Management approaches in the field of smart
Manuchehr Karbasi; Ghanbar Abbaspour Esfeden; Seyedeh Sedigheh Jalalpour; Peyman HajiZadeh
Abstract
AbstractNowadays, the development of science and technology parks and improving their performance depends on cooperation with industry and university and communication with the environment and related centers. Hence, it is important to identify cooperation network and networking indicators in science ...
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AbstractNowadays, the development of science and technology parks and improving their performance depends on cooperation with industry and university and communication with the environment and related centers. Hence, it is important to identify cooperation network and networking indicators in science and technology parks. The purpose of this research is to identify the indicators of networking in science and technology parks. The method of the current research is qualitative and in it three methods of metacomposition, fuzzy Delphi and Dimetal were used. A search was made in Persian and English databases and 10 related studies were identified and analyzed. In order to verify the networking indicators extracted from the theoretical literature, 13 experts and managers of Pardis Technology Park were surveyed and the indicators were confirmed by the experts using the fuzzy Delphi method. In order to draw the causal model of the relationships between the indicators, DEMATEL method was used. The data was analyzed using Excel software. The results showed that networking in science and technology parks has 15 indicators, such as improving the level of products, information, increasing market share, goals and creating value. According to experts, the market share increase index is the first priority and organizational learning is the last. Drawing the causal model of networking showed that indicators such as management, organizational learning, information and knowledge are effective indicators. Indicators such as new product development, market opportunity creation, relationships and opportunity exploitation are also effective indicators in the networking of science and technology parks.IntroductionNowadays, the development of science and technology parks and improving their performance depends on cooperation with industry and universities and communication with the environment and related centers. Hence, it is important to identify cooperation network and networking indicators in science and technology parks. The ultimate mission of technology parks is to be able to coordinate the results obtained from academic research with the needs of the industry and thus fill the gap between the industry and the university, and this will ultimately lead to the commercialization of knowledge. One of the major influential factors in changing the approach of science and technology parks and creating new structures and mechanisms is the birth of new concepts such as networking in the field of business. The purpose of business networking is to increase competition, cooperation and organizational expansion. Considering the importance of these centers and the impact of networking on their performance, it is essential to identify the indicators of networking in science and technology parks. So far, many researchers have investigated the relationship between science and technology parks and other actors in the innovation ecosystem, but few researchers have focused only on the indicators of park networking. In this regard, this research aims to identify the factors influencing the networking of science and technology parks and to evaluate the cause-and-effect relationships between these factors by using the method of a systematic review of previous studies (super combination) and a survey of experts. This question should answer what are the indicators of networking in science and technology parks.Literature ReviewPaztto and Burin's research (2022) indicates that management control systems are effective in inter-organizational cooperation and identification of companies. This system promotes collaborative behaviors among companies related to science and technology parks. Networking and inter-organizational partnership ultimately lead to knowledge and information sharing, increasing flexibility, improving problem-solving strategies and limiting the use of power. The research of Glitova et al. (2022) showed that for cooperation and networking between industry, university and the public sector, attention should be paid to indicators such as knowledge creation by universities, research and development centers and businesses, technology transfer, creation of new businesses, industrial clusters, Business support services, customization, building the necessary infrastructure and equipment, and legal requirements at the local level are required. The research of Khan-Mirzaei et al. (2021) showed that networking and emphasizing cooperation and communication between science and technology parks and growth centers can lead to gaining a competitive advantage for the national economy. Communication with universities and research and development centers, cooperation with companies that have a similar field of work, access to the information flow and access to the information needed in the market, or in other words, the market situation, are among the factors that create a cooperation network between Science and technology, industry, university parks are important. In confirmation of this issue, Cadorin et al. (2019) stated that talent resources and the government play an important role in promoting cooperation between science and technology parks and universities. Managers of science and technology parks should strengthen their relationship with local universities and the student community (as sources of talent) and pay attention to their relations with government representatives to receive the necessary support for the development of the park.MethodologyThe method of the current research is qualitative and in it, three methods of Meta-synthesis, Fuzzy Delphi and DEMATEL were used. A search was conducted in Persian and English databases and 10 related studies were identified and analyzed. To verify the networking indicators extracted from the theoretical literature, 13 experts and managers of Pardis Technology Park were surveyed and the indicators were confirmed by the experts using the Fuzzy Delphi method. To draw the causal model of the relationships between the indicators, DEMATEL method was used. The data was analyzed using Excel software.ResultsIn this research, a set of 62 codes and 15 indicators was obtained by extracting concepts effective on park networking from previous qualitative research. The main indicators include improving the level of products, and information, increasing market share, goals (park goals, socio-economic and environmental goals), creating value, exploiting the opportunities available in the park, optimizing resources, and developing new products, Knowledge includes the knowledge of the market-partners and co-creation of knowledge, the international and commercial performance of the park, creating opportunities through the market, management, the need for resources and operational resources, creating and developing relationships and organizational learning. According to experts, the market share increase index is the priority and organizational learning is the last. The indicators of relationships, value creation, resources, market opportunities, goals, management, knowledge, exploiting opportunities, resource optimization, performance, upgrading products, information and new product development are ranked second to fourteenth respectively. Indicators of management, organizational learning, information, knowledge, goals, resources, and upgrading of products are effective indicators. New product development, creating market opportunities, and relationships, exploiting opportunities, optimizing resources, creating value, and increasing market share and performance are also influential indicators in the networking of science and technology parks.ConclusionThe review of the subject literature showed that paying attention to the indicators obtained in this research can lead to networking in science and technology parks. For example, the implementation of the indicators of improving the level of products, increasing market share, park goals, creating value, exploiting opportunities, knowledge, creating market opportunities, relations between actors, organizational learning and technical and human resources in Nihu Technology Park and Nankang Software Park in Taipei City. Networked. Researchers have pointed out various actors in the cooperation network of science and technology parks. The review of the texts in the meta-synthesis stage showed that each of the sources identified one to three actors based on their purpose. What was tried to be considered in this research was the gathering and consensus of all actors and their placement in the form of networking indicators such as increasing market share, resources and management. Among the new findings of this research, we can mention the type of causal relationships that are established between the indicators of networking in science and technology parks. Most researchers have not paid attention to these relationships and have focused more on the relationship between the park and variables such as innovation, performance, development, etc. However, the identification of networking behavior and the type of communication between the elements of this ecosystem can lead to the improvement of performance and optimization of activities and actions, and in this research, we tried to consider more and more comprehensive indicators in the cooperation network. be placed Finally, the purpose of the formation and development of science and technology parks is to increase the capacity of innovation and the growth of the knowledge-based economy through knowledge management (creation, sharing and access to knowledge and technology) among the members of the cooperation network of parks and to develop and commercialize the product, it becomes possible by them.Keywords: Networking Indicators, Science and Technology Parks, Meta-synthesis, Fuzzy Delphi, DEMATEL.
Data, information and knowledge management in the field of smart business
Mohsen Shafiei Nikabadi; Roya Esmaeilzadeh; Mina Abfroush
Abstract
The business model is an important factor in the competitive advantage of companies، and companies need to recreate their business model by changing the business environment due to changes in technology and communication. The current research aims to design a dynamic model based on text mining and soft ...
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The business model is an important factor in the competitive advantage of companies، and companies need to recreate their business model by changing the business environment due to changes in technology and communication. The current research aims to design a dynamic model based on text mining and soft methods to determine the most important key factors of electronic business models. This research is based on the text mining method and using the system dynamics modeling approach. In order to extract the key factors، the text mining of 779 articles of the last ten years from the world's authoritative databases has been examined. After examining the experts and selecting 17 key factors from among the extracted factors، in order to investigate the causal relationships between the key factors، the DEMATEL technique was used and the DEMATEL matrix was completed by the experts، and finally، the dynamic model of the research was drawn using Vensim software. The most influential causal factor is "Internet of Things" followed by "blockchain and cloud processing"، and the most impressionable disabling factor is "provided value in the business". Also، the most influential factor on all factors was "nature of the media" and the most impressionable factor among the set of factors was "type of used technology".IntroductionThe business model is an important factor in the competitive advantage of companies، and companies need to recreate their business model by changing the business environment due to changes in technology and communication. The current research aims to design a dynamic model based on text mining and soft methods to determine the most important key factors of electronic business models. This research is based on the text mining method and using the system dynamics modeling approach.In the current research، using dynamic modeling، the key factors of electronic business models have been determined with text mining and other soft methods. Examining the causal relationships between the key factors of e-business models and determining the effect coefficients of each factor on other factors and finally determining the causal/effectual nature of the factors and prioritizing them based on the degree of influence and effectiveness can Consider the innovative aspect of research.2.Research Question(s)The main question of this research is what are the most important key factors of electronic business models and how do they interact? Literature ReviewThe business model can be considered as a type of architecture for the product، service and information flow، which includes a description of different business agents، their role in this، potential advantages for each of these agents and their sources of income (Roweley، 2002).Electronic business models are a description of work processes that are used in virtual or electronic environments such as the World Wide Web (Botto، 2003). These models are a description of the roles and relationships between customers، consumers، partners and suppliers، which seeks to determine and identify the main flows of products، information and money، and to identify major benefits for shareholders and business participants، and by using It works from the Internet to conduct interactions and create value for customers and other stakeholders (Currie، 2004).According to the literature review، it can be seen that different researchers have presented models in different spatial domains، but no research has been seen that can identify، classify and analyze all the components in different models and identify their interactions.MethodologyIn order to extract the key factors، the text mining of 779 articles of the last ten years from the world's authoritative databases has been examined. After examining the experts and selecting 17 key factors from among the extracted factors، in order to investigate the causal relationships between the key factors، the DEMATEL technique was used and the DEMATEL matrix was completed by the experts، and finally، the dynamic model of the research was drawn using Vensim software. In this research، to collect articles، integrate and clean the data، we tried to use the reliable global databases of Wiley، Taylor and Francis، Springer، Oxford، Inderscience، IGI Global، Emerald، and Elsevier.In this research، in the first step of collecting articles، merging and cleaning data for articles of the last ten years from the reliable global databases of Wiley، Taylor and Francis، Springer، Oxford، Inscience، IGI Global، Emerald، and Elsevier. Is. At this stage، the following 4 key phrases were searched;"e-business model"، "e-commerce model"، "electronic business model"، "electronic commerce model"In the second step of the research، extraction of frequent words was done in the web portal Voint. Voint Portal is an online program used for text analysis.In the third step of the research، pre-processing، normalization and clustering of frequent words and clustering evaluation were done by Rapidminer software and its output is the classification of data with different topics.In the fourth step، the key words of each cluster were extracted using the experts' opinion، and finally، the key variables of electronic business models were extracted.In the fifth step، a researcher-made questionnaire was created based on the Dimtel technique and among experts in the field of e-business (people with more than ten years of working and executive experience in the field of e-commerce and business and the development of information technology tools، in active companies in this field with master's education and above) was distributed in order to identify the causal relationships between the variables extracted in the previous step.In the sixth step، it is time to present a dynamic model of the studied factors. The dynamic modeling process used in the current research consists of two stages: "modeling cause and effect loops" and "dynamic modeling".ResultsFirst part: text mining and clustering.In the first stage of research (text mining)، the results of pre-processing، selection and selection of indicators by experts show 17 factors of "type of business and trade"، "type of value provided in business"، "Type of offered product"، "Type of customer and its features"، "Type of technology used"، "Type of market"، "Online social networks"، "Business platform and website"، "Source and Sourcing"، "Innovation in Business"، "Processes and Knowledge Management in Business"، "Nature of Supply Chain"، "Dimensions of Internet of Things"، "Blockchain and Cloud Processing"، "Competitive environment"، "Information security and privacy"، "The nature of media"، are key factors of electronic business models.The second part: combining techniques to design a dynamic model.In the first part of the second stage of the research (Dimtel technique)، the causal model of the factors، the degree of influence and the coefficients of the influence of each factor on other factors have been studied، which is used as the basis for the design of the dynamic model of the research.In the second part of the second stage of the research (system dynamics)، based on the results of the first stage and then Dimtel، the dynamic model of the key factors of the electronic business model has been designed using Vansim software.ConclusionThe most influential causal factor is "Internet of Things" followed by "blockchain and cloud processing"، and the most impressionable disabling factor is "provided value in the business". Also، the most influential factor on all factors was "nature of the media" and the most impressionable factor among the set of factors was "type of used technology ". As mentioned، the factors of "Internet of Things" and "Blockchain/Cloud Processing" are the most important causal factors. Considering the importance of Internet of Things and artificial intelligence and blockchain، which are the main driving forces in the future technology revolution، it is suggested that companies pay attention to these technologies in order to earn quick and lasting income. Also، in the prioritization based on the effect of one factor on the set of factors، "the nature of the media" is in the first place، which is a sign of the important need of business activists for the media.Keywords: E-business model، Text mining، DEMATEL، Voyant، Vensim، Dynamic modeling.
Management approaches in the field of smart
Mahnaz Saeedi Mamaghani; Mohammad Javad Ershadi; Arman Sajedinejad
Abstract
The maturity of business intelligence, which is the main goal of this research, plays an important role in intelligent decision-making, planning, control and monitoring in the field of health care. In order to identify the effective factors, the Delphi method was used and experts' opinions were, and ...
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The maturity of business intelligence, which is the main goal of this research, plays an important role in intelligent decision-making, planning, control and monitoring in the field of health care. In order to identify the effective factors, the Delphi method was used and experts' opinions were, and in order to determine the effectiveness and effectiveness of the indicators and finally to prioritize them, used the DANP method. The statistical sample includes 20 targeted academic experts and health care experts. According to the results of the Delphi section, 26 main indicators finalized in the research were identified, which are divided into three main categories including organizational, process and judgment criteria. According to the results of the DANP process, flexible and expandable technical infrastructure criteria, data and system quality and the correct definition of business intelligence problems and processes were prioritized as the three criteria with the highest ranking in the maturity of business intelligence. The business intelligence maturity model proposed by the research can be a road map for the successful implementation of business intelligence in the field of health care.IntroductionBusiness intelligence is one of the most important issues in recent decades as a decision-making system for managers of organizations in order to plan, control and intelligently monitor companies and their subordinate units and measure the achievement of organizational goals. Business intelligence includes a comprehensive set of tools, technologies and products designed to collect, aggregate, analyze and present usable data (Reinschmidt J. & Francoise A., 2000) Introduction of new and complex medical technologies, the global trend of increasing length Longevity, the unexpected development of chronic diseases and emerging diseases (such as Covid-19) can lead to an increase in health care costs to unsustainable levels (Janssen & Moors, 2013; Qaseem et al., 2012). Public or private medical care organizations have focused their efforts on achieving new, cost-effective and efficient levels of care (Romanow et al., 2012). For this purpose, information technologies play a fundamental role by transforming data into knowledge that can improve patient care, medical care facilities, and process management (Behkami & U. Daim, 2012; Li & Mao, 2015; Pai & Huang, 2011). Considering the very important role of data in supporting the improvement of the organizational level, business intelligence is one of the important areas of research for researchers and activists in the treated field (Chen et al., 2012).The field of business intelligence has improved significantly over the past decade and has promising applications in the health field (El-Gayar & Timsina, 2014; Gandomi & Haider, 2015). Indeed, business intelligence can not only improve outcomes in healthcare organizations but also help them achieve continuous improvement and precision in medicine (Christensen et al., 2008; Gastaldi et al., 2015; Tremblay et al., 2012).Investigating the process of information production and transmission in the field of healthcare is of great importance. Today, organizations active in this field need correct information at the right time, in order to make the best decision by the right person. But many of the systems used by users do not have appropriate and expected performance, and health care organizations need to act smarter, but despite the potential, business intelligence has not been widespread in the field of medical care (Hanson, 2011) and research There are limited studies on how to successfully implement a sample business intelligence solution in the field of medical care (Foshay & Kuziemsky, 2014). This research tries to fill this gap by developing a model that provides maturity levels for evaluating and improving business intelligence solutions in healthcare. Therefore, considering the explanations and issues raised in the field of business intelligence, the present research seeks to answer a main question, what is the maturity model of business intelligence in the field of health care providers? In this regard, two sub-questions are also raised, which are: What are the indicators affecting the maturity model of business intelligence in the field of health care providers, and what is the prioritization of these factors?Literature ReviewAccording to the studies of Foshay and Kuziemsky, healthcare organizations are under constant pressure to not only achieve more results with fewer resources, but also to gradually transform into information-based systems (Foshay & Kuziemsky, 2014). Considering that the amount of information recorded by electronic health records and medical record centers is growing rapidly, healthcare organizations are trying to use tools such as business intelligence to improve the efficiency and effectiveness of their operations (Kuiler, 2014; Wang et al., 2018).According to the research conducted by Naqash, business intelligence solutions help decision-makers by providing practical information in the right format, at the right time and in the right place (Negash, 2004). The business intelligence market has grown significantly and has become the first investment priority for CIOs (Gartner, 2015). Also, the awareness of the potential benefits of business intelligence is increasing (Chuah & Wong, 2011), however, the implementation of business intelligence in health and treatment organizations is progressing relatively slowly and in a case-by-case manner (Foshay & Kuziemsky, 2014).Some studies show the benefits of business intelligence to improve patient care, treatment outcomes, effective use of human resources, lower costs (Borzekowski, 2009), higher revenue (Ayal & Seidman, 2009) and improved productivity (Lucas et al., 2010). have reported As reported in other studies, the successful implementation of business intelligence in healthcare depends on understanding and analyzing the characteristics of this field (Avison & Young, 2007; Mettler & Vimarlund, 2009). Therefore, one of the most important goals of this research is to provide a maturity model for the continuous development and improvement of business intelligence solutions to healthcare professionals. In Table (1), some business intelligence maturity models in the field of health care that have been implemented in the past are reviewed. Table 1. Some business intelligence maturity models in healthcareResearch resultsResearch researchersA framework for defining and prioritizing decision support information needs in the context of specific health care processes is presented.Foshay & Kuziemsky (2014)In this study, the subject of comprehensive business intelligence in special care and understanding the basic concepts of business intelligence solutions with comprehensive features have been discussed.Pereira et al. (2016)A way to identify the capabilities and weaknesses of the intelligent information system in the hospital has been presented.Carvalho et al. (2018)The methodology of implementing the model of hospital information systems is presented.Carvalho, Rocha, & Abreu (2019)This article identifies a wide range of maturity models in the health sector and its characteristics and strengthens the belief that the maturity of the hospital information system can contribute to the quality of information and knowledge management in this field.Gomes & Romão (2018)The result of this research is the maturity model of the hospital information system based on 6 stages of maturity. The hospital information system maturity model has the feature of collecting a set of key and effective factors of maturity and related characteristics and not only enables the evaluation of the overall maturity of a hospital information system, but also the individual maturity of its different dimensions.Carvalho, Rocha, Vasconcelos, et al. (2019)The purpose of this research is to determine how the existing business continuity maturity models conform to the ISO 22301 standard and to map the existing health care model with the business continuity maturity model.Haidzir et al. (2018)In this research, while determining organizational maturity levels, effective factors in improving maturity have been identified and prioritized, and a road map for applying business intelligence in this field has been presented.(Gastaldi et al. (2018)The result of this research is to present a maturity model including six stages of the growth and maturity sequence of the hospital information system.Carvalho, Rocha, Vasconcelos, et al. (2019)The importance of scientific research on business intelligence with a focus on patients has been investigated.Zheng et al. (2018)In this research, by providing a maturity assessment framework and infrastructure development based on results, information and digital transformation in health care has been encouraged and guided.Williams et al. (2019)In this research, it has been determined that organizational business intelligence application screens at all management levels have a positive and significant effect on measurable performance indicators. In this context, when businesses monitor their operational activities through business intelligence, they have come to the conclusion that performance indicators provide less time wastage, high reliability, integrated data, quality and accurate valuation benefits in the evaluation process.Işık et al. (2021)The relevant factors for the adoption of business intelligence system have been established using a systematic literature review and a theoretical structure based on technology, organization, environment and determinants and theories of CEOs. This research deepens the literature of business intelligence system and promotes the understanding of the important decision-making elements of business intelligence system.Salisu et al. (2021)The co-creation approach will optimize the currency, accuracy and appropriateness of information in the digital health profile, understanding and use of the digital health profile and the maturity assessment tool to facilitate informed iterative discussions by Pacific Island countries on digital health maturity in order to use digital tools to strengthen use the country's health systems. Digital health profile and maturity assessment tool can rationalize the selection and use of existing tools and reduce cognitive overload.Liaw et al. (2021)In this research, an alternative solution with the benefits and possible costs of its implementation in the hospital has been shown, and the proposed initial evaluation method can be used in different health and treatment units after confirming the weight of the criteria based on the adopted strategy.Wielki & Jurczyk (2019)The results of the study enrich the recent literature of business intelligence system and improve the understanding of the decision-making processes of practitioners to obtain the maximum value from the adoption of business intelligence system.Ahmad et al., (2020)The findings support the argument that the organizational learning culture plays an important role in the business intelligence system and also affects the business performance.Arefin et al., (2021)MethodologyTo implement business intelligence in the field of health care, the characteristics of this field must be understood and analyzed; This task has been carried out in three stages. First, the subject literature was analyzed with the "systematic review" method, and in addition to the field of health care, all sectors in which the maturity of business intelligence was evaluated were also considered. In the following, a series of key success factors of business intelligence and maturity components were extracted by examining more than 23 articles in the fields related to business intelligence and further, the steps of implementing the proposed method are also described.3.1. First stage - knowledge acquisitionAt this stage, previous studies in the field of business intelligence maturity model, evaluating the value of key success factors in business intelligence and identifying maturity components were reviewed. In this study, the structured search strategy method was used as data sources from Emerald, Sage, Elsevier, IEEE, Taylor & Francis, and Springer databases in the period from 2000 to the beginning of 2022. At first, this study used the following keywords and search terms, combined and separate: "business intelligence", "factors affecting business intelligence system", "business intelligence maturity", "maturity measurement" and "business intelligence system in health care". The collection of articles presented in this research was consistent with the topic of this research in terms of questions, objectives, adopted frameworks and findings. The definitions used and their alignment with the measurement adopted were evaluated, to ensure that the factors of business intelligence investigated by different researchers are largely similar. Finally, by advancing the previous steps, an initial version of the business intelligence maturity model was adopted, which is significantly different from the final business intelligence maturity model.3.2. The second stage - identifying and categorizing the criteriaAfter extracting the main criteria influencing the success of business intelligence in three areas of organization, process and technology, using the Delphi decision-making technique, the key factors and important criteria of business intelligence maturity in health care organizations were determined and categorized, and finally the maturity model of business intelligence in the field of health care, it was confirmed by a survey of experts.3.3. The third step - determining the criteriaAfter finalizing the dimensions and criteria of the research with the Delphi method, using pairwise comparisons and the Dimtel method based on the network analysis process method, the internal and external connections of the factors were determined and each of the factors were weighted and prioritized. In this step, a committee evaluation method was used to evaluate the validity of the questionnaire (Harkness & Schoua-Glusberg, 1998). In addition, ANP-DEMATEL combined method was used to evaluate how and how much the components affect each other. Various researchers such as (Đurek et al., 2019; Rasouli et al., 2021) have used this approach in the field of maturity model.ResultsIn this research, business intelligence was investigated in three basic areas of organization, process and technology, and each of these areas has criteria. First, articles were comprehensively reviewed in the field of business intelligence maturity in order to determine the dimensions of the goal and criteria. The criteria of the designed research maturity model were finalized using the Delphi method and with the opinion of experts, and then decision-making methods with multiple criteria were used to measure the optimality. The effects of goals, dimensions, and criteria on each other were investigated with the Dimtel method, then the dimensions and criteria were weighted in terms of importance with the network analysis process method. According to the results of Dimtel, the two dimensions of technology and process are effective, and the organizational dimension is effective. The organizational field has a higher relative importance than process and technology and has more interaction with other factors of the system and is affected by two dimensions of technology and process. The results of the analysis of the questionnaires of the network analysis process method answered by the experts show that the organizational factor is the most preferred and heaviest factor in the maturity of business intelligence, and then the process factor has a higher weight and the technology factor has a lower weight than the other two areas. In line with the results of William et al. (2019) and Gastaldi et al. (2018), who have encouraged and guided information and digital transformation in health care by providing a maturity assessment framework and infrastructure development based on results, respectively, two technical infrastructure criteria Flexible and expandable (hardware and software) and data and system quality were obtained from the highest importance compared to other criteria. And in the same way, the criterion of the correct definition of business intelligence problems and processes was prioritized with the third rank compared to other criteria in the maturity of business intelligence, and the rest of the criteria were also ranked in the article. Jayanthi Ranjan (2008) has also achieved this. In this way, a comprehensive and complete business intelligence maturity model was obtained in the field of health care, which can make the path of business intelligence maturity smoother in health care and be a road map for the successful implementation of business intelligence maturity in health care. It is suggested that in future researches, the proposed maturity model should be practically implemented in health care organizations and the maturity level of business intelligence should be evaluated. Figure 1. The final research model (source: researcher's findings)Organizational field• Cooperation between the employees of the organization and the information technology department• Alignment of business strategies with business intelligence strategies• Senior management support for the business intelligence project• Clear goals and vision for business intelligence• Development of business intelligence strategy• The ability of the organization to provide sufficient resources and funds needed for business intelligence projects• Risk-taking of senior managers in investing in new information technologies• Capabilities of the team/employees/managers• Monitor information through the Business Intelligence Assessment Center• Continuous improvement of organizational processes (improvement of competence)Technology field· Flexible and expandable technical infrastructure (hardware and software)· Data and system quality· Appropriate technology/tools or the use of appropriate technology and tools for hospital conditions· Business intelligence system architecture· Integration of business intelligence systems with other systems· Quality of data analysis· ConnectorBusiness intelligence in the field of health careProcess area• Correct definition of business intelligence problems and processes• Using patterns and repeatable methods in designing business intelligence projects• Aligning business intelligence solutions with user expectations• User training and support• Effective change management• Balanced and strong composition of the business intelligence project group• Project planning and management in the implementation of business intelligence• Measuring business intelligence• Decision makingKeywords: Business Intelligence, Healthcare, DEMATEL, ANP. The maturity of business intelligence, which is the main goal of this research, plays an important role in intelligent decision-making, planning, control and monitoring in the field of health care. In order to identify the effective factors, the Delphi method was used and experts' opinions were, and in order to determine the effectiveness and effectiveness of the indicators and finally to prioritize them, used the DANP method. The statistical sample includes 20 targeted academic experts and health care experts. According to the results of the Delphi section, 26 main indicators finalized in the research were identified, which are divided into three main categories including organizational, process and judgment criteria. According to the results of the DANP process, flexible and expandable technical infrastructure criteria, data and system quality and the correct definition of business intelligence problems and processes were prioritized as the three criteria with the highest ranking in the maturity of business intelligence. The business intelligence maturity model proposed by the research can be a road map for the successful implementation of business intelligence in the field of health care.IntroductionBusiness intelligence is one of the most important issues in recent decades as a decision-making system for managers of organizations in order to plan, control and intelligently monitor companies and their subordinate units and measure the achievement of organizational goals. Business intelligence includes a comprehensive set of tools, technologies and products designed to collect, aggregate, analyze and present usable data (Reinschmidt J. & Francoise A., 2000) Introduction of new and complex medical technologies, the global trend of increasing length Longevity, the unexpected development of chronic diseases and emerging diseases (such as Covid-19) can lead to an increase in health care costs to unsustainable levels (Janssen & Moors, 2013; Qaseem et al., 2012). Public or private medical care organizations have focused their efforts on achieving new, cost-effective and efficient levels of care (Romanow et al., 2012). For this purpose, information technologies play a fundamental role by transforming data into knowledge that can improve patient care, medical care facilities, and process management (Behkami & U. Daim, 2012; Li & Mao, 2015; Pai & Huang, 2011). Considering the very important role of data in supporting the improvement of the organizational level, business intelligence is one of the important areas of research for researchers and activists in the treated field (Chen et al., 2012).The field of business intelligence has improved significantly over the past decade and has promising applications in the health field (El-Gayar & Timsina, 2014; Gandomi & Haider, 2015). Indeed, business intelligence can not only improve outcomes in healthcare organizations but also help them achieve continuous improvement and precision in medicine (Christensen et al., 2008; Gastaldi et al., 2015; Tremblay et al., 2012).Investigating the process of information production and transmission in the field of healthcare is of great importance. Today, organizations active in this field need correct information at the right time, in order to make the best decision by the right person. But many of the systems used by users do not have appropriate and expected performance, and health care organizations need to act smarter, but despite the potential, business intelligence has not been widespread in the field of medical care (Hanson, 2011) and research There are limited studies on how to successfully implement a sample business intelligence solution in the field of medical care (Foshay & Kuziemsky, 2014). This research tries to fill this gap by developing a model that provides maturity levels for evaluating and improving business intelligence solutions in healthcare. Therefore, considering the explanations and issues raised in the field of business intelligence, the present research seeks to answer a main question, what is the maturity model of business intelligence in the field of health care providers? In this regard, two sub-questions are also raised, which are: What are the indicators affecting the maturity model of business intelligence in the field of health care providers, and what is the prioritization of these factors?Literature ReviewAccording to the studies of Foshay and Kuziemsky, healthcare organizations are under constant pressure to not only achieve more results with fewer resources, but also to gradually transform into information-based systems (Foshay & Kuziemsky, 2014). Considering that the amount of information recorded by electronic health records and medical record centers is growing rapidly, healthcare organizations are trying to use tools such as business intelligence to improve the efficiency and effectiveness of their operations (Kuiler, 2014; Wang et al., 2018).According to the research conducted by Naqash, business intelligence solutions help decision-makers by providing practical information in the right format, at the right time and in the right place (Negash, 2004). The business intelligence market has grown significantly and has become the first investment priority for CIOs (Gartner, 2015). Also, the awareness of the potential benefits of business intelligence is increasing (Chuah & Wong, 2011), however, the implementation of business intelligence in health and treatment organizations is progressing relatively slowly and in a case-by-case manner (Foshay & Kuziemsky, 2014).Some studies show the benefits of business intelligence to improve patient care, treatment outcomes, effective use of human resources, lower costs (Borzekowski, 2009), higher revenue (Ayal & Seidman, 2009) and improved productivity (Lucas et al., 2010). have reported As reported in other studies, the successful implementation of business intelligence in healthcare depends on understanding and analyzing the characteristics of this field (Avison & Young, 2007; Mettler & Vimarlund, 2009). Therefore, one of the most important goals of this research is to provide a maturity model for the continuous development and improvement of business intelligence solutions to healthcare professionals. In Table (1), some business intelligence maturity models in the field of health care that have been implemented in the past are reviewed. Table 1. Some business intelligence maturity models in healthcareResearch resultsResearch researchersA framework for defining and prioritizing decision support information needs in the context of specific health care processes is presented.Foshay & Kuziemsky (2014)In this study, the subject of comprehensive business intelligence in special care and understanding the basic concepts of business intelligence solutions with comprehensive features have been discussed.Pereira et al. (2016)A way to identify the capabilities and weaknesses of the intelligent information system in the hospital has been presented.Carvalho et al. (2018)The methodology of implementing the model of hospital information systems is presented.Carvalho, Rocha, & Abreu (2019)This article identifies a wide range of maturity models in the health sector and its characteristics and strengthens the belief that the maturity of the hospital information system can contribute to the quality of information and knowledge management in this field.Gomes & Romão (2018)The result of this research is the maturity model of the hospital information system based on 6 stages of maturity. The hospital information system maturity model has the feature of collecting a set of key and effective factors of maturity and related characteristics and not only enables the evaluation of the overall maturity of a hospital information system, but also the individual maturity of its different dimensions.Carvalho, Rocha, Vasconcelos, et al. (2019)The purpose of this research is to determine how the existing business continuity maturity models conform to the ISO 22301 standard and to map the existing health care model with the business continuity maturity model.Haidzir et al. (2018)In this research, while determining organizational maturity levels, effective factors in improving maturity have been identified and prioritized, and a road map for applying business intelligence in this field has been presented.(Gastaldi et al. (2018)The result of this research is to present a maturity model including six stages of the growth and maturity sequence of the hospital information system.Carvalho, Rocha, Vasconcelos, et al. (2019)The importance of scientific research on business intelligence with a focus on patients has been investigated.Zheng et al. (2018)In this research, by providing a maturity assessment framework and infrastructure development based on results, information and digital transformation in health care has been encouraged and guided.Williams et al. (2019)In this research, it has been determined that organizational business intelligence application screens at all management levels have a positive and significant effect on measurable performance indicators. In this context, when businesses monitor their operational activities through business intelligence, they have come to the conclusion that performance indicators provide less time wastage, high reliability, integrated data, quality and accurate valuation benefits in the evaluation process.Işık et al. (2021)The relevant factors for the adoption of business intelligence system have been established using a systematic literature review and a theoretical structure based on technology, organization, environment and determinants and theories of CEOs. This research deepens the literature of business intelligence system and promotes the understanding of the important decision-making elements of business intelligence system.Salisu et al. (2021)The co-creation approach will optimize the currency, accuracy and appropriateness of information in the digital health profile, understanding and use of the digital health profile and the maturity assessment tool to facilitate informed iterative discussions by Pacific Island countries on digital health maturity in order to use digital tools to strengthen use the country's health systems. Digital health profile and maturity assessment tool can rationalize the selection and use of existing tools and reduce cognitive overload.Liaw et al. (2021)In this research, an alternative solution with the benefits and possible costs of its implementation in the hospital has been shown, and the proposed initial evaluation method can be used in different health and treatment units after confirming the weight of the criteria based on the adopted strategy.Wielki & Jurczyk (2019)The results of the study enrich the recent literature of business intelligence system and improve the understanding of the decision-making processes of practitioners to obtain the maximum value from the adoption of business intelligence system.Ahmad et al., (2020)The findings support the argument that the organizational learning culture plays an important role in the business intelligence system and also affects the business performance.Arefin et al., (2021)MethodologyTo implement business intelligence in the field of health care, the characteristics of this field must be understood and analyzed; This task has been carried out in three stages. First, the subject literature was analyzed with the "systematic review" method, and in addition to the field of health care, all sectors in which the maturity of business intelligence was evaluated were also considered. In the following, a series of key success factors of business intelligence and maturity components were extracted by examining more than 23 articles in the fields related to business intelligence and further, the steps of implementing the proposed method are also described.3.1. First stage - knowledge acquisitionAt this stage, previous studies in the field of business intelligence maturity model, evaluating the value of key success factors in business intelligence and identifying maturity components were reviewed. In this study, the structured search strategy method was used as data sources from Emerald, Sage, Elsevier, IEEE, Taylor & Francis, and Springer databases in the period from 2000 to the beginning of 2022. At first, this study used the following keywords and search terms, combined and separate: "business intelligence", "factors affecting business intelligence system", "business intelligence maturity", "maturity measurement" and "business intelligence system in health care". The collection of articles presented in this research was consistent with the topic of this research in terms of questions, objectives, adopted frameworks and findings. The definitions used and their alignment with the measurement adopted were evaluated, to ensure that the factors of business intelligence investigated by different researchers are largely similar. Finally, by advancing the previous steps, an initial version of the business intelligence maturity model was adopted, which is significantly different from the final business intelligence maturity model.3.2. The second stage - identifying and categorizing the criteriaAfter extracting the main criteria influencing the success of business intelligence in three areas of organization, process and technology, using the Delphi decision-making technique, the key factors and important criteria of business intelligence maturity in health care organizations were determined and categorized, and finally the maturity model of business intelligence in the field of health care, it was confirmed by a survey of experts.3.3. The third step - determining the criteriaAfter finalizing the dimensions and criteria of the research with the Delphi method, using pairwise comparisons and the Dimtel method based on the network analysis process method, the internal and external connections of the factors were determined and each of the factors were weighted and prioritized. In this step, a committee evaluation method was used to evaluate the validity of the questionnaire (Harkness & Schoua-Glusberg, 1998). In addition, ANP-DEMATEL combined method was used to evaluate how and how much the components affect each other. Various researchers such as (Đurek et al., 2019; Rasouli et al., 2021) have used this approach in the field of maturity model.ResultsIn this research, business intelligence was investigated in three basic areas of organization, process and technology, and each of these areas has criteria. First, articles were comprehensively reviewed in the field of business intelligence maturity in order to determine the dimensions of the goal and criteria. The criteria of the designed research maturity model were finalized using the Delphi method and with the opinion of experts, and then decision-making methods with multiple criteria were used to measure the optimality. The effects of goals, dimensions, and criteria on each other were investigated with the Dimtel method, then the dimensions and criteria were weighted in terms of importance with the network analysis process method. According to the results of Dimtel, the two dimensions of technology and process are effective, and the organizational dimension is effective. The organizational field has a higher relative importance than process and technology and has more interaction with other factors of the system and is affected by two dimensions of technology and process. The results of the analysis of the questionnaires of the network analysis process method answered by the experts show that the organizational factor is the most preferred and heaviest factor in the maturity of business intelligence, and then the process factor has a higher weight and the technology factor has a lower weight than the other two areas. In line with the results of William et al. (2019) and Gastaldi et al. (2018), who have encouraged and guided information and digital transformation in health care by providing a maturity assessment framework and infrastructure development based on results, respectively, two technical infrastructure criteria Flexible and expandable (hardware and software) and data and system quality were obtained from the highest importance compared to other criteria. And in the same way, the criterion of the correct definition of business intelligence problems and processes was prioritized with the third rank compared to other criteria in the maturity of business intelligence, and the rest of the criteria were also ranked in the article. Jayanthi Ranjan (2008) has also achieved this. In this way, a comprehensive and complete business intelligence maturity model was obtained in the field of health care, which can make the path of business intelligence maturity smoother in health care and be a road map for the successful implementation of business intelligence maturity in health care. It is suggested that in future researches, the proposed maturity model should be practically implemented in health care organizations and the maturity level of business intelligence should be evaluated. Figure 1. The final research model (source: researcher's findings)Organizational field• Cooperation between the employees of the organization and the information technology department• Alignment of business strategies with business intelligence strategies• Senior management support for the business intelligence project• Clear goals and vision for business intelligence• Development of business intelligence strategy• The ability of the organization to provide sufficient resources and funds needed for business intelligence projects• Risk-taking of senior managers in investing in new information technologies• Capabilities of the team/employees/managers• Monitor information through the Business Intelligence Assessment Center• Continuous improvement of organizational processes (improvement of competence)Technology field· Flexible and expandable technical infrastructure (hardware and software)· Data and system quality· Appropriate technology/tools or the use of appropriate technology and tools for hospital conditions· Business intelligence system architecture· Integration of business intelligence systems with other systems· Quality of data analysis· ConnectorBusiness intelligence in the field of health careProcess area• Correct definition of business intelligence problems and processes• Using patterns and repeatable methods in designing business intelligence projects• Aligning business intelligence solutions with user expectations• User training and support• Effective change management• Balanced and strong composition of the business intelligence project group• Project planning and management in the implementation of business intelligence• Measuring business intelligence• Decision makingKeywords: Business Intelligence, Healthcare, DEMATEL, ANP.
Ali Mehrabi; Foad Fasihi; Ramin Assari
Volume 3, Issue 9 , December 2014, , Pages 105-134
Abstract
Along with advances in technology and communications, global markets and competition between Companies are intensifying. Literature review of Methodologies to build Information Systems suggests that by development of Methodologies, we are seeing the involvement of users, increase System reliability, ...
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Along with advances in technology and communications, global markets and competition between Companies are intensifying. Literature review of Methodologies to build Information Systems suggests that by development of Methodologies, we are seeing the involvement of users, increase System reliability, and moving from emphasis on Tools, Techniques and programming languages to methodologies and the basic concepts of the System. In general, each generation of information systems development methodologies has a specific concentration on a specific area, whereas now in Iran for all spheres used the same methodology that will lead to reduce productivity and efficiency of Organizations and Companies at performing of assignments and decrease of competitiveness. Choose an appropriate information system for automated billing of Customs with their large number and variety of products, as well because it is difficult to evaluating the variety of methodologies, so it is more difficult and requires an integrated methodology decision. Therefore, deciding to choose between several options of an Information System is a Multi-Criteria Decision-Making Process. In this paper, we have used a combination of ANP and DEMATEL to select an appropriate Information System for Custom's billing system. Finally, after implementation of a hybrid model, revealed that in our study, second generation methodologies could be the best choice.
