Volume 12 (2023)
Volume 11 (2022)
Volume 10 (2021)
Volume 9 (2020)
Volume 8 (2019)
Volume 7 (2018)
Volume 6 (2017/18)
Volume 5 (2017)
Volume 4 (2015)
Volume 3 (2015)
Volume 1 (2012)
Volume 2 (1392)
Number of Articles: 10
Research Paper
Management approaches in the field of smart
The Evolution Path of Business Intelligence & social media Capabilities
maryam mirsharif; akbar alemtabriz; alireza motameni
Volume 12, Issue 45 , September 2023, Pages 1-38
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) ... Read More 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.
Research Paper
Management approaches in the field of smart
Exploring the lived Experience of the Concept of Touch in Purchasing Product Categories from Physical and Online Stores
meisam Aminzadeh vahedi; seyyed hamid khodadad Hosseini; Beit Allah Akbari Moghadam
Volume 12, Issue 45 , September 2023, Pages 39-69
Abstract
The touch of the product plays an important role in the final decision of the customer when purchasing from physical and online retail, and the sensations that come to be enjoyed through touch enable them to experience the product from all angles. Therefore, considering the importance of touch, ... Read More The touch of the product plays an important role in the final decision of the customer when purchasing from physical and online retail, and the sensations that come to be enjoyed through touch enable them to experience the product from all angles. Therefore, considering the importance of touch, this research has investigated the lived experience of touching the product from the point of view of customers of physical and online stores. The following article is done with qualitative method and phenomenological paradigm. The research community is made up of electronic and clothing buyers from online and physical stores: Technolife, Adak, Havadar and Happyland in Tehran, and through semi-structured interviews, evidence was collected based on the purposeful sampling method. The interviews continued until reaching the theoretical saturation, and in this research, the interviews reached saturation with 15 people. Based on the extracted results, the main themes include; Product perception is physical touch, virtual touch, touch experiences, need for touch and touch perceptions. According to the results, managers of physical and online stores should provide conditions (such as the use of modern technologies) that touch and contact with the product happen to both groups of online and physical buyers so that they can buy products based on their needs and wants, and also this research can pave the way for the development of touch literature for researchers. IntroductionThe touch of the product plays an important role in the final decision of the customer when purchasing from physical and online retail, and the sensations that come to be enjoyed through touch enable them to experience the product from all angles. Therefore, considering the importance of touch, this research has investigated the lived experience of touching the product from the point of view of customers of physical and online stores.Considering the importance of touch, this research seeks to answer this question: What are the themes of customers' lived experiences of understanding the concept of touch in physical and online shopping?Literature ReviewThere is a lot of research in the field of sensory marketing, some of which have focused on the importance of touch and the use of technologies (which create a multi-sensory experience for consumers).)Labrecque, 2020,p:1013؛ Mishra et al, 2020,p:1(.Many researchers have shown that the decision to buy will be positive after specific and positive emotional reactions that occur after touching the product (Hultén, 2020; Krishna, 2013,p:56).Retail companies recognize that product touch is an essential component of in-store experiences and explicitly encourage consumers to touch their products (Williams & Ackerman, 2011). On the other hand, online stores may use special features (for example, image magnification) to create a sense of tactile contact with products and create a so-called mental simulation with tactile products and create a favorable desire for their products (Overmars & Poels, 2015, p: 17). Meanwhile, touch interfaces may engage consumers with online shopping and influence their purchasing decisions (Chung et al, 2018, p: 795). Analyzed the main drivers for product purchase decisions and the differences between online and offline retailing. She confirmed that most of the consumers search about the product and check it mostly online, but when buying, they tend to go to physical stores (Gligorijevic, 2011). Touch in consumer behavior is a new research area (Jansson-Boyd, 2011a, p: 219), and considering the importance of touch in theoretical literature and its importance in customer decisions, this research has been conducted in this field. MethodologyThe current research is applied-developmental in terms of its purpose and was carried out based on the descriptive phenomenological approach, also practical and practical solutions for solving problems and improving processes were presented. This type of research is done to answer practical questions and meet practical needs (Maxwell et al, 2009, p: 198).The following article is done with qualitative method and phenomenological paradigm. The research community is made up of electronic and clothing buyers from online and physical stores: Technolife, Adak, Havadar and Happyland in Tehran, and through semi-structured interviews, evidence was collected based on the purposeful sampling method. The interviews continued until reaching the theoretical saturation, and in this research, the interviews reached saturation with 15 people. ResultsThe purpose of this research is to discuss how to understand the concept of touch in the process of purchasing product classes from physical (face-to-face) and online stores and to conduct a topical analysis based on descriptive phenomenology. Based on the extracted results, the main themes include; Product perception is physical touch, virtual touch, touches experiences, need for touch and touch perceptions. According to the results, managers of physical and online stores should provide conditions (such as the use of modern technologies) that touch and contact with the product happen to both groups of online and physical buyers so that they can buy products based on their needs and wants, and also this research can pave the way for the development of touch literature for researchers. DiscussionThe present study discusses how to understand the concept of touch in the process of purchasing product classes from physical (face-to-face) and online stores and conducts thematic analysis based on descriptive phenomenology. Based on this, the main theme of understanding the product includes sub-themes: understanding the personality of the product, ease of use of the product, emotional function and cognitive function, the main theme of physical touch of the product, including sub-themes: evaluation of inherent properties by hand touch and evaluation of geometric properties by hand touch, the main theme Touch and tactile perceptions include sub-topics: visual and audio virtual information and textual virtual information combined with sound and image, the content of touch experiences including sub-topics: product knowledge and familiarity with the brand; The theme of customer's need for touch includes sub-themes: instrumental touch and automatic touch, and the theme of tactile perceptions includes sub-themes: trust-seeking measures and compensatory return policy. ConclusionThe results have shown that people for whom the need to touch is important because of reducing the risk in purchasing, to better recognize the product features, to ensure the usefulness and ease of use of the product, to acquire skills and better process product information by hand before purchasing, touched so that they can have a better evaluation of the product.AcknowledgmentsWe are grateful to all the stores and buyers who helped us in conducting the interviews. Keywords: live Experience, product Touch, product Categories, physical and Online Store.
Research Paper
Data science, intelligence and future analysis
Artificial Intelligence and The Future of Scientific Progress: From Normal Science to Post Normal Science
Mohammad Hoseini Moghadam
Volume 12, Issue 45 , September 2023, Pages 71-116
Abstract
The touch of the product plays an important role in the final decision of the customer when purchasing from physical and online retail, and the sensations that come to be enjoyed through touch enable them to experience the product from all angles. Therefore, considering the importance of touch, ... Read More The touch of the product plays an important role in the final decision of the customer when purchasing from physical and online retail, and the sensations that come to be enjoyed through touch enable them to experience the product from all angles. Therefore, considering the importance of touch, this research has investigated the lived experience of touching the product from the point of view of customers of physical and online stores. The following article is done with qualitative method and phenomenological paradigm. The research community is made up of electronic and clothing buyers from online and physical stores: Technolife, Adak, Havadar and Happyland in Tehran, and through semi-structured interviews, evidence was collected based on the purposeful sampling method. The interviews continued until reaching the theoretical saturation, and in this research, the interviews reached saturation with 15 people. Based on the extracted results, the main themes include; Product perception is physical touch, virtual touch, touch experiences, need for touch and touch perceptions. According to the results, managers of physical and online stores should provide conditions (such as the use of modern technologies) that touch and contact with the product happen to both groups of online and physical buyers so that they can buy products based on their needs and wants, and also this research can pave the way for the development of touch literature for researchers.IntroductionThroughout human history, the idea of progress has been a central concern for thinkers and intellectuals, with technological advancements playing a pivotal role in shaping the development of societies (Du Pisani, 2006; Rivers, 2002). Artificial intelligence (AI), as a driving force behind the fourth industrial revolution, has had a profound impact on numerous fields, including scientific research and discovery (Velarde, 2020). AI has revolutionized scientific knowledge to such an extent that distinguishing between the discoveries made by intelligent machines and human experts has become increasingly difficult (Krenn et al, 2022). This article explores the implications of AI for the future of scientific progress and its potential to give rise to post-normal science.Here is my attempt at rewriting the text as a senior researcher:The central question examined in this article is: what role does AI play in shaping the future of scientific developments? In exploring this overarching question, several related questions are also considered: How can AI be leveraged to uncover and obtain new scientific knowledge? Can novel computing techniques based on AI not only detect unusual patterns and events in data, but also lay the groundwork for new scientific advances? Might AI furnish new theories and transform our comprehension of science? Can AI-based scientific systems determine which scientific questions are worthwhile, and for whom are they valuable? Looking ahead, what assurances will scientists have about the validity of AI-based analyses in science?In response to these pressing questions, the core hypothesis presented is that AI has become the foundation for the emergence of a new breed and style of scientific discovery, which can be characterized as post-normal science. To evaluate this hypothesis, the historical background of relevant research is reviewed. AI represents a seismic shift in the practice of science, enabling analyses and discoveries that would be impossible for humans alone. While promising, it also poses troubling philosophical questions about the nature of truth and scientific understanding.MethodologyA variety of research methods were employed to address the questions raised in this study, including a systematic review of relevant literature to identify the transition from normal to post-normal science, trend analysis to examine the influence and expansion of AI in scientific discoveries, documentary studies to obtain theoretical and conceptual foundations, and modeling to understand and describe the progress of post-normal science under the influence of AI.FindingsAI has facilitated a new model of scientific discovery, known as data-driven scientific discovery, which derives hypotheses from data rather than relying on preconceived assumptions (Wheeler, 2004). This approach has transformed traditional sciences into data sciences, with scientific patterns extracted from data and an increasing focus on intelligent automation in scientific progress (King & Roberts, 2018). As a result, a new type of epistemology has emerged, characterized by the involvement of machines in scientific discovery and the advancement of the science cycle. This development, referred to as "Science 0.4" or the fourth type of science, has integrated science into society, enabling every citizen to participate as a scientist and fostering a shift towards "open science" (Odman & Govender, 2021).AI's impact on scientific research has been guided by several key principles, including sustainability, different forms of knowledge, accountability and responsibility, values and interests, collective wisdom and rationality, and non-determinism and non-linearity in the process of scientific discovery. AI has contributed to the realization of post-normal science by facilitating simulation and modeling, improving decision-making, promoting ethics, embracing diversity, fostering interdisciplinary collaboration, expanding stakeholder engagement, and enabling big data analysis.ConclusionAI systems have fostered interdisciplinary collaborations and facilitated the integration of knowledge and expertise across various fields, allowing for the identification and resolution of complex, interdisciplinary scientific issues. This collaboration disrupts the linear progression of normal science, promoting a more integrated and cooperative approach to problem-solving. Furthermore, AI has introduced new ethical and social considerations in scientific research, necessitating a departure from conventional forms of normal science. Although it remains uncertain whether AI will replace the human role in scientific discovery, it is clear that scientists and institutions that embrace AI technology will surpass those that do not.RecommendationsTo achieve excellence in the field of AI within scientific institutions, it is crucial to understand the "state of maturity in AI" and to establish a starting point for the governance system of science and its actors. In this process, scientific institutions can be categorized along a spectrum, ranging from those seeking to familiarize themselves with AI-driven changes in scientific discovery to those actively leveraging AI technology to advance scientific knowledge.Keywords: Artificial Intelligence, Normal Science, Post Normal Science, Science Progress, Scientific Discoveries.
Research Paper
Management approaches in the field of smart
A Business Intelligence Maturity Model in Healthcare Based on the Combination of Delphi and DEMATEL-ANP Methods
Mahnaz Saeedi Mamaghani; Mohammad Javad Ershadi; Arman Sajedinejad
Volume 12, Issue 45 , September 2023, Pages 117-156
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 ... Read More 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.
Research Paper
Management approaches in the field of smart
Developing a Framework for Evaluating the Digital Platform Economy
Mehdi Elyasi; Maghsoud Amiri; Seyed Soroush Ghazinoori; Neda Jomehri
Volume 12, Issue 45 , September 2023, Pages 157-201
Abstract
The current digital revolution has given rise to a new organizational form, the Platform company. Today, the most valuable companies in the world and the first ones with a market value of more than a trillion dollars are platform companies. The Platform Economy is developing at an exponential rate and ... Read More The current digital revolution has given rise to a new organizational form, the Platform company. Today, the most valuable companies in the world and the first ones with a market value of more than a trillion dollars are platform companies. The Platform Economy is developing at an exponential rate and has become a top priority for governments across the world. The present study aims to provide a framework for evaluating the Digital Platform Economy at the international level. Utilizing a systematic review and meta-synthesis approach, the Platform Economy dimensions are identified as Digital Users, Digital Entrepreneurs, Digital Platforms, Digital Infrastructure, Innovation Capacity, and Institutional Environment and by extracting relevant indicators from international reports, the Platform Economy Composite Index is developed. Using the Partial Least Squares-Path Modelling (PLS-PM) method and specifically the Higher-Order Construct model, the measurement model is validated, and by employing a non-compensatory aggregation method, the Platform Economy Composite Index ranks 128 countries. The study is concluded by scrutinizing Iran’s current status regarding the enabling factors of the platform economy and identifying its strengths and weaknesses and providing recommendations for improvement. The results indicate that although Iran’s current status in terms of demand-side enablers is relatively good, it faces serious issues in terms of supply-side enablers.IntroductionThe emergence and proliferation of the application of big data, cloud computing and new algorithms have led to the formation of a platform economy built around platform companies. This new generation of digital businesses has disrupted several industries and often are startups that have become new market leaders (Acs et al., 2021).Companies like Apple, Microsoft, Google, Amazon, and Meta are examples of such businesses. The market value of these five companies was close to 9 trillion dollars in December 2021 (Companiesmarketcap, 2021), equivalent to 9.5% of the global GDP (O'Neill, 2021).The immense value creation power of the platform economy has made its the key to inclusive economic growth for both advanced and developing economies, and a catalyst for economic and social leapfrogging opportunities in developing countries (Chakravorti et al., 2019). However, platform economy literature has neglected the assessment of the national factors that have given rise to the platforms and therefore, it is necessary to identify the national factors that enable the emergence and growth of digital platforms (Hermes et al., 2020).However, a review of the research literature indicates that the evaluation of platform economy at the national level has not made much progress and the few studies that have attempted this (Chakravorti et al., 2019; Morvan et al., 2016), have been primarily focused on the developed countries and therefore are more compatible with the conditions of these countries. Consequently, policymakers in developing countries, despite having different conditions, must refer to the experiences of developed countries for the development of platform economy policies. Since there is a limited understanding of the effectiveness of such policies on enhancing the efficiency of the platform economy, this approach can be challenging (Szerb et al., 2022).Against this background, this study aims to develop a comprehensive framework for evaluating the platform economy of countries at different levels of development. Utilizing a systematic review and meta-synthesis approach, the enabling dimensions of the platform economy are identified as Digital Users, Digital Entrepreneurs, Digital Platforms, Digital Infrastructure, Innovation Capacity, and Institutional Environment. Based on this framework and by extracting relevant indicators from international reports, the Platform Economy Composite Index is constructed. The study concludes by closely examining Iran's current situation in terms of the enabling factors of the platform economy. It identifies the country's strengths and weaknesses and offers recommendations for improvement. Research Question(s)The main question of this research is defined as follows:What are the dimensions and components of a comprehensive framework for evaluating the platform economy and how can a composite index be developed using this framework?Literature ReviewDigital platforms serve as intermediaries that facilitate interactions and exchange of values between at least two different and interdependent user groups in platform ecosystems (Drewel et al., 2021).There is no consensus on the definition of the platform economy, and different terms such as Sharing Economy, Collaborative Economy, Access Economy, and Gig Economy have been used to refer to this phenomenon in academic and policy research (Riso, 2019). However, the term Platform Economy has gained more prevalence due to its more inclusive connotations. Kenney and Zysman (2016) consider the term Platform Economy a “more neutral term as they believe it encompasses a growing number of digitally enabled activities in business, politics, and social interaction”. Here, the platform economy is defined as a value creation system consisting of platforms and platform ecosystems (Dufva et al., 2017).A review of the research literature indicates that the evaluation of platform economy at the national level has not made much progress (Szerb et al., 2022), and the few studies that have evaluated the platform economy at the national level, have been primarily focused on developed economies e.g., Morvan et al. (2016) developed Platform Readiness Index to evaluate readiness level of 16 countries of G20 countries in the development of platforms. Furthermore, to the best of our knowledge there is no systematic review focused on the identification of platform economy enablers at the national level. Therefore, utilizing a systematic review and meta-synthesis approach, this study aims to develop a comprehensive framework for evaluating the platform economy of countries at different levels of development.MethodologyThe main steps for developing a composite index include developing a conceptual framework, selecting individual indicators, imputation of missing data, multivariate analysis, normalization, aggregation, and composite index validation (OECD, 2008).The first step of constructing a composite index is the development of a conceptual framework that encompasses the dimensions and components of the phenomenon being measured. To this end, based on a meta-synthesis approach, a systematic review was conducted. The meta-synthesis approach was implemented using the Noblit and Hare (1988) seven-step method: 1. getting started; 2. deciding what is relevant; 3. reading the studies; 4. determining how the studies are related; 5. translating the studies into one another; 6. synthesizing translations; 7. expressing the synthesis. This resulted in the extraction of 6 dimensions and 16 components as platform economy enablers which are presented in the proposed conceptual framework for the platform economy evaluation.Based on this framework and by extracting relevant indicators from international reports, the Platform Economy Composite Index is constructed. Using the Partial Least Squares-Path Modelling (PLS-PM) method and specifically the Higher-Order Construct model, the measurement model is validated, and by employing a non-compensatory aggregation method, the Platform Economy Composite Index ranks 128 countries.ConclusionThis study attempted to develop a comprehensive framework for evaluating the efficiency of the platform economy of countries at different levels of development. Using a systematic review and meta-synthesis approach, Digital Users, Digital Entrepreneurs, Digital Platforms, Digital Infrastructure, Innovation Capacity, and Institutional Environment were identified as the evaluating dimensions of the platform economy.Furthermore, Iran's current situation in terms of the enabling factors of the platform economy was closely examined and country's strengths and weaknesses were identified. The results from the Platform Economy Composite Index indicate that while Iran is in a relatively good position regarding demand-side enablers, it is facing significant challenges with supply-side enablers.Keywords: Digital Platform, Platform Economy, Composite Index, International Ranking.
Research Paper
Data science, intelligence and future analysis
Identifying and prioritizing fifth-generation wireless mobile communications (5G) applications in smart manufacturing
Mehdi Fasanghari; Mohammad Asarian
Volume 12, Issue 45 , September 2023, Pages 203-231
Abstract
The fifth-generation networks of smart manufacturing and smart factory is rapidly evolving as a technology that integrates industrial production and smart Internet, bringing new support for the digital transformation of the industry and the development of a high-quality economy. Therefore, this ... Read More The fifth-generation networks of smart manufacturing and smart factory is rapidly evolving as a technology that integrates industrial production and smart Internet, bringing new support for the digital transformation of the industry and the development of a high-quality economy. Therefore, this article, with emphasis on the fifth generation of the Internet and with the aim of identifying 5G-based intelligent manufacturing projects, seeks to prioritize these projects using the hierarchical analysis method. Therefore, after reviewing the literature and interviewing with 17 experts, 5 main criteria for project prioritization were selected and weighted by AHP method using an expert questionnaire. Then, using the opinions of experts, 22 identified smart factory projects were prioritized according to the criteria weight. The criteria were calculated according to the income, cost and risk level of the project. Also Intelligent production line, intelligent logistics, intelligent resource allocation and process automation were identified as the most important intelligent production projects.IntroductionPrompt technological progress is driving a substantial paradigm shift in the manufacturing sector, empowering manufacturers to innovate and better satisfy consumer needs. In order to maintain a competitive edge on an international scale, manufacturers must implement technological advancements such as flexible production, robotics, automation, and smart factories to reduce expenses and increase efficiency (M Attaran & Attaran, 2020; Mohsen Attaran, 2023).5G technology is integrating intelligent internet with industrial production at an accelerated rate. Its provision of superior network services, including ample bandwidth, extensive connectivity, minimal latency, and dependable performance, serves as a catalyst for the advancement of the wireless industrial internet (Agiwal et al., 2016; Zhang et al., 2022)With augmented reality, artificial intelligence, and automation, 5G enables smart factories to perform troubleshooting (Wang, 2021). It addresses production obstacles while improving connectivity, speed, and quality (Yit et al., 2020). By facilitating intelligent management and agile production, 5G IoT provides factories with increased flexibility, reduced change turnaround times, and enhanced cost-effectiveness. It centralizes product lifecycle management, enhances communication, and streamlines smart factory operations (Siddiqui et al., 2022).5-G improves smart manufacturing by enabling real-time machine-to-machine communication, connectivity, and smart factory capabilities (Gangakhedkar et al., 2018). Early adoption of 5G has limited commercial applications in manufacturing, despite its potential (Wang, 2021). This article uses research, expert interviews, and project prioritization criteria to identify promising 5G applications in smart factories to aid 5G adoption decisions.Literature Review2.1. The fifth generation of mobile networksAmong the continuously evolving communication technologies, 5G emerges as a transformative entity. It follows the digitization of voice in 2G, the incorporation of multimedia in 3G, and the introduction of high-speed wireless broadband in 4G, constituting the fifth generation of mobile networks. Present communication technologies are facing challenges in keeping pace with the exponential growth of demand for mobile services, communication capabilities, and network traffic (Mu et al., 2020).5G, designated IMT-2020 by the International Telecommunication Union in 2015, will revolutionize connectivity and capabilities. Its features include user-centric network architecture, cloud radio access network architecture, beamforming antennas, millimeter-wave hybrid and standalone networks, and user plane separation. 5G offers over 1000-fold increased communication capacity, 10-100 times faster data transfer speeds, less than 1 millisecond latency, 10-100 times larger large-scale connectivity, lower costs, and a vastly improved user experience (Agiwal et al., 2016; Alqahtani et al., 2023; Li et al., 2020).2.2. Smart factorySmart manufacturing, or smart factories, uses 5G technology to improve efficiency, reduce production time, and optimize processes. It uses smart sensors to monitor and control production. These sensors can adapt to external stimuli, make logical decisions, and relay information, enhancing manufacturing efficiency and intelligence (Hozdić, 2015; Soori et al., 2023; Temesvári et al., 2019; Zuehlke, 2010).2.3. Smart manufacturing technologiesM2M and D2D communication are part of smart manufacturing. Active communication, data-driven decision-making, and control commands are enabled by M2M connections between humans, machines, and systems. It helps implement IoT smart connections. Conversely, D2D allows peer devices in a network to communicate directly. Communication is routed and managed autonomously by each device, optimising resource usage and network efficiency to improve connectivity (Ding & Janssen, 2018).2.4. Smart officeThe impact of 5G technology transcends the boundaries of the manufacturing facility. It enables employees to optimize their productivity by means of virtual assistants, digital communication tools, and rapid data transfer, thereby empowering intelligent workplaces. The realization of a mobile digital office is facilitated by 5G, which also promotes employee collaboration, adaptability, and uninterrupted communication (M Attaran & Attaran, 2020; Rao & Prasad, 2018).2.5. Automation and supply chain management and 5GBy facilitating communication and data exchange between and within organizations, 5G has a substantial effect on supply chain management. By improving the ability to integrate suppliers, customers, and internal logistics processes, it grants organizations a competitive edge. 5G enhances the overall efficiency of supply chains through the optimization of processes, reduction of costs, improvement of quality, and implementation of real-time monitoring capabilities (Liu, 2021; Rejeb & Keogh, 2021; Taboada & Shee, 2021).2.6. BlockchainBlockchain is a decentralized, global technology that works like a "large computer." It processes digital asset transactions like money, personal data, health records, and others as a distributed ledger. Blockchain accelerates computation through encryption and data improvement. Blocks of transaction records form a blockchain, ensuring data integrity. Blockchain combined with 5G technology allows real-time ownership and location tracking, improving transparency, validating products, preventing fraud, and improving supply chain efficiency. Monitoring KPIs ensures network performance transparency and ensures material sourcing, manufacturing, and supply chain security (Han et al., 2023; Jovović et al., 2019; Tahir et al., 2020).MethodologyThis study uses pragmatism-based applied research. Its main goal is to identify 5G network projects and applications in smart factories. The study is mixed-method, using qualitative and quantitative methods.First, 5G in smart factory projects literature was reviewed and expert interviews were conducted to identify relevant projects. These projects were refined using content analysis.17 industry experts were interviewed to evaluate and prioritize the projects in the second phase. After statistical analysis, 31 projects were reduced to 17. After comparing these projects to the literature, 22 were chosen.The third stage involved choosing five project evaluation and weighting criteria. The 17 experts were given a questionnaire to weight each criterion by importance.The fourth stage scored projects using the five criteria. Our Analytic Hierarchy Process (AHP) determined each project's final weight and ranking. The Analytic Hierarchy Process helps decision-makers prioritize options in complex and uncertain situations. It organises factors into a hierarchical tree structure and solves decision-making problems by breaking down large problems into smaller ones. This method clarifies problem relationships and concepts.ConclusionThe 5G wireless communication technology has emerged as an indispensable component in the advancement and administration of intelligent manufacturing. Exploring the applications of 5G connectivity in smart factories, this study seeks to identify projects that are feasible. By conducting interviews with IT specialists and reviewing prior articles in this field, the research identified 22 implementable projects. The prioritization of these projects was determined by the following five factors: total project cost, project revenue, social benefits, project feasibility, and project risk level. The findings indicated that project revenue was the most pivotal criterion, with project cost and risk level following suit. Both intelligent logistics and smart production lines, which are the top two recommended project categories, stand to gain substantially from 5G integration. Additionally, intelligent supply chain management, intelligent resource allocation, and process automation are crucial initiatives that can augment smart manufacturing.Keywords: Fifth-generation internet wireless mobile communications (5G), Analytic Hierarchy Process (AHP), Smart factory, Smart manufacturing, Technology.The fifth-generation networks of smart manufacturing and smart factory is rapidly evolving as a technology that integrates industrial production and smart Internet, bringing new support for the digital transformation of the industry and the development of a high-quality economy. Therefore, this article, with emphasis on the fifth generation of the Internet and with the aim of identifying 5G-based intelligent manufacturing projects, seeks to prioritize these projects using the hierarchical analysis method. Therefore, after reviewing the literature and interviewing with 17 experts, 5 main criteria for project prioritization were selected and weighted by AHP method using an expert questionnaire. Then, using the opinions of experts, 22 identified smart factory projects were prioritized according to the criteria weight. The criteria were calculated according to the income, cost and risk level of the project. Also Intelligent production line, intelligent logistics, intelligent resource allocation and process automation were identified as the most important intelligent production projects.IntroductionPrompt technological progress is driving a substantial paradigm shift in the manufacturing sector, empowering manufacturers to innovate and better satisfy consumer needs. In order to maintain a competitive edge on an international scale, manufacturers must implement technological advancements such as flexible production, robotics, automation, and smart factories to reduce expenses and increase efficiency (M Attaran & Attaran, 2020; Mohsen Attaran, 2023).5G technology is integrating intelligent internet with industrial production at an accelerated rate. Its provision of superior network services, including ample bandwidth, extensive connectivity, minimal latency, and dependable performance, serves as a catalyst for the advancement of the wireless industrial internet (Agiwal et al., 2016; Zhang et al., 2022)With augmented reality, artificial intelligence, and automation, 5G enables smart factories to perform troubleshooting (Wang, 2021). It addresses production obstacles while improving connectivity, speed, and quality (Yit et al., 2020). By facilitating intelligent management and agile production, 5G IoT provides factories with increased flexibility, reduced change turnaround times, and enhanced cost-effectiveness. It centralizes product lifecycle management, enhances communication, and streamlines smart factory operations (Siddiqui et al., 2022).5-G improves smart manufacturing by enabling real-time machine-to-machine communication, connectivity, and smart factory capabilities (Gangakhedkar et al., 2018). Early adoption of 5G has limited commercial applications in manufacturing, despite its potential (Wang, 2021). This article uses research, expert interviews, and project prioritization criteria to identify promising 5G applications in smart factories to aid 5G adoption decisions.Literature Review2.1. The fifth generation of mobile networksAmong the continuously evolving communication technologies, 5G emerges as a transformative entity. It follows the digitization of voice in 2G, the incorporation of multimedia in 3G, and the introduction of high-speed wireless broadband in 4G, constituting the fifth generation of mobile networks. Present communication technologies are facing challenges in keeping pace with the exponential growth of demand for mobile services, communication capabilities, and network traffic (Mu et al., 2020).5G, designated IMT-2020 by the International Telecommunication Union in 2015, will revolutionize connectivity and capabilities. Its features include user-centric network architecture, cloud radio access network architecture, beamforming antennas, millimeter-wave hybrid and standalone networks, and user plane separation. 5G offers over 1000-fold increased communication capacity, 10-100 times faster data transfer speeds, less than 1 millisecond latency, 10-100 times larger large-scale connectivity, lower costs, and a vastly improved user experience (Agiwal et al., 2016; Alqahtani et al., 2023; Li et al., 2020).2.2. Smart factorySmart manufacturing, or smart factories, uses 5G technology to improve efficiency, reduce production time, and optimize processes. It uses smart sensors to monitor and control production. These sensors can adapt to external stimuli, make logical decisions, and relay information, enhancing manufacturing efficiency and intelligence (Hozdić, 2015; Soori et al., 2023; Temesvári et al., 2019; Zuehlke, 2010).2.3. Smart manufacturing technologiesM2M and D2D communication are part of smart manufacturing. Active communication, data-driven decision-making, and control commands are enabled by M2M connections between humans, machines, and systems. It helps implement IoT smart connections. Conversely, D2D allows peer devices in a network to communicate directly. Communication is routed and managed autonomously by each device, optimising resource usage and network efficiency to improve connectivity (Ding & Janssen, 2018).2.4. Smart officeThe impact of 5G technology transcends the boundaries of the manufacturing facility. It enables employees to optimize their productivity by means of virtual assistants, digital communication tools, and rapid data transfer, thereby empowering intelligent workplaces. The realization of a mobile digital office is facilitated by 5G, which also promotes employee collaboration, adaptability, and uninterrupted communication (M Attaran & Attaran, 2020; Rao & Prasad, 2018).2.5. Automation and supply chain management and 5GBy facilitating communication and data exchange between and within organizations, 5G has a substantial effect on supply chain management. By improving the ability to integrate suppliers, customers, and internal logistics processes, it grants organizations a competitive edge. 5G enhances the overall efficiency of supply chains through the optimization of processes, reduction of costs, improvement of quality, and implementation of real-time monitoring capabilities (Liu, 2021; Rejeb & Keogh, 2021; Taboada & Shee, 2021).2.6. BlockchainBlockchain is a decentralized, global technology that works like a "large computer." It processes digital asset transactions like money, personal data, health records, and others as a distributed ledger. Blockchain accelerates computation through encryption and data improvement. Blocks of transaction records form a blockchain, ensuring data integrity. Blockchain combined with 5G technology allows real-time ownership and location tracking, improving transparency, validating products, preventing fraud, and improving supply chain efficiency. Monitoring KPIs ensures network performance transparency and ensures material sourcing, manufacturing, and supply chain security (Han et al., 2023; Jovović et al., 2019; Tahir et al., 2020).MethodologyThis study uses pragmatism-based applied research. Its main goal is to identify 5G network projects and applications in smart factories. The study is mixed-method, using qualitative and quantitative methods.First, 5G in smart factory projects literature was reviewed and expert interviews were conducted to identify relevant projects. These projects were refined using content analysis.17 industry experts were interviewed to evaluate and prioritize the projects in the second phase. After statistical analysis, 31 projects were reduced to 17. After comparing these projects to the literature, 22 were chosen.The third stage involved choosing five project evaluation and weighting criteria. The 17 experts were given a questionnaire to weight each criterion by importance.The fourth stage scored projects using the five criteria. Our Analytic Hierarchy Process (AHP) determined each project's final weight and ranking. The Analytic Hierarchy Process helps decision-makers prioritize options in complex and uncertain situations. It organises factors into a hierarchical tree structure and solves decision-making problems by breaking down large problems into smaller ones. This method clarifies problem relationships and concepts.ConclusionThe 5G wireless communication technology has emerged as an indispensable component in the advancement and administration of intelligent manufacturing. Exploring the applications of 5G connectivity in smart factories, this study seeks to identify projects that are feasible. By conducting interviews with IT specialists and reviewing prior articles in this field, the research identified 22 implementable projects. The prioritization of these projects was determined by the following five factors: total project cost, project revenue, social benefits, project feasibility, and project risk level. The findings indicated that project revenue was the most pivotal criterion, with project cost and risk level following suit. Both intelligent logistics and smart production lines, which are the top two recommended project categories, stand to gain substantially from 5G integration. Additionally, intelligent supply chain management, intelligent resource allocation, and process automation are crucial initiatives that can augment smart manufacturing.Keywords: Fifth-generation internet wireless mobile communications (5G), Analytic Hierarchy Process (AHP), Smart factory, Smart manufacturing, Technology.
Research Paper
Data, information and knowledge management in the field of smart business
Information systems evaluation model in world-class by balanced scorecard approach in sports organizations
Majid Sabet Rasekh; Mehdi Salimi; Ghasem Rahimi
Volume 12, Issue 45 , September 2023, Pages 235-264
Abstract
The aim of the current research was to provide a world-class information systems development model using the balanced scorecard approach in sports organizations. The current research is practical in terms of purpose; In terms of how to collect information, it was a survey. The statistical population ... Read More The aim of the current research was to provide a world-class information systems development model using the balanced scorecard approach in sports organizations. The current research is practical in terms of purpose; In terms of how to collect information, it was a survey. The statistical population of the research was made up of the employees of all 31 general sports and youth departments of the country's provinces (5882 people) and the statistical sample was selected using the Karjesi and Morgan table of 361 people. To collect data, a researcher-made questionnaire was used according to the balanced scorecard approach (4 components and 48 items). The validity of the questionnaire was confirmed by 10 sports management professors and the reliability was 0.86, which indicated its good reliability. Data analysis was done using confirmatory factor analysis and structural equation modeling with PLS software. The findings from the analysis of the conceptual model of the research show that the development model of world-class information systems in sports organizations, in the financial perspective using 5 indicators, in the customer perspective with 12 indicators, in the business processes perspective with 14 indicators and in the growth perspective and learning was confirmed with 17 indicators. Therefore, it is concluded that the development of world-class information systems in sports organizations by increasing efficiency and effectiveness will improve organizational productivity and be considered as a sustainable competitive advantage. IntroductionToday, work processes are increasingly performed with high complexity, multitasking and time pressure. Among these organizations, sports organizations need more flexible information systems due to their communication and interaction with different stakeholder groups and their geographic scope is both national and international. One of the important functions of organizational information systems, in addition to the flow and integration of information throughout the scope of an organization, is the sharing of relevant and required organizational information with stakeholders and other related organizations; And due to the interconnected nature of some organizations and the important role of stakeholders in organizational growth and development, it is very important.Considering the advantages mentioned for information systems, most organizations have now realized that the use of these systems in all economic and social fields is an inevitable necessity. Physical education and sports are not exempted from this rule, so one of the fields that need to use these information systems for transformation is the country's sports department, for this purpose, the current research seeks to examine the question that the system evaluation model How is the world-class information in the country's sports organizations? Literature ReviewIn a research, Jafarzadeh et al. (2019) investigated the future research of information technology infrastructure in sports organizations and by presenting a model, they stated that managers of sports organizations should pay attention to the identified variables of the optimal infrastructure path in the future. Technology, such as technology knowledge, network communication, technology management, etc., emphasize this issue and improve it. In another study, Najafi and Ghasemi (2019) identified the main indicators and calculated the performance efficiency of information systems and knowledge management in the oil industry and found its position in this industry to be better than other industries.Also, in their research, Salimi and Tayibi (2022) investigated a model of information systems in sports organizations and examined the variables of system quality, information quality, service quality, usability, user satisfaction and net profit, which The difference between this research and the current research is in the model that is evaluated. Norton and Kaplan (2021) also investigated the importance of the balanced scorecard method in a research and called it a revolutionary tool for realizing the mission of organizations and more than an evaluation system, as a management system that can use all energy, abilities, knowledge and skills. Employees are introduced to achieve the strategic goals of the organization. Benbiya et al. (2020) and Sora et al. They know a great help to solve these complications. Boranbayu et al. (2020) also evaluated the reliability of information systems using multi-criteria decision-making and its information security risks in a study and provided solutions to find and neutralize risks. MethodologyThe current research is practical in terms of purpose; And in terms of method, it is placed in the category of survey descriptive research, which is specifically based on structural equation modeling. The statistical population was made up of the employees of all 31 general departments of sports and youth in the provinces of the country (Iran) (this number was estimated to be 5882 people); And the sample size was considered 361 people based on the table of Karjesi and Morgan, with maximum confidence. For sampling, the provinces of the country were divided into 5 geographical regions, and in each region, one general office was randomly selected as a sample and 73 questionnaires were distributed in that office. Due to the geographical dispersion of the selected general sports and youth departments (5 departments from five different geographical regions of the country), as well as the communication limitations caused by the corona disease, the questionnaires were sent in person and through an electronic address (or WhatsApp application) and etc. were distributed (a total of 73 questionnaires were distributed in each General Directorate of Sports and Youth, which was a total of 365 questionnaires and 361 questionnaires could be examined). In this research, the tool used to collect data was a researcher-made questionnaire. For this purpose, with the help of theoretical literature and existing research background, including reliable sources and instructions issued by the Ministry of Sports and Sports and Youth Departments, the indicators of the questionnaire were designed according to the balanced scorecard approach; which includes 4 general components and 48 items: the financial perspective in the development of the organization's information systems (5 items), the customer's perspective in the development of the organization's information systems (12 items), the perspective of internal processes in the development of the organization's information systems (14 items), the perspective Learning and growth in the development of organization information systems (17 items). The validity of the questionnaire was accepted and confirmed by 10 sports management professors after removing, adjusting or modifying some questions, and its reliability was confirmed using Cronbach's alpha coefficient of 0/86. At the end and after data collection, using confirmatory factor analysis and structural equation modeling with the help of Smart PLS software, the construct validity was confirmed and the research model was explained. ConclusionThe existence of appropriate information systems in the country's sports organizations, which have a wide range in the provinces and cities and also include many financial and non-financial resources, can be beneficial in the field of education, learning and organizational growth. also played a very important role and by providing various information to the organization's human resources, it helped to perform their job duties with better and more quality, increased the speed of performing duties and also assessed the training needs of jobs in the future. turn on another part of the findings from the analysis of the research model states that one of the most important aspects of the balanced scorecard in the investigation of the information systems of sports organizations is the customer's perspective; Because the existence of loyal customers is significant and valuable, which primarily gives credibility to an organization and causes its establishment, stability and growth; Also, the existence of information systems in various organizations, including sports organizations, which have customers from different strata of people with different ages, economic status, and social status, and they have different demands and expectations, is very important, and obtaining their maximum satisfaction is achieved when There should be more transparency in various organizational and executive stages, which can help attract more customers in addition to retaining customers.The perspective of internal processes is also another aspect investigated in the balanced scorecard approach, which the findings from the analysis of the research model show that the existence of information systems in sports organizations, which, like many other organizations, are subject to changes and developments. are located globally, it can examine various processes that affect customer satisfaction such as time, quality, employee skills and productivity in general, and identify its competitive advantages in different sectors and with quantitative measurements and clarify and improve the different quality of this issue with transparency.Keywords: Balanced Scorecard, World Class, Sports Organizations, Information Systems.v
Research Paper
Management approaches in the field of smart
Strategic Factors Affecting Banks' Cooperation with FinTechs
alireza rezanezhad kookhdan; peyman ghafari ashtiani; Mohammad Hasan Maleki; Majid Zanjirdar
Volume 12, Issue 45 , September 2023, Pages 265-311
Abstract
Traditional banking needs new fintech innovations and technologies to improve its processes and services. Various factors affect the cooperation of banks and fintechs, some of which are related to banks and others to the banking environment.The purpose of this study is to identify and analyze the strategic ... Read More Traditional banking needs new fintech innovations and technologies to improve its processes and services. Various factors affect the cooperation of banks and fintechs, some of which are related to banks and others to the banking environment.The purpose of this study is to identify and analyze the strategic factors affecting the cooperation of banks and fintechs in Bank.The present study is applied in terms of orientation and has a quantitative nature in terms of methodology. Two methods of fuzzy Delphi and fuzzy dematel were used to analyze the data. The fuzzy Delphi method was used to screen the strategic factors of the research and the fuzzy dematel technique was used to identify the most effective factors. Two tools of interview and questionnaire were used to collect data. The research questionnaires were:Fuzzy Screening Questionnaire and effect analysis Questionnaire. Initially, through literature review and interviews with experts, 28 strategic factors were identified.These factors were screened by fuzzy Delphi technique.10 internal factors and eight external factors had a defuzzy number greater than 0.7 and were selected for analysis with fuzzy dematel.Analysis of internal factors with fuzzy dematel showed that the factors of the nature of the needs of the bank's customers,the future thinking of the bank's senior managers, the culture of risk-taking between managers and senior experts and the agility of the bank's structure and processes have the most net effect In relation to external factors, the factors of intensity of competition between banks, effective factors on the cooperation between banks and fintechs. IntroductionThe relationship with banks is not only beneficial for them but also brings threats and challenges. So, banks have resorted to using different strategies to deal with the possible threats of FinTechs, the most important of which is the formation of strategic partnerships. A strategic partnership is a cooperative arrangement between organizations, contributing to the competitive advantage of the parties. Some advantages of the strategic partnership between the banking system and FinTech are efficiency in speed, agility, cost, and attracting new customers. Some of the challenges faced by traditional banks are having complex structures, high level of formality, increasing operating costs, providing expensive and time-consuming banking services, lack of service innovation, and failure to meet customer expectations (Soltani and Tahmasebi Aghbolaghi, 2020). Through strategic partnerships with FinTechs, banks can overcome many of their inefficiencies.Most of the studies on banks and FinTechs have investigated the effects of financial innovations on the operational variables of banks, such as costs and performance. The challenges and opportunities of bank and FinTech partnerships have been evaluated by some studies. Moreover, some studies have extracted the patterns of bank and FinTech partnerships from the point of view of bank and fintech managers. Factors affecting the partnership between banks and FinTechs have been examined by a few studies. They obtained limited factors from the perspective of a few stakeholders. The strategic partnership between banks and FinTechs is affected by various factors, some of which are intra-organizational and some are extra-organizational. Accordingly, the study questions are as follows:What are the strategic factors affecting the partnership between banks and FinTechs?Which strategic factors have the most impact on the partnership between banks and FinTechs? Literature ReviewBy providing customer-oriented services, using Internet-based technologies, and facilitating the use of financial services, FinTechs have competed with traditional financial services (Suryono et al., 2021). FiTtechs offer more innovative, faster, and cheaper services than banks. On the other hand, banks have slower structures and processes than FinTechs. Many traditional institutions, such as banks, do not have a positive view of Fintechs (Romānova & Kudinska, 2016; Temelkov, 2018). However, the trend towards bank-FinTech partnerships has increased significantly recently (Buchak et al., 2018; Iman, 2019; Ky et al., 2019; Cole et al., 2019; Ya, 2020; Cheng & Qu, 2020; Saphyra & Zahra, 2021; Hoang et al., 2021). Banks and their managers have two important approaches to FinTechs. The first approach does not have a positive view of FinTechs, arguing that the risk of partnering with and investing in them is very high and that partnering with them can lead to various threats such as security risks. The second approach suggests that partnering with them, especially in research and development, can lead to the agility of banking structures and processes. Partnerships between banks and FinTechs can have various reasons, the main of which are reducing costs, increasing profitability, growing revenues, developing market share, reducing each other's risks, and providing optimal and unique services (Tahmasebi Aghbolaghi et al., 2021). Many studies have investigated the effects of FinTechs on banking indicators. Th These studies, which form an important part of the literature, aim to explain the effects and functions of FinTechs and their innovations in the banking sector. This relationship is accompanied by challenges such as regulatory (Buchak et al., 2018; Omarova, 2020), customer management (Suryono et al., 2020), security (Lee & Shin, 2018), integration and partnership (Phan et al., 2020), fee system (Koshesh Kordsholi et al., 2019), receiving international licenses (Payandeh et al., 2014; Koshesh Kordsholi et al., 2019), authentication and validation systems (Suryono et al., 2020), wallets (Agarwal & Zhang, 2020), and low financial literacy of users (Suryono et al., 2020). One of the most important challenges faced by FinTechs is the lack of effective and supportive laws. The laws enacted are mainly for the benefit of traditional institutions. They are mostly ambiguous and unpredictable. Banks and large financial institutions are reluctant to partner with FinTechs due to the ambiguity of laws and regulations. Materials and MethodsThis study was conducted to provide a framework for identifying and analyzing strategic factors affecting the partnership between banks and FinTechs. For this purpose, fuzzy Delphi and fuzzy DEMATEL techniques were used. These are quantitative techniques and use quantitative data for analysis. The fuzzy Delphi technique was used to screen the strategic factors of partnership between banks and FinTechs and the fuzzy DEMATEL technique was used to analyze the effectiveness of these factors. Since these techniques are quantitative, the study has multiple quantitative methodologies. Moreover, it is an applied study because of the benefit of its findings for the banking industry and FinTechs.The study was conducted in three steps. In the first step, the factors affecting the partnership between banks and FinTechs were extracted through a literature review and interviews with FinTech experts. In the next step, these factors were screened using the fuzzy Delphi technique. In the third step, the effectiveness of the screened factors was determined through the fuzzy DEMATEL technique. ConclusionThis study was conducted to identify and analyze the strategic factors affecting the partnership between banks and FinTechs. 28 factors were extracted through a literature review and expert interviews. 14 of the extracted factors were intra-organizational and the rest were extra-organizational. They were screened using the fuzzy Delphi technique, and 10 factors were eliminated. The intra-organizational and extra-organizational strategic factors were then analyzed separately through the fuzzy DEMATEL technique. Among the intra-organizational strategic factors, the nature of the needs of the bank's customers, the forward-thinking of the bank's senior managers, the culture of risk-taking among managers and senior experts, and the agility of the bank's structure and processes were the most effective, respectively. Among the extra-organizational strategic factors, the intensity of competition between banks, the fee system, the performance of the regulator in legislation, and the risks and security considerations concerning FinTechs, had a greater effect on the partnership between banks and FinTechs, respectively.Keywords: Financial Technology, FinTech, Banking Industry, Banking FinTechs, Fuzzy Approach.Traditional banking needs new fintech innovations and technologies to improve its processes and services. Various factors affect the cooperation of banks and fintechs, some of which are related to banks and others to the banking environment.The purpose of this study is to identify and analyze the strategic factors affecting the cooperation of banks and fintechs in Bank.The present study is applied in terms of orientation and has a quantitative nature in terms of methodology. Two methods of fuzzy Delphi and fuzzy dematel were used to analyze the data. The fuzzy Delphi method was used to screen the strategic factors of the research and the fuzzy dematel technique was used to identify the most effective factors. Two tools of interview and questionnaire were used to collect data. The research questionnaires were:Fuzzy Screening Questionnaire and effect analysis Questionnaire. Initially, through literature review and interviews with experts, 28 strategic factors were identified.These factors were screened by fuzzy Delphi technique.10 internal factors and eight external factors had a defuzzy number greater than 0.7 and were selected for analysis with fuzzy dematel.Analysis of internal factors with fuzzy dematel showed that the factors of the nature of the needs of the bank's customers,the future thinking of the bank's senior managers, the culture of risk-taking between managers and senior experts and the agility of the bank's structure and processes have the most net effect In relation to external factors, the factors of intensity of competition between banks, effective factors on the cooperation between banks and fintechs. IntroductionThe relationship with banks is not only beneficial for them but also brings threats and challenges. So, banks have resorted to using different strategies to deal with the possible threats of FinTechs, the most important of which is the formation of strategic partnerships. A strategic partnership is a cooperative arrangement between organizations, contributing to the competitive advantage of the parties. Some advantages of the strategic partnership between the banking system and FinTech are efficiency in speed, agility, cost, and attracting new customers. Some of the challenges faced by traditional banks are having complex structures, high level of formality, increasing operating costs, providing expensive and time-consuming banking services, lack of service innovation, and failure to meet customer expectations (Soltani and Tahmasebi Aghbolaghi, 2020). Through strategic partnerships with FinTechs, banks can overcome many of their inefficiencies.Most of the studies on banks and FinTechs have investigated the effects of financial innovations on the operational variables of banks, such as costs and performance. The challenges and opportunities of bank and FinTech partnerships have been evaluated by some studies. Moreover, some studies have extracted the patterns of bank and FinTech partnerships from the point of view of bank and fintech managers. Factors affecting the partnership between banks and FinTechs have been examined by a few studies. They obtained limited factors from the perspective of a few stakeholders. The strategic partnership between banks and FinTechs is affected by various factors, some of which are intra-organizational and some are extra-organizational. Accordingly, the study questions are as follows:What are the strategic factors affecting the partnership between banks and FinTechs?Which strategic factors have the most impact on the partnership between banks and FinTechs? Literature ReviewBy providing customer-oriented services, using Internet-based technologies, and facilitating the use of financial services, FinTechs have competed with traditional financial services (Suryono et al., 2021). FiTtechs offer more innovative, faster, and cheaper services than banks. On the other hand, banks have slower structures and processes than FinTechs. Many traditional institutions, such as banks, do not have a positive view of Fintechs (Romānova & Kudinska, 2016; Temelkov, 2018). However, the trend towards bank-FinTech partnerships has increased significantly recently (Buchak et al., 2018; Iman, 2019; Ky et al., 2019; Cole et al., 2019; Ya, 2020; Cheng & Qu, 2020; Saphyra & Zahra, 2021; Hoang et al., 2021). Banks and their managers have two important approaches to FinTechs. The first approach does not have a positive view of FinTechs, arguing that the risk of partnering with and investing in them is very high and that partnering with them can lead to various threats such as security risks. The second approach suggests that partnering with them, especially in research and development, can lead to the agility of banking structures and processes. Partnerships between banks and FinTechs can have various reasons, the main of which are reducing costs, increasing profitability, growing revenues, developing market share, reducing each other's risks, and providing optimal and unique services (Tahmasebi Aghbolaghi et al., 2021). Many studies have investigated the effects of FinTechs on banking indicators. Th These studies, which form an important part of the literature, aim to explain the effects and functions of FinTechs and their innovations in the banking sector. This relationship is accompanied by challenges such as regulatory (Buchak et al., 2018; Omarova, 2020), customer management (Suryono et al., 2020), security (Lee & Shin, 2018), integration and partnership (Phan et al., 2020), fee system (Koshesh Kordsholi et al., 2019), receiving international licenses (Payandeh et al., 2014; Koshesh Kordsholi et al., 2019), authentication and validation systems (Suryono et al., 2020), wallets (Agarwal & Zhang, 2020), and low financial literacy of users (Suryono et al., 2020). One of the most important challenges faced by FinTechs is the lack of effective and supportive laws. The laws enacted are mainly for the benefit of traditional institutions. They are mostly ambiguous and unpredictable. Banks and large financial institutions are reluctant to partner with FinTechs due to the ambiguity of laws and regulations. Materials and MethodsThis study was conducted to provide a framework for identifying and analyzing strategic factors affecting the partnership between banks and FinTechs. For this purpose, fuzzy Delphi and fuzzy DEMATEL techniques were used. These are quantitative techniques and use quantitative data for analysis. The fuzzy Delphi technique was used to screen the strategic factors of partnership between banks and FinTechs and the fuzzy DEMATEL technique was used to analyze the effectiveness of these factors. Since these techniques are quantitative, the study has multiple quantitative methodologies. Moreover, it is an applied study because of the benefit of its findings for the banking industry and FinTechs.The study was conducted in three steps. In the first step, the factors affecting the partnership between banks and FinTechs were extracted through a literature review and interviews with FinTech experts. In the next step, these factors were screened using the fuzzy Delphi technique. In the third step, the effectiveness of the screened factors was determined through the fuzzy DEMATEL technique. ConclusionThis study was conducted to identify and analyze the strategic factors affecting the partnership between banks and FinTechs. 28 factors were extracted through a literature review and expert interviews. 14 of the extracted factors were intra-organizational and the rest were extra-organizational. They were screened using the fuzzy Delphi technique, and 10 factors were eliminated. The intra-organizational and extra-organizational strategic factors were then analyzed separately through the fuzzy DEMATEL technique. Among the intra-organizational strategic factors, the nature of the needs of the bank's customers, the forward-thinking of the bank's senior managers, the culture of risk-taking among managers and senior experts, and the agility of the bank's structure and processes were the most effective, respectively. Among the extra-organizational strategic factors, the intensity of competition between banks, the fee system, the performance of the regulator in legislation, and the risks and security considerations concerning FinTechs, had a greater effect on the partnership between banks and FinTechs, respectively.Keywords: Financial Technology, FinTech, Banking Industry, Banking FinTechs, Fuzzy Approach.
Research Paper
Data science, intelligence and future analysis
A model for detecting abnormal claims in crop insurance using deep learning
Yaqub Ahmadlou; Alireza pourebrahimi; jafar tanha; Ali Rajabzadeh Ghatari
Volume 12, Issue 45 , September 2023, Pages 313-346
Abstract
Fraud cases have increased in recent years, especially in important and sensitive financial and insurance fields. Therefore, to deal with such frauds, there is a need for different measures than traditional inspection methods. Agricultural insurance is also not exempted from this threat due to its nature ... Read More Fraud cases have increased in recent years, especially in important and sensitive financial and insurance fields. Therefore, to deal with such frauds, there is a need for different measures than traditional inspection methods. Agricultural insurance is also not exempted from this threat due to its nature and wide extent and every year a lot of money is spent on paying fake damages. This research was presented with the aim of providing a model to discover unrealistic damage claims in agricultural insurance by using data mining and machine learning techniques. It was used to build a deep learning model. The data used was obtained from the Agricultural Insurance Fund and related to wet and rainfed wheat insurance policies of Khuzestan province, for which compensation was paid in the 2018-2019 crop year. After preparing and preprocessing the data, using deep learning to discover unusual cases, the action and results were evaluated by the experts of the Agricultural Insurance Fund. After analyzing the results, it was found that 1% of the damages paid were related to unrealistic requests and more care should be taken in paying the damages. The accuracy of the model in detecting unusual cases for wet and dry wheat was 53.53 and 63.37 percent, respectively. In the review of the results, it was found that 5 categories of unusual behavior have led to the payment of unrealistic damages, and the behavior of not providing damage documentation was more frequent than the others.IntroductionInsurance fraud refers to the immoral act of committing a crime with the intention of abusing an insurance policy to obtain illegal profit from an insurance company; In general, insurance is made to protect the assets and business of individuals or organizations against financial loss and may occur at any stage of the insurance process by anyone such as customers or fraudulent agents (Al -Hashedi & Magalingam, 2021). Insurance fraud not only reduces the profit of the insurance company and leads to major losses, but also affects the pricing strategy of the insurance company and its socio-economic benefits in the long term (Yaram, 2016). Every year, significant sums of money are defrauded from the insurance industry, but not all of them are discovered. According to the statistics published by the Insurance Anti-Fraud Coalition, an amount of about eighty billion dollars is added to customers' expenses in the United States through fraud, and they must compensate for the amount of fraud by paying higher insurance premiums in the following year (Fraud statistics, 2020). In Iran, there is no accurate estimate of the amount of compensations paid to unreal damage claims or any other fraud, and one of the goals of this research is to estimate the amount of fraud in wheat crop insurance using deep learning. Research Question(s)This research seeks to find answers to these questions: In rainfed and irrigated wheat crop insurance, what percentage of the paid compensations are related to unrealistic and fictitious damage claims, and what is the accuracy of deep learning detection for this purpose?Literature ReviewGhahari et al. (2019) in their study investigated the use of deep learning in predicting agricultural performance in time and space with unstable weather conditions. They compared the performance of machine learning next to weather stations with conventional methods. Their findings showed that deep learning provides the highest prediction accuracy compared to other approaches. It can also be inferred from this result that the use of deep learning can play a role in reducing agricultural insurance costs by knowing the exact measures of crop yield (Newlands et al., 2019). Gomez et al. (2021) presented a new deep learning method to gain pragmatic insight into the behavior of an insured individual using the unsupervised effective variable. Their proposed method can be used in the fields of pension insurance, investment and other broader areas of the insurance industry. Their proposed method enables auto encoder and variable auto encoder to be used in semi-supervised/unsupervised effective variable analysis to identify cheating agents (Gomes et al., 2021). Xia et al. (2022) in their study proposed a deep learning model to detect car insurance fraud by combining convolutional neural network, long-term and short-term memory, and deep neural network. In their proposed method, more abstract features were extracted and helped the experts in the complex process of feature extraction which is very critical in traditional machine learning algorithms. The results of the experiments showed that their method can effectively improve the accuracy of car insurance fraud detection.MethodologyThe current research method is practical from the point of view of the objective and is data-oriented from the point of view of its nature. For machine learning modeling, the standard CRISP process has been used, which includes the stages of data collection, data preparation and preprocessing, modeling and model evaluation, and obtaining results. Figure 1 shows the general process of anomaly detection and analysis.Figure 1. Anomaly detection process framework In this research, the data related to one agricultural year of wet and dry wheat crop were obtained from the Agricultural Insurance Fund. The national code of the insurers has been removed from the data set to maintain confidentiality. The extracted data is related to the crop insurance policies of wet and rainfed wheat for the crop year 2018-2019 of Khuzestan province. In this crop year, compensation has been paid for these insurance policies according to the claim of the damage they had, in other words, the data set includes those insurance policies of wet and dry wheat whose product is damage Seen and compensated for them. The data were obtained from the comprehensive system of the insurance fund in the form of a csv report. The obtained data set had 23 features.ConclusionThe results of the research show that in wheat insurance, about 1% of the compensations paid are allocated to unrealistic claims, so they need to be further investigated by experts before payment. This amount of compensations paid to unrealistic claims was close to the prediction of insurance fund inspection experts who stated that about 1.5% of claims are unrealistic. Also, according to the results, 5 categories of behavior or methods were identified in the beneficiaries to receive compensation for unrealistic claims, which are mentioned below:Lack of sufficient documentation to prove the damage: This means that the necessary documents that should be uploaded in the system according to the implementation methods are not available or some of them have not been uploaded. Payment of compensation without the existence of documents indicating the occurrence of damage can be caused by the negligence or collusion of the appraiser or broker with the insured.The documents are not in accordance with the declared damage: the documents uploaded in the system according to the relevant instructions do not show the occurrence of the type of registered damage. For example, the speed of storm damage is mentioned as 50 km/h, but in meteorological documents it is 15 km/h.The damage documentation is not true: for example, in some documents, the risk factor is mentioned in the expert form of drought, but the picture sent shows flood damage. In this case, it is probably due to negligence. In another possibility, it is also possible to send the image of damaged agricultural land instead of healthy agricultural land. Non-observance of the damage notification period: According to the executive instructions of the insurance fund, the time limit for the declaration of damage until the time of payment of compensation is one month. Outside of that, it is against the instructions. Sometimes it was observed that the damage had been declared before the accident. The date of damage does not match with the time of its announcement: according to the executive instructions of the insurance fund, in the case of damage to agriculture, the visit must be done one week after the occurrence of the damage; before removing the damage, the type and amount of the damage should be carefully checked. In some cases, it was observed that the announcement date was recorded one month after the damage occurred. It is clear that after removing the effects of damage, the payment of compensation can seem suspicious because there may not have been any damage in the past.Keywords: Anomaly Detection, Crop Insurance, Deep Learning, Auto Encoder.
Research Paper
Management approaches in the field of smart
Providing Agent-based Conceptual Model for the Hospital Evaluation and Accreditation System
Javad Keshvari Kamran; Mohammad ali Keramati; Abbas Toloie Eshlaghy; Seyed Abdollah Amin Mousavi
Volume 12, Issue 45 , September 2023, Pages 347-389