Data, information and knowledge management in the field of smart business
Mohammad Kazemi; Mohammad Ali Keramati; Mehrzad Minooie
Abstract
The effort of this article is to solve one of the main problems in the field of banking, which is closely related to the field of information technology. The combination of the management discussion of this issue with the field of information technology will be one of the important topics in the field ...
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The effort of this article is to solve one of the main problems in the field of banking, which is closely related to the field of information technology. The combination of the management discussion of this issue with the field of information technology will be one of the important topics in the field of information technology management. The main purpose of this article is the clustering of bank customers.At first, all customer characteristics were extracted from the bank's database, which was randomly extracted for 900,000 customers and will be provided as input to the proposed method of this article. All the characteristics of these customers were extracted and 10 characteristics (except four characteristics of the LRFM method) were listed using the opinions of experts. The proposed method should be able to choose among these 10 features for customer clustering that results in more resolution in clustering. This makes more suitable features to be placed next to the four features of LRFM and improve the performance of LRFM. Due to the high number of variations in this problem, it is not possible to do it manually and the proposed method tries to provide a separate pattern for clustering for the customers of each bank by examining different situations. Also, the problem of choosing the right value for the number of clusters in the K-means method is solved by the method proposed in this article. The results show that it is better than the basic RFM and LRFM methods.IntroductionToday, the Achilles heel of all customer-oriented businesses is customer satisfaction and providing services tailored to each customer's situation. This issue has gone so far that regardless of customer satisfaction, any organization will face failure (Otto et al., 2019). One of the main current challenges for customer-oriented organizations is understanding the differences and ranking customers in order to optimally allocate resources. This issue is very important in managing the correct relationship with the customer. Banks are one of the main customer-oriented institutions in the country (Morzdashti et al., 2022). The bank does not do any proper clustering to know its customers and plan future goals. More precisely, it does not have information about the total number of customers and their distribution. Because of this, more time and money is wasted. As far as the research of this article has followed; The clustering that currently exists for customers does not have the necessary dynamics and people are clustered based on some characteristics such as transaction amounts, occupation or other general characteristics. LRFM model is a method used to cluster customers in customer relationship management. In this model, customers are clustered based on four characteristics of customer relationship, novelty of exchange, number of times of exchange and monetary value exchanged. In fact, the customer relationship length has been added to the RFM model and created the LRFM model. Because, the RFM model was not able to identify loyal customers (Moslehi et al., 2013).In the proposed model of this article, an attempt will be made to provide a dynamic method for using variables with the LRFM method to provide the possibility of implementing different clusters depending on the time of use. This issue will lead to more compliance of the proposed clustering method with reality.Research Question(s)What methodology is used to follow the process of presenting the proposed model?What features can be placed next to the LRFM model to provide appropriate results?What methodology is used to follow the process of presenting the proposed model?What features can be placed next to the LRFM model to provide appropriate results?What will be the structure of particle swarm algorithm?What similarity measure or clustering method would be suitable for customers?How can the LRFM model be improved by the particle swarm algorithm and the creation of different clusters based on the K-means method? Literature ReviewShrahi and Ali Qoli have implemented a clustering method for the customers of one of Sepeh Bank branches in Tehran (Shrahi and Ali Qoli, 2015). This model is based on K-means clustering algorithm. In this method, an attempt has been made to identify sixty companies loyal to the bank from among all legal customers. However, the K-means algorithm has some problems (Bagatini et al., 2019, Santini, 2016):Determine the optimal value for the number of clusters.The initial points that are chosen randomly at the beginning of the algorithm have a great impact on the final result.The order of data entry and their review is effective in the final result.Ayoubi has tried to cluster bank customers using Kohonen neural networks (Ayoubi, 2016). In this method, the training of a neural network is done using the training data, and after that it is possible to cluster the new customer.Yousefizad and Sorayai have also used the RFM model to cluster customers in order to design a model for providing services to customers, which consists of two stages (Yosefizad and Sorayai, 2017).suggested method:In this section, the proposed method of the article is described in full detail. MethodologyIn this part, how to improve the LRFM method using the combination of particle swarm algorithm and K-means method is described. All the steps of particle swarm algorithm are followed and its functions and parameters are specified. The steps of the proposed method will be as follows:Initialization: The schematic of the initial population matrix will be as shown in Figure (2). This matrix consists of two parts. The first part has one element that tries to suggest the number of clusters using the K-means method, and the second part will have 10 binary elements.Calculating the fitness of each particle: Using the fitness function, the fitness level is determined for each particle present in the population. This fitness level is based on clustering using the K-means method. The appropriateness of the clustering done is measured based on the intraclass variance criterion, which corresponds to the image of the fitness of each particle (Ahmar et al., 2018).Update of particle values: Using two parameters, local optimum (LBEST) and global optimum (GBEST), the values present in the particles can be updated. By LBEST, we mean the best value that the I-th particle has reached so far (the best-fit value for the I-th particle). Also, GBEST means the value that has the best fit until T iterations. These two values are used to update the values of other particles. ConclusionThis article tries to provide a dynamic method for clustering bank customers in order to improve their service. The LRFM method has four important features in the field of banking, but its problem is lack of dynamics. More precisely, it is possible that other characteristics such as financial, occupational, or daily transaction characteristics can be added to the four LRFM characteristics and improve the performance of this method. Among all the features that can be placed next to the four features of LRFM; Depending on the customer's data, the appropriate features should be selected. This choice is the responsibility of the particle swarm algorithm. This algorithm tries to put appropriate features along with the four LRFM features depending on the data conditions and customer information to get a better result in clustering. Also, because this algorithm methodK-means helps in finding the number of clusters.It is also possible to replace the particle swarm with other meta-heuristic methods and compare its results with the results in the article.
Management approaches in the field of smart
Javad Keshvari Kamran; Mohammad ali Keramati; Abbas Toloie Eshlaghy; Seyed Abdollah Amin Mousavi
Abstract
The aims of this study is to provide a conceptual model of hospital accreditation in Iran through qualitative research. The grounded theory model was compiled using the results of the analysis of the interviews. Then, with the help of the grounded theory model and the results of qualitative analysis, ...
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The aims of this study is to provide a conceptual model of hospital accreditation in Iran through qualitative research. The grounded theory model was compiled using the results of the analysis of the interviews. Then, with the help of the grounded theory model and the results of qualitative analysis, a state diagram was obtained. Using the state diagram, initial sampling, systematic review of sources and results of interviews, 9 conceptual agents "governance organizations, management and leadership, clinical personnel, support personnel, hospital infrastructure, assessor, standards, assessment method and service recipient" were identified. Finally, the conceptual model of agent-based, environment, behavioral rules of agents and their input and output interactions was presented. In future researches, reinforcement learning models can be designed according to the conceptual model of this study, so that by using it, software developers can develop a suitable framework for solving complex problems in the field of hospital accreditation. Because the field of hospital management systems is one of the desirable types of socio-technical systems that have high capacities.IntroductionThe ecosystem of hospital accreditation is a triangle with “standard, accreditation method, and accreditation assessors” sides (Mosadeghrad et al.,2017). Hospital accreditation in Iran has faced challenges, the most important of which are: “a large number of standards and measures, lack of transparency and ambiguity in the measures, incompleteness and defects in the standards and high emphasis on structure and documentation, lack of systemic thinking and following that, a lot of focus on the sectoral approach” (Mosadeghrad & Ghazanfari, 2020). The results of a systematic review of sources and documents indicate that as a result of the lack of new approaches to solving “social-technical” problems such as “use of agent-based systems”, the above-mentioned challenges have become more prominent and ultimately cause the credibility and ranking of hospitals to become unrealistic (Ghazanfari et al., 2021). This study aims to present new models such as the agent-based conceptual model in Iran's hospital accreditation system. This model will create a study foundation for the environmental simulation process and the creation of a multi-agent hospital accreditation system to provide useful guidelines to the relevant policymakers.Therefore, it seems that the result of the current research covers the research gap in this field to some extent. Also, this study aims to answer the main research question; “What are the rules, position, behavior, and relationships of each of the agents in the multi-agent hospital accreditation system and how are they formulated?” It has started working.Literature ReviewHospital accreditationHospital accreditation is the process of systematic evaluation and determination of hospital credit by an external organization using the desired structural, process, and outcome standards (Chehrzad et al., 2019).Figure 1. The main elements of the hospital accreditation system, Source: (Mosadeghrad & Ghazanfari, 2021) Figure 1 shows the main elements of the hospital accreditation system. The hospital accreditation system is a triangle that includes the sides of “standard, accreditation method, and accreditation assessors”. The governance element is the regulator and controller of the sides of this triangle.Agent-based systemsThe agent-based system can be used to solve problems that are difficult or impossible to solve for a “single agent” or an integrated system. Agent-based systems provide new methods for solving complex computing problems and implementing social-technical software projects (Dorri et al.., 2018). The elements of agent-based systems are: “environment, objects, a set of agents, a set of relationships, and a set of agent behaviors” (Bonabeau, 2002).Research backgroundTable 1 shows the summary report of the background of the most important research conducted in the fields of hospital accreditation and agent-based models.Table 1. Summary report of the background of the researchSummary of study resultsResearcherA comprehensive hospital accreditation model was developed and validated. Paying attention to structures, processes, outcomes, and systemic thinking during model development is one of the advantages of this study.(Mosadeghrad & Ghazanfari, 2021)The challenges of hospital accreditation standards were categorized into two groups: standards development process and standards content.(Ghazanfari et al., 2021)The identified agents describe the consumer's impulse buying behavior as an economic analysis based on the relationship between the customer and the product.(Abbasi Siar et al., 2022)The multi-agent model and process simulations provide useful information for generating strategies to reduce the risks of COVID-19 transmission inside the facility.(Cuevas, 2020)The results of the agent-based simulation show the advantages of the proposed model for reducing the response time to requests compared to the current maintenance system.(Yousefli et al., 2020)The proposed model of pre-hospital management operation was presented. The identified agents are: “Management Center, Ambulance, Traffic Station, Medical Service Provider, Patient, Counseling Center, National Medical Record System, and Service Quality Monitoring”.(Safdari et al., 2017)MethodologyTo collect data, library and field methods have been used. Using qualitative analysis and obtained results, conceptual models were created. Therefore, the approach of this research is of a hybrid type. Also, the snowball sampling method was used to collect the required information. By using primary sampling, agents, the environment, and their relationships were extracted. By conducting six interviews, theoretical saturation was achieved regarding the conceptual model. To collect the information needed to know the elements and processes, a systematic review of sources and semi-structured interviews were used. The interviewees were selected from among the professors, managers, and employees of the hospitals. Finally, the interviews were summarized using grounded-theory-based methods, approaches, and systematic approaches. To calculate the reliability of the interviews, the method of two inter-coder agreements was used. Finally, the fuzzy Delphi method with triangular fuzzy numbers was used to validate the extracted conceptual model. ResultsConceptual model of the agent-basedUsing the results obtained from qualitative data analysis and the grounded theory model, examples and independent agents of each agent group were identified. All the interactions of the agents are included in the final model in the form of input and output. Figure 2 shows the agent-based conceptual model of the hospital accreditation system.Figure 2. Conceptual model of the agent-based hospital accreditation system (source: findings of the present research) DiscussionThis study aimed to provide a conceptual model of the agent-based system in Iran's hospital accreditation system. Also, agents, the environment, general behavioral rules, and their interactions with the environment were obtained. Because, so far, a lot of research has been conducted to provide an optimal model in the hospital accreditation ecosystem, there have been no studies that have new methods such as agent-based design. Therefore, it seems that the findings of the current research have covered some research gaps in this field because agent-based design is one of the newest and most efficient solutions available for solving distributed problems and complex human processes and environments. The agent-based conceptual model of the current research can create a suitable study base for the environmental simulation process and the creation of a multi-agent hospital accreditation system. Also, future researchers are suggested to carry out relevant research in this field, considering the wide application of agent-based modeling in the field of social-technical hospital systems and the importance of using reinforcement learning algorithms in them.ConclusionThe background analysis of the research was done with the method of systematic review of sources. Using experts' opinions, broad and general questions were asked about the results of the research, and then their description and analysis were addressed through grounded theory-based tools (MAXQDA), and a conceptual model of the grounded theory was obtained. Then, to the main research question; “What are the rules, position, behavior, and relationships of each of the agents in the multi-agent hospital accreditation system and how are they formulated?” The appropriate answer was given so that by using qualitative analysis, the dimensions of the problem were fully understood and the obtained results were converted into the final conceptual model. Also, agents, the environment, and their relationships were obtained. Then their general rules of conduct were compiled. All interactions of the agents with the environment were included in the model as input and output.Keywords: Agent-Based Conceptual Model, Hospital Accreditation, Multi-Agent System, Simulation.The aims of this study is to provide a conceptual model of hospital accreditation in Iran through qualitative research. The grounded theory model was compiled using the results of the analysis of the interviews. Then, with the help of the grounded theory model and the results of qualitative analysis, a state diagram was obtained. Using the state diagram, initial sampling, systematic review of sources and results of interviews, 9 conceptual agents "governance organizations, management and leadership, clinical personnel, support personnel, hospital infrastructure, assessor, standards, assessment method and service recipient" were identified. Finally, the conceptual model of agent-based, environment, behavioral rules of agents and their input and output interactions was presented. In future researches, reinforcement learning models can be designed according to the conceptual model of this study, so that by using it, software developers can develop a suitable framework for solving complex problems in the field of hospital accreditation. Because the field of hospital management systems is one of the desirable types of socio-technical systems that have high capacities.IntroductionThe ecosystem of hospital accreditation is a triangle with “standard, accreditation method, and accreditation assessors” sides (Mosadeghrad et al.,2017). Hospital accreditation in Iran has faced challenges, the most important of which are: “a large number of standards and measures, lack of transparency and ambiguity in the measures, incompleteness and defects in the standards and high emphasis on structure and documentation, lack of systemic thinking and following that, a lot of focus on the sectoral approach” (Mosadeghrad & Ghazanfari, 2020). The results of a systematic review of sources and documents indicate that as a result of the lack of new approaches to solving “social-technical” problems such as “use of agent-based systems”, the above-mentioned challenges have become more prominent and ultimately cause the credibility and ranking of hospitals to become unrealistic (Ghazanfari et al., 2021). This study aims to present new models such as the agent-based conceptual model in Iran's hospital accreditation system. This model will create a study foundation for the environmental simulation process and the creation of a multi-agent hospital accreditation system to provide useful guidelines to the relevant policymakers.Therefore, it seems that the result of the current research covers the research gap in this field to some extent. Also, this study aims to answer the main research question; “What are the rules, position, behavior, and relationships of each of the agents in the multi-agent hospital accreditation system and how are they formulated?” It has started working.Literature ReviewHospital accreditationHospital accreditation is the process of systematic evaluation and determination of hospital credit by an external organization using the desired structural, process, and outcome standards (Chehrzad et al., 2019).Figure 1. The main elements of the hospital accreditation system, Source: (Mosadeghrad & Ghazanfari, 2021) Figure 1 shows the main elements of the hospital accreditation system. The hospital accreditation system is a triangle that includes the sides of “standard, accreditation method, and accreditation assessors”. The governance element is the regulator and controller of the sides of this triangle.Agent-based systemsThe agent-based system can be used to solve problems that are difficult or impossible to solve for a “single agent” or an integrated system. Agent-based systems provide new methods for solving complex computing problems and implementing social-technical software projects (Dorri et al.., 2018). The elements of agent-based systems are: “environment, objects, a set of agents, a set of relationships, and a set of agent behaviors” (Bonabeau, 2002).Research backgroundTable 1 shows the summary report of the background of the most important research conducted in the fields of hospital accreditation and agent-based models.Table 1. Summary report of the background of the researchSummary of study resultsResearcherA comprehensive hospital accreditation model was developed and validated. Paying attention to structures, processes, outcomes, and systemic thinking during model development is one of the advantages of this study.(Mosadeghrad & Ghazanfari, 2021)The challenges of hospital accreditation standards were categorized into two groups: standards development process and standards content.(Ghazanfari et al., 2021)The identified agents describe the consumer's impulse buying behavior as an economic analysis based on the relationship between the customer and the product.(Abbasi Siar et al., 2022)The multi-agent model and process simulations provide useful information for generating strategies to reduce the risks of COVID-19 transmission inside the facility.(Cuevas, 2020)The results of the agent-based simulation show the advantages of the proposed model for reducing the response time to requests compared to the current maintenance system.(Yousefli et al., 2020)The proposed model of pre-hospital management operation was presented. The identified agents are: “Management Center, Ambulance, Traffic Station, Medical Service Provider, Patient, Counseling Center, National Medical Record System, and Service Quality Monitoring”.(Safdari et al., 2017)MethodologyTo collect data, library and field methods have been used. Using qualitative analysis and obtained results, conceptual models were created. Therefore, the approach of this research is of a hybrid type. Also, the snowball sampling method was used to collect the required information. By using primary sampling, agents, the environment, and their relationships were extracted. By conducting six interviews, theoretical saturation was achieved regarding the conceptual model. To collect the information needed to know the elements and processes, a systematic review of sources and semi-structured interviews were used. The interviewees were selected from among the professors, managers, and employees of the hospitals. Finally, the interviews were summarized using grounded-theory-based methods, approaches, and systematic approaches. To calculate the reliability of the interviews, the method of two inter-coder agreements was used. Finally, the fuzzy Delphi method with triangular fuzzy numbers was used to validate the extracted conceptual model. ResultsConceptual model of the agent-basedUsing the results obtained from qualitative data analysis and the grounded theory model, examples and independent agents of each agent group were identified. All the interactions of the agents are included in the final model in the form of input and output. Figure 2 shows the agent-based conceptual model of the hospital accreditation system.Figure 2. Conceptual model of the agent-based hospital accreditation system (source: findings of the present research) DiscussionThis study aimed to provide a conceptual model of the agent-based system in Iran's hospital accreditation system. Also, agents, the environment, general behavioral rules, and their interactions with the environment were obtained. Because, so far, a lot of research has been conducted to provide an optimal model in the hospital accreditation ecosystem, there have been no studies that have new methods such as agent-based design. Therefore, it seems that the findings of the current research have covered some research gaps in this field because agent-based design is one of the newest and most efficient solutions available for solving distributed problems and complex human processes and environments. The agent-based conceptual model of the current research can create a suitable study base for the environmental simulation process and the creation of a multi-agent hospital accreditation system. Also, future researchers are suggested to carry out relevant research in this field, considering the wide application of agent-based modeling in the field of social-technical hospital systems and the importance of using reinforcement learning algorithms in them.ConclusionThe background analysis of the research was done with the method of systematic review of sources. Using experts' opinions, broad and general questions were asked about the results of the research, and then their description and analysis were addressed through grounded theory-based tools (MAXQDA), and a conceptual model of the grounded theory was obtained. Then, to the main research question; “What are the rules, position, behavior, and relationships of each of the agents in the multi-agent hospital accreditation system and how are they formulated?” The appropriate answer was given so that by using qualitative analysis, the dimensions of the problem were fully understood and the obtained results were converted into the final conceptual model. Also, agents, the environment, and their relationships were obtained. Then their general rules of conduct were compiled. All interactions of the agents with the environment were included in the model as input and output.Keywords: Agent-Based Conceptual Model, Hospital Accreditation, Multi-Agent System, Simulation.vv
Majid Kalantari; Jalal Haghighat Monfared; Mohammad'Ali Keramati
