solutions help decision-makers by providing practical information in the right format, at the right time and in the right place (Negash, 2004). The business intelligence market has grown significantly and has become the first investment priority for CIOs (Gartner, 2015). Also, the awareness of the potential benefits of business intelligence is increasing (Chuah & Wong, 2011), however, the implementation of business intelligence in health and treatment organizations is progressing relatively slowly and in a case-by-case manner (Foshay & Kuziemsky, 2014).Some studies show the benefits of business intelligence to improve patient care, treatment outcomes, effective use of human resources, lower costs (Borzekowski, 2009), higher revenue (Ayal & Seidman, 2009) and improved productivity (Lucas et al., 2010). have reported As reported in other studies, the successful implementation of business intelligence in healthcare depends on understanding and analyzing the characteristics of this field (Avison & Young, 2007; Mettler & Vimarlund, 2009). Therefore, one of the most important goals of this research is to provide a maturity model for the continuous development and improvement of business intelligence solutions to healthcare professionals. In Table (1), some business intelligence maturity models in the field of health care that have been implemented in the past are reviewed. Table 1. Some business intelligence maturity models in healthcareResearch resultsResearch researchersA framework for defining and prioritizing decision support information needs in the context of specific health care processes is presented.Foshay & Kuziemsky (2014)In this study, the subject of comprehensive business intelligence in special care and understanding the basic concepts of business intelligence solutions with comprehensive features have been discussed.Pereira et al. (2016)A way to identify the capabilities and weaknesses of the intelligent information system in the hospital has been presented.Carvalho et al. (2018)The methodology of implementing the model of hospital information systems is presented.Carvalho, Rocha, & Abreu (2019)This article identifies a wide range of maturity models in the health sector and its characteristics and strengthens the belief that the maturity of the hospital information system can contribute to the quality of information and knowledge management in this field.Gomes & Romão (2018)The result of this research is the maturity model of the hospital information system based on 6 stages of maturity. The hospital information system maturity model has the feature of collecting a set of key and effective factors of maturity and related characteristics and not only enables the evaluation of the overall maturity of a hospital information system, but also the individual maturity of its different dimensions.Carvalho, Rocha, Vasconcelos, et al. (2019)The purpose of this research is to determine how the existing business continuity maturity models conform to the ISO 22301 standard and to map the existing health care model with the business continuity maturity model.Haidzir et al. (2018)In this research, while determining organizational maturity levels, effective factors in improving maturity have been identified and prioritized, and a road map for applying business intelligence in this field has been presented.(Gastaldi et al. (2018)The result of this research is to present a maturity model including six stages of the growth and maturity sequence of the hospital information system.Carvalho, Rocha, Vasconcelos, et al. (2019)The importance of scientific research on business intelligence with a focus on patients has been investigated.Zheng et al. (2018)In this research, by providing a maturity assessment framework and infrastructure development based on results, information and digital transformation in health care has been encouraged and guided.Williams et al. (2019)In this research, it has been determined that organizational business intelligence application screens at all management levels have a positive and significant effect on measurable performance indicators. In this context, when businesses monitor their operational activities through business intelligence, they have come to the conclusion that performance indicators provide less time wastage, high reliability, integrated data, quality and accurate valuation benefits in the evaluation process.Işık et al. (2021)The relevant factors for the adoption of business intelligence system have been established using a systematic literature review and a theoretical structure based on technology, organization, environment and determinants and theories of CEOs. This research deepens the literature of business intelligence system and promotes the understanding of the important decision-making elements of business intelligence system.Salisu et al. (2021)The co-creation approach will optimize the currency, accuracy and appropriateness of information in the digital health profile, understanding and use of the digital health profile and the maturity assessment tool to facilitate informed iterative discussions by Pacific Island countries on digital health maturity in order to use digital tools to strengthen use the country's health systems. Digital health profile and maturity assessment tool can rationalize the selection and use of existing tools and reduce cognitive overload.Liaw et al. (2021)In this research, an alternative solution with the benefits and possible costs of its implementation in the hospital has been shown, and the proposed initial evaluation method can be used in different health and treatment units after confirming the weight of the criteria based on the adopted strategy.Wielki & Jurczyk (2019)The results of the study enrich the recent literature of business intelligence system and improve the understanding of the decision-making processes of practitioners to obtain the maximum value from the adoption of business intelligence system.Ahmad et al., (2020)The findings support the argument that the organizational learning culture plays an important role in the business intelligence system and also affects the business performance.Arefin et al., (2021)MethodologyTo implement business intelligence in the field of health care, the characteristics of this field must be understood and analyzed; This task has been carried out in three stages. First, the subject literature was analyzed with the "systematic review" method, and in addition to the field of health care, all sectors in which the maturity of business intelligence was evaluated were also considered. In the following, a series of key success factors of business intelligence and maturity components were extracted by examining more than 23 articles in the fields related to business intelligence and further, the steps of implementing the proposed method are also described.3.1. First stage - knowledge acquisitionAt this stage, previous studies in the field of business intelligence maturity model, evaluating the value of key success factors in business intelligence and identifying maturity components were reviewed. In this study, the structured search strategy method was used as data sources from Emerald, Sage, Elsevier, IEEE, Taylor & Francis, and Springer databases in the period from 2000 to the beginning of 2022. At first, this study used the following keywords and search terms, combined and separate: "business intelligence", "factors affecting business intelligence system", "business intelligence maturity", "maturity measurement" and "business intelligence system in health care". The collection of articles presented in this research was consistent with the topic of this research in terms of questions, objectives, adopted frameworks and findings. The definitions used and their alignment with the measurement adopted were evaluated, to ensure that the factors of business intelligence investigated by different researchers are largely similar. Finally, by advancing the previous steps, an initial version of the business intelligence maturity model was adopted, which is significantly different from the final business intelligence maturity model.3.2. The second stage - identifying and categorizing the criteriaAfter extracting the main criteria influencing the success of business intelligence in three areas of organization, process and technology, using the Delphi decision-making technique, the key factors and important criteria of business intelligence maturity in health care organizations were determined and categorized, and finally the maturity model of business intelligence in the field of health care, it was confirmed by a survey of experts.3.3. The third step - determining the criteriaAfter finalizing the dimensions and criteria of the research with the Delphi method, using pairwise comparisons and the Dimtel method based on the network analysis process method, the internal and external connections of the factors were determined and each of the factors were weighted and prioritized. In this step, a committee evaluation method was used to evaluate the validity of the questionnaire (Harkness & Schoua-Glusberg, 1998). In addition, ANP-DEMATEL combined method was used to evaluate how and how much the components affect each other. Various researchers such as (Đurek et al., 2019; Rasouli et al., 2021) have used this approach in the field of maturity model.ResultsIn this research, business intelligence was investigated in three basic areas of organization, process and technology, and each of these areas has criteria. First, articles were comprehensively reviewed in the field of business intelligence maturity in order to determine the dimensions of the goal and criteria. The criteria of the designed research maturity model were finalized using the Delphi method and with the opinion of experts, and then decision-making methods with multiple criteria were used to measure the optimality. The effects of goals, dimensions, and criteria on each other were investigated with the Dimtel method, then the dimensions and criteria were weighted in terms of importance with the network analysis process method. According to the results of Dimtel, the two dimensions of technology and process are effective, and the organizational dimension is effective. The organizational field has a higher relative importance than process and technology and has more interaction with other factors of the system and is affected by two dimensions of technology and process. The results of the analysis of the questionnaires of the network analysis process method answered by the experts show that the organizational factor is the most preferred and heaviest factor in the maturity of business intelligence, and then the process factor has a higher weight and the technology factor has a lower weight than the other two areas. In line with the results of William et al. (2019) and Gastaldi et al. (2018), who have encouraged and guided information and digital transformation in health care by providing a maturity assessment framework and infrastructure development based on results, respectively, two technical infrastructure criteria Flexible and expandable (hardware and software) and data and system quality were obtained from the highest importance compared to other criteria. And in the same way, the criterion of the correct definition of business intelligence problems and processes was prioritized with the third rank compared to other criteria in the maturity of business intelligence, and the rest of the criteria were also ranked in the article. Jayanthi Ranjan (2008) has also achieved this. In this way, a comprehensive and complete business intelligence maturity model was obtained in the field of health care, which can make the path of business intelligence maturity smoother in health care and be a road map for the successful implementation of business intelligence maturity in health care. It is suggested that in future researches, the proposed maturity model should be practically implemented in health care organizations and the maturity level of business intelligence should be evaluated. Figure 1. The final research model (source: researcher's findings)Organizational field• Cooperation between the employees of the organization and the information technology department• Alignment of business strategies with business intelligence strategies• Senior management support for the business intelligence project• Clear goals and vision for business intelligence• Development of business intelligence strategy• The ability of the organization to provide sufficient resources and funds needed for business intelligence projects• Risk-taking of senior managers in investing in new information technologies• Capabilities of the team/employees/managers• Monitor information through the Business Intelligence Assessment Center• Continuous improvement of organizational processes (improvement of competence)Technology field· Flexible and expandable technical infrastructure (hardware and software)· Data and system quality· Appropriate technology/tools or the use of appropriate technology and tools for hospital conditions· Business intelligence system architecture· Integration of business intelligence systems with other systems· Quality of data analysis· ConnectorBusiness intelligence in the field of health careProcess area• Correct definition of business intelligence problems and processes• Using patterns and repeatable methods in designing business intelligence projects• Aligning business intelligence solutions with user expectations• User training and support• Effective change management• Balanced and strong composition of the business intelligence project group• Project planning and management in the implementation of business intelligence• Measuring business intelligence• Decision makingKeywords: Business Intelligence, Healthcare, DEMATEL, ANP.
Mohammadreza Taghva; Iman Raeesi Vanani; Zohreh Dehdashti Shahrokh; Mana Shakerin
Abstract
IntroductionToday, the strategic importance of information is obvious to all businesses. In addition, the competitive environment of each company is constantly changing. The Spring 2020 event was a testament to this fact. Due to the health and economic crisis caused by the emergence and spread of an ...
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IntroductionToday, the strategic importance of information is obvious to all businesses. In addition, the competitive environment of each company is constantly changing. The Spring 2020 event was a testament to this fact. Due to the health and economic crisis caused by the emergence and spread of an unknown virus, various teams found it difficult to convey their advertising messages, campaigns and services. They could no longer rely on their assumptions about what customers buy and why and how they buy it (Johnson, 2020). Access to rich information for businesses that operate both in the field of e-commerce and in the retail sector of perishables is crucial. These products have a short life cycle and should be consumed faster. If the market intelligence model is properly designed for such businesses based on the supply chain of perishables, then managers will be able to correctly identify their customers, competitors and the business environment and run their business more successfully and grow as a result. In Iran, not much research has been conducted to provide a model that simultaneously addresses the aspects related to supply chain, market intelligence and online retail of fast-moving (perishable) products. and each of the models or patterns in the literature address one aspect of the issue. If market intelligence is at the service of the supply chain, it can create opportunities to reduce costs and increase customer satisfaction through collaborative decisions. Based on what was presented in the introduction, the main question of the research is extracted as follows.Research QuestionRQ1: what are dimensions and components of Market Intelligence model in the supply chain of FMCG (perishables) products in online retailing.Literature ReviewThe concept of market intelligence has attracted a lot of attention in recent years. Various experts have defined market intelligence in some way: market intelligence is formed through detailed and accurate information about business environment in general, consumer needs and preferences, technology and changes in the business environment that can affect buyers. (Hedin,2014). Market intelligence enables small businesses to identify market attractiveness and create value and drive innovation (Del Vecchio, 2018). 2.1. Supply Market IntelligenceThe relationship between market intelligence and supply chain can be found under concept of supply market intelligence. (SMI). Market intelligence is a process for gaining competitive advantage and reducing risk by increasing knowledge about market dynamics and includes market intelligence, process and price benchmarking to evaluate sourcing performance, competitive sourcing identifying strategic opportunities in markets that lead to lower prices ,emerging supply channels and markets (Hanfield,,2010). 2.2. Organization Information Processing Theory (OIPT)One of the theories which is the basis for market intelligence and business intelligence is organization information processing theory (OIPT), which was introduced by Galbraith in late 1973. According to Galbraith, when uncertainty is low, organizations can be managed by relying on rules and programs and hierarchical referrals but in situations where the organization is facing high uncertainty, the need for information processing increases and there are two general solutions in this regard: organizations must either reduce the need for information processing or increase information processing capabilities by investing in information systems (Galbraith, 1974).2.3. Market OrientationThe root of market intelligence can also be traced to a concept called market orientation. The concept of market orientation has been developed from two perspectives: behavioral perspective and market intelligence perspective. According to Kohli and Jaworski, market orientation is a set of behaviors or activities related to market intelligence, dissemination of market intelligence among different units of the organization and responsiveness based on it (Kohli & Jaworski, 1990). According to Narver and Slater, Market Intelligence has three main components: customer orientation, competitiveness, and cross-sectoral coordination. In short, market orientation states that customer orientation helps companies to understand the needs and wants of their customers and take basic steps to meet them. Competitiveness will enable companies to create more value for customers than competitors and thus achieve a sustainable competitive advantage. The role of market intelligence is in collecting, analyzing and disseminating this information (Narver & Slater, 1990). MethodologyIn this study mixed method approach has been adopted. First, in order to achieve the research objectives and identify the indicators of market intelligence in the supply chain of perishable products (fruits and vegetables), the seven-step approach of Sandelowski and Barroso’s (2003) meta-synthesis method was used. The statistical population covers the research published in 3 databases of ProQuest, Science Direct and Google Scholar during the period time of 2010-2021 for keywords of market intelligence and supply market intelligence. For other keywords, different period time was applied. In the second part, to obtain additional indicators, semi-structured interviews were conducted by an exploratory approach. In this regard, interviews were conducted with experts in the field of retail of fast-moving and perishable products, service providers of fruits in Iran’s e-commerce environment. ResultsIn order to achieve the most relevant research to enter the meta-synthesis process, criteria for inclusion and exclusion of research were considered.. A total of 1654 studies were reviewed, of which 276 studies had related topics, and with elimination of duplicated studies, There were 202 researches available, of which 113 had abstracts, 48 had content and 31 had appropriate quality and analysis method. In order to combine the findings of the research, the approach of Sandelowski and Barroso has been followed, in the sense that after careful study of studies and articles, codes have been identified from their texts and the researcher has formed a classification based on it and Similar classifications were placed on the topic that best described it. The sample of Codes, concepts and category identified in meta-synthesis phase is illustrated in table 1.Table 1. An example of coding in meta-synthesis processCodesConceptCategoryCustomer Demographic InformationCustomer InsightCustomer & Market Insight Customer personalizationCustomer interests and NeedsFocus group sessions with customersCustomer EngagementCall Center interaction with customerCustomers surveysThe coding process at the meta-synthesis stage led to the identification of 5 categories (supply chain intelligence, market and customer insight, business intelligence, social business intelligence and competitive intelligence), 23 concepts and 5 categories.In the second phase of the research, the new items identified in the theme analysis of semi-structured interviews with experts which included Order, Co-Branding, Customer Club, and Financial Issues. By comparing and combining the dimensions and components obtained in the two qualitative stages of the research, the market intelligence model for perishable products in the field of online retail was presented in the form of the model presented in Figure 1. Figure 1. Supply market intelligence (research model) In order to validate the model, the conditions for establishing reliability and validity (convergent and divergent validity) and fit indices must be met according to Table 2. Table 2. Conditions for establishing Reliability & ValidityindicatorsAllowable ValidityReliabilityComposite Reliability > 0.7 and Cronbach's alpha>0.6Convergent validityLoading Factor >0.5CR>AVEAVE>0/5Rho_A>0/6Discriminate validityAVE>MSVFit IndicesGOF>0/36SRMR<0/1NFI>0/9Descriptive statistics and central indicators such as mean, standard deviation, skewness and kurtosis for each of the components and dimensions and indicators are reported in the above table 3.Table 3. Sample of Descriptive indicators and first-order confirmatory factor analysis The reliability index was evaluated by measuring the factor loads and the reliability of the latent variables was evaluated by the compositional reliability . Cronbach's alpha results, compositional reliability and are shown in Table 4.Table 4. Sample of Cronbach's alpha results, composite reliability and convergent validityDimensionComponentsCronbach’s AlphaCA>0/6rho_A>06Composite ReliabilityCR>0/7Average variance extractedAVE>0/5Supply chain intelligenceSuppliers club & insight0/6920/7150/8650/762Service Provider Portal0/9250/9260/9380/656Competitive intelligenceResponse to Competition0/8440/8480/8950/682Tactical competition0/8910/8940/9330/822Customer & Market InsightCustomer Engagement0/9000/9000/9380/834Social Business IntelligenceCompetitive insight in social network0/7160/7160/8760/779Social Customer Interaction0/8450/8450/9280/866According to Table4, the Cronbach's alpha value for all variables is greater than the appropriate limit of 0.6 . Also the value of the compositional reliability coefficient for each variable is more than the desired limit of 0.7. In this model, the convergent validity of the model variables is all higher than 0.5, all of which are at an appropriate level. ConclusionIn this study, the aim was to develop a market intelligence model in the supply chain of perishable products in the field of online retailing. Handfield (Handfield, 2006), introduced the supply market intelligence concept and considered business intelligence and market intelligence as the information drivers of supply chain processes. According to the meta-synthesis of literature and analysis of semi-structured interviews with 14 experts, the components of each of the proposed dimensions were identified and social business intelligence and supply chain intelligence were identified as new dimensions of supply market intelligence model. In fact, a complete and optimal supply chain should include those activities that the customers value and are willing to pay for the resulting services or products. Therefore, understanding customer behavior is very important. What is very important in the supply chain is that supply is aligned with demand across the supply chain, so a better understanding of suppliers and end customers is the best way to reduce costs in the supply chain., As a summary, the identified dimensions and the importance and role of each in the supply market intelligence model is discussed. - Supply chain intelligence. In this dimension, the components related to the to the links that make up the chain (logistics, sourcing, service provider gateway ...) should be considered to ensure that these links work efficiently. In e-commerce, logistics and service provider portals (such as website or mobile App) are very important because they are the connection point with customers and if the delivery is not done properly, especially for perishable products, in addition to customer dissatisfaction will cause product waste. Also, the service provider portals should have appropriate features such as speed, graphics, user friendliness, user experience, security, providing complementary services, ease of payment and other important features to make users and customers will revisit the website.- Market and customer insights. In this dimension, 4p components and customers are defined. It is crucial to identify market trends as well as the position that the business has with its customers. In fact, depending on the type of product and service that customers are willing to pay for, supply chain processes can be restructured. - Competitive intelligence. The way competitors market their products and services and the scanning of the external business environment are crucial in shaping the business supply chain. According to the resource-based view theory, a service should be defined in the supply chain that cannot be easily copied or provided by competitors and brings a competitive advantage to the firm, and this requires knowledge of the technologies adopted by competitors and the type of service and price offered by them.- Business intelligence. One of the important dimensions of the supply market intelligence model is business intelligence. In fact, the revenue model, sales volume, statistics and financial information and value that the retailer has created for itself, and the and the evaluation of incentives provided in the form of discount plans, provide insight to managers to focus on those products and services in the supply chain that they bring better and more to the business, and according to these factors, the company's revenue model can be defined.- Social business intelligence. Social networks have had a significant impact in the last decade. Social customers are able to share information with countless members of these networks, so analyzing social customer relationships and current trends in these networks and analyzing the performance of competitors in these networks is very important. In fact, these networks have created a new potential market for businesses and require their own sourcing and marketing.Based on what was covered in this study, it can be concluded that those businesses that operate in the field of online retailing, always need to find themselves in the path of information flow, which is an attempt to reduce uncertainty.
mehrnoosh mehdi sasan; Ghasem Bakhshandeh
Abstract
Given the rapid economic, technological and even political developments in today's world, the use of business intelligent systems in organizations is inevitable and the present study also examines the relationship between intelligent business systems and organizational performance in previous research ...
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Given the rapid economic, technological and even political developments in today's world, the use of business intelligent systems in organizations is inevitable and the present study also examines the relationship between intelligent business systems and organizational performance in previous research has examined the community. In this regard, 1244 studies were identified and reviewed by systematic review, and finally 8 articles had the necessary criteria to enter the meta-analysis were selected as the basis for entering the meta-analysis. The obtained data were analyzed using CMA2 software. The results of N safe from error test indicated that there was no diffusion bias in the data, and in terms of homogeneity or heterogeneity, both I2 index and Q statistic indicated that the observed effects were heterogeneous; hence, a random model was used to evaluate the total effect size. The results showed that the relationship between business intelligent systems and organizational performance is positive and has an effect size of 0.474.
Seyed Mohammadbagher Jafari; Atefe Sadat Rouhani; Fatemeh Yousefi
Abstract
Due to the widespread use of information systems, a large amount of data has been stored in organizations, and the analysis of this data is a valuable resource to help organizations making better decisions. This concept is known as business intelligence (BI) and is one of the most important factors for ...
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Due to the widespread use of information systems, a large amount of data has been stored in organizations, and the analysis of this data is a valuable resource to help organizations making better decisions. This concept is known as business intelligence (BI) and is one of the most important factors for the success of organizations in the contemporary world. Given the importance of BI, it is necessary to systematically review the research conducted in Iran on it. The purpose of this research is to identify the topics researched in selected journals, analyze the dispersion and frequency of studies, identify research methods, the purpose of articles, the level of analysis of BI research and finally identify research gaps and provide the future research suggestions. The research was conducted by analyzing secondary data and systematically mapping the existing knowledge of BI in 6 selected databases. For this purpose, 2108 articles were reviewed and in the initial review, 1274 articles were selected and 91 articles were approved by studying the abstracts. Finally, considering the exclusion criteria, 81 final articles were selected and analyzed. The results show that the most thematic coverage of BI in these journals was the specific topic of knowledge management and general topic was BI tools with 44.45%. The most used research method was descriptive-survey with 34.57%, the most purpose was practical with 82.71% and the highest level of analysis used was community analysis with 66.66%. Finally, research gaps were presented in the form of future research suggestions.
Roghyeh Nouri; Mohammadreza Motadel
Abstract
Dashboards are an important tool for monitoring the performance of the organization and the decisions of managers, so their proper design in an integrated and orderly manner, taking into account the needs of customers and users and in accordance with organizational goals, is essential. The purpose of ...
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Dashboards are an important tool for monitoring the performance of the organization and the decisions of managers, so their proper design in an integrated and orderly manner, taking into account the needs of customers and users and in accordance with organizational goals, is essential. The purpose of this study is to provide a framework for designing management dashboards with QFD approach. For this purpose, after in-depth study in literature review, a conceptual research model was proposed, then using a mixed exploratory research Approach, management dashboard design framework, was presented. The statistical population of the qualitative section included 40 articles out of 452 articles related to the period 2005 to 2020 and related to dashboard topics and its design, which was done by Meta-Synthesizing method. Also, in quantitative part of the research, in order to validate the proposed framework and screen the indicators, fuzzy Delphi technique was used. The statistical population at this stage was 18 managers in different fields and 12 IT specialists and thus the framework. The design of management dashboards consisted of 4 dimensions, 11 components and 102 final indicators.
farajallah rahimi; Mohammad hassan baghalinejad shooshtari; mahdi nadaf
Abstract
Organizations face different problems in the optimal use of data. Business intelligence helps organizations achieve that. This article is based on the qualitative research, using Grounded Theory approach, and it aims to design a business intelligence modelin petrochemical companies located in Mahshahr ...
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Organizations face different problems in the optimal use of data. Business intelligence helps organizations achieve that. This article is based on the qualitative research, using Grounded Theory approach, and it aims to design a business intelligence modelin petrochemical companies located in Mahshahr Special Economic Zone. In this study, 13 executives/experts were interviewed as a sample using semi-structured method and the collected data were analyzed with the open coding, axial and selective stages. and the model was tested and confirmed by the interviewers using the Delphi method. The results of this research provide a model the petrochemical companies, the motivations and the factors affecting successful implementation of Business Intelligence, transition into an intelligent enterprise, and optimized decision-making in all areas.
Seyyed Ali Akbari Hashem; Hasan Alvedari; Mohammad Reza Daraei; Rohollah Razini
Abstract
Organizational Dashboard is a tool full of rich indicators, reports, and graphs that dynamically acts to help managers to control the performance of their organization at any time. For data productivity, using Business Intelligence(BI) logic is inevitable. BI is a system in which different, dispersed ...
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Organizational Dashboard is a tool full of rich indicators, reports, and graphs that dynamically acts to help managers to control the performance of their organization at any time. For data productivity, using Business Intelligence(BI) logic is inevitable. BI is a system in which different, dispersed and heterogeneous data of an organization is integrated and through the establishment of analytical database for managers to make decisions. This study is a developmental and practical research. After reviewing literature and interviewing with 38 experts in universities and organizations, organizational dashboard developmental factors with the business intelligence logic were presented that were 357 codes. Using content analysis and focus group methods, these codes were grouped into 24 based content and 7 organizer contents. In the next step, in two distinct stages, using interpretative structural modeling method, the modeling of the basic themes and the organizing content separately and considering the consistency of the two models, their integration and creation of the final model of evaluation of the development of organizational dashboard with the logic of business intelligence has been made. This can be a precise roadmap for organizations to design and implement this system.
