Research Paper
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.
Research Paper
Neda Kavand; Yoseph Mohammadi Moghadam
Abstract
In today's fast-paced and changing world, all the evidence points to the centrality of the human role in overcoming problems, opening bottlenecks, creating advanced technology and producing various technologies. In this field, managers are the foundation stone of any organization, and the organization ...
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In today's fast-paced and changing world, all the evidence points to the centrality of the human role in overcoming problems, opening bottlenecks, creating advanced technology and producing various technologies. In this field, managers are the foundation stone of any organization, and the organization can achieve capability and development if it is properly managed and has skilled managers. Therefore, in this article, an attempt has been made to examine the unlearning of managers in skill enhancement in the digital age. The present study is a mixed-method research, in which data were collected through semi-structured interviews with elites of the Tax Affairs Organization who were selected through a purposeful method. The validity of the interview was assessed through a formal and content judgment approach based on the opinions of 7 experts (who had experience working or collaborating with the Tax Affairs Organization and were familiar with academic and organizational issues in the field of tax affairs). And the reliability was calculated and confirmed by the percentage of agreement method between the two coders as 85 and 88 percent, respectively. Thematic analysis was used to analyze the data. Ultimately, 60 basic themes, 10 organizing themes, and 3 super themes were obtained. The validity of the results of the qualitative section was verified by using the Kendall correlation test and referring to experts in the field of qualitative studies, who were evaluated and approved in a targeted manner. The results showed that managers' skills, including technical, human, and perceptual skills, are significant at three levels: senior, middle, and operational managers. Also, three key factors in understanding digitalization (having a digital vision, critical thinking, and cognitive flexibility), Digital platforming (redefinition of tasks and responsibilities, adaptability and adaptability, digital literacy, digital mindset) And leading changes (social-emotional intelligence, networking, reflective thinking) are essential skills for managers to succeed in the digital journey of organizations. Keywords: Unlearning, skill-building, transformational, digital age, managers.. IntroductionOrganizations investing in digital transformation, They will prepare for the future scenarios and will be ready to overcome the challenges of the market(Li et al.,2023).There are many factors in the way of keeping up with the developments of the present age, among the main obstacles is the lack of required skills and experience(Jones et al.,2021).Therefore, organizations and its members must abandon beliefs, norms, values, procedures and routines and outdated knowledge to gain new knowledge in order to achieve success and survive in this changing and extremely chaotic environment, the lack of ability of organizations and their members in this matter,or in other words, unlearning, is the fundamental weakness of many organizations and their members. Organizational ability to unlearning allows organizations to adapt better and faster to new situations(Vătămănescu et al.,2020). Unlearning is an important step to strengthen learning and innovation for organizations and their members, but it is doubly important for managers. Because they influence organizational performance through the values, personalities, behaviors and strategic choices they make. Regarding the influence of managers in organizations, they must fundamentally change their skills along with the changes of the current era, which is referred to as the digital era. Managers have a central role in overcoming crises and effectively exploiting the opportunities facing organizations in the digital age.They identify opportunities and allocate resources, and evaluating the competitive landscape, creating a digital transformation roadmap and setting and developing digital transformation strategies will be the foundation and facilitator of the digital journey of organizations.In this regard, the specific needs of the organization's digital journey require abandoning outdated skills and new skills specific to managers.Therefore, the aim of the present study is to analyze the unlearning of managers in skill enhancement in the digital age.Literature ReviewUnlearning is the abandonment of a particular set of knowledge, values or behaviors stored in memory, which are no longer relevant or valuable.This process occurs when individuals and organizations become aware that they need to acquire new knowledge, values, or behaviors. Since organizations do not have mental activities and cannot learn on their own, it is the people within the organization who must carry out this process(Kmieciak,2020). Therefore, unlearning is a process that first occurs at the individual level and then spreads to the entire organization(Itacaramby Pardi metal,2023). Unlearning in the organization is the reorientation of organizational values, norms and behaviors by changing cognitive structures, mental models, dominant logic and central assumptions, which guide the behavior. It can improve skills, as it helps change old skills. Skill is the ability to effectively apply personal knowledge and experience and the continuous ability of a person to perform a task quickly and accurately. Management skills are of particular importance due to the key role of managers in ensuring the success and efficiency of an organization or team(Suryakant Chandekar,2023). Management skills are the tools through which managers put their favorite style, strategy and techniques into practice.Efficient managers have many skills, which allows them to effectively lead and direct their team towards achieving the set goals.MethodologyThe present study is a mixed-method study, and data was collected using semi-structured interviews with 15 managers of the Tax Affairs Organization who were selected using a purposive and snowball method. The interview questions were reviewed with a formal and content judgment approach based on the opinions of seven experts who had experience working or collaborating with the Tax Affairs Organization and were familiar with academic and organizational issues in the field of tax affairs, and were calculated to be 85 percent, which according to Chin (1998), is a very desirable value. Therefore, the validity of the instrument is supported (Moghadam, 2016). In order to examine the reliability of the interview protocol, the percentage of agreement method between the two coders was used. The reliability coefficient of the interview protocol in this study was 88 percent, which was approved by the researchers as a desirable reliability percentage. The collected data was then analyzed using thematic analysis. After conducting the qualitative stage and identifying the factors of unlearning in improving the skills of managers in the digital age, a research model was designed. Then, in the quantitative phase, the validity of the results of the qualitative section was evaluated using the Kendall correlation test. Therefore, a questionnaire was designed based on the results of the qualitative section and the Likert scale. In order to assess the validity of the questionnaire, the opinions of 7 experts were used, and after making the necessary corrections, its validity was confirmed. The reliability of the questionnaire was also examined based on Cronbach's alpha, and all questions obtained values above 0.7, confirming the reliability of the questionnaire. Next, a panel of experts in the qualitative field, who were selected through purposive sampling to conduct interviews, was formed and the designed questionnaire was provided to them. Then, the data obtained from collecting questionnaires were analyzed in three rounds using the Kendall correlation test. Discussion and ConclusionThe purpose of this study is to analyze managers' unlearning in skill enhancement in the digital era.For this purpose, a semi-structured interview was conducted with experts. And the data were analyzed using the theme analysis method,which obtained 60 basic themes,10organizing themes and3advanced themes.The results showed that according to Katz's(1955) classification, managers' skills include technical, human and perceptive skills, which are significant in response to digital transformation at the three levels of senior, middle and operational managers. Based on the results, all three types of management skills are necessary for managers at every level, but the depth and need of managers for some skills is more than other managers at other levels. The traditional skills of managers in environments that are changing rapidly, It can add complexity and be misleading. Path dependence mechanisms should be challenged by unlearning managers' skills, because they inhibit managers' innovative and creative abilities. Therefore, based on the data, basic measures are taken to unlearn the skills of managers with the aim of increasing their skills in the digital era, which is a response to the changes and instabilities of this era, can be categorized into three key actions:"Understanding digitalization","Digitalization platform"and"Leading change".Digital infrastructure refers to formal organizational structures, processes and resources, which are created to enable digital transformation. The data shows, managers provide a formal basis for digitalization by creating a digital mindset, acquiring digital literacy and re-engineering tasks and processes.Based on the data, critical thinking, cognitive flexibility and having a digital perspective are skills, which provides the possibility for managers to develop targeted actions and responses and prepare the organization for the challenges created by digital transformation.Knowledgeable and successful managers must overcome challenges by adopting strategic and general solutions.This requires progressive changes in management skills so that managers can facilitate the organization's digital journey.Based on the data, the leading changes include skills such as reflective thinking, social-emotional intelligence, and networking.
Research Paper
Management approaches in the field of smart
Faezeh Zamani; Ahmad Ebrahimi; Roya Soltani; Babak Farhang Moghaddam
Abstract
This research aims to investigate the effective factors in predicting lead time (LT) and create a predictive model of LT to improve sustainability and resilience for Kanban orders in the lean supply chain (LSC). The study follows the data mining (DM) method, and the dataset includes 103023 observations ...
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This research aims to investigate the effective factors in predicting lead time (LT) and create a predictive model of LT to improve sustainability and resilience for Kanban orders in the lean supply chain (LSC). The study follows the data mining (DM) method, and the dataset includes 103023 observations from the Kanban system, which were extracted in compliance with the requirements of the dataset quality indicators in the period 1402/6 to 1402/11. First, indicators affecting the LT of orders were extracted. Process mining was used to identify influential variables in high-variance processes to improve performance and accuracy. A stepwise analysis approach was used to select features for the model fitting stage. Also, tuning the parameters of non-parametric approaches was used. The predictive model uses Multiple Linear Regression, Multiple with curvature, Lasso, Elastic Net, Boosted Decision Tree, Bootstrap Random Forest, K-Nearest Neighbor, and Boosted Multi-Layer Perceptron. The performance of the fitted regression models has been confirmed using R^2, RASE, and validation of the results and model. The results showed that the logistical features are effective in LT, and the Boosted Multi-Layer Perceptron is the best for predicting orders' LT with an accuracy of 96% and an error of 5.84. Using the model's predictive capability for new data in the Kanban system, the results obtained within four months have been used. The improvements from using DM capabilities in the Kanban system all express the significant impact of combining lean and machine learning (ML) tools to empower and resilient Lean Supply Chain Management (LSCM).IntroductionThe main problem in this research is identifying the factors that effectively predict the LT of orders in the LSC, choosing the best ML algorithm for predicting the exact LT, and how process mining can effectively identify the most repeatable variables in the main variants and investigate how DM can reduce waste in LSC.Despite classification studies on risk, disruption, and delay prediction in the literature, to our knowledge, fewer articles were found regarding the use of DM to predict the accurate LT of orders in the LSC with logistical features. Also, according to researchers, DM is considered a tool to overcome the limitations of lean tools and strengthen their performance. However, the studies corresponding to the executive case did not observe the results and improvements from the ML application in predicting the LT of orders.Therefore, in this research, in terms of innovation, 1) machine learning has been used to accurately predict the LT of Kanban orders, considering logistical factors, 2) Process mining has been used in the identification stage of influential variables, 3) The results and improvements obtained from predicting the LT of orders regarding risk reduction and sustainability improvement have been examined and compared.Research Question(s)The main questions in this research are specified as follows:What factors affect LT's prediction in the lean supply chain?How do we predict the LT in the lean supply chain?How can DM effectively reduce waste in the lean supply chain?Literature ReviewRegarding the issue's importance and urgency, transparency and accurate prediction of the LT have reduced risk and improved sustainability and resilience in the LSC. These effects are significant in both theoretical and operational dimensions, such as reducing logistic costs, safety stock, working capital, stoppage, level of inventories, storage cost, energy consumption, and risk. After reviewing the literature, the most relevant articles in the field of ML are listed in Table2.MethodologyThis research is practical from the objective point of view, and from the data point of view, it is quantitative. This study includes four main processes: 1) reviewing the literature and data collection, 2) research method and pre-processing, 3) model construction, and 4) model evaluation and results (Jayanti, 2022 & Wasesa). First, influential variables were extracted by reviewing the literature. Then, the dataset was extracted from the Kanban system in compliance with the requirements of the data set quality indicators from 6/1402 to 11/1402. Then, process mining was used to identify the features with the most repeatability in the main variants, and finally, influential variables were extracted through brainstorming. An integrated stepwise analysis approach has been used to select features. The predictive model uses MLR, curvature, Lasso, Elastic Net, Boosted DT, Bootstrap RF, KNN, and Boosted Multi-Layer Perceptron. The parameters of non-parametric approaches are tuned to improve forecasting performance and accuracy. In this research, evaluation and validation are the main criteria for evaluating the model's predictive power, and error and accuracy indices have been used together. Therefore, the performance of the fitted regression models using R^2 and RASE evaluation indices and validation of the results and the model are confirmed.ResultsAfter fitting the regression models, for each row of test data, predict the LT and compare it with the actual values of the LT; then, to identify the best model, R^2, RASE, and model comparison approaches are used.The results show that the Boosted Multi-Layer Perceptron, with one hidden layer, five activation functions, and a learning rate of 0.1, has the highest accuracy at 96% and the lowest root average square error at 5.84, compared to other fitted models.Discussion and ConclusionThe obtained results show that the identified independent variables are related to customer factors (safety stock), manufacturer factors (inspection status, quality paint), logistic factors (vehicle, distance), part factors (name, part-expert), and order factors (number of holidays, Kanban issue date) are effective on the LT. As the selected model in this research, the regression model of the Boosted Multi-Layer Perceptron has the highest R^2 and the lowest RASE criteria. Process mining is practical and helpful in identifying the main variants. By using the model's predictive capability for new data in the Kanban order issuing system within four months, the improvements all express the significant impact of combining lean tools and ML to empower LSCM. The practical implications of this research can guide managers in implementing practices with lean tools, improving sustainability, eliminating waste, and being more competitive in the current challenging business environment. Academics can benefit from the present study because it provides ML practices that can be further tested and validated.This research generalizes and develops the use of DM as a decision-making support tool in predicting the LT to overcome the limitations of lean tools, and it can improve the efficiency and stability of the LSC and reduce the risk. While this research provides valuable insights, it also has limitations, including the lack of data on influential variables identified in the literature. In implementing this research, there are suggestions for future research that examine factors such as production capacity, weather, and location conditions and deep learning to fit more reliable and accurate results and investigate prescriptive analyses to optimize the LT of orders based on the fitted regression models, the design of the experiment and using the profiler's capabilities.Keywords: Machine Learning, Regression, Lean Supply Chain Management, Kanban, Lead Time.
Research Paper
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.
Research Paper
Management approaches in the field of smart
seyed Mohsen Safavi koohsareh; seyed amin hosseini sano; Amirhossein Mohajerzadeh
Abstract
The primary objective of mobile network operators is arguably to maximize their efficiency. Beyond operational and investment costs, maximizing the utilization of available resources can help them achieve this goal. To this end, operators offer discounted data plans during off-peak hours to encourage ...
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The primary objective of mobile network operators is arguably to maximize their efficiency. Beyond operational and investment costs, maximizing the utilization of available resources can help them achieve this goal. To this end, operators offer discounted data plans during off-peak hours to encourage users to utilize the network during these times. These data plans are typically based on the average traffic load across the entire network at different times of the day. However, they often overlook the fact that traffic patterns can vary significantly across different population areas within a city at various times. In this paper, different population areas are automatically identified using clustering based on traffic patterns. By identifying these areas and considering the traffic patterns specific to each area, the allocation of appropriate data plans for users, based on the regions they frequent, is analyzed and discussed. Additionally, other potential applications of this clustering method for offering various services are presented, followed by a conclusion.IntroductionThe number of cellular network users and their required bandwidth are continuously increasing (Ericsson, 2022). However, limited wireless frequency bands constrain network capacity, prompting operators to deploy dense base stations to reuse radio frequencies in smaller coverage areas, thereby enhancing capacity. Operators plan for peak usage, leading to base station layouts that often remain underutilized for extended periods, resulting in inefficient use of capital (equipment) and operational (energy and maintenance) costs (Liu et al., 2023). To address this, operators offer discounted plans during low-traffic periods but overlook the varying traffic patterns across urban areas, which could enable tailored offers for different regions. This paper proposes a hierarchical clustering-based method to identify and segment urban areas, design region-specific traffic-based plans, and target appropriate users. The main contribution is improving efficiency by maximizing the utilization of existing cellular networks without expanding capacity, benefiting both operators through increased revenue and users through enhanced satisfaction.MethodologyThe best approach to evaluate proposed solutions in cellular networks is to use real-world datasets from mobile operators. Cellular network logs are vast, contain sensitive user and network information, and require algorithms capable of handling large-scale data. In this study, we use a publicly available dataset (Barlacchi et al., 2015) containing telecommunication, weather, news, social media, and power grid data from Milan and Trentino, Italy, spanning November 1, 2013, to June 1, 2014. Our focus is on telecommunication data, specifically Call Detail Records (CDRs), to evaluate the proposed method.The dataset is processed and analyzed using Python and libraries such as NumPy, Pandas, Scikit-learn, and Matplotlib. The proposed method involves clustering base stations based on traffic patterns, designing region-specific data plans, and targeting users during low-traffic periods.3.1. Traffic Pattern-Based Region IdentificationAs mentioned earlier, traffic patterns of cellular base stations vary across urban areas. These patterns are heavily influenced by the stations' locations. For example, base stations in residential areas exhibit different traffic patterns compared to those in commercial, transportation, or recreational zones (Xu et al., 2017).Figure 1: Traffic Patterns of Base Stations in Three Different Population Zones Figure 1 illustrates the traffic patterns of base stations in three different population zones over a week. Zone 3 likely corresponds to recreational areas like amusement parks, with higher traffic on weekends. Zone 1 may represent office areas, with reduced traffic on weekends, while Zone 2 could be industrial or transit areas with consistent traffic throughout the week.To separate these zones, hierarchical clustering is employed (Abubakar et al., 2022). Instead of using Euclidean distance, which fails to distinguish adjacent zones with different traffic patterns, we use traffic time series as the clustering criterion. The chosen algorithm is agglomerative hierarchical clustering (Kassambara, 2017), as shown in Figure 2. Base stations first remove noise from their data and send average traffic data to a central node every x minutes. At the central node, Euclidean distance is used to measure traffic similarity between stations, reducing dimensionality from two dimensions (time series traffic volume) to one (distance between clusters). Over 80% of time series similarity studies use this metric, though some employ deep learning for feature extraction to improve clustering.The Euclidean distance between two base stations' traffic time series Q and C is calculated as:1 To mitigate sensitivity to variations, preprocessing steps include removing outliers, adjusting offsets, and smoothing noise using moving averages (Keogh & Pazzani, 1998).The hierarchical clustering dendrogram (Figure 2) determines the optimal number of clusters by identifying the best cut-off line. Two strategies are proposed:Predefine the number of clusters based on comprehensive traffic pattern analysis and use k-means clustering.Use silhouette scoring to dynamically determine the optimal number of clusters based on traffic similarity.We adopt the second approach, using average silhouette scores (Almeida et al., 2015) to select the optimal number of clusters. This method eliminates the need for predefined cluster counts and provides precise cluster identification.Once clusters are identified, data plans are designed for each cluster based on their traffic patterns.3.2. Designing Data PlansFor each cluster, the average traffic profile is calculated, and data plans are designed inversely proportional to traffic volume. The number of offers q in time interval t is determined by:2 where A and B are the traffic range bounds, S is the current traffic, N is the maximum number of offers, and C is the minimum (0).Alternative models, such as linear, exponential growth/decay, and logarithmic growth/decay, are also explored (Safavi et al., 2024), as shown in Figures 5 and 6.3.3. Targeting UsersUsers with higher overlap with low-traffic periods are prioritized for data plan offers. A user’s average monthly presence in low-traffic intervals is used to rank them. The longest data plans are assigned to users with the highest presence in low-traffic periods, ensuring efficient resource allocation.ResultsSimulations represents the method proposed in this paper, utilize 100% of the network's bandwidth capacity.results demonstrate the optimal utilization of existing equipment and resources, which directly correlates with increased operator profitability. Those also show that the proposed method can maximize resource efficiency by approximately 40%, representing the highest possible improvement in network resource utilizationWe can conclude, the proposed method significantly enhances resource utilization and operator profitability by fully leveraging network capacity. While other scenarios improve resource usage to some extent, only the proposed method achieves 100% utilization, highlighting its effectiveness in optimizing network performance and operational efficiency.Keywords: Mobile Network Operator, Maximizing the Utilization, Cellular Data Plan, Clustering, Traffic Pattern.
Research Paper
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.
Research Paper
Management approaches in the field of smart
Mohammad Taghi Taghavifard; Payam Hanafizadeh; Saeedeh Mehri; Iman Raeesi Vanani
Abstract
Business model change in startups is essential for adapting to evolving market demands and increasing competitiveness, playing a critical role in their success. However, most research related to business model change has mainly focused on established firms. To address this research gap, the present study ...
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Business model change in startups is essential for adapting to evolving market demands and increasing competitiveness, playing a critical role in their success. However, most research related to business model change has mainly focused on established firms. To address this research gap, the present study provides a framework that contributes to understanding the role of design themes in startup business model changes. In this study, a systematic literature review was used to analyze theoretical foundations and prior researches. After defining the research question, designing a search strategy, and applying screening criteria, 95 articles published between 2000 and 2024 were reviewed. In the first stage, a conceptual model was developed to analyze the dimensions of business model change by reviewing theoretical foundations and prior researches. This model was then refined through inductive-deductive thematic analysis, using evidence from empirical articles and case studies. The identified themes—novelty, efficiency, lock-in, complementarity, and adaptability—were examined across four main dimensions of business model change: content, structure, governance, and stream. The findings showed these themes interact synergistically and contribute to competitive advantage and business sustainability. The research results suggest that these five overarching themes provide a suitable framework for understanding and managing business model change in startups.IntroductionIn recent years, the topic of entrepreneurship and startups has attracted significant attention and has emerged as a key driver of economic growth. The business model of startups plays a fundamental role in this process, serving as a mechanism for exploiting entrepreneurial opportunities and creating value (Amit & Zott, 2001; George & Bock, 2011; Guo et al., 2020). However, one of the key characteristics of the startup and entrepreneurial environment is high uncertainty, which static business models are unable to effectively adress (Demil & Lecocq, 2010). Accordingly, the main objective of this research is to develop a framework for changing the business models of digital startups and to offer practical insights for entrepreneurs. To explore the dynamics of startup business model change, this study addresses the following key research question:Research QuestionHow do startup business models evolve, and which business model themes drive this change?To answer this question, a conceptual framework was developed based on the business model structure proposed by Amit and Zott (2001) and the business model innovation framework proposed by Spieth and Schneider (2016). Literature ReviewThe existing literature on business model change follows two major approaches: the evolutionary approach and the theme-based approach (Guo et al., 2020).2.1. Evolutionary Approach to Business ModelsFrom an evolutionary approach, the business model of the startup changes and evolves in a dynamic manner through flexibility (Bock et al., 2012), experimentation (Andries et al., 2013), and trial-and-error learning (Chesbrough, 2010; Sosna et al., 2010). This approach draws upon methodologies such as the Lean Startup (Ries, 2011) and Customer Development (Blank, 2013), which suggest that business models evolve through iterative testing and customer feedback. Studies based on this approach show that incremental and continuous changes in business models require careful attention to the firm's internal resources and competencies, as explained by the resource-based view (RBV) and dynamic capabilities theory (Schneider & Spieth, 2013).2.2. Theme-Based Approach to Business ModelsThe theme-based approach focuses on using specific themes to structure business models and value creation. The four conventional themes are novelty, efficiency, lock-in, and complementarity (Amit & Zott, 2001, 2012; Kulins et al., 2016; Pati et al., 2018):Novelty: Refers to innovation in products or services or underlying resources and capabilities.Efficiency: Focuses on cost optimization and resource utilization.Lock-in: Helps strengthen long-term relationships with customers and partners.Complementarity: Enhances synergies among different value propositions.Recent studies (Zott & Amit, 2007; Ojala, 2016; Costa & Haftor, 2021) have demonstrated the effectiveness of this approach in fostering strategic entrepreneurship, allowing startups to integrate external opportunities with internal capabilities for sustained growth.2.3. Research Gap and Initial Conceptual ModelBusiness model change research has primarily focused on established firms (Achtenhagen et al., 2013; Bohnsack et al., 2014), while business model change in startups - a broadly defined type of organization with limited resources, high uncertainty, flexibility, and differences in value creation sources - has been less examined in the academic literature (Kesting & Günzel-Jensen, 2015; Guckenbiehl & Corral de Zubielqui, 2022). Amit and Zott (2001) recommend more research on the dynamics of business model themes, a gap reiterated by Randhawa et al. (2020). In this study, based on the business model structure by Amit and Zott (2001) and the stream dimension of Spieth and Schneider (2016), we analyze startup business model change themes across four dimensions: content, structure, governance, and stream.MethodologyThis study adopts an inductive–deductive approach and conducts a systematic literature review on startup business model change. The review process adhered to Tranfield et al.'s (2003) framework: planning the review; conducting the review; analyzing the findings. The articles included in the literature review were from articles published between 2000–2024 and they were retrieved from the Scopus and Web of Science databases. Initial output of 197 articles were ultimately reduced to 95 articles for in-depth review after duplicates and irrelevant articles were removed. To identify core concepts and themes related to business model change, the data were coded and analyzed using ATLAS.ti software.ResultsThematic analysis using the theme network tool led to the identification of five primary themes: novelty, efficiency, lock-in, complementarity, and adaptability. Each of these themes represents business model change in four dimensions: content, structure, governance, and stream (see Table 1) Table 1. Final Framework of Startup Business Model Change Based on Business Model Design ThemesDimensions NoveltyEfficiencyLock-InComplementarityAdaptabilityContent Products, services, information, or value propositionsüüüüüResources and assetsüü üüCapabilities and competenciesü üüStructure Customer segments and their relationsü üParticipants and their relationsü üüCommunication mechanismsüüüüüGovernance Controllersü ü Formal and legal structure üIncentivesü ü Stream Revenue streamsü üüCost structuresüü üInvestment structuresü üDiscussionThe framework proposed in this study introduces an additional dimension—stream—which extends the three-dimensional concep outlined by Amit and Zott (2001): content, structure, and governance. Furthermore, the inclusion of adaptability, which is not present in Amit and Zott’s model, is a significant innovation. Supported by Sharma et al.'s (2016) model, this framework integrates adaptability into a unified business model design, addressing dynamic environments and emerging market challenges beyond operational management.ConclusionThis study offers two important contributions to the literature by providing a new conceptual framework for startup business model change (Table 1). First, in addition to the four conventional business model themes (novelty, efficiency, lock-in, complementarity), the adaptability theme is introduced to demonstrate the importance of adaptability in changing and evolving business models. Second, this study provides new insights into how business models change by adding the stream alongside the content, structure, and governance dimensions.Keywords: Startup, Business Model Change, Business Model Design Themes.
