Research Paper
Management approaches in the field of smart
Javad Nazarian-Jashnabadi; MohammadHossein Ronaghi; moslem alimohammadlu; Abolghasem Ebrahimi
Abstract
AbstractThe maturity of business intelligence is a result of the evolution and advancement of technology and management approaches that help to provide accurate information, predictive analyzes and improve decisions in organizations using advanced technologies such as artificial intelligence and data ...
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AbstractThe maturity of business intelligence is a result of the evolution and advancement of technology and management approaches that help to provide accurate information, predictive analyzes and improve decisions in organizations using advanced technologies such as artificial intelligence and data analysis. Despite technological maturity that improves the efficiency and performance of organizations over time, business intelligence is far from becoming a mainstream trend in organizations. According to numerous researches in the field of business intelligence, the aim of this research was to present the framework of factors affecting the maturity of business intelligence using a meta-composite approach. In order to reach a comprehensive framework that includes all the maturity factors of business intelligence, 221 scientific studies were reviewed. Relevant codes were extracted using content analysis in metacomposite method. The categories were leveled using the comprehensive interpretive structural modeling method and the most influential ones were determined. The findings show that a total of 93 codes were extracted and divided into 6 categories. These categories include organization and management factors, environment, technology infrastructure, human resources - knowledge, data management and data analysis. The categories of technology infrastructure, data management and data analysis were placed at level three and have the greatest impact on the maturity of business intelligence.IntroductionIn today's world, digital transformation has become one of the prominent and fundamental phenomena in the field of technology and business. This transformation has placed organizations in a process of change and evolution, significantly altering their approaches and operational methods (Hilbert, 2022). One of the concepts that has emerged as a result of these developments is business intelligence (Ragazou et al., 2023). The primary objective of business intelligence is to convert scattered, raw, and unstructured data into usable and valuable information. By integrating internal and external data and utilizing advanced analytics methods such as data mining and artificial intelligence, business intelligence facilitates more effective and precise decision-making for organizations (Sinarasri & Chariri, 2023). However, given the multifaceted nature of business intelligence, companies must operate more intelligently and strive for maturity by identifying critical factors in the successful implementation of business intelligence. This plays a crucial role in reducing the likelihood of business failures. In general, the shortage of appropriate knowledge resources for companies operating in this field, coupled with a lack of proper understanding among managers, has resulted in minimalist views on business intelligence, limiting its scope to basic services and reports.Given the extensive use of business intelligence, addressing the topic of business intelligence and its influencing factors is crucial. On the other hand, the existence of numerous domestic and international research studies in various aspects of business intelligence necessitates the creation of a comprehensive and coherent framework to connect these research efforts. Considering the current concern, the main question of this research is to provide a comprehensive and coherent framework of the factors affecting business intelligence maturity. The results of this research play a role in advancing theoretical discussions on the maturity of business intelligence and provide suitable indicators for companies seeking to optimize their use of business intelligence. The use of quantitative approaches alongside systematic review can add significant value; therefore, the "Total Interpretive Structural Modeling" (TISM) approach is used to determine the levels of concepts. The research questions are as follows:(1) What are the influential factors on business intelligence maturity?(2) What is the classification of factors affecting the maturity of business intelligence?(3) What are the most important concepts influencing business intelligence maturity?(4) Among researchers, which factors influencing business intelligence maturity are most commonly used?Literature ReviewThe concept of business intelligence maturity refers to an organizational growth stage in which organizations and businesses harness intelligent technologies and leverage their most powerful features. This stage signifies that achieving maturity in business intelligence is considered a strategic goal for organizations in the digital age. Business intelligence maturity offers several advantages, as highlighted in various studies: improved decision-making (Aparicio et al., 2023), enhanced customer satisfaction (Ramos, 2022), increased flexibility (Aparicio et al., 2023), and reduced costs and time required for work (Niazi, 2019).The research conducted in the field of business intelligence across various domains has highlighted several advantages. These include data analytics and dashboards (Sinarasri & Chariri, 2023), security and privacy (Halper & Stodder, 2014), as well as forecasting and advanced analytics (Darwiesh et al., 2022). However, it's important to note that the topics and benefits mentioned here represent only a fraction of the research conducted in the field of business intelligence maturity. Most of these studies are domain-specific, focusing on industries such as banking (Rezaei et al., 2017; Monshy, 2021; Najmi et al., 2010), insurance, small businesses (Ragazou et al., 2023; Sinarasri & Chariri, 2023), e-commerce (Ramos, 2022), the manufacturing industry (Ahmad et al., 2020), and supply chain management (Arunachalam et al., 2018).Some of these research studies have adopted a quantitative approach (Rangriz and Afshari, 2015). This type of research often focuses on the maturity of business intelligence using structural equations (Monshy, 2021; Poti et al., 2017; Khrisat et al., 2023; Golestanizadeh et al., 2023; Mbima & Tetteh, 2023) and examines the relationships between various latent variables and the maturity of business intelligence. However, these studies have not employed a systematic review approach to comprehensively explore the underlying concepts. Business intelligence encompasses diverse dimensions and extends beyond a few latent variables.Another part of the researches has dealt with the modeling of business intelligence with a qualitative method; However, their investigation has reached limited variables and does not include all aspects of business intelligence (Fallah and Kazemi, 2019; Adineh et al., 2022). On the other hand, it should be clear what level of the organization the model is for (readiness, growth, maturity and decline). Because every organization with the conditions it lives in needs a certain level of business intelligence to progress and it is not possible to prescribe the advanced use of business intelligence to a newly established organization, which has not been observed in various researches (Ahmadizad et al., 2015; Srivastava & Venkataraman, 2022).MethodologyThis study is objective in nature and employs a qualitative approach. Its aim is to identify the factors that affect the maturity of business intelligence. To achieve this, a meta-synthesis approach is used to examine existing articles in the field and extract the relevant factors. The statistical population for this research includes credible and relevant articles published until 2023. Meta-synthesis entails reviewing prior studies and reinterpreting concepts by integrating previous results. In this research, the seven-stage Sandelowski & Barroso (2003) method is employed to conduct the meta-synthesis, as it is widely recognized as the most commonly used method for meta-synthesis in recent university research studies. The seventh and final step of the meta-synthesis method involves presenting the findings. In this phase, the TISM is utilized to categorize the meta-synthesis outputs into two categories: "impactful" or "influenced." Eventually, a comprehensive framework for understanding the factors that influence the maturity of business intelligence is established by employing TISM.ResultsThe aim of this research was to provide a framework for understanding the factors that influence business intelligence maturity using a meta-synthesis approach. To develop a comprehensive framework encompassing all aspects of business intelligence maturity, 221 scientific studies were reviewed. Relevant codes were extracted through content analysis using the meta-synthesis method. The categories were stratified using the Total Interpretive Structural Modeling method, and the most influential ones were determined. The findings indicate that a total of 93 codes were extracted, which were categorized into 6 groups. These categories encompass organizational and managerial factors, the environment, technological infrastructure, Human resources - knowledge, data management, and data analysis. The categories of technological infrastructure, data management, and data analysis were placed at level three and exhibited the greatest impact on business intelligence maturity.Discussion and ConclusionThis research investigates the factors influencing the maturity of business intelligence with the aim of establishing a comprehensive framework. The results obtained through the meta-synthesis method reveal six categories crucial to business intelligence maturity. These categories are categorized using the TISM method. Technology infrastructure, data management, and data analysis are placed at the third level and exhibit the most significant impact on other levels. Human resources - knowledge and organization and management factors were placed at the second level. This level is influenced by the third level and, in turn, influences the first level. The environment is categorized at the first level.Among the factors affecting business intelligence maturity, the power of analysis, decision-making quality, and quick and easy access to data exhibit the highest recurrence rate in previous research. The ability to analyze data accurately and with a focus on data-centricity extracts comprehensive insights from the data (Lilly & Renjberfred, 2018), enabling precise predictions of trends, patterns, and behaviors both within and outside the organization (Hernández-Julio et al., 2021). The power of analysis empowers organizations to make strategic decisions based on accurate and reliable information and data (Batra, 2022). Most researchers assert that the quality of decision-making is one of the key advantages of implementing business intelligence in organizations (Fu et al., 2022). Regarding the aspect of fast and easy data access, scholars argue that it is a prerequisite for achieving business intelligence maturity (Sinarasri & Chariri, 2023).
Research Paper
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.
Research Paper
Management approaches in the field of smart
vahid sharifi; Gholamreza hashemzadeh Khorasgani; Seyed Alireza Derakhshan; Ashraf Shahmansouri; Abotorab Alirezaee
Abstract
AbstractIn recent years, studies on the paradigm of social Manufacturing and its applications have been developed as a new production paradigm and have led to the production of diverse and scattered knowledge in this field. Knowing the sub-fields, new topics and the research process of the social production ...
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AbstractIn recent years, studies on the paradigm of social Manufacturing and its applications have been developed as a new production paradigm and have led to the production of diverse and scattered knowledge in this field. Knowing the sub-fields, new topics and the research process of the social production paradigm can be of great help to researchers in this field. The current research has been carried out with the aim of identifying and categorizing research in the field of social Manufacturing, recognizing sub-fields and achieving a coherent view of its research process.This research has investigated the research field of social Manufacturing using bibliometric analysis. The data of this research was collected from 200 articles of the Scopus database and an analysis of the co-occurrence analysis of key words and bibliographic pairs was performed on them, and in this way the sub-fields and the research process of this field were identified.Based on the findings of this study, the research in the field of social Manufacturing has been categorized into 5 clusters and it has also been determined that in recent years, topics such as cloud computing, smart production, blockchain, Internet of Things, social physical cyber systems, innovation systems, society 5.0 and Digital twins have received more attention in research in this field. This research provides a framework of concepts and main topics of interest in the research field of social production, which provides a comprehensive perspective for researchers in this field that can help in choosing their research path.1.IntroductionThe paradigm of social manufacturing can provide suitable solutions for problems of traditional manufacturing such as (non-demand production, high cost production, non-creative production, etc.), However, considering the freshness of this paradigm and the partial studies around it, currently, there is no clear understanding of it in the manufacturing. the present research has been done for manufacturers, organizations and manufacturing companies in case of confronting the market changes so that they can take safe steps to face environmental changes. The main problem is the lack of recognizing theparameters that should be taken into account in order to use all the possible capabilities of social manufacturing. However, no specific research has been done in this field so far. In fact, this research seeks out to answer the following questions:What are the main areas of social manufacturing?What are new topics and emerging trends associated with research in the field of social manufacturing? Literature Reviewever since the last ten years now, researchers have given attention to the concept of social manufacturing. Many researchers have studied the technology and applications of social manufacturing (Pingyu Jiang & Ding, 2012). Similarly, an organizational communication network model has been developed by some based on the social manufacturing. (P. Jiang et al., 2015) in a research, Hamalainen and his colleagues have proposed the basic characteristics of social manufacturing as well (Hamalainen, 2018) also Shang and his colleagues, have designed a social manufacturing model for producing shoes and clothes. (Xiong, Helo, 2022).Correspondingly, Xiang and colleagues (2022) in an investigation have explored the key factors of the transition from mass production to social manufacturing. The outcome of this research is that expanding the concept of Internet of Things, deploying multiple sensors and the usage of data mining with the purpose of managing production data with a large volume, variety and speed in the physical system, will lead to the continuous growth of the industry of manufacturing.3.Methodologythe present research seeks out to identify the playing field of social manufacturing, its subfields and research trends by examining 200 articles from the Scopus database using bibliometric analysis. "VOS Viewer" software is used for bibliometric as well. the software has been using for providing bibliometric maps, visualization of the coinciding of keywords, citation, analysis of bibliographic pairs, co-citation map and other things, through distance-based maps. In order to analyze the new topics, two different parts have been used. the first part is the usage of a cover map and the second part is the analysis of the average lifetime of words. Building a cover map is one of the methods to identify the changes in scientific fields and examine their developments. Cover maps are things that are the outcome of combining two maps with each other. For instance, we use these maps when we need to display the role of time on a science map (Mousavi and colleagues, 1400, quoted by Rafols et al., 2010).4.ResultsBased on the result of the clustering of the keywords co-occurrence map (Figure 5), the five identified research clusters are as follow:Cluster 1: This cluster deals with social manufacturing and correlated technologies such as 3D printing, 3D modeling, social sensors, social cyber-physical systems, RFID and social computing. This indicates that research efforts are dedicated to explaine the infrastructure technologies of social manufacturing.Considering the average publication year of 7/2016 for keywords in the cluster, these topics are more developed in social manufacturing than the others.Cluster 2: The researches of the second cluster are associated with industry 4 and various new production methods with social manufacturing.Industry 4 is a concept which attempts industries become smart, dynamic and flexible. This industry seeks to overcome new challenges such as global competition, market fluctuations, development of customization, establishment of innovation and product life cycle management (Ostadi and Nasiri, 1401). Cloud manufacturing, intelligent manufacturing, digital manufacturing, crowdsourcing, outsourcing, and mass customization. As revealed in figure 5, the most frequent words of this cluster are: industry 4, crowdsourcing and cloud manufacturing, which include a significant number of associated researches with social manufacturing. The age of the keywords in this cluster illustrates the consideration to industry 4 in social manufacturing initiated roughly from 2014 and then the cluster has been inclined towards crowdsourcing and mass customization with an average of 2016.5.Cluster 3: As illustrated in figure 5, a significant number of researches connected with social manufacturing have been carried out in applying new technologies. the most frequent keywords of this cluster include: Internet of Things, cloud computing, deep learning, big data, blockchain, 5thG internet and digital twin.The average year of publishing the keywords of the field is 7/2018, which indicates the researches, especially Blockchain and Internet of Things, can be considered as a research field which has been recently examined.Cluster 4: Researches of this cluster refers to the effect of consumer demand and participation. keywords such as manufacturing platforms, personalized production, personalized products, social media, and the role of the consumer are among the keywords of this cluster.Cluster5: Social sustainability is one of the notions connected with sustainable development, which was counted in the developing programs of different countries from the 1960s onwards. nevertheless, due to the lack of consensus on its components and its place among other apparatuses, it has been treated in many different ways practically.Social sustainability refers to the capability of a society to preserve the necessary means of producing wealth, prosperity and social contribution in order to expand integration and cohesion. As a concept, it also seeks to preserve the social and cultural components of an integrated society with the environmental and economic dimensions. the role of social sustainability is precisely significant in sustainable development (Vaezzadeh and others, 2015). ConclusionThe present study identifies five research clusters as follow:Social manufacturing and its infrastructural technologiesResearches correlated with the connection between new methods of production and industry 4 with social manufacturingResearches related to new technologies such as blockchain and Internet of Things with social manufacturingResearch connected to the concepts of consumer participation in manufacturing (the role of supply and demand) such as mass customization and crowdsourcingStudies in social sustainability, sustainable development, collaborative economy and the fifth generation of societyMoreover, by means of a cover map the present research has achieved the newest topics in social manufacturing such as cloud computing, intelligent production, social computing, blockchain, Internet of Things, cyber- physical social systems, innovative systems, digital twins, the fifth generation of society, and machine learning by using the analysis of the average life of words.discoveries of the present research will help manufacturers and manufacturing companies, to know the emerging areas and components of social manufacturing, and equipped in case of changes and use all the capabilities of social manufacturing. what's more, the analysis of keywords identifies the intellectual bases of discourse in social manufacturing. Furthermore, the findings of bibliographic pair analysis identify influential articles in this field as well, so that researchers can benefit from them as the theoretical foundations of this field.
Research Paper
Management approaches in the field of smart
Mohammad Bashokouh; golsum akbari arbatan; mehdi ebrahimzade
Abstract
Although digitization brings important possibilities, implementing its technologies in practice can be challenging. One of the current major developments in this field is to discover the potential of gamification for the empowerment of knowledge-based companies. Based on this, the current research was ...
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Although digitization brings important possibilities, implementing its technologies in practice can be challenging. One of the current major developments in this field is to discover the potential of gamification for the empowerment of knowledge-based companies. Based on this, the current research was conducted with the aim of identifying the qualitative components of gamification implementation in the working environment of knowledge-based companies. This research has a qualitative approach, through in-depth semi-structured interviews, it has compiled and validated the conceptual framework with thematic analysis method. The statistical population includes experts and experts of knowledge-based companies, among whom 12 people were selected by purposeful sampling and participated in this study. The number of samples follows the rule of saturation. The findings of the research show that concepts were identified in the form of 4 main themes, including the acceptance process with 2 organizing themes, promotion strategies with 4 organizing themes, development and design strategies with 4 organizing themes, evaluation and implementation platforms with 3 organizing themes Gamification actually provides various possibilities to increase the motivation of employees for knowledge-based activities. But to reveal its potential, it needs a suitable environment.
Introduction
In the last decade, a growing trend towards gamification activities and information systems has been observed to motivate positive behaviors to adapt to users' needs. The growing process of gamification of fields such as education and marketing has become powerful and has opened its way to the environment of companies, especially knowledge-based companies. Therefore, this study examines the impact of gamification on the different roles of employees of knowledge-based companies and their behavioral patterns and "visualizes" different communities that have appeared in a company but have not been noticed before. These observations, in turn, can show the adjustment of the working methods of knowledge-based companies.
Research Question(s)
How can gamification become an innovation in the knowledge-based field? What consequences will the adopted solutions have for improving the performance of knowledge-based companies?
Literature Review
Gamification is a way to enhance knowledge management with game design elements to increase user interaction, content creation and satisfaction (Duriník, 2014). Understanding the fundamental characteristics of the impact of gamification on knowledge work may be important for the future development of expert and intelligent systems, which are increasingly used to support knowledge work in a variety of ways. First, embedding gamification in the design of expert and intelligent systems may help to organically implement such a system in work practices, for example by addressing issues of inappropriate use (Spanellis et al, 2020). Secondly, the implementation of expert and intelligent systems can be supported by using gamification, and this potential advantage can be enhanced by knowledge-based workers who are generally accustomed to gamification (González et al., 2016).
Since in knowledge-based companies, managers are considered as one of the main decision-making factors, having their innovative features and capabilities has an impact on the improvement and success of the knowledge-based company's efficiency. Therefore, it can be said that innovation is considered to be the most key and important factors of companies' profitability (Lopez-Nicilas & Merono-Cerdan, 2011).
Methodology
This research is based on the purpose of applied research and based on the approach of qualitative research. The statistical population studied in this research is managers, experts and experts of knowledge-based companies. Therefore, the selection of people was made based on the purposeful sampling of 12 people and the respondents are familiar with gamification technology and have records in this field. Sampling adequacy follows the rule of theoretical saturation. Brown and Clark's (2006) 6-step thematic analysis method was used to analyze the interview text.
Results
Analyzing the research data in three stages of basic themes, organizing themes, and overarching themes finally led to 71 basic themes, 10 organizing themes, and 4 overarching themes, which include all the emerging themes, which are shown in the table below. mentioned.
Discussion
This research has been conducted with the aim of identifying the components of gamification implementation in the working environment of knowledge-based companies. In this context, it can be stated that knowledge-based companies can, based on the extracted factors, establish the necessary conditions. To accept, promote, design and develop and evaluate and implement their activities based on gamification. Also, according to the factors extracted in this research, using the correct implementation method of gamification in the environment of knowledge-based companies is completely different from other information technology environments. The results showed that design and training are among the most important factors identified in the implementation of gamification in the working environment of knowledge-based companies; Therefore, knowledge-based companies should plan that the systems should have an appropriate training stage for learning, identifying and modifying patterns and act accordingly.
Research Paper
Management approaches in the field of smart
Esmaeil Mazroui Nasrabadi; Zahra Sadeqi Arani; Mostafa Salmannejad
Abstract
AbstractThe implementation of Industry 4.0 in the healthcare sector to improve community health is of great importance. Therefore, it is crucial to identify the critical success factors for implementing Industry 4.0 in the healthcare sector, model them, and analyze scenarios for targeted interventions. ...
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AbstractThe implementation of Industry 4.0 in the healthcare sector to improve community health is of great importance. Therefore, it is crucial to identify the critical success factors for implementing Industry 4.0 in the healthcare sector, model them, and analyze scenarios for targeted interventions. This issue has not been investigated in previous studies, and this research aims to fill this research gap. This research was conducted in two qualitative and quantitative stages. The statistical population was experts in both stages, and the judgmental and snowball sampling methods were used. The first stage had a population size of 17, determined based on theoretical saturation, while the second stage had a population size of 10. Thematic analysis was used as the data analysis method in the first stage, and fuzzy cognitive mapping was used in the second stage. The results showed that "competent managers," "support and cooperation," and "competent human resources" have the most significant impact, while "project management," "appropriate planning," and "support and cooperation" are the most susceptible. Furthermore, "support and cooperation," "appropriate planning," and "project management" are the most central. Three forward and three backward scenarios were designed for more effective interventions. It is recommended to improve the organization's educational system, strengthen the succession system, implement transparent contracts, and improve the quality of human resource management to achieve independent variables. IntroductionThe health sector is of great importance to governments. Recent developments in technology (especially industry 4.0) have led to a transformation in the health sector, and if the health sector fails to adapt to these developments, it will face serious damage. These developments have led to widespread benefits such as reduced costs, increased productivity, and increased quantity and quality of service. Failure to pay attention to this transformation can lead to the loss of competitive advantage and lack of proper control of the disease, so the factors that can lead to successful implementation of Industry 4.0 must be identified. This research has three steps. The first step is to identify critical success factors in the implementation of Industry 4.0 in health care.In the second step, the modeling of these factors is discussed to determine the role of each CSF and their relationships. Finally, by identifying the backward and forward scenarios, it is possible to apply targeted interventions. Literature ReviewThe 4th Generation Industrial Revolution has transformed healthcare into healthcare 4.0. Like the industry that has gone through different generations, healthcare has also had different generations: Healthcare 1.0, Healthcare 2.0, Healthcare 3.0, and Healthcare 4.0 (Oduncu, 2022). Health 4.0 is a continuous and transformative process for converting the entire healthcare supply chain. With the help of health care 4.0 patients get rid of negative conditions such as the progression of their disease, and new inventions in the field of health that reduce human death, and prevent the prevalence of diseases. The patient's records are also safe and used if necessary (Oduncu, 2022). In this generation of health, IOT, intelligent measurement, cloud computing, big data analysis, artificial intelligence, automatic control, and automatic and robotic implementation are combined, to create not only digital health products and technologies but also digital health services. (Pang et al., 2018). MethodologyTo answer the research questions, two qualitative and quantitative steps have been performed. In the qualitative stage, the CSFs for the implementation of Industry 4.0 in the healthcare sector were identified. The method of sampling is purposeful, and the sample size was determined by theoretical saturation. Semi-structured interviews and thematic analysis method has been used to gather and analyze the data. In the second stage, the conceptual model of the CSFs of Industry 4.0 implementation was extracted. Using a researcher-made questionnaire data was collected and modeling CSFs was carried out using the fuzzy cognitive map method and Pajek and FCMapper software. ResultsIn the first stage, based on interviews, 32 CSFs were identified in the healthcare sector and categorized into 11 groups that is:Future study and gaining experienceProject ManagementCompetent managersCompetent human resourceThe rule of lawProper hardware and softwareOrganizational readiness analysisComplete ecosystemProper planningFinancingSupport and cooperationPublic education (for the community)The fuzzy cognitive map method was used to identify CSFs model. Out of the 12 CSFs studied, 2 variable is drivers, "competent managers" and "organizational readiness analysis"; and two receivers are "project management" and "public education" factors. The remaining 8 factors have ordinary status, meaning that they have both effective and effective roles. In the first backward scenario, the "project management" factor was considered as the target factor to create a scenario path. During the process of drawing this scenario, the "proper hardware and software" factor was determined as the starting point of the scenario. The path of this scenario shows the high importance of the "proper hardware and software" factor in improving the current state of project management. The second and third scenario pathways are part of the first scenario, which indicates the high importance of "proper hardware and software" and "support and cooperation" in Healthcare 4.0.The first forward scenario path shows that as the "competent managers" factor improves, the maximum improvement in the "competent human resource" will be formed because competent managers are unable to work with ordinary human resources and feel more damaged. The second and third scenarios as part of the first scenario also emphasize its double importance. Overall, it can be claimed that the existence of competent managers and competent human resources and their support and cooperation with each other are considered to be the most important factors that will strengthen the success of the health sector in the implementation of the fourth-generation industry. Discussion and ConclusionThe research findings include important suggestions for healthcare managers in the implementation of the fourth-generation industry. According to the findings from the backward scenario, it is suggested that the most priority for the “proper hardware and software” factor is considered as the essential starting point in this project; because if it is ignored, the human resources involved will not take this project seriously and therefore will not take action for other factors. But the presence of this factor, as can be seen in all three paths, can have the most important impact on the support and cooperation of the organization's managers and human resources. These factors will also activate management activities such as project management and planning. It is also recommended to use competent and specialist managers at the starting point of the project. Experienced and expert managers will be able to make the most of the organization's human resources capacity and provide the best possible way to provide the required hardware and software.
Research Paper
Data science, intelligence and future analysis
mehdi mohammadiraz; maryam sharif nejad; Mohammad Hassan Fotros
Abstract
AbstractAmong the growth and development of businesses, managing financial flows and increasing its effectiveness is an important achievement that causes value stability, efficiency of systems and procedures throughout the chains of Business. Therefore, paying attention to uncontrollable and controllable ...
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AbstractAmong the growth and development of businesses, managing financial flows and increasing its effectiveness is an important achievement that causes value stability, efficiency of systems and procedures throughout the chains of Business. Therefore, paying attention to uncontrollable and controllable variables that lead to value creation is inevitable. The purpose of this study is to study the impact of social and biological factors (production and recycling of polymer parts) of environment on the value creation of the company along the supply chain. Also the key role of financial ratios has also been considered in this regard.The present research is of quantitative and applied types and is based on mathematical modeling. It is the result of a combination of genetic algorithms and Simulated Annealing. The financial analysis parameters of the model include current ratios, debt to equity, Instantaneous ratio, net profit margin, cash ratio and rate of return. The analysis of the results shows that considering financial goals and indicators leads to improved profitability and by removing financial indicators from the model, profitability is reduced. Therefore,this is means that the environmental and social performance of the supply chain is improved.Companies can pay special attention to social issues and environmental factors along with their profitability Increase their economic value. Profitability can also be improved by exposing social responsibilities and the mission of environmental protection.IntroductionAmong the growth and development of businesses, managing financial flows and increasing its effectiveness is an important achievement that causes value stability, efficiency of systems and procedures throughout the chains of Business. Therefore, paying attention to uncontrollable and controllable variables that lead to value creation is inevitable. The purpose of this study is to study the impact of social and biological factors (production and recycling of polymer parts) of environment on the value creation of the company along the supply chain. Also the key role of financial ratios has also been considered in this regard.The present research is of quantitative and applied types and is based on mathematical modeling. It is the result of a combination of genetic algorithms and Simulated Annealing. The financial analysis parameters of the model include current ratios, debt to equity, Instantaneous ratio, net profit margin, cash ratio and rate of return. The analysis of the results shows that considering financial goals and indicators leads to improved profitability and by removing financial indicators from the model, profitability is reduced. Therefore,this is means that the environmental and social performance of the supply chain is improved.Companies can pay special attention to social issues and environmental factors along with their profitability Increase their economic value. Profitability can also be improved by exposing social responsibilities and the mission of environmental protection.Literature ReviewIn the literature review of the research, various factors such as economic issues, laws and regulations, social responsibility, and stakeholder pressures have been given as drivers to lead organizations to implement sustainable supply chain management infrastructure. In some cases, issues such as climate change,factors related to environmental issues and social parameters are ignored. This factor caused criticism in the conventional accounting topics and the topic was raised under the title of environmental management accounting and sustainability, environmental and social accounting) Schaltegger et al. 2013(. The drivers of the organization's movement towards a sustainable supply chain are different from the point of view of the final customer, government institutions, private organizations and legislative institutions. Laws and regulations are the main driver that dictates environmental issues to organizations. On the other hand, some organizations implement these laws in order to increase profitability or customer requests (Ko,Evans et al., 2016). The market and competitors are one of the drivers of the organization towards the adoption of sustainable supply chain management. In today's global business, the competition between organizations is very intense, and in order to impress customers, organizations need to put themselves in a position of superiority over their competitors. Being environmentally friendly and adapting to environmental requirements and paying attention to the organization's social responsibilities is a way to differentiate from other competitors. If competitors have benefited from sustainable supply chain management, the company will be under more pressure to establish sustainable supply chain management(Jian Zhang et al., 2021). In another article, it is suggested that through research in the field of supply chain management and corporate social responsibility, a hierarchical structure of supply chain management is proposed and a multipurpose measurement scale is presented to show the specific management practices of supply chain management(Zhang et al., 2019).MethodologyThe research methodology and the stages of its completion are in the category of quantitative and applied research. Considering that the purpose of applied research is to improve the product or process and test theoretical concepts in real problem situations, the purpose of this research is also to develop practical knowledge in the field of investigating the effects of environmental performance and social parameters along with financial flows on the profitability of the company and its chains. The supply is stable. Therefore, while studying the theoretical background of the research, the research problem is firstly expressed conceptually and then by means of a mathematical model, and in the next step, the mathematical model is solved and the data and results are analyzed. Research data is collected by field method and interviews with experts. To solve the model, the memetic hybrid algorithm has been used.ResultsIn this research, a three-objective mixed integer linear programming model for optimizing financial flows in a sustainable supply chain was presented. The goals of the model include three economic, social, bio-environmental and financial dimensions, and the functions of each are complementary. Although these dimensions depend on each other and affect the performance of others, it is necessary to make a single decision and manage how to implement each of these dimensions in a way that has the highest efficiency. Based on the surveys and analyzes of the research and its results, we state that paying attention to economic goals and financial factors leads to the improvement of profitability. On the other hand, if in the supply chain we only seek to improve the functioning of the environment and social factors, the profitability of the profit-making unit will decrease. In this research, we examined the simultaneity of the mentioned factors in order to realize more value and to improve the financial flows in the supply chain, we analyzed the different financial ratios that are needed by the decision makers in each organization.To analyze the sensitivity of the model, the waste loss rate index was used and it was observed that no matter how much the product loss rate increases in the collection of waste, it causes the profit function to decrease accordingly. The same issue has been proven to reduce negative environmental and social effects.
Research Paper
Management approaches in the field of smart
Amir Valafar; Morteza Maleki MinBashRazgah; Azim Zarei; feiz davood
Abstract
AbstractBlockchain technology is one of the most promising technologies of this century that has the potential to bring about fundamental changes in business models in a wide range of industries. The current research seeks to identify the antecedents of the development of digital marketing ...
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AbstractBlockchain technology is one of the most promising technologies of this century that has the potential to bring about fundamental changes in business models in a wide range of industries. The current research seeks to identify the antecedents of the development of digital marketing based on blockchain technology from the point of view of marketing experts in the aviation industry.This research is practical in terms of orientation and positivist from a philosophical point of view, which was carried out using a mixed method.,In this study, the antecedents of the development of digital marketing based on blockchain technology were first extracted by literature review and semi-structured interviews, and with the help of experts, the final factors were identified;Then, these factors in the form of Q cards were provided to 22 marketing experts in the aviation industry who were selected purposefully and finally their views were analyzed using exploratory factor analysis.The participants in this research have 6 different views and their mental patterns are categorized based on market variables, internal, content, technological, human and environmental factors.The results of this research have given useful insight to managers and decision makers in the aviation industry, so that by knowing these factors, they can strengthen and develop digital marketing in airline companies.IntroductionThe transformation arising from the fourth industrial revolution is based on the integration of digital technologies. This transformation is effective in all dimensions of business activities and causes fundamental changes in the way organizations function and provide value to consumers. Blockchain technology is one of the most promising technologies of this century that has the potential to bring about fundamental changes in business models in a wide range of industries. In the current competitive environment, marketing managers know that forming and maintaining relationships in the digital space is essential(Hussain Zahid,2021). The benefits of digital marketing in the aviation industry include making the user more active, providing rich content to the customer, providing airport services to the customer digitally, increasing the speed and convenience of transactions, increasing customer satisfaction, reducing workload and increasing the utilization of airlines(Keke, 2022).Today, blockchain has strongly influenced business models(Jain et al,2022)Blockchain has also changed the way digital marketing works(Nilsson&Ali,2018). The most important requirements in the aviation industry are high data security, protection against unauthorized access and privacy. Blockchain technology can help digitize the aviation industry and create new business models(Kehoe&Hallahan,2017). One of the capabilities of blockchain technology is providing a customer loyalty program. Blockchain technology can help passengers to buy tickets using digital currency and eliminate the chance of selling and buying duplicate tickets) Ahmad et al, 2021 (. Blockchain creates complete transparency and brand traceability in digital marketing (Stone&Woodcock,2014). The purpose of this study is to identify and classify the antecedents of the development of digital marketing based on blockchain technology from the point of view of airline marketing experts. Literature ReviewMarketing is a social and managerial process through which people and organizations get what they need by creating and exchanging value with others) Kotler et al,2017(Many companies invest in digital marketing as a factor in the development and sustainability of future business(Al-bawaia,2022). Blockchain is a peer-to-peer digital ledger of transactions that may exist publicly or privately between users(Rennock et al,2018). Blockchain features are decentralization, immutability, transparency and auditability of transactions(Monrat et al,2019). Due to the need to develop air transportation in the country, 18 airlines are engaged in air transportation activities. The airline industry is part of a highly interconnected ecosystem of various players including aircraft and aircraft component manufacturers, lessors, airports, freight forwarders, global distribution system providers and online travel agencies. Airlines are at the heart of this ecosystem. Today, the airline industry must include the use of modern technologies in its plan to provide better services to passengers(Riechmann,2020) MethodologyThe current research seeks to identify the antecedents of the development of digital marketing based on blockchain technology from the perspective of marketing experts in the aviation industry, which was conducted in a combined method, in this research, first by reviewing the literature and semi-structured interviews, the factors affecting the development of digital marketing based on blockchain technology China was extracted and finalized with the help of experts. Then these factors were given to 22 aviation industry marketing experts in the form of Q cards. These people were selected purposefully and finally their opinions were analyzed using exploratory factor analysis. ResultsThe participants in this research have 6 different views and their mental patterns are categorized based on market variables, internal, content, technological, human and environmental factors. According to the views of the participants, 6 identified mental patterns explain 75.194% of the total variance. The first mental pattern is 19.283%, the second mental pattern is 13.459%, the third mental pattern is 11.585%, the fourth mental pattern is 11.375%, the fifth pattern is 10.080% and the sixth pattern is 9.142%.Discussion and ConclusionInvestigations show that no such research has been done in this organization and similar organizations. The results were also compared with previous researches. Although the method used in this research has not been used in previous researches; But some of the results obtained are similar to the researches (Lopes et al, 2021), (Antoniadis et al, 2019) and(Hosseinpouli Mamaghani et al, 2021) and the effect of variables The development of digital marketing based on blockchain technology is confirmed in this research. Market variable, existing competition, competitors' strategy and increasing demand in digital marketing in the aviation industry have been the most important from the point of view of marketing experts in the aviation industry. It seems that the focus on market factors is due to the fierce competition between airlines, which shows that managers in this industry should pay more attention to these categories. The results of this research can help decision makers and policy makers in adopting a suitable strategy for the development of digital marketing and strengthen digital marketing in airlines.
Research Paper
Data, information and knowledge management in the field of smart business
Payam Faghihi; Mehrdad Kazerooni
Abstract
AbstractAccelerating the agility of production control systems in today's dynamic production environment is one of the challenges that many types of research have been conducted using multi-agent systems to improve it. The current models of these systems have shortcomings such as limited predictability, ...
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AbstractAccelerating the agility of production control systems in today's dynamic production environment is one of the challenges that many types of research have been conducted using multi-agent systems to improve it. The current models of these systems have shortcomings such as limited predictability, low reliability in the decision-making process, poor ability to understand and interpret the current state of the system, control with many limitations, and generally the existence of error-prone systems. In order to solve these problems, the current research presents a new methodology for multi-agent production control based on integration with ERP, which improves the capabilities of the system in the face of the above deficiencies. The research method employed in this study is qualitative, and developmental-applicative, aiming to enhance the integration of multi-agent production control systems with ERP. The objective is to improve the flow of material, production, and the quality of semi-finished products on the production line by considering the parameters that influence them. The key accomplishment of this research is the development of a reliable production control methodology that encompasses three components: a data exchange framework, tools, and implementation. These components are derived from existing ERP information systems that are functionally mature and designed based on best practices with a focus on maintenance, modification, and performance, aiming to minimize errors. The developed methodology offers a practical and agile solution for enhancing production control using an ERP system, with a lower implementation cost than the implementation of a commercial ERP system with a separate multi-agent system. IntroductionAccelerating the agility of production control systems in today's dynamic production environment is one of the challenges that many types of research have been conducted using multi-agent systems to improve it. The current models of these systems have shortcomings such as limited predictability, low reliability in the decision-making process, poor ability to understand and interpret the current state of the system, control with many limitations, and generally the existence of error-prone systems. In order to solve these problems, the presented research introduces a versatile methodology developed to enhance the efficiency of data and material flow control within a production system. The methodology emphasizes the role of data flow in regulating material flow, making it agile and autonomous.The innovation lies in elevating the role of ERP modules from process flow reporting to that of decision-making software agents, aligning with the common nature of both systems. Consequently, higher levels of data integration between the production system and the Multi-Agent Production Control System (MAPCS) integrated with ERP are achieved, leveraging agent technology and best practices from ERP modules.This approach enables real-time responsiveness to changes in the production system, establishing an agile production control methodology capable of managing material flow dynamics. Furthermore, it represents a step toward addressing current MAPCS limitations.Literature ReviewThe advent of affordable computer technology marks a pivotal moment in the adoption of advanced IT-based production control systems (Karrer, 2012). Leveraging technologies that continually monitor and gather information concerning the real-time status of production systems, such as machines equipped with sensors actively participating in the production process and offering virtual representations of the production system's state, enhances data integrity for improved decision-making in production control (Huang, 2022).Over the last decade of the 20th century, agent technology emerged, giving rise to agent-based production planning and control models and extensive research into technology development based on these principles (Bär, 2022; Groß et al., 2021).Agent-based systems represent the next generation of software, capable of dynamic adaptation to the evolving business environment and addressing a wide array of production system challenges (Mesbahi et al., 2014). However, they do present ongoing challenges, including limitations in system state comprehension, restricted control, reduced decision-making reliability, and a generally increased risk of errors in design and implementation (De la Prieta et al., 2019; Balaji & Srinivasan, 2010).Concurrently, Enterprise Resource Planning (ERP) systems emerged as IT-based solutions in the final decade of the 20th century, witnessing rapid expansion in research and implementation across various organizations (Scharf et al., 2022; De Brabander et al., 2022; Febrianto & Soediantono, 2022; Senaya et al., 2022).The integration of agents with ERP systems holds the promise of enhancing ERP intelligence, allowing them to autonomously interact with their environment and execute self-directed actions while collaborating with other systems (Faghihi & Kazerooni, 2023).This paper introduces a novel solution: the development of a Multi-Agent Production Control Methodology (MAPCM) integrated with ERP system that encompasses three components: data exchange framework, tools, and implementation.MethodologyIn this study, a developmental-applicative research method has been employed with the goal of building upon the findings of prior fundamental research. The objective is to enhance and refine various aspects, including behaviors, methods, tools, devices, structures, and patterns. This iterative process aims to address the practical needs of the society's industries.Additionally, to gather the desired data, a qualitative research method has been employed. This approach is particularly useful for tackling complex problems and deriving meaningful, easily comprehensible conclusions accessible to a wide audience.Results4.1. Data exchange frameworkThe development of the Final MAPCM integrated with ERP framework proceeded in a systematic four-layer approach. To enhance comprehension of the progress in each stage and the data exchange within these layers, we represent the first layer's data in black, while the data from the second and third layers are depicted in blue and red, respectively.4.1.1. Layer 1: A Framework for streamlining production control data exchangeFigure 1, illustrates an exemplary data-exchange framework for production control, which serves as the foundation for the proposed framework (Frazzon et al., 2018). This framework leverages a Manufacturing Execution System (MES) as the central data hub, facilitating seamless data exchange to bridge the physical manufacturing and production system with a multi-agent system.The data-exchange framework, depicted in Figure 2, emphasizes the implementation of real-time inventory distribution, dispatching limitations, and delivery constraints throughout the production process. Also, effectively addresses the dynamic handling of inventory distribution and delivery constraints in response to unplanned and unscheduled maintenance operations. This capability is achieved through the collaborative efforts of the inventory control and the maintenance modules of the ERP system. After upgrading the ERP quality control module to a software agent, it conducts three-phase quality checks utilizing data from both human and cyber-physical systems. (Figure 3):- Phase 1:This phase is dedicated to assessing the quality of raw materials and consists of two sections:The quality of incoming warehouse inventoryThe quality of warehouse inventory during storage periods- Phase 2:Semi-product quality control during the manufacturing process- Phase 3:Quality of finished productsFigure 3. MAPCM integrated with ERP – based on quality control framework 4.1.4. Layer 4: Final MAPCM integrated with ERP frameworkThe final MAPCM integrated with ERP framework (Figure 4) was developed through concurrent implementation and application of the preceding layers.Figure 4. Final MAPCM integrated with ERP framework 4.2. ToolsCyber-physical systems offer rich sensory data. A network of sensors continuously monitors the condition of machine tools on the shop floor and tracks the work-in-progress status in the production system.4.3. ImplementationWhile constructing complex software agents from the ground up using Agent-Oriented Programming (AOP) languages can be challenging due to the skills and knowledge required, readily accessible agent-building toolkits like JAFMAS, JATLite, ZEUS, and Sodabot provide valuable alternatives.DiscussionAgent-based approaches are essential for future production control systems due to their decentralized decision-making, flexibility, and complexity-reducing capabilities. Integrating ERP modules into software agents and enabling data exchange and direct interactions among these agents can enhance self-management and intelligence in production systems. This integration reduces implementation costs compared to using separate commercial ERP software and a multi-agent system. Furthermore, real-time soft sensors become more accessible and user-friendly due to the software-based nature of production control agents.ConclusionThe developed methodology offers a practical, cost-effective, and agile solution to enhance production control through ERP integration. By harnessing the synergistic capabilities of agents and ERP modules for monitoring, decision-making, and control, the limitations of traditional MAPCS models have been resolved. This transition results in autonomous production control systems that reduce reliance on human intervention. This methodology leverages well-established ERP information systems, following best practices to minimize errors, and enhance maintenance, modification, and performance, ultimately striving for error reduction.
Research Paper
Management approaches in the field of smart
Mahsa Akbari; mostafa bigdeli; Parvaneh Charestad
Abstract
AbstractGamification is a relatively new concept that has seen a significant increase in its use in recent years. Gamification involves the application of game elements in a non-gaming environment to create a gaming experience related to a product or service. The aim of this research is to investigate ...
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AbstractGamification is a relatively new concept that has seen a significant increase in its use in recent years. Gamification involves the application of game elements in a non-gaming environment to create a gaming experience related to a product or service. The aim of this research is to investigate the impact of different aspects of gamification (immersion, achievement, social) on customer engagement (emotional, cognitive, social) in the online store of Digikala. The research population consists of consumers and users of the Digikala website who have made at least one purchase on this site. In this regard, 222 questionnaires obtained from Digikala website users' data were analyzed. The research model was designed by reviewing the literature related to the research topic and previous studies, and it was analyzed using structural equation modeling. Finally, it was determined that the aspects of gamification have a positive and significant effect on customer engagement. The immersion aspects of gamification have a positive impact on emotional aspects of customer engagement, the achievement aspects affect the cognitive aspects of customer engagement, and the social aspects of gamification also have a stronger positive impact on the social aspects of customer engagement.IntroductionGamification has gained recognition as a powerful tool for establishing customer engagement in recent years, garnering significant attention both in industry and academia (Huotari, 2017), (Hamari et al., 2014), (Hamari et al., 2014b). This is because the inherent nature of play and the potential for possible achievement evoke positive emotions in people. In marketing, gamification is a means to elicit positive emotions in customers for the sale of a product or service. The use of gamification helps consumers spend more time on your website, increasing the likelihood of them making a purchase. In this context, gamification can be described as the use of game design in a non-gaming environment (Deterding et al., 2011). In other words, gamification seeks to replicate the effects of games, including motivation, excitement, and repetition, in a real-world context. Therefore, gamification technologies have the capability to manipulate social and individual factors to motivate customers and influence their intentions (Shang & Lin, 2013), (Jackson, 2009).As online games and social software continue to advance and become integrated into e-commerce businesses, they create new patterns that enhance user experiences and encourage active participation (Hsu & Chen, 2018).With the expansion of online businesses, the use of effective marketing techniques to attract customers has become crucial. In this regard, Digikala, the largest online retailer in Iran, has been no exception. Therefore, the use of gamification has great importance in branding and improving customer experiences.Literature ReviewGamification is an innovative concept that has not only impacted the gaming industry but has also opened avenues in management sciences to provide maximum effectiveness for organizations in competitive conditions. Initial studies on gamification were conducted in 2008 by Brett Terrill. However, its scientific popularity and extensive research began around 2010 (Alhamed & Morano, 2018).In terms of the effectiveness and the impact of gamification on marketing concepts, particularly in customer engagement, extensive research has not been conducted. However, most studies indicate a positive impact of gamification on customer engagement. In this regard, we will review some important research studies.In a study that examined the effects of gamification aspects on customer engagement dimensions and brand value among customers of Huawei and Xiaomi in social networks, it was found that gamification aspects have a significant impact on customer engagement dimensions and brand value (Xi & Hamari, 2019). In another study that investigated the impact of gamification on customer engagement and online sales, it was revealed that gamification aspects such as social interactions, goal orientation, and rewards lead to increased customer engagement and online sales (Eisingerich et al., 2019). In a study that focused on the impact of gamification on participation in online programs, the results indicated that gamification significantly affects participation (Looyestyn et al., 2017).MethodologyThis study is descriptive & applied in nature and employs a quantitative approach. Data collection was done through questionnaire. In this study, the statistical population consists of customers of the Digikala website who have made at least one purchase from this site. Since this website is the most well-known online shopping site in Iran, a structured questionnaire was distributed to 300 Digikala users using convenience sampling method. After filtering out incomplete and problematic questionnaires, a total of 222 questionnaires were gathered. Data analysis was conducted using Structural Equation Modeling (SEM) through the Lisrel Software.ResultsBased on the results obtained from the hypothetical model, we conclude that:The influence of immersion aspects of gamification on the emotional dimension of customer engagement was confirmed (Hypothesis 1).The influence of immersion aspects of gamification on the social and cognitive dimensions of customer engagement was not confirmed (Hypotheses 2 and 3).The influence of achievement aspects of gamification on the emotional and cognitive dimensions of customer engagement was confirmed (Hypotheses 4 and 5).The influence of achievement aspects of gamification on the social dimension of customer engagement was not confirmed (Hypothesis 6).The influence of achievement aspects of gamification on the emotional, cognitive, and social dimensions of customer engagement was confirmed (Hypotheses 7, 8, and 9).The immersion aspects of gamification have a stronger and more significant impact on the emotional dimension of customer engagement compared to other dimensions (Hypothesis 10).The achievement aspects of gamification have a stronger and more significant impact on the cognitive dimension of customer engagement compared to other dimensions (Hypothesis 11).The social aspects of gamification have a stronger and more significant impact on the social dimension of customer engagement compared to other dimensions (Hypothesis 12).Discussion & ConclusionIn general, the findings of the present research indicate that immersion aspects of gamification (such as creating avatars, customizing applications and web pages, storytelling, and narrative) have a greater impact on the emotional dimensions of customer engagement.When compared to immersion aspects, achievement aspects of gamification, such as giving prizes, medals, digital currency, coins, points, and gift cards, have a greater influence on the cognitive dimensions of customer engagement. They also affect the emotional aspect. Providing rewards and medals leads to customers forming a better rational and cognitive perception of our brand.Moreover, social aspects of gamification, like organizing competitions and teamwork activities and using social networks, have a more significant impact on the social dimension of customer engagement. However, they also have an effect on the emotional and cognitive dimensions.The findings of this research are consistent with the results of previous studies, including Madura (2015), Zhi and Hamari (2019), Harwood and Garry (2015), Yin et al. (2017), and Eisingerich et al. (2019).Based on the results obtained, it is recommended that online stores and smart businesses employ various gamification elements to increase customer engagement
Research Paper
Data, information and knowledge management in the field of smart business
Fatemeh Rezaimehr; Chitra Dadkhah
Abstract
AbstractRecently, the Internet has played a significant and substantial role in people's lives. However, the content available in the global web environment should align with users' daily needs, providing them with useful and up-to-date information tailored to their tastes. In this context, recommender ...
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AbstractRecently, the Internet has played a significant and substantial role in people's lives. However, the content available in the global web environment should align with users' daily needs, providing them with useful and up-to-date information tailored to their tastes. In this context, recommender systems assist users by suggesting items that closely match their preferences in less time. Today, with the exponential growth of data, the utilization of recommender systems has surged. Conversely, these systems encounter challenges such as evolving user preferences over time, cold start problem, sparsity within the user-item matrix, the infiltration of fake users in the systems, and their adverse impact on the recommendation lists. The objective of this paper is to propose a recommender system grounded in time and trust factors to enhance the efficiency and precision of system recommendations. Initially, the proposed system addresses the data sparsity dilemma by incorporating reliable implicit ratings into the user-item matrix. Subsequently, it constructs a weighted user-user network based on user rating timestamps and trust relationships among users, thereby mitigating the cold start problem and accounting for changing user preferences over time. The proposed recommender system employs a novel community detection algorithm introduced in this paper to identify the nearest neighbors of active users and recommends the top @k items based on the collaborative filtering approach. Evaluation results of the proposed system, tested on a film recommender system using the Epinions dataset, demonstrate its superior efficiency compared to basic systems.IntroductionToday, with the increasing tendency of users to use websites for obtaining information, online shopping, and using social networks for expressing personal opinions, the ways of obtaining information and establishing connections among users have undergone significant changes. Consequently, users are confronted with the big of data. Managing this data and selecting the appropriate options from this vast collection and presenting it to users is one of the main reasons for the development of information retrieval systems and search engines. In this regard, Recommendation Systems (RSs) help users choose the best options and recommend items that are closer to their preferences in the shortest possible time. Different models of RS such as collaborative filtering, content-based, knowledge-based, and newly developed context-aware RS, have been presented by researchers (Casillo et al., 2022). Each has its own advantages and disadvantages, which can be combined to create a hybrid RS. It should be noted that RS face challenges, including changes in user preferences over time, cold start for new users or items, sparsity of the user-item matrix, attack by fake users, and their negative impact on the recommendation list. In this paper, a time- and trust-based recommendation system is presented to enhance the performance and accuracy of recommendations. Our proposed system initially solves the data sparsity problem by adding reliable implicit ratings to the user-item rating matrix. It then generates a weighted user-user network based on the time of user feedback on items and trust relationships among users. This approach addresses the cold start problem and the change in user preferences over time. Our system is based on a novel community detection algorithm presented in this article, which identifies the nearest neighboring users with similar tastes to the active user and recommends the top-k items using the collaborative filtering method. The evaluation of the proposed system is performed on an Epinions dataset for a movie recommendation system. The evaluation uses metrics such as accuracy, recall, F1 score, mean absolute error, and root mean square error. The experimental results indicate the superior performance of the proposed system compared to similar systems.Literature ReviewIn the recent years, the researchers attempt to improve the accuracy of their recommendation for retaining the users and increasing the profit. Some of the papers has worked on optimizing the performance of their proposed RS using evolutionary algorithms (Tohidi & Dadkhah, 2020) and the others used the additional information such as time, location, etc. Trust-based RSs have been recently introduced to the community of computer science. Recent studies have shown that incorporating social factors or trust statements in RSs leads to the improvement of recommendation quality (P. Moradi & Ahmadian, 2015; S. Ahmadian, M. Meghdadi, & Afsharchi, 2018b). So far, several trust-based CF approaches have been proposed to overcome data sparsity and cold-start problems as well as to increase recommendable items (Ghavipour & Meybodi, 2016; Moradi, Ahmadian, & Akhlaghian, 2015; P. Massa & Avesani, 2007; Ranjbar Kermany & Alizadeh, 2017). Trust statements can be explicitly collected from users or can be implicitly inferred from users behaviors (S. Ahmadian, M. Meghdadi, & Afsharchi, 2018a; S. Ahmadian, P. Moradi, & Akhlaghian, 2014). Liu and Lee proposed a specific approach which does not directly use the trust information; instead they take into account the number of exchanged messages among the users of the system to construct the trust network (Liu & Lee, 2010). Alahmadi and Zeng presented a framework to apply short texts posted by users friends in microblogs as an additional data source to build the trust network (Alahmadi & Zeng, 2015). Since explicit trust statements are directly specified by the users, they are more accurate and reliable than implicit ones in determining social relationships among users (Cho, Kwon, & Park, 2009; Ingoo, Kyong, & Tae, 2003; Lathia, Hailes, & Capra, 2008; Manolopoulus, Nanopoulus, Papadopoulus, & Symeonidis, 2008).The research In (Abdul-Rahman & Hailes, 2000) has been shown that a user constructs his/her social connections with someone who has similar tastes. Massa and Avesani showed that adding social network data to traditional collaborative filtering improves the recommendation results (P. Massa & Avesani, 2007). Gharibshah and Jalili studied the relation between RSs and connectedness of users-items bipartite interaction network (Gharibshah & Jalili, 2014). Guo et al. proposed a method which merged the ratings of users trusted neighbors with the other information sources to identify their preferences (G. Guo, J. Zhang, & Thalmann, 2014). Yang et al. proposed a Bayesian inference based recommendation method for online social networks (X. Yang, Y. Guo, & Liu, 2013). In this method, the similarity value between each pair of users is measured using a set of conditional probabilities derived from their mutual ratings. Jiang et al. introduced a framework to incorporate interpersonal influences of users in social network with their individual preferences to improve the accuracy of social recommendation (Jiang, Cui, Wang, Zhu, & Yang, 2014).Purchase/rating time is one of the most important contextual information that can be used to design RSs with high precision (Xiong, Chen, Huang, Schneider, & Carbonell, 2010). The main motivation for time-aware RS is that in realistic scenarios users tastes might change over time.MethodologyWe propose a time and trust-aware RS using a graph-based community detection method consists of four steps: 1: developing a user-item rating matrix, 2: constructing a time weighted user-user network, 3: performing graph- based community detection, 4: recommending Top-N items. In the first step, the user-item rating matrix is developed by adding some implicit ratings and the quality of the implicit ratings is evaluated using a reliability measurement. In the second step, a time-weighted user-user network is constructed based on the combination of trust relationships and similarity between users. Moreover, the timestamps of user-item ratings are considered to calculate the similarity between users. In the third step, a graph-based community detection method classifies similar users into appropriate communities. Finally, in the fourth step, it predicts the rating for each unobserved item and top-N recommendations is generated for the target user.We proposed a new community detection method that consists of three phases. First, the initial centers of communities are obtained using a sparsest subgraph of weighted user-user network. It should be noted that the initial centers must have the maximum dissimilarities with each other based on the general concept of clustering and community detection algorithms. Then users can be assigned to their nearest communities. For each user proposed system calculated the fitness function. User has associated to community which has high value of fitness function. Then the centers of communities were updated in order to maximize a fitness function. This process is iteratively repeated until members of communities do not change and steady state is achieved. A set of communities are identified where the users are assigned to their corresponding communities. Some of the communities may have overlap and they can be merged. The final communities were used as the nearest neighbors set of the active user in the same community for the recommendation.ConclusionOur proposed algorithm solves the sparsity of rating matrix by adding the implicit rating and solved cold-start problem for new users by considering the trust between the users. We applied the proposed algorithm on extended Epinions dataset and compared its performance with similar algorithms. The experimental results showed that our proposed algorithm outperforms the other algorithms according to the accuracy and recommends the top@N items with high precision.
