Data, information and knowledge management in the field of smart business
Mohammadreza Saadi; Mohammad Bashokouh; Golsum Akbariarbatan
Abstract
Objective: Social gamification has been widely used in various industries to increase user engagement and change the rules of engagement in business management. Designing social gamification can help shape user behavior to some extent and increase the level of business performance. Considering the role ...
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Objective: Social gamification has been widely used in various industries to increase user engagement and change the rules of engagement in business management. Designing social gamification can help shape user behavior to some extent and increase the level of business performance. Considering the role and impact of gamification in speeding up the development of businesses, the importance of this issue is highlighted. Methodology: With the aim of providing a model for the role of social gamification in the development of businesses in a descriptive manner, this research developed and validated a conceptual framework with the foundational data method through in-depth semi-structured interviews. The statistical population includes experts and experts in the fields of gamification, online businesses, experts in information science and technology, and social media, among whom 12 people were selected and participated in this study by purposeful sampling. The number of samples follows the rule of saturation. Findings: Causal conditions including (facilitating learning and training, strengthening social interaction, promoting participation motivation, improving work experience and competition), background conditions including (participation and social responsibility, training and development of human resources, marketing and game-like advertising) intervening conditions ( lack of correct understanding of gamification by users, incompatibility of gamification with business goals, incorrect design of gamification), strategies (creativity and innovation, development and continuous improvement of processes, simulation and role playing), consequences including (better analysis and feedback, simplification of issues) complex, business performance improvement, synergy and business growth with gamification, sales and revenue convergence) were categorized.
Data, information and knowledge management in the field of smart business
Reza Hosseingholizadeh; Mahmood Alborzi; Abbas Toloie Eshlaghy; Hamid Zargham Boroujeni
Abstract
Forecasting cryptocurrency market trends remains a significant challenge due to its fundamental differences from traditional currencies. This complexity arises from the interplay between conventional financial indicators, advancements in information technology, and government macroeconomic policies influencing ...
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Forecasting cryptocurrency market trends remains a significant challenge due to its fundamental differences from traditional currencies. This complexity arises from the interplay between conventional financial indicators, advancements in information technology, and government macroeconomic policies influencing market acceptance. This study introduces a novel decision support framework that, rather than analyzing individual cryptocurrencies, focuses on the overall acceptance of the cryptocurrency market. The proposed approach enables a more precise and realistic assessment of market trends, facilitating the generation of buy and sell guidance tables for any specified time interval. To achieve this, maximum likelihood estimation and Bayesian belief networks are employed, allowing for a comparative analysis of these methodologies. Additionally, a high-edge-strength Bayesian belief network is constructed from the generated networks to enhance prudent trading decisions. The method is validated using 1155 weekly and 484 daily time points across 21 cryptocurrencies with the highest market capitalization, covering two periods: the last quarter of 2024 and March–May 2025. The findings demonstrate that the proposed framework, with its high precision, accuracy, recall, and model robustness, supports buy and sell decisions with an average accuracy of at least 78% on a daily basis and 64.5% on a weekly basis. This approach offers a valuable tool for navigating the dynamic and uncertain nature of the cryptocurrency market.
Data, information and knowledge management in the field of smart business
, Parisa karaminiya; , Ali Rajabzadeh Ghatari; Mohmoud Dehghan Nayeri,
Abstract
This research was conducted with the aim of modeling the drivers and consequences of digital transformation in the country's steel industry business ecosystem Iran. The present study is an applied-developmental research in terms of its purpose and a descriptive-survey research in terms of its data collection ...
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This research was conducted with the aim of modeling the drivers and consequences of digital transformation in the country's steel industry business ecosystem Iran. The present study is an applied-developmental research in terms of its purpose and a descriptive-survey research in terms of its data collection method. In line with the purpose, an exploratory mixed research design was used. The qualitative section's participant population includes management professors and managers of the country's steel industry. Theoretical saturation was achieved after 20 interviews using the theoretical sampling method. In the quantitative section, a sample of 140 managers and experts of the country's steel industry was selected using the Cohen power analysis method. The data collection tool was a semi-structured interview and a researcher-made questionnaire. The validity of the qualitative section was examined based on reliability, transferability, confirmability, and reliability, and the Holst coefficient was estimated to be 0.707 and Cohen's kappa was 0.658, which is desirable. The questionnaire was validated by estimating the content validity ratio, convergent validity, and divergent validity. Also, Cronbach's alpha, coefficient of resiliency and composite reliability of all constructs were estimated above 0.7. Qualitative content analysis, structural-interpretive modeling and partial least squares methods were used to analyze the data. The research findings showed that business ecosystem factors, management factors, hardware and software platforms are driving factors that affect the digital transformation strategy. The digital transformation strategy also affects the digital transformation of the steel industry, and digital transformation in turn affects digital innovation and digital communications, and affects innovative performance, social performance and marketing performance, and ultimately enables the achievement of financial performance.IntroductionThe steel industry in Iran is known as a vital and mother industry due to its rich mineral resources and potential capacities. This industry has a strategic position in Iran and is considered the second largest non-oil export industry after petrochemicals. Steel is the most practical metal in terms of quality and value, and about 95% of the world's metals are steel and iron. Transformation is a critical factor in the success of steel companies' supply chains, and customer demands in the competitive market of this industry require fundamental changes in current processes. In other words, it can be said that transformation has become a vital issue in the steel industry ecosystem, and accepting and keeping up with changes is a necessary and continuous matter that ultimately ensures the health of this industry. The advancement of digital technology has led to the development of new organizational networks, which are called digital business ecosystems. Digital technology plays a pivotal role in achieving business goals, and its scope and effects are so extensive that it can even transform the nature of an industry as a whole. It is not possible to study business ecosystems without considering digital transformation. In general, it can be said that digital transformation has become the dominant paradigm in the industrial world today. In order to solve the country's major problems by utilizing the capacity of transformative technologies and with the aim of developing the digital economy, the Ministry of Communications and Information Technology has compiled and submitted to the Cabinet the "Digital Transformation Document" since the beginning of 1400. Specifically, in the country's steel industry, embracing digital transformation will bring many benefits, but this transformation requires contexts and platforms that are known as drivers of digital transformation in the steel industry. Creating software platforms that are appropriate for the business ecosystem processes of this steel industry, which has a continuous value chain, along with speed and agility in decision-making for managers, is a very vital issue that will have significant consequences. On the other hand, the digital transformation of the steel industry is inevitable, and from a negative perspective, this issue is also very important. The rapid movement of countries such as China and India towards digital development in the steel industry has greatly affected global markets and, of course, Iran, and can be a warning for the Iranian steel industry. This issue is also very important theoretically, and various studies have been conducted on "digital transformation" and "business ecosystems." However, an independent study that examines the country's steel industry business ecosystem based on digital transformation has not yet been recorded in the country's domestic scientific interventions. In studies that have implicitly addressed this issue, providing an applied model in this area has been neglected. Finally, it should be said that there is no doubt that the gap between the scientific and practical fields in the field of digital transformation in the country is large, therefore, this study attempted to present a model for digital transformation with an applied-developmental approach in the country's steel industry. The present study will answer this key question: what is the model of the drivers and consequences of digital transformation in the country's steel industry business ecosystem? Methodology This research is an applied-developmental research in terms of its purpose, which seeks to model the drivers and consequences of digital transformation in the country's steel industry business ecosystem. It is also considered a descriptive-survey research based on the data collection method. In order to achieve the research objective, a mixed exploratory research design (qualitative-quantitative) was used. The qualitative part's participant population includes management professors and managers of the country's steel industry. Theoretical saturation was achieved after 20 interviews using the theoretical sampling method. In the quantitative part, a sample of 140 managers and experts of the country's steel industry was selected using the Cohen power analysis method. The data collection tool was a semi-structured interview and a researcher-made questionnaire. The validity of the qualitative part was examined based on credibility, transferability, confirmability, and reliability, and the Holst coefficient was estimated to be 0.707 and Cohen's kappa was estimated to be 0.658, which is desirable. The questionnaire was validated by estimating the content validity ratio, convergent validity, and divergent validity. Also, Cronbach's alpha, coefficient of resiliency, and composite reliability of all constructs were estimated to be above 0.7. Thematic analysis method and Maxqda software were used for data analysis in the qualitative part. Structural-interpretive modeling method and MicMac software were used to identify the relationship between constructs. In the quantitative part, partial least squares method and Smart PLS software were used. Results and DiscussionIn the research findings section, the interviews were analyzed using qualitative thematic analysis based on the six-step Atread-Stirling method. In the open coding stage, 514 codes were identified, which were ultimately identified through axial coding as 4 overarching themes, 12 organizing themes, and 72 basic themes. ConclusionThe results showed that business ecosystem factors, management factors, hardware and software platforms are the driving factors that affect the digital transformation strategy. The digital transformation strategy also affects the digital transformation of the steel industry, and digital transformation in turn affects digital innovation and digital communications, and affects innovative performance, social performance, and marketing performance, and ultimately achieves financial performance.Key words: digital transformation, business ecosystem, steel industry of the country Iran.
Data, information and knowledge management in the field of smart business
bahman khodapanah; Seyyed Ali Hosseini; Mojtaba Babaeihezejan
Abstract
The origins of modern technological change provide the necessary context for understanding today's technological developments, examining the impact of new digital technologies, and examining the phenomenon of digital disruption in emerging industries and businesses. Time will tell how new technologies ...
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The origins of modern technological change provide the necessary context for understanding today's technological developments, examining the impact of new digital technologies, and examining the phenomenon of digital disruption in emerging industries and businesses. Time will tell how new technologies transform industries and institutions. But what is significant is the role of data in technological developments and creative destruction that causes digital transformation and new business models, business strategies, innovation and capabilities at the global, national, corporate and local levels. The main purpose of the current research; Design is a framework for developing data-driven creative destruction. The research method was conducted qualitatively with the grounded theory approach and Strauss and Corbin theory and systematic approach. The statistical population of the research includes those who have theoretical knowledge in relation to entrepreneurship theories, especially creative destruction, as well as those who have sufficient experience in the field of data-driven businesses. In order to analyze the qualitative data, open, central and selective coding steps were carried out and finally, the paradigm model of grounded theory was based on the comprising five main dimensions. and 21 sub-dimensions including causal factors (technology, personality and behavior, institutional framework) underlying factors (data behavioral, textual data, psychological data, demographic information, geographical data, destructive intent), intervening factors (organizational technology, the degree to which new value is created, effectiveness and cost management), strategies (creative development strategy, strategy Based on the structure, the strategy of redefining the business model, the strategy of sensing and shaping, the strategy of identification and seizure, the strategy of transformation and reconfiguration) and consequences (technological, socio-economic) were formed.IntroductionIncreasing globalization, e-commerce, and cross-border information sharing have led to the need for most companies worldwide to be digitally active. The key driver of creative destruction in the modern economy is data, and data is the backbone of emerging industries such as artificial intelligence, machine learning, and the Internet of Things. However, the current "data economy" is centralized and decentralized. Currently, it is very difficult for businesses to access and use the data they need to innovate and grow. Two decades ago, the popularity of the Internet led to what we refer to here as the first digital destruction; File-sharing, and the reordering of content-based industries from music to film to news, etc., have led us to the second digital destruction, driven by the ability of streaming platforms to collect massive amounts of data combined with powerful computing about Consumer preferences and consumption patterns. The data collected by Companies like Netflix, Spotify, and Apple leverage consumer data to gain granular insights into preferences. This has led to the emergence of "data-driven creativity" in business marketing. A review of the research literature related to the phenomenon of creative destruction and data-driven economy shows that so far there is no research that has investigated the relationship between these two phenomena. We show how businesses use streaming data not only to organize and suggest content to consumers, but even to shape creative decisions. Therefore, the organization of this article will be as follows: in the second part, the theoretical foundations and background of the research will be examined; In the third part, the research method is determined, and in the fourth part, the research findings will be evaluated. Finally, in the fifth section; We will draw conclusions and make suggestions from a policy perspective.Literature Review Creative destruction, coined by Austrian economist Joseph Schumpeter in 1942 in his work, Capitalism, Socialism and Democracy. It is an evolutionary process in capitalism that overturns the economic structure from within, continuously destroying the old structure and creating a new structure. Every business operates in this "permanent storm," where "every part of the business strategy takes on real importance." From this definition; It can be concluded that Schumpeter saw the market and competition as dynamic. One of the most important features of the theory of creative destruction is that it can be transferred from the analysis of economic institutions to political institutions. Schumpeter made this transition by revising - in a radical and subtle way - the neoclassical theory of decision making and the formation of supply and demand.MethodologyThis study employs a qualitative approach rooted in Strauss and Corbin’s grounded theory methodology. The participant pool comprises experts in entrepreneurial theory (particularly creative destruction) and practitioners in data-driven businesses. Data were collected via semi-structured interviews, with purposive and iterative sampling conducted until theoretical saturation. Coding—a "vital link" between data collection and interpretation (Charmaz, 2001; Saldaña, 2021)—followed Strauss and Corbin’s (1967) three-stage process: open, axial, and selective coding. Categories were organized into causal, contextual, intervening, strategic, and consequential factors.ResultsThe most important step in the process of analyzing the data obtained from the interviews is coding. A code, in qualitative research, means a short word or phrase that symbolically and succinctly represents a salient and comprehensive feature of an element of data. Charmaz (2001) describes coding as the "vital link" between data collection and the interpretation of their meanings (Saldana, 2021). In the grounded theory approach, the process of data analysis based on the theory of Strauss and Corbin (1967) includes three stages of coding (open, central, selective) and six categories, including the determination and identification of causal, central, intervening, contextual or background factors. strategies and finally consequences.DiscussionIn this study; the grounded theory paradigm model has five main dimensions and 21 sub-dimensions, including causal factors, background factors, intervening factors, strategies, and consequences were formed.ConclusionIn the data economy, huge amounts of data are growing rapidly through various sources, including social media, sensors, and other digital technologies. These data are often used to improve society by improving the efficiency and effectiveness of various systems such as transportation and healthcare. However, the collection and use of data can raise concerns about privacy, security, and other social issues that may negatively impact society. In order to assess the nature and scope of these impacts on society, it is important to consider factors such as the parties' access to this information and the ways in which it is used. Gaining a clear understanding of the social implications of the data economy is critical to ensuring the responsible and ethical use of data.Legal enablers are laws, regulations and other legal frameworks that enable the development and growth of the data economy while guaranteeing the rights of all. These enablers can include various legal frameworks such as data protection laws that ensure the privacy and security of personal data and laws that govern the collection, use and sharing of data. Legal enablers are essential to provide a stable and predictable legal environment in which companies and individuals can operate and innovate in the data economy. They can also help protect the rights and interests of individuals and ensure that the data economy is fair and transparent.Keywords: Creative Destruction, Technological Changes, Data-Driven Economy.
Data, information and knowledge management in the field of smart business
Maral Shadpour; Kambiz Shahroodi; , Narges Delafrooz
Abstract
The turning away of customers is one of the main threats in the competitive insurance industry, so the use of new technological approaches such as artificial intelligence to communicate with customers and reduce their loss has become a focal issue in this industry. In terms of the purpose of this study, ...
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The turning away of customers is one of the main threats in the competitive insurance industry, so the use of new technological approaches such as artificial intelligence to communicate with customers and reduce their loss has become a focal issue in this industry. In terms of the purpose of this study, it is an applied-developmental study that seeks to provide a model for reducing customer churn using artificial intelligence-based customer relationship management in the insurance industry. From the point of view of the data collection method, it is a cross-sectional survey research. To achieve the goal, a mixed exploratory research design (qualitative-quantitative) was used. In the qualitative part, the theme analysis method was used, and in the quantitative part, the partial least square method was used. The community of participants of the qualitative part included the managers of Iran Insurance Company, 17 of whom were selected by purposive sampling method. In the quantitative part, the statistical population consisting of managers and experts of Iranian insurance and managers of Iranian insurance agencies in Gilan province, with the method of effect size and power analysis, 130 people were selected by cluster-random sampling method. The data collection tool in the qualitative part was semi-structured interview and in the quantitative part, researcher-made questionnaire. Research findings showed that technical factors of artificial intelligence, managerial factors of artificial intelligence and relationship marketing affect the management of relationship with customers. Customer relationship management improves customer experience by influencing service personalization and customer orientation. This factor by influencing customer loyalty, customer satisfaction and customer participation leads to the reduction of customer churn. Therefore, it was found that artificial intelligence is an infrastructure structure that, from a technical and managerial point of view, can help to improve customer relationship management in Iranian insurance agencies and reduce customer turnover and loss. IntroductionThe loss of customers is an alarming issue in the insurance service sector. In competitive and saturated markets such as the insurance industry market, customer turnover can always be expected and there are several reasons for it. This may be due to various reasons such as dissatisfaction, higher costs, low quality, lack of handling of complaints, lack of certain facilities and services, or concerns about privacy and other such issues. On the other hand, competitive prices, higher service quality, gifts, promotions, marketing campaigns, accessibility and competitors' driving activities can lead to the loss of customers. The economic value of customer retention in the insurance industry has compelled insurance companies to try to reduce the loss of their customers. In the highly competitive insurance industry, customer-oriented strategies must be set to minimize the rate of customer loss both in the short- and in the long- term. Customer churn reduction programs require large databases and big data analysis. The processing and analysis of such data to improve the ability of companies to achieve their desired goals requires the use of new technologies based on artificial intelligence (AI). The present study investigates the potential role of customer relationship management in reducing customer attrition rates in the insurance industry. The number of active companies in the insurance industry and the large number of agencies have greatly intensified the competition in this industry. In such a situation, customers have a lot of freedom in choosing, and this has increased the rate of customer churn in the industry. In such a situation, customer relationship management can be effective in reducing the loss of customers. In this regard, application of artificial intelligence can be very fruitful in understanding the needs, wishes and demands of insurance customers and providing correct, timely and pioneering responses. However, few studies have dealt with customer relationship management based on artificial intelligence in insurance market. This represents a research gap in the literature; hence there is an urgent need for further research. To address the aforementioned gap, this research presents a new framework that highlights the role of AI in reducing customer churn in the insurance industry. For model sophistication, in-depth interviews with business experts were conducted, by which the relevant variables were identified. Thematic analysis - as a robust qualitative method - was used to identify key components. For further validation, the proposed model was evaluated in the form of a survey. The main contribution of this research is to identify the key components of the application of AI in the insurance industry in terms of preventing customer churn. The authors believe that the findings of this study can have a combination of managerial and research implications because the model highlight themes and areas that have not received much attention in previous research. Literature ReviewCustomer churn:“Churn” is the equivalent of the polysemous morpheme Churn, which is composed of the two words Change, meaning change, and Turn, meaning rotation. Churn refers to the fact that a customer changes their service provider by turning away from their current service provider. According to another definition, churn or turning away refers to a customer changing service providers or a customer’s tendency to disconnect from an organization within a certain period of time (Forghani Dehnavi et al., 2022).Customer relationship managementCustomer relationship management refers to the methods, strategies, and technologies that marketing managers use to manage a company’s relationship with customers and gain more profit through customer satisfaction and loyalty (Sudirjo et al., 2024).Artificial intelligence:Artificial intelligence was introduced in 1950 with the study of Alan Turing, a British mathematician. Turing asked the question, “Can machines think?” After this initial question, artificial intelligence was formally proposed and defined as a new field of research at the Dartmouth Academic Conference in 1956. Then, in 1965, John McCarthy introduced the concept of artificial intelligence in its current common sense. Then came the first spring of artificial intelligence, when the field was rapidly applied in various fields (Strieth-Kalthoff et al., 2024). Methodology:This is an applied research carried out with inductive-analogical approach. An exploratory mixed design (qualitative-quantitative) was used to conduct the research. The participants in qualitative phase of the research included 17 experts (both university professors and market practitioners) having significant experience in the fields of technology-based marketing who were adopted by purposeful sampling using snow-ball method. Sample size was determined based on theoretical saturation during interviews.The statistical population in the quantitative part included managers and experts of Iranian insurance and managers of Iranian insurance agencies in Gilan province. To calculate the sample size, Cohen's power analysis rule (1992) and G*Power software were used. Using the rule of power analysis, a minimum sample size of 130 people was estimated with an effect size of 0.15 and a statistical power of 80%. A cluster-random method was used for sampling in the quantitative part. Data was collected by semi-structured interviews (qualitative phase) and self-administered questionnaires. The interview included 6 primary questions and was conducted in a semi-structured manner. The research questionnaire includes 11 main constructs and 63 items with a five-point Likert scale. Qualitative data were analyzed by theme analysis method using MaxQDA 20 software. Partial least squares using Smart PLS 3 software was used for validation of the model in the quantitative phase. Resultsbased on the results, 3 global themes, 11 organizing themes and 63 basic themes were obtained through axial coding.The results showed that technical and managerial factors of artificial intelligence and also relationship marketing affect customers relationship management. Customer relationship management improves customer experience by influencing service personalization and customer orientation. By influencing customer loyalty, customer satisfaction and customer participation, this factor leads to reduction of customer churn. Therefore, Iran's insurance agencies can prevent their customers churn by means of customer relationship management based on artificial intelligence capabilities. Discussion and ConclusionCustomer relationship management is a process of collecting and integrating information for effective and targeted use. This information can be related to customers, sales, effective marketing, sensitivity and market needs. Given the strong fluctuations in demand and increased competition in the markets, many companies are trying to create a strategy that integrates all components of an organization, shares information among all users and prevents unnecessary repetition of work. This philosophy creates an environment in the organization in which information is shared so that it is available to those who need it at the right time, meaning that all employees and everything are connected and connected to each other and the departure of one person from the organization will not cause anything in the organization to fall apart. Customer relationship management is a strategy that has been implemented with the help of technology, of course, it should be noted that customer relationship management is not just a software tool that improves the performance of the company, but rather customer relationship management is a philosophy that tries to create a strategy in this direction.Keywords: Customer Churn, Customer Relationship Management, Artificial Intelligence, Insurance Industry of the Country.
Data, information and knowledge management in the field of smart business
Mehri Chehrehpak; Abbas Tolouei Ashlaghi; Kamran Mohammadkhani
Abstract
Effective knowledge-based processes are essential for companies operating in the information technology industry. These
Effective knowledge-based processes are essential for companies operating in the information technology industry. These processes rely on the expertise of skilled workers and play ...
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Effective knowledge-based processes are essential for companies operating in the information technology industry. These
Effective knowledge-based processes are essential for companies operating in the information technology industry. These processes rely on the expertise of skilled workers and play a crucial role in the value chain of such organizations. Decision-making is a critical element of knowledge-based processes, highlighting the need to identify decision rules and models accurately. In this paper, we examine the process of identifying and deciding on proposed ideas in the software industry, analyzing decision logs from a leading software company. The Rough sets theory and fast Reduction algorithm are employed to provide a step-by-step approach to data analysis and decision mining. The algorithm identifies vital features used in decision-making and presents the decision model as if-then rules, utilizing existing equivalence rules between data. The results demonstrate that this model can significantly reduce the direct involvement of decision-makers and the duration of the decision-making process. In today's competitive landscape, effective knowledge-intensive processes are fundamental for companies in the information technology (IT) industry. These processes are highly dependent on the expertise of skilled professionals and are integral to value creation across various organizational fronts. Decision-making—considered a cornerstone of knowledge-intensive processes—underscores the necessity of accurately identifying decision rules and models. This paper focuses on the methods of identifying and evaluating proposed ideas within the software industry, specifically analyzing decision logs from a leading software company. By employing the Rough Set Theory along with the Fast Reduction Algorithm, we provide a detailed methodological framework for data analysis and decision mining. This structured algorithm identifies critical features relevant to decision-making and presents the resulting decision model in the form of if-then rules, which are derived from pre-existing equivalence relations among data. Our results illustrate that the implemented model can significantly lessen the direct involvement of decision-makers as well as the time taken in the decision-making process, highlighting a potential path for enhancing operational efficiency in IT firms.
Introduction
The field of information technology is constantly evolving, marked by rapid developments and intense competition. To navigate this landscape successfully, organizations must rely on effective knowledge-based processes that are essential for sustaining competitive advantages. These processes hinge on the expertise of skilled workers who play a pivotal role in various stages of product development and innovation.
This paper aims to illuminate the decision-making facets of knowledge-intensive processes in the context of new idea generation within software companies. By scrutinizing decision logs from a prominent software firm, we aspire to discern decision rules and models that could significantly optimize decision-making efficiencies, ultimately positively impacting innovation outcomes.
Research Questions
This research is driven by several key inquiries aimed at uncovering various dimensions of decision-making in IT innovation processes:
What methods can be employed to identify decision points in the innovation processes of IT companies?This question targets the analytical techniques used to pinpoint where crucial decisions occur during the innovation lifecycle.
How can critical decision-making features be identified within these organizations, and what are the characteristics of these features?Identifying these features assists in understanding what influences decisions, including both internal and external factors.
In what ways can structured procedures be developed to expedite and improve the decision-making processes in IT innovation?This question seeks to establish procedural guidelines that can streamline decision-making, allowing companies to react swiftly to new information and emerging market trends.
Literature Review
The importance of Business Process Management (BPM) and decision mining in enhancing organizational efficiency is well documented in the literature. Earlier studies have primarily focused on implementing process mining techniques across various sectors, including healthcare and manufacturing, to improve overall decision-making efficiency. However, there exists a relative scarcity of research that specifically addresses decision mining in the context of IT innovation processes.
This study builds on existing frameworks, particularly leveraging the Rough Set Theory and the Fast Reduction Algorithm. These methodologies facilitate a thorough analysis of decision-making features, enabling the development of a tailored decision model for the software industry. By filling this notable gap, our research generates insights that can be applied to enhance decision-making within knowledge-intensive sectors.
Methodology
This research employs a comprehensive case study methodology, focusing on a well-established Iranian IT firm with over 25 years of industry experience. Our approach is structured into several key phases:
Identifying Decision Points: We apply a four-stage model, as outlined by Bazhenova and Weske (2016), to systematically pinpoint decision-making instances throughout the innovation process.
Analyzing Decision Logs: In this phase, we extract and scrutinize decision logs to identify critical features that influence decision-making. This analysis involves various statistical and data mining methods to validate findings.
Utilizing Rough Set Theory and Fast Reduction Algorithm: Following feature extraction, we employ Rough Set Theory alongside the Fast Reduction Algorithm to develop a robust decision model. This model is articulated through if-then rules that encapsulate significant decision-making aspects.
Evaluating Model Effectiveness: To ascertain the model's effectiveness, we conduct an extensive analysis of the product development process within the company, assessing how well the model predicts decision outcomes.
Results
The results of implementing the proposed decision model revealed several significant features critical to decision-making processes:
Idea Relevance: The relationship of the proposed idea to existing business operations emerged as a crucial factor.
Idea Source: Determining whether the idea originated from internal staff or external consultants significantly influenced the decision-making progression.
Anticipated Customer Acceptance: Factors related to customer acceptance and assessments of the competitive landscape were also primary considerations in the decision-making process.
The model showcased a remarkable 91.5% accuracy rate in predicting decision outcomes based on the identified features, illustrating its effectiveness. More importantly, the implementation resulted in a pronounced reduction in the direct involvement of decision-makers and a considerable decrease in the duration required for decision-making processes.
Conclusion
The research findings underscore the potential of applying Rough Set Theory along with decision mining techniques to significantly enhance the efficiency of decision-making in IT innovation processes. By systematically identifying and modeling essential decision features, organizations can streamline operations, minimize redundant tasks, and improve the overall effectiveness of their innovation strategies.
This study contributes to the growing body of knowledge on decision mining in the software industry, offering a structured approach that can be adapted to various knowledge-intensive environments. Looking ahead, further research is needed to explore the adaptability of this model in larger organizations and diverse contexts, further expanding its applicability within the broader IT landscape.
The implications of this research extend beyond the immediate findings, suggesting that strategic implementation of structured decision-making models can enhance operational efficiency across various sectors. Future studies could investigate the scalability of these models in larger organizations and their applicability in other innovation-driven industries.
Keywords: Process Mining, Decision Mining, Rough Set Theory, Knowledge-Intensive Process, Information Technology.
Data, information and knowledge management in the field of smart business
mehran rezvani; Mehrdad Forouzandeh; kamal sakhdari
Abstract
Entrepreneurial entry is one of the most important stages of the entrepreneurial process that businesses can complete by taking advantage of the benefits of social media. To identify and analyze the role of social media in facilitating the entrepreneurial entry process of small and medium businesses, ...
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Entrepreneurial entry is one of the most important stages of the entrepreneurial process that businesses can complete by taking advantage of the benefits of social media. To identify and analyze the role of social media in facilitating the entrepreneurial entry process of small and medium businesses, the current research specifically focuses on the initial stage of starting a business. This research is applied in terms of purpose and descriptive-documentary in terms of research. This research is based on 1608 articles from the Scopus scientific database from 2016 to 2023. After the initial screening, we found 47 articles and finally 15 central articles for extracting findings. The findings show that social media, by providing facilities such as networking, digital marketing and access to information, play a central role in reducing the risk of entering the market, increasing awareness of business opportunities and building relationships with stakeholders. This research identified four main structures including social networking, brand management, customer acquisition and organizational learning, along with 11 key concepts. The proposed theoretical framework of this research can be used as a road map for entrepreneurs and a strong theoretical foundation for future research in this field.IntroductionEntrepreneurship is a social process where entrepreneurs utilize market opportunities with available resources. The process includes pre-startup (identifying opportunities), startup (business planning), and post-startup (investment development) stages. Successful entry into entrepreneurship requires information and access to resources, with social media emerging as a valuable tool for acquiring market information. Social media allows entrepreneurs to gain knowledge about customers, identify opportunities, and manage relationships effectively.Early startup stages involve proving product quality, attracting customers, and establishing a brand for growth. Factors like customer feedback, technology use, and digital skills are key to success. Networking plays a significant role in entrepreneurial success, with social media enhancing networking capabilities. Entrepreneurs can expand their networks, access resources, and manage relationships effectively using social media platforms.The use of social media has transformed how entrepreneurs interact, identify opportunities, and engage with stakeholders. Leveraging social media features can enhance business activities. However, there is a gap in understanding the functions and practices of social media in the entrepreneurial entry stage. It is essential for entrepreneurs to understand and utilize social media effectively to achieve their business goals in the rapidly changing and competitive environment.Research Question(s)The main questions of this research are:What is the function of social media in the entrepreneurial entry of small and medium-sized businesses?What factors affect the function of social media in the entrepreneurial entry of small and medium-sized businesses?Literature ReviewA comprehensive review of literature identifies multiple levels influencing entrepreneurial entry. At the individual level, factors such as education, knowledge, family background, and certain psychological traits—like the desire for independence and social contribution—play critical roles. Entrepreneurship education and cognitive skills are particularly significant in fostering entrepreneurial intentions. At the macroeconomic level, taxation stands out as a key determinant; variations in tax rates and policies can significantly influence the decision to pursue entrepreneurship. Additionally, geographical factors, including proximity to relevant industries and local resources, can either facilitate or inhibit entry into specific markets. Lastly, the required capital and skill levels in various industries also influence the ease of starting new ventures, with some markets favoring those with specialized skills.MethodologyThe method of this research is qualitative, using meta-synthesis. This research focused on analyzing articles from the Scopus scientific database to investigate the role of social media in entrepreneurial entry. Using a structured approach with multilayer filters, relevant studies were filtered based on source type, document type, language, subject area, and year of publication. A keyword search yielded 1608 articles, which were narrowed down to 47 based on relevance. Following a detailed examination of titles and abstracts, 15 articles were selected for their strong alignment with the research question. The analysis identified key concepts related to social media's functions in entrepreneurship, categorizing them into central functions such as communication, marketing, finance, strategic development, and value creation. To ensure the reliability of the findings, a coding agreement method was applied between two independent coders, resulting in a reliability coefficient of 86.6%, indicating very good reliability of the analysis.ResultsSocial media is crucial for entrepreneurs, providing networking and marketing opportunities. It helps create diverse networks, engage directly with customers, and gather competitive intelligence. By being present on social media, companies can receive feedback, turn customers into brand advocates, and boost loyalty and revenue. Positive customer experiences enhance brand image and awareness, increasing profitability. Social media platforms enable businesses to create cost-effective promotional content, engage with customers, and drive web searches through viral marketing. It benefits small and medium-sized enterprises by promoting repeat purchases and boosting revenue. Additionally, social media is valuable for crowdfunding campaigns and refining offerings based on customer trends. Overall, social media significantly influences entrepreneurial processes, enhancing effectiveness and opportunity recognition. It also empowers women entrepreneurs in developing nations by fostering innovation and networking opportunities, ultimately improving business performance and competitiveness.DiscussionThis study focused on how social media is used in the early stages of starting a business. It found that social media has 15 basic functions that can help entrepreneurs achieve their goals. By understanding and utilizing these functions, businesses can overcome challenges they face in the beginning stages. The study highlights the importance of paying attention to social media in entrepreneurship, as its impact can vary depending on factors such as company size, industry, and how it is used. While social media can have positive effects on entrepreneurship, it can also have negative consequences if not used properly. The functions of different social media platforms in entrepreneurship also differ based on various factors.ConclusionThe study aimed to create a framework for understanding the functions of social media in entrepreneurship. Social media was found to have many advantages in supporting entrepreneurial activities. The proposed framework can help entrepreneurs use social media effectively to achieve their business goals. Previous studies focused on specific functions of social media in entrepreneurship, but this study provided a more comprehensive overview. By considering variables like business size and industry, the study explored the complex interaction between social media and the entrepreneurial process. Future research could further categorize and explore the functions of social media in entrepreneurship, considering the element of time and expanding the scope for a more comprehensive framework.Keywords: Social Media, Entrepreneurial Entry, Small and Medium Businesses, Entrepreneurship.
Data, information and knowledge management in the field of smart business
Nahid Entezarian; Mohammad Mehraeen
Abstract
New technologies in the field of Industry 4.0 enable companies to enhance their business processes and customize products and services through the generation of new knowledge. The creation and sharing of this new knowledge depends on both the optimal use of Industry 4.0 technologies and interactions ...
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New technologies in the field of Industry 4.0 enable companies to enhance their business processes and customize products and services through the generation of new knowledge. The creation and sharing of this new knowledge depends on both the optimal use of Industry 4.0 technologies and interactions along the value chain. However, achieving business benefits is highly dependent on human resources and their digital skills and competencies. Therefore, companies approaching the Industry 4.0 paradigm should consider these new technologies as tools that facilitate the creation and sharing of new knowledge. They should pay attention to the digital skills and competencies required to manage this technological transformation and enhance internal competencies. The purpose of this research is to combine the results and findings obtained from qualitative studies, providing new insights from previous research. In this study, a meta-composite approach was used to investigate qualitative case studies, examining the relationship between knowledge management and Industry 4.0 capabilities in organizations. The results show that knowledge management capabilities in the field of Industry 4.0 are examined in two dimensions: business models and organizational innovation. This research also emphasizes that in order to address organizational challenges, knowledge management strategies and the maturity level of Industry 4.0 technologies within organizations must be understood.IntroductionIndustry 4.0, driven by digital technologies such as smart sensors, IoT, cloud computing, big data, and AI, holds significant importance in the realm of organizational knowledge management. It enables convenient access to vast repositories of data that can be meticulously scrutinized to drive improvements in processes. Moreover, Industry 4.0 seamlessly merges the physical and virtual domains, thereby enhancing both production processes and resulting products (Wilkesmann, 2018). This study endeavors to propose a model that seamlessly integrates knowledge management and Industry 4.0 to gain a competitive advantage. The researchers will utilize the Meta-synthesis method to identify capabilities and develop a new framework, thus contributing to a deeper understanding in this field.Literature ReviewThe theoretical foundations are categorized into two components: Industry 4.0 and knowledge management.2.1. Industry 4.0Industry 4.0 emerged in 2011 as the fourth industrial revolution, focusing on fully automated and intelligent production systems. It involves the integration of production systems through real-time information exchange and flexible production. The internet and related technologies play a crucial role in connecting physical objects, machines, and processes across organizations (Ghobakhloo, 2018). Industry 4.0 relies on data-driven decision-making and recognizes the value of real-time data utilization. It disrupts traditional competition and impacts various aspects of organizational strategy, business models, innovation, supply chains, production processes, and stakeholder relationships (Pozzi et al., 2023).2.2. Knowledge management strategies and approaches in Industry4.0Knowledge is essential for decision-making in implementing Industry 4.0 technologies. Industry 4.0 significantly influences knowledge management within organizations. These technologies facilitate knowledge management by enhancing existing knowledge and generating new knowledge. Knowledge sharing and storage are key components of knowledge management in the context of Industry 4.0 (Salvadorinho & Teixeira, 2021). The cost-effective and high-performance nature of Industry 4.0 technologies makes them suitable for storing and sharing knowledge. Industry 4.0 technologies enhance value creation through knowledge sharing within organizations and enable organizational innovation and competitive advantage maximization through knowledge management (Gupta et al., 2022).MethodologyThis research proposes Meta-synthesis as a suitable method for effectively combining the various factors involved in knowledge management capabilities and Industry 4.0 technologies within organizations. Meta-synthesis serves as a valuable instrument in formulating a comprehensive theory by systematically amalgamating these elements. The selection of the Hoon model (Hoon, 2013) for this research is based on its comprehensive and innovative nature in comparison to other Meta-synthesis models. It is characterized as an exploratory and inductive research design that integrates qualitative case studies to extend the findings of the original studies. Hoon's proposed Metasynthesis entails eight specific steps, which are briefly outlined below:Step 1 involves designing and framing the research question related to knowledge management capabilities in Industry 4.0. Step 2 includes searching for articles using specific keywords and selecting relevant research. Step 3 involves screening and selecting suitable texts based on inclusion criteria. Step 4 entails extracting and coding evidence from selected studies. Step 5 analyzes individual studies using a causal network technique. Step 6 synthesizes findings on an across-study level. Step 7 involves building theory from meta-synthesis.Results and DiscussionThe convergence of Industry 4.0 and knowledge management within organizational frameworks serves to amplify the influence of knowledge management on the performance of organizational innovation (Tortorella et al., 2022). This study furnishes valuable perspectives for formulating an adoption strategy and prioritizing tasks in the integration of Industry 4.0. It underscores the significance of knowledge dissemination in expediting the assimilation of Industry 4.0 and recommends a focus on cultivating affiliations with strategic counterparts. The development of internal capabilities and competencies stands as pivotal for meaningful engagement in knowledge dissemination for Industry 4.0. Effective knowledge exchange among organizations can offset the dearth of internal resources and knowledge during the adoption process. This study accentuates the cost-effectiveness of knowledge sharing as an alternative to external consultants. In sum, it furnishes invaluable insights for managers seeking to augment organizational innovation, fortify stakeholder associations, and attain a competitive edge in the landscape of Industry 4.0.ConclusionThe Meta-synthesis approach used in this study has limitations, including a smaller sample size of only 8 studies, which raises concerns about the generalizability of the findings. The reliance on a limited number of keywords for searching and identifying studies is another limitation. However, the study's analysis revealed similarities among the chosen articles, and the selection process followed the criteria set by Hoon (2013). The Meta-synthesis protocol allows for the development of causal networks, meta-causal network, and case comparison table, showing a wider context of knowledge management and Industry 4.0 capabilities in organizations. Future studies should encompass a wider scope, as organizations in the Industry 4.0 environment need to share and manage knowledge both internally and externally. The Meta causal network developed in this study can be used as a foundation for developing strategies that generate value and foster a competitive advantage in the realm of Industry 4.0.Keywords: Knowledge Management, Industry 4.0, Meta-Synthesis, Case Study.Figure 1. Meta-causal network of selected analyzed studies (research findings)
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.
Introduction
Today, 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 Review
Shrahi 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.
Methodology
In 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.
Conclusion
This 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 method
K-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.
Keywords: Relationship Management with Bank Customers, Clustering, RFM model, LRFM Model, Particle Swarm Algorithm, K-Means Method.
Data, information and knowledge management in the field of smart business
fateme abadi; Gholamreza Jamali; Ahmad Ghorbanpour
Abstract
AbstractSmart technologies have brought changes in the supply chain. This study was conducted with the aim of investigating the impact of the Internet of Things on the intelligent management of the supply chain, which evaluates the relationships between variables and their impact and effectiveness with ...
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AbstractSmart technologies have brought changes in the supply chain. This study was conducted with the aim of investigating the impact of the Internet of Things on the intelligent management of the supply chain, which evaluates the relationships between variables and their impact and effectiveness with the fuzzy cognitive mapping method. The statistical population is academic experts and active experts in the drug distribution company in Bushehr province. After identifying the components from the background of the research, an interview was conducted. Then the questionnaire was presented to 10 experts and experts and it was analyzed in several stages, and finally, the main factors of the use of Internet of Things in the supply chain were determined in 9 categories of criteria and 41 sub-criteria. The criteria include: intelligent management of inventory and warehousing, intelligent management of operations, intelligent management of information, intelligent management of products, intelligent management of costs, intelligent management of corporate productivity, intelligent management of customers and drug suppliers, intelligent management of sales and marketing, and intelligent management of the environment.The results showed that intelligent information management was obtained as the most important indicator; Because it affects all indicators. intelligent management of customers, intelligent management of sales and marketing, and intelligent management of operations are the second most influential. Therefore, managers of the drug distribution industry should use Internet of Things technology to intelligently manage information in their organization, improve relationships with customers, improve operations and focus on the sales process, and optimize supply chain processes and profitability. IntroductionThe fourth industrial revolution, through its smart technologies, has greatly affected the management models and traditional supply chain operations (Chen & et al., 2020). Supply chains must be smarter in order to overcome their problems and complexities, such as reducing uncertainty regarding demand and delivery time, poor flow of information, costs, product quality, communicating effectively with customers, etc. (Chbaik, 2022). Application of the mentioned technology in the supply chain in drug distribution industry will play a very important role toward efficiency and effectiveness. In this research, by examining the indicators of Internet of Things in the supply chain, the relationship between these indicators in the supply chain in the pharmaceutical distribution company have been studied. Literature ReviewInternet of Things (IOT) refers to the connection of sensors and devices with a network through which they can interact with each other and with their users. Internet of Things integrates various sensors, objects and smart nodes that can communicate without human intervention and currently has wide applications in smart networks, healthcare and transportation (Dadhaneeya & et all, 2023). Tavakli Moghadam and et al (2022) investigated the use of Internet of Things (IOT) in the food supply chain (FCS) in a research. By reviewing the literature, six basic functions obtained for this type of network including transportation logistics, food production, resource management, food safety, food safety, food quality maintenance and FSC transparency were obtained. Also, a clustering method was used. Disin (2022), investigated the barriers to the adoption of the Internet of Things in the healthcare supply chain in India with a fuzzy approach. In this research, it is stated that the Internet of Things plays an important role in the health care supply chain. It improves the quality of patient care, reduces the cost of medical procedures, maintains flawless operations, and supports clinical decisions. This research identified and analyzed the potential barriers that prevent the healthcare industry from adopting the Internet of Things. In this research, it is stated that the legal and regulatory standards and the lack of information technology infrastructure are the main obstacles affecting the adoption of the Internet of Things in the health supply chain. MethodologyThe statistical population of this research were all academic experts, managers and experts of drug distribution in Darupakhsh Company of Bushehr province, were familiar with the concept of Internet of Things and supply chain and had related work experience and bachelor's degree or higher. Their opinions were used to determine the importance of indicators. The statistical sample for determining the relationship between indicators using the Fuzzy Cognitive Map (FCM) method was 10 out of experts. After identifying indicators from previous studies, a questionnaire was provided to the sample, some less important indicators were removed from the questionnaire. In the second phase questionnaire was designed and then from the point of view of the sample, 41 key indicators were identified, which were classified into 9 categories and used in the fuzzy cognitive map method. Resultsfindings of this research were analyzed based on the process of creating a fuzzy cognitive map. The initial matrix of success for 9 main effective indicators in the intelligent management of the supply chain under Internet of Things technology with a case study in the drug distribution company in Bushehr province. Based on the value and points that 10 experts gave to these indicators in the range of 0 to 100, was formed and after several steps of calculation, we reached the final matrix which is related to the results.Table 1. Final MatrixIndicatorFactorC1C2C3C4C5C6C7C8C9Intelligent management of inventory and warehousingC1 0.860.85 0.810.94 0.93 Intelligent operation managementC20.86 0.98 0.740.670.94 0.58Intelligent information managementC30.850.98 0.780.740.650.930.83 Intelligent product management (pharmaceutical)C40.670.78 0.780.740.57Intelligent cost managementC5 0.74 0.77 0.830.82Intelligent management of corporate productivityC6 0.65 0.77 0.920.50Intelligent management of drug customers and suppliersC70.880.940.93 0.83 0.870.72Intelligent management of sales and marketingC80.930.83 0.740.830.92 0.78Intelligent environmental managementC9 0.58 0.820.50 0.78 Based on the results presented in the final matrix, a fuzzy cognitive map diagram is drawn. It can be seen that the intelligent information management index has the greatest impact on other indices. Then, three indicators of intelligent management of customers including intelligent management of sales and marketing, and intelligent management of operations were also ranked second in terms of influence. On the other hand, four indicators of intelligent management including operations, cost, sales and marketing and productivity are the indicators that have the most influence from other indicators, the highest correlation between the index of intelligent management of information and the intelligent management of company operations with a value of 0.98 and the lowest correlation between productivity intelligent management index and environmental intelligent management index was 0.50, which are examined and analyzed in the research results section. ConclusionAccording to the obtained results, the relationship between all the indicators of the use of the Internet of Things in the supply chain of the pharmaceutical industry is consistent and positive.With intelligent information management, the automatic decision-making process in the company is supported, and with rapid information cooperation in internal operations and cooperation with suppliers and customers, the drug distribution industry is able to respond to the environmental changes. Another influential indicator is the intelligent management of customers, which by using the Internet of Things in the drug distribution industry, succeeded in expanding online services and delivering products on time to the customers, focusing more on customer relationship management and receiving effective feedback on the disadvantages of products purchased by customers. Another influential indicator is the intelligent management of sales and marketing of products, which through an intelligent system to receive the needs of patients of medical centers and other drug applicants, lead to the improvement of the sales of the company's products and services, and respond to the market demand of pharmaceutical products and optimal management. Another effective indicator is the intelligent management of operations, which is optimized by using the Internet of Things in the supply chain processes of pharmaceutical companies in Bushehr province, helping to make the operations flawless and improve the production and delivery process, integrating internal, customer and supply processes, and cooperation and coordination takes place throughout the supply chain.AcknowledgmentsWe are grateful to all the experts who cooperated with the researchers in the process of data collection and favored us.Keywords: Intelligent Technologies, Intelligent Supply Chain Management, Internet of Things, Fuzzy Cognitive Map.
Data, information and knowledge management in the field of smart business
Ali Memarpour Ghiaci; Morteza Abbasi; Morteza Piri; Peyman Akhavan
Abstract
AbstractIn the digital age, blockchain technology is recognized as an operational innovation that is rapidly joining the field of supply chain and humanitarian logistics. Hence, blockchain technology has the potential to fundamentally change the field of humanitarian aid, but still relatively little ...
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AbstractIn the digital age, blockchain technology is recognized as an operational innovation that is rapidly joining the field of supply chain and humanitarian logistics. Hence, blockchain technology has the potential to fundamentally change the field of humanitarian aid, but still relatively little research has been published aimed at improving understanding of the various barriers to blockchain adoption in humanitarian logistics. The aim of this research is to provide an integrated framework for evaluating the barriers to blockchain adoption in the field of humanitarian logistics. To assess the barriers, integrated approach has been applied in three phases. In the first phase of this approach, based on the literature, 10 barriers to the adoption of blockchain in humanitarian logistics are identified and evaluated using the FMEA method. In the second phase, using the opinions of experts, the weights of the three factors are calculated. Then, in the third phase and according to the outputs of the previous phases, obstacles are prioritized using the proposed Z-ARAS method. In addition to assigning different weights to the three factors considering uncertainty and reliability in barriers is also considered in this approach through the theory of Z numbers. The proposed approach of current study was implemented in the evaluation of blockchain adoption barriers in humanitarian logistics. According to the results, the most critical barriers concern with integrating issues, risk of cyber-attacks, and technology risks. The results shown the capability and superiority of the proposed approach compared to other traditional methods such as FMEA and Fuzzy ARAS.IntroductionIn the context of the Fourth Industrial Revolution, advanced technologies are reshaping production and business models across various industries, offering new opportunities for enhanced competitiveness but also introducing challenges in terms of adoption and optimization (Wong et al., 2020; Khan et al., 2021). Notably, the convergence of advanced technology and humanitarian logistics is crucial, especially in addressing natural and man-made disasters (Ar et al., 2020; Dubey et al., 2020). This necessitates effective management and the combination of humanitarian logistics with blockchain technology, although this integration comes with multifaceted challenges (Baharmand et al., 2021).To address these challenges, we explore the Failure Modes and Effects Analysis (FMEA) method as a systematic approach to identify and assess barriers and risks. Traditional FMEA approaches rely on subjective evaluations, which introduce uncertainty into the results. In this context, our research aims to introduce an innovative approach that addresses these limitations by integrating the ARAS method and Z-numbers theory. This approach allows for more reliable prioritization of barriers related to blockchain technology adoption in humanitarian logistics, enhancing the robustness and effectiveness of decision-making processes. In this extended abstract, we present our method and compare its outcomes with traditional approaches to prioritize barriers and risks in blockchain technology adoption within humanitarian logistics. Also, the barriers to blockchain technology adoption in humanitarian logistics and how to prioritize these barriers are among the main research questions. Literature ReviewBlockchain technology is gaining traction in supply chains due to its diverse applications and unique advantages. As supply chains face increasing disruptions, blockchain technology adoption can address challenges and enhance performance (Akhavan & Philsoophian, 2022; Hald & Kinra, 2019). Blockchain structures data into interconnected blocks, ensuring the security and transparency of transactions (Akhavan & Namvar, 2021; Azizi et al., 2021). Blockchain technology is appealing for supply chains due to four main characteristics: encouraging data sharing, minimizing fraudulent transactions, ensuring data immutability, and providing asset security (Babich & Hilary, 2020; Cole, Stevenson, & Aitken, 2019; Rahimi, Akhavan, Philsofian, & Darabi, 2022).Research on blockchain applications in humanitarian logistics primarily focuses on motivations, such as improved collaboration, transparency, trust, cost reduction, intermediary removal, and shared participation (Baharmand, Maghsoudi, et al., 2021; Seyedsayamdost & Vanderwal, 2020). However, more research is needed in this area (Sahebi, Masoomi, & Ghorbani, 2020). Existing studies have identified barriers to blockchain adoption in humanitarian supply chains, including financial constraints, senior management support, organizational readiness, technological complexity, infrastructure, technology compatibility, and regulatory issues (Baharmand & Comes, 2019).Multi-criteria decision-making methods (MCDM) have been used to improve FMEA's performance (Ghoushchi et al., 2021; Ghoushchi et al., 2022). These approaches often combine FMEA with methods like GRA, BWM, TOPSIS, and AHP in various fuzzy environments. Such integrated methods have been proposed for barrer identification in the context of blockchain adoption (Li, Li, Sun, & Wang, 2018; Lo & Liou, 2018; Kolios, Umofia, & Shafiee, 2017; Carpitella, Certa, Izquierdo, & La Fata, 2018; Sayyadi Tooranloo & Ayatollah, 2017). Additionally, unified methods like MOORA have been applied to address specific challenges in different contexts (Jafarzadeh Ghoushchi, Memarpour Ghiaci, et al., 2022).The literature indicates a gap in research on blockchain applications in humanitarian logistics, as most studies focus on business supply chains. Using insights from business supply chains to inform decisions in humanitarian logistics can be misleading, given their fundamental differences (Baharmand, Saeed, Comes, & Lauras, 2021). Consequently, this study aims to address these gaps by proposing an extended FMEA approach based on MCDM methods to identify and prioritize barriers to blockchain adoption in humanitarian logistics, using Z-numbers theory. MethodologyThe proposed approach of this research is presented, utilizing FMEA and Z-ARAS methods for barrier assessment. The proposed approach consists of three phases. In the first phase, barriers are identified, and the values of the criteria are scored by the FMEA team using linguistic variables from Z-number theory. In the second phase, considering the differences in the importance of criteria, the weight of each criterion is determined based on expert opinions as triangular fuzzy numbers. In the third phase, based on the results of the first and second phases, barrier prioritization is performed while taking into account the criterion weights, using the Z-ARAS method. Unlike the conventional fuzzy ARAS method, the Z-ARAS method can consider uncertainty and reliability for each criterion concerning the options. In this method, after determining the decision matrix, which comprises fuzzy numbers and reliability values (Z-numbers), these values are transformed into triangular fuzzy numbers, and then the Z-ARAS method is executed. ConclusionHumanitarian logistics is a relatively new area of research. The impact of humanitarian logistics is crucial, as it saves lives and improves conditions. Research has shown that effective humanitarian logistics is a key driver for the performance of humanitarian organizations. Currently, there exists a significant gap in humanitarian logistics research, particularly in developing countries, between theoretical research and practical implementation.The adoption of blockchain technology will play a pivotal role in the future development of humanitarian logistics. Therefore, the identification and prioritization of barriers to adopting blockchain technology in humanitarian logistics have gained increasing importance. In this study, an enhanced approach to FMEA is proposed using the Z-ARAS method. Based on the results obtained, "Integration Issues," "Cybersecurity Risks," and "Technology Risks" have been chosen as critical barriers to blockchain technology adoption in humanitarian logistics and are given priority for mitigation and resource allocation. The use of this enhanced approach has addressed some of the limitations of the conventional FMEA method, such as not providing a complete ranking of options. While the developed FMEA approach using the Z-ARAS method is a promising and reliable method, it has limitations. This model may be complex for decision-makers, and it is expected that software tools will be developed to assist decision-makers using this enhanced approach. Additionally, the interaction and impact of barriers were not discussed in this study. Future work can analyze the interplay between barriers to identify critical barriers. Furthermore, researchers can consider multi-criteria decision-making methods like PIPRECIA, SWARA, BWM, and others to determine the importance and weights of criteria. Developing the FMEA method using multi-criteria decision-making methods such as MARCOS, EDAS, CoCoSo, and others for ranking barriers in uncertain environments, including pythagorean, q-rung, and spherical fuzzy scenarios, is also suggested for future studies. Regardless of the issue used for implementing the proposed approach in this research, this approach can be applied to identify and analyze risks and failure modes in various scenarios..Keywords: Blockchain, Humanitarian logistics, FMEA, Multi-criteria decision-making, Z-number theory.
Data, information and knowledge management in the field of smart business
Ehsan allah Khoshkhoy Nilash; Mansour Esmaeilpour; Behrooz Bayat; Alireza Isfandyari Moghaddam; Erfan Hassannayebi
Abstract
Banks have complex and long-term processes for facilities, including many stages, control points and approvals. Continuous analysis of such processes is increasingly important for continuous improvement and gaining knowledge from them. The main goal of the present research is to provide a comprehensive ...
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Banks have complex and long-term processes for facilities, including many stages, control points and approvals. Continuous analysis of such processes is increasingly important for continuous improvement and gaining knowledge from them. The main goal of the present research is to provide a comprehensive methodological framework based on process mining and data mining regarding the analysis of fixed capital facility processes. The method used in the present research is derived from the techniques of process mining and data mining based on the event log of the facility system, an active bank in Iran. This method includes nine phases of initiation, preparation, inspection, exploration and analysis, evaluation, multi-dimensional analysis, prediction, review of results and improvement. Among the results of the present research is the extraction of the real process model, identification of bottlenecks, frequent activities in a case and all cases and process variant. In addition to this identification of branches and people with the most important roles and based on data features in reducing the time of payment of facilities, the analysis of the process from dimensions such as the province was one of the other findings. One of the initiatives of the present research was the use of data mining to predict the payment time of facilities. In the comparison of various methods, the decision tree algorithm had the best performance with 72% accuracy. In addition to identifying deviations, based on the creation of event log and its analysis, the improved process of extracting which showed a 67% improvement.
Introduction
Today's businesses benefit from a number of processes in order to earn more income and better services (Dakich et al., 2018). They are looking for processes that have better and more successful performance in order to achieve organizational goals and optimal use of resources in the operational environment (Van Der Aalst, 2016). Therefore, continuous analysis of processes for continuous improvement in organizations is very important.
Considering that the processes of providing facilities, especially fixed capital, are very effective in the creation and development of industrial, mineral and tourism units, having knowledge of them is of increasing importance. One of the efficient and effective methods for analyzing and improving business processes is process mining. With the help of its various concepts and techniques this method provides useful knowledge for the detailed examination of processes and how they are realized.
On the other hand, the efficient method of data mining, which provides the possibility of extracting knowledge from historical and predictive data (Basha, 2017), can be combined with the process mining method. With the investigations carried out, the methodological framework in order to provide process-centric and data-centric analysis, including the discovery of the real process model of facility payment, performance analysis of such processes, analysis of process varints, multi-dimensional process-centric analysis, payment time prediction, recommendations for improvement and process improvement based on event log simulation is not presented. Also, due to the novelty of the process mining method, the purpose of this research is to provide a comprehensive methodological framework using these techniques, concepts and tools of process mining in combination with data mining methods regarding the analysis of business processes with the study of fixed capital facilities processes.
Research Question(s)
How to provide a methodological framework for the analysis of fixed capital processes by using the techniques and concepts of process analysis and data mining methods?
Literature Review
In Table No. 1, a number of related studies are compared with each other.
Table 1. Summary of the research conducted
Research
Business
Components used
Event log
Miners
(Urrea-Contreras et al., 2017)
SME organizations
Event Log extraction, discovery, conformance checking, extend model, and return integrated model
software development system (JIRA)
inductive
(EL KODSSI & Sbai, 2024)
Smart environments
Data selection, data transformation, generation of event log, discovery, enhancement
Unstructured sensor generated data
MDA and machine learning
(Rashed et al., 2023)
hospital
Preprocessing, model discovery and analysis
Heart surgery unit in a hospital in Egypt
heuristic, inductive, ILP and ETM
(Erdogan & Tarhan, 2022)
Emergency
Determining goals, extracting event log, pre-processing, applying multi-perspective process mining, analysis, recommendation for improvement and evaluation of results.
Emergency system log
fuzzy
(Pan & Zhang, 2021)
Construction project
Event log generation and preparation, discovery and validation
Example of a construction project
Fuzzy and inductive
(Lorenz et al., 2021)
Production business
Mapping, analysis and improvement
Production business event log
fuzzy
(Augusto et al., 202)
Healthcare trends
Planning, data extraction, data processing and evaluation
Patients in Victoria, Australia
fuzzy
(Pang et al., 2021)
Acute care and treatment processes
Coding and categorizing activities, extracting and filtering event log, discovering and improving the process model and performance analysis
Stroke care process
IDHM miner, alpha, fuzzy and heuristic
(Ramos et al., 2021)
ERP configuration, intelligent agriculture and computer configuration
Extract configuration event log, control and clean data based on feature model, build data clusters and discover related workflow.
Greed, hierarchy and genetics
A number of studies are not comprehensive in using the concepts of data mining and process mining. Some of them lack features such as multidimensional process centric analysis, event log simulation for improvement, evaluation of results with field specialists and so on. Comparing the studies, each of these cases can be expressed as a research gap. It is also necessary to consider all the components and phases as a methodological framework as another research gap.
Methodology
The method used in the present research is based on the techniques, concepts and methods of the process mining in its manifest (Will van der Alast et al., 2011). In this research, the event log of the fixed capital facility system of one of the active banks in Iran has been used. The proposed framework includes nine phases of initialization, preparation, inspection, analysis, evaluation, process centric analysis, prediction, transfer results and finally improvement. Figure 1 depicts the mentioned methodological framework.
Figure 1. The mentioned methodological framework
Results
Process models were discovered based on alpha, alpha++, heuristic, genetic, fuzzy and inductive techniques. By comparing inductive and fuzzy model, fuzzy model is very effective due to less edge filter and coverage of all activities. Process bottlenecks, people and branches with the most important roles were identified.
The heuristic algorithm with a value of 0.833 had the best performance in the average values of the quality indicators of the process model. In Figure 2, the mentioned methods are compared.
Figure 2. Comparison of miners
Analyzing the impact of data features with a target throughput time of 271 days, according to the dimensions of the Civil Partnership Bases contract, Riyal Civil Partnership Contracts and SME customers had the greatest impact in reducing the process throughput time.
The J48 decision tree algorithm had the best performance with 72% accuracy compared to all the data mining methods used.
Figure 3. Results of data mining analysis with J48 algorithm
203 records were used to simulate new event data. The results of the analysis showed a 67% improvement.
Keywords: Fixed capital processes, methodological framework, event log, process mining, data mining.
Data, information and knowledge management in the field of smart business
Mohsen Aazami; Mohaddes Nadershahi; Ali Asghar mobasheri; Sayedeh Nahid Hosseini
Abstract
The current research was conducted with the aim of designing a model for using cloud computing in Small and medium-sized enterprise. This study is a developmental in terms of its purpose and it is a qualitative research in terms of the nature of data collection and analysis, and was conducted using the ...
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The current research was conducted with the aim of designing a model for using cloud computing in Small and medium-sized enterprise. This study is a developmental in terms of its purpose and it is a qualitative research in terms of the nature of data collection and analysis, and was conducted using the grounded theory method. The statistical population consists of experts and entrepreneurs in the field of handicrafts and university professors of Kermanshah city, among which 14 people were selected as sample members by snowball sampling. semi-structured interviews was used for data collection. The results show that improving competitive advantages and improving operational processes explain why cloud computing should be used in these Enterprise. The findings also indicate that cultural-management facilitators, infrastructural facilitators and facilitators related to cloud computing are among the factors that can act as contextual factors. In addition, two categories of intervening factors (promoting and inhibiting factors) can affect the use of these technologies in these enterprises. The strategies of using cloud computing are also identified at two enterprises and environmental levels, and the consequences of using these technologies are also identified in four categories of operational, managerial-executive, entrepreneurial and competitive consequences.
Introduction
Throughout history, small and medium-sized enterprises (SMEs) have always been considered as a place for job creation, transformation and innovation, and they have drawn the attention of many economic development policy makers around the world. On the other hand, the increasing global changes in recent years have strongly affected the environment of SMEs and made their stability and resilience dependent on the use of new technologies. In this new situation, SMEs should be able to build their business processes based on new technologies such as cloud computing. The right use of cloud computing not only increases the accuracy and reliability of the operations of SMEs such as handicrafts, but can also lead to improved services, reduced costs, and improved competitive advantage. Nevertheless, the use of cloud computing technology in handicraft businesses requires detailed and scientific studies in order to explain the mechanisms affecting this process. However, the review of literature related to the subject shows that the use of cloud computing in handicraft businesses has not received the attention of researchers so far and And the questions related to this topic are unanswered questions. Therefore, the current research has been carried out in order to design a model for using cloud computing in handicraft businesses and to answer these questions.
Research Questions
Why should cloud computing be used in craft businesses?
What factors affect the use of these technologies in handicraft businesses?
What strategies are needed to use cloud computing in SMEs such as handicrafts?
What are the consequences of using cloud computing in crafts businesses?
Literature Review
2.1. SMEs
Small and medium businesses refer to businesses that employ less than 250 people. These businesses are one of the most important elements of the global economic system, which play a very important role in improving the economic situation of different countries. Handicraft businesses are also included in the category of SMEs that play an important role in improving the living standards of local communities by creating employment and increasing the income of local residents.
2.1. cloud computing
Cloud computing has its roots in communication technologies such as the Internet, networking, virtualization, and the like, and in fact, it is an evolutionary process of communication and information technologies for which no standard and universal definition has been provided so far. Cloud computing is a new method of processing in which scalable and often virtualized resources are provided as processing services through communication networks such as local area networks and the Internet. The characteristics of cloud computing, such as universality and freedom from time and place limitations, scalability, flexibility, ability to share resources and pay per use, distinguish it from common communication technologies, facilitate its use for businesses, especially sm SMEs.
Methodology
This study is an exploratory qualitative research that was conducted using grounded theory, which is a systematic method for conducting qualitative research. The statistical population of the research consists of experts and entrepreneurs in the field of handicrafts and university professors of Kermanshah city. Among them, 14 people were selected as sample members by non-probability (targeted) snowball sampling method. Individually semi-structured interviews were used in order to collect data and theoretical saturation rule was in order to select the number of interviewees (sample size).
Results
In the process of data collection, after conducting each interview, the collected data were carefully checked and the appropriate phrases for the purpose of the research were extracted from the topics. After that, the extracted phrases were examined and coding of these phrases and identification of primary themes was done. In the following, all the created codes (initial themes) were reviewed and while removing duplicate codes and merging similar codes, 66 final codes (themes) were identified. Then, by categorizing the final codes (themes) with semantic and conceptual commonality, the basic themes were identified. Then, in the axial coding stage, the basic themes were categorized in the form of grounded theory dimensions. After the end of the axial coding, in the process of selective coding (the third stage of the grounded theory), while creating a connection between the main themes (the six dimensions of the grounded theory), the paradigm model of the research was developed in the format of Figure 1.
Figure 1. the paradigm model of the research
Causal factors
* Competitive factors
* Improving operational processes
Strategies
* Business level strategies
* Environmental strategies
contextual factors
* Cultural-managerial factors
*Infrastructure factors
*Characteristics of cloud computing technology
consequences
* operational
* Managerial-executive
* Entrepreneurial
Intervening factors
* Promoting factors
* inhibiting factors
central phenomenon
using cloud computing in SMEs
Conclusion
The results can provide practical guidelines for managers and planners at the micro (business) and macro (economic policy) levels. The identified causal factors can increase the awareness of managers and planners regarding the necessity of using cloud computing in SMEs and thereby increase their mental readiness to accept these technologies. Contextual factors can also improve the awareness of business managers regarding the prerequisites of using cloud computing in businesses and introduce them to the areas that need to be strengthened in this regard. Identifying the intervening factors also increases the awareness of the policy makers of the economic system regarding the factors affecting the use of cloud computing in SMEs. The strategies also describe how to create better conditions for the use of cloud computing at the micro (business) and macro (environmental) levels for managers and planners and introduce the necessary measures to them.
Keywords: Cloud Computing, Small and Medium-Sized Enterprises, Handicraft Enterprises, Grounded Theory.
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.
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.
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.
Data, information and knowledge management in the field of smart business
Majid Sabet Rasekh; Mehdi Salimi; Ghasem Rahimi
Abstract
The aim of the current research was to provide a world-class information systems development model using the balanced scorecard approach in sports organizations. The current research is practical in terms of purpose; In terms of how to collect information, it was a survey. The statistical population ...
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The aim of the current research was to provide a world-class information systems development model using the balanced scorecard approach in sports organizations. The current research is practical in terms of purpose; In terms of how to collect information, it was a survey. The statistical population of the research was made up of the employees of all 31 general sports and youth departments of the country's provinces (5882 people) and the statistical sample was selected using the Karjesi and Morgan table of 361 people. To collect data, a researcher-made questionnaire was used according to the balanced scorecard approach (4 components and 48 items). The validity of the questionnaire was confirmed by 10 sports management professors and the reliability was 0.86, which indicated its good reliability. Data analysis was done using confirmatory factor analysis and structural equation modeling with PLS software. The findings from the analysis of the conceptual model of the research show that the development model of world-class information systems in sports organizations, in the financial perspective using 5 indicators, in the customer perspective with 12 indicators, in the business processes perspective with 14 indicators and in the growth perspective and learning was confirmed with 17 indicators. Therefore, it is concluded that the development of world-class information systems in sports organizations by increasing efficiency and effectiveness will improve organizational productivity and be considered as a sustainable competitive advantage. IntroductionToday, work processes are increasingly performed with high complexity, multitasking and time pressure. Among these organizations, sports organizations need more flexible information systems due to their communication and interaction with different stakeholder groups and their geographic scope is both national and international. One of the important functions of organizational information systems, in addition to the flow and integration of information throughout the scope of an organization, is the sharing of relevant and required organizational information with stakeholders and other related organizations; And due to the interconnected nature of some organizations and the important role of stakeholders in organizational growth and development, it is very important.Considering the advantages mentioned for information systems, most organizations have now realized that the use of these systems in all economic and social fields is an inevitable necessity. Physical education and sports are not exempted from this rule, so one of the fields that need to use these information systems for transformation is the country's sports department, for this purpose, the current research seeks to examine the question that the system evaluation model How is the world-class information in the country's sports organizations? Literature ReviewIn a research, Jafarzadeh et al. (2019) investigated the future research of information technology infrastructure in sports organizations and by presenting a model, they stated that managers of sports organizations should pay attention to the identified variables of the optimal infrastructure path in the future. Technology, such as technology knowledge, network communication, technology management, etc., emphasize this issue and improve it. In another study, Najafi and Ghasemi (2019) identified the main indicators and calculated the performance efficiency of information systems and knowledge management in the oil industry and found its position in this industry to be better than other industries.Also, in their research, Salimi and Tayibi (2022) investigated a model of information systems in sports organizations and examined the variables of system quality, information quality, service quality, usability, user satisfaction and net profit, which The difference between this research and the current research is in the model that is evaluated. Norton and Kaplan (2021) also investigated the importance of the balanced scorecard method in a research and called it a revolutionary tool for realizing the mission of organizations and more than an evaluation system, as a management system that can use all energy, abilities, knowledge and skills. Employees are introduced to achieve the strategic goals of the organization. Benbiya et al. (2020) and Sora et al. They know a great help to solve these complications. Boranbayu et al. (2020) also evaluated the reliability of information systems using multi-criteria decision-making and its information security risks in a study and provided solutions to find and neutralize risks. MethodologyThe current research is practical in terms of purpose; And in terms of method, it is placed in the category of survey descriptive research, which is specifically based on structural equation modeling. The statistical population was made up of the employees of all 31 general departments of sports and youth in the provinces of the country (Iran) (this number was estimated to be 5882 people); And the sample size was considered 361 people based on the table of Karjesi and Morgan, with maximum confidence. For sampling, the provinces of the country were divided into 5 geographical regions, and in each region, one general office was randomly selected as a sample and 73 questionnaires were distributed in that office. Due to the geographical dispersion of the selected general sports and youth departments (5 departments from five different geographical regions of the country), as well as the communication limitations caused by the corona disease, the questionnaires were sent in person and through an electronic address (or WhatsApp application) and etc. were distributed (a total of 73 questionnaires were distributed in each General Directorate of Sports and Youth, which was a total of 365 questionnaires and 361 questionnaires could be examined). In this research, the tool used to collect data was a researcher-made questionnaire. For this purpose, with the help of theoretical literature and existing research background, including reliable sources and instructions issued by the Ministry of Sports and Sports and Youth Departments, the indicators of the questionnaire were designed according to the balanced scorecard approach; which includes 4 general components and 48 items: the financial perspective in the development of the organization's information systems (5 items), the customer's perspective in the development of the organization's information systems (12 items), the perspective of internal processes in the development of the organization's information systems (14 items), the perspective Learning and growth in the development of organization information systems (17 items). The validity of the questionnaire was accepted and confirmed by 10 sports management professors after removing, adjusting or modifying some questions, and its reliability was confirmed using Cronbach's alpha coefficient of 0/86. At the end and after data collection, using confirmatory factor analysis and structural equation modeling with the help of Smart PLS software, the construct validity was confirmed and the research model was explained. ConclusionThe existence of appropriate information systems in the country's sports organizations, which have a wide range in the provinces and cities and also include many financial and non-financial resources, can be beneficial in the field of education, learning and organizational growth. also played a very important role and by providing various information to the organization's human resources, it helped to perform their job duties with better and more quality, increased the speed of performing duties and also assessed the training needs of jobs in the future. turn on another part of the findings from the analysis of the research model states that one of the most important aspects of the balanced scorecard in the investigation of the information systems of sports organizations is the customer's perspective; Because the existence of loyal customers is significant and valuable, which primarily gives credibility to an organization and causes its establishment, stability and growth; Also, the existence of information systems in various organizations, including sports organizations, which have customers from different strata of people with different ages, economic status, and social status, and they have different demands and expectations, is very important, and obtaining their maximum satisfaction is achieved when There should be more transparency in various organizational and executive stages, which can help attract more customers in addition to retaining customers.The perspective of internal processes is also another aspect investigated in the balanced scorecard approach, which the findings from the analysis of the research model show that the existence of information systems in sports organizations, which, like many other organizations, are subject to changes and developments. are located globally, it can examine various processes that affect customer satisfaction such as time, quality, employee skills and productivity in general, and identify its competitive advantages in different sectors and with quantitative measurements and clarify and improve the different quality of this issue with transparency.Keywords: Balanced Scorecard, World Class, Sports Organizations, Information Systems.v
Data, information and knowledge management in the field of smart business
seyed rasoul hoseini; sahel Farokhian; Hadi Taghavi
Abstract
IntroductionCurrent global statistics indicate that 80% of startups fail within a short period, with one of the primary reasons being weak branding strategies. Startups often lack precise knowledge of branding, which increases the risk of failure. To reduce this risk, marketers need a phenomenon called ...
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IntroductionCurrent global statistics indicate that 80% of startups fail within a short period, with one of the primary reasons being weak branding strategies. Startups often lack precise knowledge of branding, which increases the risk of failure. To reduce this risk, marketers need a phenomenon called co-creation branding.Branding in startups can increase access to suppliers, customer purchases, and innovative business models (Drakoulis & Lipovsek, 2015). Despite these advantages, startups face challenges such as gaining consumer trust, creating demand for their products and services, establishing an identity, and providing unique and differentiated value to consumers (Sonja et al., 2022). Therefore, to reduce these challenges and the risk of failure, marketers need co-creation branding in startups (Bonamigo et al., 2022). Co-creation branding involves active customer and company participation and interaction to improve brand image, increase brand value and awareness, and ultimately increase customer loyalty, achieving a competitive position in the market (Dehdashti Shahrokh et al., 2022).Unfortunately, very few studies have been conducted on both co-creation branding and startups, and extensive research is needed (Wong & Merrilees, 2005; Lagerstedt & Mademlis, 2016). Therefore, this research aims to identify the factors affecting on co-creation branding in startups. The main question of this research is defined as follows: What are the factors affecting on co-creation branding in startups?Literature ReviewThe literature review of startups offers various definitions for the term. For instance, Avnimelech and Teubal (2006) define startups as young companies with advanced technology whose primary activity, from idea to initial sales, lasts between one to five years.Brand co-creation is a recent trend in branding (Hatch & Schultz, 2010), which is largely based on the dominant logic of service (Vargo & Lusch, 2008) and co-creation of value (Prahalad & Ramaswamy, 2004), starting with the identification of customer value creation processes (Juntunen, 2012). Co-creation leads to offering more suitable products and services to consumers and encouraging their participation (Nadeem et al., 2020). The theory of brand co-creation assumes that the consumer is no longer a passive brand buyer but desires and seeks active participation in creating brand experiences (Kamboj et al., 2018), and therefore, customers can play a vital role in determining the success of brands. Brand co-creation begins with the relationship between shareholders and customers (Prahalad & Ramaswamy, 2004; Snyder, 2019), where shareholders define and create their brand identity through this relationship. Finally, it can be said that brand co-creation, in addition to strengthening a company's innovation capability, is also a reliable way to enhance brand relationships (Chang & Hsieh, 2016).Broeke and Paparoidamis (2021) demonstrate in their research that the co-creation of brand value occurs when customers are more sensitive to quality and less sensitive to price, and there is high demand for the product. Under such conditions, product quality is enhanced, and the company's flexibility increases. Nadeem et al. (2020) show in their research that social support affects ethical perception, and both are effective in co-creation. Ethical understanding also has an impact on consumers' trust, satisfaction, and commitment. However, trust and commitment do not have a significant impact on the co-creation of value. Tajvidi et al. (2020) demonstrate that concerns about privacy can disrupt the effects of brand co-creation, and social support, quality of relationships, and information sharing on social media have a positive impact on consumers' intention to co-create brand value on social media. Additionally, there is a meaningful relationship between customer participation in brand communities on social media and the quality of the relationship.MethodologyThis study is objective in nature and employs a qualitative approach. Its aim is to identify the factors that affect co-creation branding in startups. To achieve this, a meta-synthesis approach is used to examine existing articles in the field and extract the relevant factors. The statistical population of the research is credible and relevant articles published between 2007 and 2022 (a 15-year time span). Meta-synthesis involves reviewing previous studies and reframing concepts through interpretive integration of previous results. In this research, the seven-stage Sandelowski & Barroso (2006) method is used to carry out the meta-synthesis, as it is the most commonly used method for meta-synthesis in recent university research studies.ResultsThis research conducted a systematic review of 41 research studies to identify the factors influencing co-branding in startups. The meta-synthesis method was used to analyze the research literature. After studying and extracting text, key codes were clustered using MAXQDA software and organized into concepts and components. Ultimately, the factors influencing co-branding in startups were extracted and classified into four themes, eight concepts, and 33 distinct codes. These themes include environmental factors (financial and social factors), strategic brand management factors (brand value and brand creation), marketing factors (promotional activities and customer-related factors), and individual entrepreneurial factors (entrepreneurial personal characteristics and entrepreneurial skills).Discussion and ConclusionThe objective of this research is to identify the factors that influence co-branding in startups using a meta-synthesis method. To accomplish this objective, the scattered factors mentioned in various studies and case studies in this field were collected and classified into similar categories as concepts and themes using the meta-synthesis method and following the seven steps proposed by Sandelowski and Barroso. Startups can fulfill their responsibility and duty to society by engaging in activities that help the community, which has a significant impact on co-branding in the startup ecosystem (Kennedy & Guzman, 2016). Moreover, the social position of companies has been shown to influence co-branding (Twrsnick, 2016; Kennedy & Guzman, 2016). The availability of financial resources has a critical impact on co-branding activities in startups. Financial performance in this context refers to the extent to which the resources under the company's control generate profitability, which is vital for accepting and developing co-branding programs in the future. Therefore, it is considered one of the influential factors (Hatch & Schultz, 2010; Huang & Lai, 2011; Todor, 2014; Setiyati & Wijaya, 2015; Du Plessis et al., 2015; Tavares, 2015; Twrsnick, 2016; Kennedy & Guzman, 2016). The process of brand creation refers to a set of factors that lead to the development of a brand, encompassing brand design, brand strategy, brand identity, brand positioning, and brand objectives. These factors have been examined in most studies conducted in this area (Spence & Essoussi, 2008; Bresciani & Eppler, 2010; Bergström et al., 2010; Huang & Lai, 2011; Dai & Pietrobon, 2012; Sonja et al., 2022). Understanding the value-creating factors of a brand is a requirement for creating a strong brand. A brand's value is defined as a set of assets related to the brand name and company symbol that depend on the name or symbol of a brand and the increase in value created by the company's products or services. The value-creating factors of a brand include brand awareness, perceived brand quality, brand associations, brand image, brand experience, brand value, brand trust, brand commitment, and brand love (Boyle, 2007; Carvalho, 2007; Spence & Essoussi, 2008; Hamidi et al., 2021; Sonja et al., 2022; Bahagir et al., 2022). Promotional activities are all actions taken to raise awareness and persuade customers and the target audience to use a product or service and represent the fourth element of the marketing mix (Hagili et al., 2017; Kamboj et al., 2018; Rialti et al., 2018; Tajvidi et al., 2020; Sonja et al., 2022; Bahagir et al., 2022). To implement and execute the co-creation approach, companies create their own channels to establish connections with customers, which is essentially the fundamental aspect of co-creation, involving individuals' participation in creating valuable experiences together. By employing this approach, companies cause customers to feel a sense of belonging to the brand and develop loyalty towards the brand (France et al., 2015; Setiyati & Wijaya, 2015; Du Plessis et al., 2015; Twrsnick, 2016; Kauffman et al., 2016). Previous research has shown that the personal characteristics and traits of entrepreneurs have an impact on their success and the success of their startup companies. Therefore, knowledge and experience play a significant role in branding, and many entrepreneurs have been able to use their previous knowledge and experience to pave the way for their future (Carvalho, 2007; Juntunen, 2012; Tavares, 2015; Lagerstedt & Mademlis, 2016; Twrsnick, 2016; Giannopoulos et al., 2021). The role of entrepreneurs in guiding and integrating the branding approach in startup companies has been emphasized in previous studies, which can be achieved in line with the innovation of entrepreneurs (Spence & Essoussi, 2008; Payne et al., 2009; Tavares, 2015; Setiyati & Wijaya, 2015; Twrsnick, 2016; Giannopoulos et al., 2021). Hence, it is advisable for entrepreneurs to place significant emphasis on networking and bolstering their social networks, as well as improving communication with their customers, to foster increased and superior engagement with them, and ideally, to capitalize on enhanced brand credibility. In this regard, startup firms can enhance and expedite their brand acceptance process by encouraging customers to partake in and collaborate on the branding process through co-creation. Moreover, considering the frequent reiteration of brand identity in numerous studies, it is recommended that startup company executives devote greater attention to establishing and reinforcing brand identity in the minds of customers. : Brand, Branding, Co-Creation, Start-Up, Meta-Synthesis
Data, information and knowledge management in the field of smart business
Samaneh Sheibani; Hassan Shakeri; Reza Sheibani
Abstract
Among the various applications of recommender systems, their use in estimating and suggesting points of interest (POIs) for tourists has expanded significantly in recent years. A common approach to identify user interests is to use collaborative filtering (CF) technique. However, the accuracy and efficiency ...
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Among the various applications of recommender systems, their use in estimating and suggesting points of interest (POIs) for tourists has expanded significantly in recent years. A common approach to identify user interests is to use collaborative filtering (CF) technique. However, the accuracy and efficiency of CF can be improved by applying different parameters and complementary approaches. In this paper, a new solution for promoting POI offers to tourists is presented, which uses a five-dimensional time model including the dimensions of day and night hours, days of the week, days of the month, months of the year, and occasions, and by calculating the Euclidean distance between the time of recommendation and the time of previous experiences of the active user and his similar users identifies and suggests suitable venues. The proposed solution also uses the trust parameter to increase the accuracy of POI suggestion. To improve the accuracy of trust evaluation, a new criterion based on a similarity tree structure between contexts is introduced. The results of experiments conducted on three well-known datasets show that the proposed model outperforms the state-of-the-art methods in term of efficiency and accuracy.
Introduction
Recommender systems estimate the interests and preferences of each user and suggest items and services to them, thus helping users to make a quick and favorable choice. Among the various applications of these systems, their use in estimating and suggesting points of interest (POIs) for tourists has expanded significantly in recent years. A common approach to identifying user interests is to use the collaborative filtering (CF) technique. However, the accuracy and efficiency of CF can be improved by applying different parameters and complementary approaches. In this research, a new solution for promoting POI offers to tourists is presented, which uses a five-dimensional time model including the dimensions of day and night hours, days of the week, days of the month, months of the year, and occasions, and by calculating the Euclidean distance between the time of recommendation and the time of previous experiences of the active user and his similar users identifies and suggests suitable venues. The proposed solution also uses the trust parameter to increase the accuracy of POI suggestions. To improve the accuracy of trust evaluation, a new criterion based on a similarity tree structure between contexts is introduced. The results of experiments conducted on three well-known datasets show that the proposed model outperforms the state-of-the-art methods in terms of efficiency and accuracy.
Research Question(s)
The main question of the current research is whether considering the different dimensions of the time parameter in touristic place recommendation systems, along with the trust parameter between users, can significantly increase the accuracy of the system's recommendations.
Literature Review
Various research works have been done with the aim of investigating the impact of social relations, time, place, and context on the efficiency of recommender systems. Savage et al. (2012) presented a location-based recommendation algorithm to improve the accuracy of recommended items based on learning according to the analysis of the user's profile in social networks and his location. Bedi (2020) presents a cross-domain approach for group recommender systems. In this approach, the suggestions provided by reliable and well-known users in the group improve the acceptance of recommendations compared to the suggestions of other people in the group. The system is designed in such a way that it takes into account the information of different sub-domains of the tourism domain. El Yebdri et al. (2021) proposed a context-aware trust-based post-refining approach to overcome the problems of data sparsity and cold start in recommender systems. This approach uses the average relative difference between fields. The authors first calculate the average score for each contextual condition and balance all evaluations based on the contextual condition of each tuple.
On the other hand, in the new era, which is known as the post-Fordism era, the supply and demand patterns in the field of tourism have faced significant changes which should be considered in the strategies of tourism service providers (Liasidou, 2022).
Methodology
According to the main goal of the current research, which is to increase the accuracy of systems recommending points of interest to tourists by introducing the influence of time dimensions, the research includes several stages. At first, a new approach to represent time in terms of hours, days of the week, days of the month, months of the year, and occasions is presented. Then, this time representation approach is combined with a trust computing model and a context-aware collaborative filtering technique to build a computational model for extracting and recommending points of interest to tourists. In the next stage of the research, to evaluate the effectiveness of the proposed model in increasing the accuracy of the system's recommendations and the level of user satisfaction, the presented model was implemented on several datasets in the field of tourism.
Results
In this research, several experiments have been performed to evaluate the performance of the proposed model. Experiments have been conducted on three real public datasets in the field of tourism, namely Yelp, Foursquare, and Gowalla. Some common criteria have been used to evaluate the proposed approach and compare its accuracy and efficiency with the existing methods:
Precision: the ratio of the number of relevant items in the list of top N items to N.
Recall: the ratio of the number of relevant items in the list of N suggested items to the total number of relevant items.
The results of the proposed model in this research were compared with three existing similar research works, including USSTC, MEAP-T, and LOCABAL+, which were respectively conducted by Kefalas and Manolopoulos (2017), Ying et al. (2019) and Ardisono and Mauro (2020).
The first experiment was performed to analyze the sensitivity of the proposed model in terms of precision and recall criteria to changes in the value of N for the top N item suggestion. As expected, the precision decreases as the number of suggested venues increases. On the other hand, as N increases, the recall increases as well.
Subsequent experiments were conducted to measure and compare the accuracy and recall criteria and showed that the proposed method provides the best accuracy values for different datasets compared to existing research works.
Discussion
The results of the evaluations based on three well-known data sets in the field of tourism-related recommendation systems showed that the application of these parameters significantly improves the accuracy of the system's recommendations, and therefore they should be considered more seriously in the recommender systems.
It is worth noting that if the absolute values of the results are evaluated, the improvement of the results in the proposed model may seem insignificant compared to the previous models. But if the relative amount of the improvement of the results is considered, for example, in the case of the Yelp dataset, it can be seen that the proposed model has provided a significant increase in precision and recall criteria even compared to its closest competitor, LOCABAL+.
Conclusion
In this research, with the aim of improving the performance of systems recommending venues to tourists, a model based on the estimation of trust between people was presented and evaluated. In the proposed model, the level of trust between two users in choosing their favorite places to visit is estimated based on the similarity level of their feedback and previous comments. In this regard, in the proposed model, parameters of time, location of the tourist, and classification of POIs were considered. In the proposed solution, a five-dimensional time model is used, and suitable venues are identified and suggested by calculating the distance between the time of recommendation and the time of previous experiences of similar tourists. The improvement of the results of this approach, which is evident in the results of this research, shows that systems that apply different dimensions of time in offering places to tourists, provide more accurate recommendations and a higher level of satisfaction for users.
Keywords: Tourism Recommender System, POI, Location-Based Services, Time-Aware Recommendation, Trust-Based Recommendation, Context-Aware Recommendation.
Data, information and knowledge management in the field of smart business
nafiseh rafiei; Zahra Zakeri Nasrabadi; Nikta Rey Shahrizadeh
Abstract
The purpose of this research was to design a model of the job competencies of online business consultants. The research method was qualitative with a contextual approach. The samples were first selected purposefully and then through snowball method. The interviews were conducted in an in-depth, semi-structured ...
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The purpose of this research was to design a model of the job competencies of online business consultants. The research method was qualitative with a contextual approach. The samples were first selected purposefully and then through snowball method. The interviews were conducted in an in-depth, semi-structured manner. With the process of open, central, and selective coding, 5 selective categories and 24 central categories were extracted. Causal conditions (the effort to acquire specialized knowledge, mastery of information and communication technology, experience in online business, having discipline in work and flexible management), background conditions (management and structural performance of the government, development of consulting culture in society), intervening conditions (non The stability and lack of structure of the country's economy, inefficient and cumbersome bureaucracy, lack of laws supporting business owners and weaknesses within the job, strategies (strategies planned by the government, acquiring up-to-date knowledge and information in the field Online business, practical training of online business, having discernment, use of reference groups) and consequences (strategic thinking, self-empowerment, civil ethics in work, performance management) were extracted. The obtained categories, while differentiating the job of consultants, achieved a model that can be the basis of the performance of online business consultants.
Introduction
Competence in its best definition is a combination of visible and measurable knowledge, skills, abilities, and characteristics that help improve employee performance and ultimately lead to organizational success. (Müller-Frommeyer, 2017). Therefore, having proficiency in the field of job competencies related to online business will help them to prosper more (Hakak et al., 2020).
One of the main reasons for the failure to survive or achieve the expected growth in online businesses is the lack of knowledge and expertise in online business management. Therefore, the presence of competent entrepreneurship consultants plays an important role in this field. (Reid et al., 2019).
Since the purpose of business consulting is specific and strategic, the chosen approach should be a combination of providing advice based on the consultant's experiences and coaching. In addition to this skill, having general business experience will play a significant role in guiding clients in aspects of strategic planning, business development, and responding to existing business challenges. Of course, these services will be efficient enough when the consultants have sufficient competence in personality, moral and skill dimensions (Rajab pour, 2020).
Research Questions
In the context of which causal, contextual, and intervening factors, job competencies of online business consultants are formed? What strategies do online business consultants take to strengthen job skills? What consequences will the adopted solutions have for improving the performance of online business consultants?
Literature Review
Researchers have identified different components of job competence including: motivation, social skills, self-awareness, empathy, self-regulation, cognitive skills (Liikamaa, 2015), strategic contribution, business knowledge, personal credit, technology (Mufti et al., 2016), systemic thinking, acceptance of interdisciplinary diversity, intrapersonal competence, practical and strategic management (Solansky, 2020).
Some researches show the competencies needed by business consultants, including the competencies of motivating and giving hope, keeping entrepreneurs' information confidential and protecting their intellectual property rights, alertness to new work opportunities, and the ability to prepare a business plan (Hatami&Azizi, 2015). Also, the job competence of employees has been identified in terms of personal characteristics, knowledge, and skills (Babashahi et al., 2017). The competencies of the consultants of organizations, in addition to specific personality competencies, were also extracted in the sub-categories of intelligence, knowledge of management and organization, strategic thinking, situational assessment, and leadership of leaders (Vakili et al., 2021).
Methodology
The method of this research was qualitative based on a contextual approach. The area investigated in the current qualitative research was formed by experts of online business consultants. In this research, the samples were selected purposefully and the sampling process continued as a snowball. Semi-structured in-depth interviews were completed with 18 experts until the theoretical saturation criterion was reached. The duration of each interview was between 50 and 80 minutes.
Results
The analysis of the research data in the three stages of open, central, and selective coding finally resulted in 24 central categories, 5 major categories, and one core category which covers all the emerged categories, which is mentioned in the table below.
Table 1. Coding results (source: findings of the current research)
Selective coding
Axial coding
First order axial code
Second order axial code
Job competency
Knowledge-oriented, skill-oriented
and ethical
Get updated information
-
Having multiple skills
practical skill
Communication skills
Speaking skill
Listening skills
Ethics of consultants
-
gaining experience
and knowledge in the age of information
and communication
Trying to acquire specialized knowledge
Mastery of information and communication technology
Online business experience
Having order and discipline at work
Correct management of programs
Background
conditions from
macro to
micro levels
Administrative performance of the government
Structural performance of the government
The growth of counseling culture in society
Economy
bureaucracy
and restrictive
culture
Instability and unstructured economy in Iran
Inefficient and cumbersome bureaucracy
Lack of laws supporting business owners
Limitations and weaknesses within the job
Strategies
structural-
operational) to
strengthen job
competence
Selective
coding
Systematic and planned government strategies
Get up-to-date business knowledge and information online
Practical and practical online business training
Having the power of discernment
Axial coding
Strategies
structural-
operational)
Use the experience of others and reference groups in your field of work
Personal,
professional
and social promotion
Strategic thinking
Self-empowerment
Civil ethics in performing job duties
performance management
Finally, during selective coding (central extraction, causal and contextual conditions, interventional conditions, strategies, and consequences), central categories in each sector, systematically related to other categories, relationships in a clear communication framework, and the research paradigm model were drawn that narrate the process of forming job competencies online. The model is illustrated in Figure1.
Figure 1. Derived contextual model
(source: findings of the present research)
Intervening conditions of economy, bureaucracy and restrictive culture
* Instability and unstructured economy
* Inefficient and cumbersome bureaucracy
*Lack of laws supporting business owners *Limitations and weaknesses within the job
Causal conditions for gaining experience and knowledge in the age of information and communication
* Trying to acquire specialized knowledge
* Mastery of technology
Information and communication
* Online business experience
* Having order and discipline at work
*Flexible management
Background conditions from macro to micro levels
* Administrative performance of the government
* The structural function of the government
* Growth of counseling culture in the society
Consequences of personal, professional and social promotion:
* Strategic thinking
* Empowering yourself
* Civil ethics in
Performing job duties
*performance management
The central phenomenon of online business consulting job competencies model
* science-oriented
* Skill oriented
* Moral oriented
Strategies structural-operational) tostrengthen job competence
* Systematic and planned strategies of the government
*Acquiring up-to-date knowledge and information in the field of online business
*Practical and practical online business training
* Having the ability to recognize
*Using the experience of others
Conclusion
The main goal of this research was to design a model of the job competencies of online business consultants. According to the obtained results, the conceptual model of the research was extracted in six main sections, including causal, contextual, intervening, strategic, consequences, and central conditions. The extracted model shows that for the formation of a science-oriented, skill-oriented, and ethical multi-dimensional desirable occupational competency model, both the implementation of strategies at the macro-management and structural levels of society and the agency and active role of actors in this field are needed. It is important to achieve this by acquiring the necessary specialized knowledge and strengthening one's civic capabilities. In this regard, inefficient and cumbersome bureaucracy, lack of sufficient supporting laws for business owners, instability, and unstructured economy in the society are the most important limitations in the model based on the mentality and immediate experiences of the interview. Those involved in online business consulting are depicted. Therefore, if the competency model of online business consultants of this research is implemented, it can play a significant role in improving the performance management of consultants to their clients in a scientific way, not based on personal experiences and based on trial and error.
Keywords: Job Competency, Consultant, Online Business.
