Research Paper
Management approaches in the field of smart
Fahime Mahavarpour; Feiz Davood; Morteza Maleki Min Bash Razgah
Abstract
Augmented reality technology has emerged as one of the main trends in the digital market in recent years. This new technology has been successively used in innovative businesses due to its attractiveness and potential. The aim of the current research was a systematic and comprehensive review ...
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Augmented reality technology has emerged as one of the main trends in the digital market in recent years. This new technology has been successively used in innovative businesses due to its attractiveness and potential. The aim of the current research was a systematic and comprehensive review in the form of a bibliometric approach on the citation data of the purchase decision-making literature based on augmented reality technology. A pragmatic paradigm allowed the researcher to collect citation data from the Scopus database in a systematic strategy and preprocess it in the form of the standard Prism protocol. Finally, 239 studies were included in the bibliometric analysis basket by R Studio and Vosviewer software. The final results in the functional section identified the most influential documents, authors, journals, organizations, and countries, and then added highlights to buyer behavior and purchase decision-making with augmented reality technology in virtual businesses in the form of interaction patterns between elements and data content analysis.. It is also worth noting that this concept mainly revolves around five main areas: the virtual and psychological experiences of shoppers in online shopping with augmented reality; The effects of artificial intelligence on new technologies in the behavior of buyers; virtual and psychological experiences of buyers in online shopping with augmented reality; Interactions of technology, perceived value and cognition on shoppers' experience in retail using mobile augmented reality; The role of augmented reality technology in purchasing decisions and smart purchases in virtual space. The influential school of augmented reality technology in buyers' decision-making is related to the stimulus-organism-response theory, which formed the intellectual foundation of this field.
Introduction
Try to conduct marketing without using augmented reality technology; you will not succeed. Try to create an inspiring marketing strategy without augmented reality technology; this will not be effective either. While traditional marketing is effective, it involves high costs and difficulties in engaging and providing personalized services to customers, as it cannot adjust services to recognize customers' needs or expectations. Marketing with this novel technology (AR) represents a new and potentially impactful subfield that has transformed how buyers experience goods and services. Augmented reality aids in the development of marketing, offering benefits that enhance the company's image, strengthen customer interaction, and increase sales. These advanced new technologies have already positively boosted marketing. AR technology is in its early stages and there are ample opportunities for its improvement. This innovative technology (AR) provides an excellent option for enriching perceptual and interactive desires. While many established consumer behavior theories may extend naturally into virtual spaces, many of them may require significant updates to align with buyers' search, selection, and usage practices. During purchase decision-making, whether interacting with physical or online stores, buyers encounter various touchpoints that determine the shopping experience. Academic knowledge is expanding exponentially. This has made it challenging to keep up with the latest innovations in research and evaluate the collective evidence in a particular field of study. One such database is Scopus, created in 2004 and published by Elsevier. Managing such knowledge using traditional tools and techniques is difficult, if not unlikely, due to the rapid growth. This is why literature review as a research method has become more popular than ever in various natural and social science disciplines. The purpose of a systematic review is to identify all empirical evidence that meets pre-specified criteria to answer a specific research question or hypothesis. Bibliometrics is a new style of theoretical literature review and a type of systematic review that has created related theories and bibliometric analysis in various fields of knowledge, including management sciences. The value of bibliometric methods lies in their ability to analyze the evolution of scientific literature over time and reveal intellectual relationships in this field. Therefore, based on the present research problem, which seeks to describe performance indicators, examine the interactive patterns of these indicators, and analyze the content of citation information in the field of augmented reality technology in the academic space, the researcher aims to demonstrate the study gap in this field and contribute to filling the knowledge gap. In the introduction section, a systematic methodology is first defined to specify the steps for collecting, preprocessing, and analyzing information. Then, in the results section, an objective and tangible interpretation is provided using tables and charts from professional bibliometric software. Finally, the obtained results are discussed and concluded within the context of expanding the present literature.
Methodology
Systematic reviews indicate a precise approach to integrating and evaluating scientific evidence literature reviews are no exception and are guided by a systematic process. This process aids researchers in systematically and comprehensively gathering, analyzing, and evaluating existing studies, providing an overall picture of the state and trends within a scientific field. Compared to systematic literature reviews, this method helps prevent author bias. The present bibliometric methodology emerges from a pragmatic paradigm that designs various stages of research based on common assumptions and beliefs among review researchers Based on the outputs of scientific research, the researcher has the freedom to use various quantitative and qualitative approaches within this philosophical framework. According to bibliometric studies, the methodology of bibliometric research consists of five steps, which are detailed below.
Chart 3: The Methodological Process of Bibliometric Studies (Moradi & Miralmasi, 2020b, p. 570)
Results
In this study, the focus is on the topic of augmented reality technology and its evolution up to the year 2024. While augmented reality technology saw erratic growth in buyer behavior and purchase decision-making during its nascent stage up to 2015, this was largely due to the limited understanding of its long-term effects owing to the lack of metrics, measurable elements, and research studies at that time.
There is a 50-year span of articles, indicating over half a century of discussion surrounding augmented reality technology. However, from 2015 onwards, it has experienced an annual growth rate of 25.92%, showing an upward trend.
Approximately 36.46% of the research has been authored through international collaborations, as this field requires expertise from various research disciplines.
Developing countries, despite having fewer scientific outputs, publish their articles as international collaborative studies to ensure publication in reputable journals and to increase citations to their studies.
Keywords: Augmented Reality Technology, Marketing, Buyer Decision Making, Buyer Behavior, Virtual Businesses.
Research Paper
Management approaches in the field of smart
Homa Soufi; Habib Roodsaz; Davoud Hosseinpour; Hosein Aslipour
Abstract
Digital transformation in the banking sector is recognized as a critical driver of growth and change. This study investigates the anticipated outcomes (both outputs and impacts) following the implementation of digital banking policies, utilizing an exploratory and applied-developmental approach. The ...
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Digital transformation in the banking sector is recognized as a critical driver of growth and change. This study investigates the anticipated outcomes (both outputs and impacts) following the implementation of digital banking policies, utilizing an exploratory and applied-developmental approach. The research adopts a mixed-method design, incorporating both qualitative and quantitative phases. In the qualitative phase, experts in banking and policymaking were interviewed using purposive sampling, reaching theoretical saturation after 25 interviews. In the quantitative phase, data was collected from 354 bank managers and professionals through a researcher-developed questionnaire. The data was analyzed using thematic analysis for the qualitative part and the Partial Least Squares (PLS) method for the quantitative part. The results indicate that the initial outcomes of digital banking policy implementation include improvements in revenue and market positioning, cost efficiency, customer acquisition and satisfaction, bank infrastructure, data management, banking products and services, banking technologies and channels, and risk management. Additionally, the long-term and sustainable impacts of digital transformation in the banking system encompass transparency and justice, the creation of new opportunities, the future outlook of banking and the economy, digital leadership and mindset, social and environmental impacts, long-term policymaking and planning, as well as internal and external networking.
Introduction
Digital transformation is a crucial driver of development, particularly in the banking sector, where it reshapes traditional business models and operational processes, enabling efficient online financial services. Despite these advancements, Iranian banks face infrastructural and regulatory challenges, such as weak IT systems and traditional organizational cultures, which hinder the full adoption of digital solutions. To address these issues and align with global standards, comprehensive digital banking policies are necessary. This study explores the effects of these policies, focusing on the changes they bring about in both the short and long term. In this context, the fundamental research question is: What are the expected dimensions and components of the outputs and long-term impacts following the implementation of digital banking policies during the evaluation period?
Literature Review
Digital transformation in banking refers to the use of digital technologies to fundamentally improve and change the processes and structures of traditional banking. This transformation includes the application of technologies such as artificial intelligence, blockchain, big data, and cloud computing, which enable banks to offer innovative and customized services to customers (Vial, 2014). The emergence of fintech companies and the provision of digital financial services have pressured traditional banks to digitalize their structures. As a result, banks have developed digital transformation policies to remain competitive and align with market changes (Salamatitaba et al., 2017). In digital banking policy-making, evaluation is recognized as one of the most critical stages in the policy cycle. According to various literature and definitions, outputs are the immediate results obtained in the short term following the implementation of the policy, including direct improvements in digital banking services and processes. These results are typically observable within 1 to 3 years after the policy is implemented. In contrast, impacts refer to the long-term outcomes that emerge between 5 to 10 years post-implementation, encompassing the lasting effects and consequences of the policy. Research indicates that digital transformation in banking leads to improved customer satisfaction, enhanced transaction security, and optimized processes.
Methodology
This applied-developmental study employs a mixed-method approach. In the qualitative phase, 31 banking and policy experts were selected through purposive and snowball sampling, with saturation reached after 25 interviews. Data were analyzed using thematic analysis with MAXQDA software. In the quantitative phase, a survey was administered to 354 bank managers and experts, with the sample size calculated using Cochran's formula. The researcher-developed questionnaire included 36 items based on a five-point Likert scale. Reliability was evaluated using Cronbach’s alpha, yielding a score above 0.7. Data analysis was conducted using the Partial Least Squares (PLS) method with SmartPLS software to validate the findings.
Results
The thematic analysis of the qualitative data identified 88 descriptive codes, 21 interpretative codes, and 8 overarching themes for the outputs, as well as 54 descriptive codes, 15 interpretative codes, and 7 overarching themes for the impacts following the implementation of digital banking policies. The outputs were categorized into areas such as revenue and market positioning, cost efficiency, customer acquisition and satisfaction, bank infrastructure, data management, products and services, banking technologies and channels, and risk management. The long-term impacts included dimensions such as transparency and justice, the creation of new opportunities, the outlook for banking and the economy, digital thinking and leadership, social and environmental impacts, long-term policymaking and planning, and internal and external networking. Quantitative data analysis was conducted using the Partial Least Squares (PLS) method with SmartPLS software. The model was validated through relevant statistical tests, demonstrating a satisfactory fit and reliability.
Discussion & Conclusion
The findings align with key studies. Czerwińska et al. (2021) confirmed that investment in digital technologies enhances banks' competitive positions, which this study supports. Similarly, Lydiana et al. (2022) highlighted the role of digital transformation in fostering innovation, with this study further expanding the focus to service development. Consistent with Agboola et al. (2019), this research shows that digital transformation improves cost efficiency and operational performance. The impact on customer satisfaction and acquisition aligns with Aydin & Onayli (2020), emphasizing the role of digital experiences in customer growth. The analysis of organizational transformation matches Mirković et al. (2019), and the emphasis on data management is consistent with Sadigh et al (2022).
This study differentiates between the short-term outputs and long-term impacts of digital transformation in banks, providing a unique perspective compared to previous studies. It offers localized insights tailored to the Iranian market, making the findings particularly relevant for policymakers. It emphasizes that policymakers and regulatory bodies, such as the Central Bank, should prioritize strengthening IT infrastructure, developing comprehensive data security and privacy regulations, and focusing on new technologies like open banking and big data. The results indicate that the long-term effects of digital transformation on economic indicators and customer behavior require further investigation. Future studies could explore these effects over different time periods and examine how technologies such as AI and blockchain influence customer psychology, including decision-making, trust, and satisfaction, as well as social factors like access to banking services and the distribution of opportunities.
Keywords: Digital Transformation, Digital Banking, Policy Evaluation, Policy Outcomes.
پسآیندهای خطمشی بانکداری دیجیتال
Research Paper
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.
Research Paper
Management approaches in the field of smart
Aryan Setareh Tabrizi; Ali Mohtashami
Abstract
Shipping industries in ports, as one of the main and strategic industries, have always performed an important role in the optimized management of the transportation business in a country. Moreover, it has an impact on the country's economy. The future status of port’s business management can be ...
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Shipping industries in ports, as one of the main and strategic industries, have always performed an important role in the optimized management of the transportation business in a country. Moreover, it has an impact on the country's economy. The future status of port’s business management can be seen in planning and forecasting, access to new technology in order to smartness, access to logistics, increase in specialized human resources and the impact of ports globalization. For this reason, the ports have to provide their services in a sutaible way in order to plan for their business development and supply demands. Also, the process of these changes in the future will be more than that, it will have a direct impact on the technology and facilities of the port. According to the mentioned points, in this article, in order to promote the ports and shipping industry, in the first step, the main indicators of green and smart ports are identified by library study, and then interviews are conducted with thematic analysis system approach. Next, the conceptual model of the research was drawn.Consequently, each index in the drawn pattern and the final index are evaluated in MATLAB software by Fuzzy Inference. As result of that, it is possible to determine the smartness and environmental indicators in the ports of the country for the first time. In this regard, Anzali port, one of the country's most important ports, will be implemented and concluded as a real example.
Introduction
Ports and harbors face serious competition to deliver a more efficient and safer flow of goods around the world. In this context, a new concept has emerged, which is called a smart port (2019, Molavi). Targeted smart port initiatives seek to remove specific barriers at ports. These initiatives are largely focused on specific applications of information and communication technology and regulation-based approaches in smart ports. These rules are aimed at improving port sustainability, the implementation of new technologies and providing port performance evaluation standards. In this part, the generalities of the research are presented in the form of stating the problem and the necessity of conducting the research, and in the next part, the goals and assumptions of the research are presented. Finally, the executive structure of research and innovation is presented.
Research Methodology
In this article, two library and field methods have been used to collect information. We reviewed educational theses, foreign and Iranian published books, Persian and English publications and some textbooks. Then, the basis the questions for the interview was designed. In this research, the snowball method was used and the number of experts in this field reached fifty people. Then, the subject method is used to obtain the data and information needed to identify the indicators. Semi-structured interviews were used based on the interview protocol. (2019, Yarahmad Ghasemi)
Innovation and novelty of the research:
Background and up-to-date information on research innovation:
Research Port 360 has recently prepared a comprehensive report in collaboration with the World Ports Association which report aims to examine the activities related to the management of smart port markets in the period 2023-2028 and is intended to be used in the agenda of all ports in the world.
Through the analysis and research conducted in this report, the dynamic chain of the global smart ports industry market during the period 2023-2028 has been well studied and an overview of how ports are becoming smart has been provided.
In the regulation of technology, it can be said that a digital port describes a connected port that uses broadband communication infrastructure, flexible and service-oriented computing infrastructure, and innovative services to meet demands. On the other hand, intelligence, along with international laws and regulations in the field of environment, results in a developed port.
Some of the things that distinguish this research from other research in terms of innovation are as follows:
In this research, services in the field of ports are considered in both implementation forms combined with a strategy and which have been thoroughly examined in research interviews, which has not been seen in the articles in this way as it has been accurately displayed in the maximum research table.
In this research, all four service levels that should be considered in ports, such as traffic management, safety, environment and ship management, have been examined in the research to extract concepts and ultimately the main indicators.
The managerial and executive application of this research is ultimately the implementation of environmental and smart components in the country's ports, which leads to the sustainable development of ports.
Research findings
According to the thematic analysis, the smartness and greenness of ports is drawn in Figure 4. We should evaluate all the indicators obtained from the evaluation model with the Mamdani logic in FIS with MATLAB software in order to determine the level of implementation of the intelligence and environmental model of ports. The inputs are: 1) port intelligence 2) greenhouse gas production 3) smart infrastructure 4) water and waste management 5) renewable resources utilization 6) environmental management 7) cultural support 8) energy consumption 9) safety management 10) greenness of the ports. Considering the average value for all the inputs, we will see that the result for the output is equal to the average. For instance, according to Figure 9, the environmental model and intelligence in the areas where the value of the smart technology is very high and high, and the amount of the greenhouse gas production is low and very low, the result for the environmental and intelligence model is high. After that, Anzali port, was considered as a real model and the results is obtained as Figure 12. It indicates that the organization of this port is not ready to implement the designed model.
Conclusions and Discussion
Evaluating the efficiency of ports in terms of compliance with green and intelligence indicators is very important and strategic. (Tabrizi, 2023) In this study, first the main indicators for evaluating green and smart ports were identified through a library study and a thematic analysis system, and then the conceptual model of the research was drawn. Each indicator was evaluated based on the fuzzy inference, and finally all indicators were embedded in the form of a final model in FIS, which can be implemented for the first time in the country's port business. In this regard, it was implemented in Anzali Port as a real example and conclusions were drawn. It is necessary that the issue of greenness or the environment of ports be given priority attention by ports of countries, because for international communications especially European ports, the greenness of ports is of great importance. The issue of greenness of ports is put on the agenda, and the need to implement smart indicators in ports requires more attention, because in analyzing the indicators by fuzzy inference and also implementing the model on the real example, it shows that the smartness component is given little importance in the country's port business, which is hoped to be resolved separately. Therefore, evaluating the efficiency of ports in terms of compliance with green and intelligence indicators is very important and strategic.
Keywords: Transit Ports, Intelligent Factors of Port Business Management, Green Factor, Thematic Analysis, Fuzzy Inference.
Research Paper
Data science, intelligence and future analysis
Fatemeh Akhoondi Barzoki; MohammadReza NiliAhhmadabadi; Mehdi ShamiZanjani
Abstract
The emergence of advanced technologies alongside the Fourth Industrial Revolution in higher education has gradually introduced new concepts such as digital transformation, digital education, and ultimately the digital university. The digital university is an emerging concept that requires definition, ...
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The emergence of advanced technologies alongside the Fourth Industrial Revolution in higher education has gradually introduced new concepts such as digital transformation, digital education, and ultimately the digital university. The digital university is an emerging concept that requires definition, and it can be clarified by identifying its features. Therefore, this research aims to comprehensively study the theoretical foundations of the digital university by outlining its characteristics, enabling a deeper understanding and comprehension in this field, and contributing to the development of studies from both theoretical and practical perspectives. To thoroughly analyze the theoretical foundations of the characteristics of the digital university, a systematic review method was employed. After formulating the research question and establishing the search strategy, 43 relevant scholarly articles published between 2000 and 2022 were identified and analyzed. Through the systematic review of the theoretical foundations and by utilizing open, axial, and selective coding methods, eight characteristics were identified: ecosystem and comprehensiveness, digital curriculum, socialization, digital strategies, digital experience, platform-centricity, data-centricity, innovation, and agility. Each dimension comprises various components that reflect the similarities and differences between traditional universities and digital universities which were approved by the experts. The results of this research can serve as a guide for higher education institutions to evaluate and align their characteristics, determining their position in the move towards digitalization. Institutions can consider these characteristics in their overall management strategies to enhance competitiveness in response to the evolving demands of modern society.
Introduction
With the emergence of new technologies and the advent of the digital transformation era, higher education institutions face new challenges and opportunities. Managers encounter pressures from global competition, demographic changes, and student expectations, using digital transformation strategies to improve processes and create new models.
Digital transformation involves the convergence of emerging technologies and the redefinition of educational services, steering universities towards the digital or 4.0 university model. This model includes extensive use of technologies, digital management, and creating engaging experiences for stakeholders.
The digital university utilizes digital technologies and artificial intelligence to enhance decision-making, reduce bureaucracy, and ensure global competitiveness. The COVID-19 pandemic accelerated the need for digital transformation and increased the importance of digital education quality.
Research aim
Explaining the characteristics of the digital university and explain them through a systematic review of theoretical foundations.
Literature Review
In domestic research, Rahimian, Arasteh, and colleagues have focused on identifying the features and policy models of the digital university. Foreign studies include works by Jones and Goodfellow (2014), who examined the meanings and definitions of the digital university; Smyth et al. (2015), who focused on the strategy and practice of the digital university; and Khalid et al. (2018), who explored emerging technologies and their impacts. Grigoriev and Mishota (2019), Doroshenko et al. (2021), and Akhmetshin et al. (2021) have proposed various models for the digital university that include different levels of services and technologies. Despite the importance of the topic, further research is needed to explore the structure and features of the digital university.
Methodology
In this study, due to the lack of a precise definition of the concept of a digital university, a systematic review of the theoretical foundations has been employed. This method consists of eight stages: identifying the objective, creating a protocol, screening, literature search, information extraction, quality assessment, synthesizing research, and writing the systematic review.
The search was conducted in reputable databases using keywords such as "digital university," resulting in 43 articles. The articles were examined and filtered based on various criteria such as language, date, population, and type of study. Ultimately, 19 top articles were selected for analysis. The articles were assessed for content quality using the CASP evaluation method. Concepts and models were extracted through qualitative content analysis, then which were approved by the experts.
Results
In this study, the activities of the digital university extracted from selected articles are considered as codes, which are categorized into 9 themes. The themes, which represent the characteristics of the digital university, include digital ecosystem, digital curriculum, digital socialization, digital strategies, digital experience, platform-centricity, data-centricity, innovation and agility.
Figure 1. Features of digital university
Discussion
The key features of the digital university include eight categories, the Digital Ecosystem Focuses on interaction with institutions and changes in traditional university functions. The Digital Curriculum Concerns the digitization of content and teaching-learning methods, creating cyber-physical educational environments. Digital Socialization Aims to establish digital interaction between students and graduates to create a digital community ultimately. Digital Strategy Addresses the need for changes in organizational strategies to advance long-term goals. Digital Experience Focuses on enhancing stakeholder experiences by developing competencies and digital literacy. Platform-centric involves the use of innovative technologies and intelligent systems. Data-centric involves collecting and analyzing data for effective decision-making. Innovation and Agility Refers to the use of novel approaches in education and addressing challenges.
Conclusion
Therefore, for success in the digital age, universities must pay attention to innovation and quality in educational and managerial processes and confront the challenges of digitalization. Emphasis on strengthening digital literacy, creating appropriate infrastructure for digital transformation, and creating a digital university is essential.
Keywords: Digital Transformation, Digital University, characteristics of the digital university, Systematic Review.
Research Paper
Management approaches in the field of smart
Elnaz Valizadeh Hamzekolaei; Ameneh khadivar; Fatemeh Abbasi
Abstract
Abstract
Social networks have become vital for sharing opinions and feelings through user-generated content. Many organizations leverage analytics to enhance decision-making, yet most sentiment analysis studies focus on commercial businesses, neglecting non-profits despite their significant social media ...
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Abstract
Social networks have become vital for sharing opinions and feelings through user-generated content. Many organizations leverage analytics to enhance decision-making, yet most sentiment analysis studies focus on commercial businesses, neglecting non-profits despite their significant social media presence. This research investigates the impact of user-generated content on the financial performance of non-profit organizations using a dataset of 26,714 tweets from 23 accounts. Results indicate that while positive emotions do not affect financial performance, negative emotions and retweets harm it, whereas likes positively influence revenue. Future research should explore additional social networks and broader data collection methods.
Introduction
Free market societies comprise three main sectors: the public sector, the private business sector, and private non-profit organizations, collectively known as the "three-sector economy" (Weisbrod, 1975). Non-profit organizations are characterized by being organized, private, self-managed, non-profit distributing, and voluntary (Salamon et al., 1996). This research focuses on non-profits dedicated to animal welfare, which aims to prevent animal abuse and ensure proper care (Navigator Charity, n.d.). Recently, these organizations have gained prominence and significantly influenced societal modernization (Lee & Nowell, 2014). Evaluating their performance is crucial for enhancing efficiency amid increasing competition for funding. Given the challenges of measuring performance in non-profits, this study employs sentiment analysis of user-generated content on Twitter to assess organizational effectiveness.
Research Question(s)
How is the performance of non-profit organizations evaluated using user opinion analysis?
Literature Review
This literature review examines existing studies relevant to the research topic and identifies gaps that necessitate this investigation. Non-profit organizations generate revenue and publish annual financial statements (Rathi et al., 2016). They increasingly use social networks to engage with stakeholders (Lai et al., 2017), producing content that can yield valuable insights through user opinion analysis (Miller, 2011). Social networks enable these organizations to gather stakeholder feedback, enhancing decision-making (Waters & Lo, 2012). Non-profits typically focus on measuring performance through donor revenue and budget progress, emphasizing the importance of both financial and non-financial metrics (Epstein & McFarlan, 2011; Kaplan, 2001).
Research has explored the effects of user-generated content on the performance of both non-profit and for-profit organizations. For instance, studies have shown that Twitter content can predict sales for commercial enterprises (Liu et al., 2016) and that negative user messages elicit more responses, prompting businesses to adapt their communication strategies (Hewett et al., 2016). Additionally, analysis of user content has identified key factors influencing millennial engagement online (Saura et al., 2019). Another study linked customer feedback on Twitter to satisfaction and dissatisfaction factors in the hotel industry (Xu et al., 2017), while research demonstrated that emotions in text comments significantly affect product sales performance (Li, 2018).
In non-profit contexts, stakeholder-generated content can enhance participation strategies (Saxton & Waters, 2014), and increased social campaign popularity correlates with heightened discussion (Tayal & Yadav, 2016). However, most existing research focuses on emotional impacts on specific campaigns rather than overall organizational performance, revealing a significant gap. This study aims to fill this gap by evaluating non-profit performance through sentiment analysis of social media content, presenting an innovative model that incorporates econometric methods. Thus, this research represents a novel contribution to understanding non-profit performance evaluation.
Methodology
This study examines selected non-profit organizations evaluated through "Charity Navigator," a prominent charity evaluator in the United States. To refine our sample and focus on larger, more active organizations on social media, we began with a pool of 9,000 non-profits and used advanced search filters (see Table 1) to identify 60 organizations. From this group, five organizations were randomly chosen for further analysis.
Table 1. Title of filters for advanced search of non-profit organizations on charitynavigator website
The title of the characteristics of non-profit organizations
Characteristics of non-profit organizations
Social network
Twitter
Select category-type
Place
Income
Site ranking
Work area
Animals - rights, welfare and services to animals
The entire United States of America
No restrictions
No restrictions
international
Data collection involved manually extracting financial reports detailing total income for each organization from 2010 to 2020 via the "ProPublica" website. We also identified 23 English-language Twitter accounts related to these organizations, from which we gathered tweet data using a Python-based web crawler.
Data preprocessing included removing non-English texts, hashtags, mentions, URLs, punctuation, and stop words, as well as performing tokenization and lemmatization. This resulted in a dataset of 22,829 tweets from the five selected non-profits.
Data Visualization
Word Cloud: We generated a word cloud using the hyperwords package in Python, displaying the 50 most frequently used words, with their size reflecting usage frequency.
Topic Modeling: To explore underlying topics in the tweets, we applied Dirichlet’s hidden allocation algorithm, identifying five main themes:
Vegetarian education
Addressing cruelty and rescuing animals
Animal protection
Monitoring organizational actions
Supporting organizational activities
Results showed vegetarian education was the most discussed topic, while support for non-profits was the least frequent, indicating users prioritize other issues.
Sentiment Analysis
We conducted sentiment analysis using a vocabulary-based approach. Texts were standardized to lowercase, and stop words and punctuation were removed. A dataset of 1,000 tagged examples was used to evaluate sentiment accuracy.
Econometric Analysis
This research assesses how user-generated content on Twitter impacts the annual income of non-profits. After necessary tests and model estimation using Evioz 10 software, we evaluated the effects of independent variables on total revenue, summarized in Table 2.
Table 2. Variables used in the model
Mathematical symbol
English symbol
Mean
1
y
Total Revenue
Total Revenue
X1
X2
X3
X4
X5
Volume
Neg
Positive
Retweet
Favorite
Volume of tweets
Negative sentiments
Positive sentiments
Number of retweets
Number of favorits
The regression model is expressed as:Total Revenue = αi + β1X1it + β2X2it + β3X3it + β4X4it + β5X5it + eit
We conducted Chow or Flimer tests to determine data structure and used Fisher’s test for the unit root test, confirming stationarity at a 95% confidence level.
Model Estimation
Using the OLS method, we found the model significant at the 95% confidence level, with a coefficient of determination of 0.512638, indicating that over half of the variability in total revenue is explained by the model. The Durbin-Watson statistic indicated no autocorrelation among residuals.
Results showed a significant relationship between Twitter content and financial performance. Negative sentiments and retweets inversely affected financial outcomes, while likes positively correlated with revenue, indicating active support from followers. No significant relationship was found between tweet volume or positive sentiments and financial performance.
Results
The results show that users' opinions have a significant impact on the financial performance of these organizations. Therefore, non-profit organizations should pay special attention to their users and donors to increase their satisfaction. Otherwise, the lack of proper management of the organization's actions can damage its credibility. Also, there is a need for further investigation to determine the cause of the negative relationship between the number of retweets and financial performance of organizations. Modeling of topics discussed by users shows that managers of non-profit organizations should focus more on building trust among their users, because the results of topic modeling show that support for the organization is the least frequent.
Keywords: Sentiment Analysis, Nonprofit Organizations, Topic Modeling, Social Network, Panel Data.
Research Paper
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.
