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Purpose: The purpose of this study was to prioritize the factors affecting consumer-buying behavior in online social media marketing.Method: The analysis and validation of indicators were conducted using the mixed method of structural equation modeling and partial least squares. Additionally, the IPMA matrix was utilized to determine the importance and type of performance of each factor. The research data were influenced by consumer information, predominantly consisting of women with a master's degree aged between 36 and 50 years. The study employed a non-probability sampling method common for online surveys, and 466 individuals were examined using Cohen's effect size formula.Findings: The research findings indicated that consumer engagement was the most important factor, and ease of use exhibited the highest level of performance in the overall social media model. Furthermore, consumer engagement was identified as the most important factor, while electronic word-of-mouth (EWOM) demonstrated the highest performance level on Instagram. Conversely, EWOM was deemed the most important factor, with consumer engagement displaying the highest performance level on Telegram. These results can be utilized by marketers to influence consumer purchasing behavior and craft online marketing strategies accordingly.Conclusion: Companies utilizing online social media platforms are advised to enhance consumer engagement and performance by implementing short-term techniques and effective strategies, such as incorporating online chat functionalities in the user environment. Instagram, being the most widely used social media platform and an online shopping hub for consumers, showcased acceptable performance in terms of consumer engagement. Hence, companies need to take measures to elevate performance levels to match their importance. In the case of Telegram, EWOM emerged as the most crucial factor among others, with a commendable performance rating. Businesses can bolster their Customer Relationship Management (CRM) units and enhance WOM initiatives to the fullest extent possible.

This paper provides a systematic review of the literature on big data capabilities, business model innovation, firm performance, and environmental uncertainty. It aims to establish a foundation for theoretical modeling, research proposition refinement, and the overall research framework by meticulously examining the theoretical backgrounds of existing studies and identifying research gaps. An initial search yielded 1,360 articles, which were filtered to remove duplicates and irrelevant studies, resulting in 475 articles for final analysis. These articles were classified into three main categories: the relationship between big data capabilities and business model innovation, the impact of business model innovation on firm performance, and the integrated relationship involving environmental uncertainty. Additionally, it examines the mediating role of business model innovation on firm performance as well as the moderating effect of environmental uncertainty on these relationships. Finally, the paper formulates research hypotheses and discusses identified research gaps, establishing a solid groundwork for methodological discussions in future research and contributing to the advancement of knowledge in the field.

Today, gamification is being used in various areas such as education, health, and business to enhance engagement and increase the system's efficiency. Despite significant scholarly interest, in many cases, undesirable results have been achieved using gamified solutions. This highlights the need for further research to explore these challenges through innovative methodologies and to devise new solutions. Addressing this gap, we conducted a systematic review of the literature on the emerging and growing subject of gamification using the PRISMA methodology and proposed a novel model for the strategic development of future gamification studies. The research led to the identification of 48 qualified empirical studies which have been analyzed to outline the existing views, gaps, and consequently the implications for future research. Through the analysis, we delineate the impact and effectiveness of gamification, highlighting its potential to transform user experience positively when implemented with strategic finesse. Consequently, we propose a novel model for the strategic development of future gamification studies, presenting it in three main dimensions: Contexts, Users, and Elements, and for each dimension, significant and less-paid topics are discussed. In addition, we represent six main suggestions for the design of the entire gamified system: Decision-Making Methods, Success Factors, Validation Methods, Dynamic Design Approach, Timeframe, and Modern Technology. Our proposed model not only facilitates a deeper understanding of gamified systems but also offers actionable insights and guidelines for both academics and practitioners. It is meticulously designed to assist researchers and practitioners in crafting more effective gamified systems that are customized to meet specific user needs and environmental contexts. By doing so, it aims to maximize the sustainable benefits of gamification, ensuring that these systems deliver significant and lasting impacts. This strategic approach integrates the latest advancements in technology and dynamic design principles, establishing a robust framework for the future of gamification research and application.

Purpose: In contemporary times, augmented reality is a cornerstone technology within the tourism sector. Nonetheless, the inclination towards embracing and utilizing augmented reality applications in tourism hinges upon multifaceted determinants, meriting examination through the integrated lens of technology acceptance and use theory. Concurrently, these determinants sway individuals' hedonistic motivations to engage with such apps. This juncture holds significance for managerial considerations, delineating pivotal factors influencing the adoption of this technology. Hence, this present inquiry explores the nexus between the technology acceptance and use theory and the hedonistic motivations for tourism augmented reality apps on mobile platforms within the Iranian context.Method: The current research's statistical population consists of users of tourism augmented reality apps. The sample size is 201 users. The data collection tool is a questionnaire. A structural equation test was used to analyze the data.Findings: The research findings indicate that four factors, namely performance expectancy, effort expectancy, social influence, and facilitating conditions, have a positive and significant impact on the hedonic motivation to use tourism augmented reality apps. Moreover, all four factors of the Unified Theory of Acceptance and Use of Technology (UTAUT) positively influenced the intention to use augmented reality applications on mobile phones. Additionally, hedonic motivation was shown to positively and significantly affect the intention to use tourism augmented reality apps on mobile phones. Notably, hedonic motivation is a crucial mediator between the Unified Theory of Acceptance and Use of Technology (UTAUT) dimensions and the intention to use augmented reality applications. However, the mediating role of hedonic motivation was not supported for performance expectancy and behavioral intention.Conclusion: This research investigates the factors influencing the motivation to use augmented reality (AR) apps in tourism. It finds that performance expectancy, effort expectancy, social influence, and facilitating conditions positively impact hedonic motivation for AR app usage. The study validates the Unified Theory of Acceptance and Use of Technology (UTAUT) for predicting AR app adoption. Notably, hedonic motivation mediates between UTAUT dimensions and intention to use AR technology, suggesting implications for enhancing AR app design in the tourism sector.

The article justified the feasibility of an investment project by analysing the performance indicators while taking into account risk and uncertainty of the use of information technologies. The impact of the above calculations of the investment project results is due to the fact that the evaluation of the investment performance depends on the projected cash flows. The purpose of the article is to assess the impact of risks on making investment decisions using information technologies in order to increase the financial security of enterprises. Methodological and practical aspects of risk modelling of the investment project were further developed, using the Monte Carlo method, which allows to construct a model by minimizing data, as well as to maximize the value of data used in the model. This model involves the use of probability theory and random number tables. The results show the distribution of probabilities of the successful project variable and the coefficient of variation of the performance indicator, allowing the investor to take uncertainty into account when making a decision.

Purpose: The key to startup success is a brand-centric approach that combines brand leadership strategy with creative, targeted, and cost-effective business methods. In the brand-centered approach, all company processes revolve around the development of brand identity in continuous interaction with customers, to create long-term competitive advantages for the brand. The purpose of this research is to investigate the impact of entrepreneurship-based branding in cyberspace on the performance of startups.Method: The current research is of an applied type and with a mixed method and a sequential qualitative-quantitative approach, first qualitative and then quantitative data was collected and analyzed. Due to the small size of the population, sampling was not done and all 52 people were examined. Smart PLS software was used to analyze quantitative data and test research hypotheses and implement structural equation modeling. Confirmatory factor analysis was also used to test the significance of the relationships and the fit of the obtained measurement models. To analyze the data and test the research hypotheses, the structural equation modeling method was applied.Findings: Qualitative findings showed that the branding model based on entrepreneurship in cyberspace includes six main themes and 18 sub-themes. Causal factors include risk-taking categories, business situation analysis, and brand content capabilities; Background factors include the categories of brand organizational structure, market orientation, and brand orientation; intervening conditions include the categories of legal infrastructure, customer characteristics, and economic uncertainty; the central category includes the three categories of brand vision depiction, brand integrity and entrepreneurial capabilities; strategies including three categories of brand positioning, strategic planning for branding and value creation; And the consequences include three categories of competitive advantage, strategic brand development and brand identity development. Quantitative findings showed that the causal conditions of branding with a score of 26.719 has a positive and significant effect on entrepreneurial branding; Entrepreneurial branding with a score of 4.873 on value-based brand positioning; And value-based positioning with a score of 29.960 has a positive and significant effect on the performance of startups.Conclusion: The results indicate that the branding model based on entrepreneurship in cyberspace and its impact on the performance of startups includes six main themes and 18 sub-themes, and branding based on entrepreneurship in cyberspace has a positive and significant effect on the performance of startups.

This paper provides a comprehensive overview of the evolving Artificial Intelligence (AI) threat to cybersecurity, emphasizing the urgent need for finance leaders and cybersecurity professionals to adapt their strategies and controls to effectively combat AI-powered scams and cyber-attacks. The study delves into the specific ways in which AI is being used maliciously in cybercrime, such as enhanced phishing and Business Email Compromise (BEC) attacks, the creation of synthetic media including deepfakes, targeted attacks, automated attack strategies, and the availability of black-market AI tools on the dark web. Furthermore, it highlights the critical need for enhanced cybersecurity strategies and international cooperation to combat cyber threats effectively. The findings of this study provide valuable insights for finance leaders, cybersecurity professionals, policymakers, and researchers in understanding and addressing the challenges posed by generative AI in the cyber threat landscape.

Purpose: Economic and social status play a prominent role in many human activities and their  function is accentuated in the theory of the knowledge gap. According to the idea, the knowledge of the people with higher socio-economic status increases compared to those with lower socio-economic status. The purpose of this study was to determine the role of socio-economic status (based on knowledge gap theory) in the information-seeking behavior of fellow members of staff at  Qom University.Method: The study was an applied research in terms of purpose and in terms of strategy and data collection was correlational. The population consisted of 761 university employees. Based on Cochran’s formula the sample of the study included 255 employees. A researcher-made questionnaire was used to collect data. Spearman and X2 statistical tests were applied to analyze data.Findings: People who have a higher socio-economic status (with higher employment, income and education levels) are more motivated to search and obtain information, and there is a significant relationship between the components of individuals' socio-economic status and the type of the used information resources. Socio-economic status affects the criteria for evaluating information resources, and people with higher rate use various evaluation criteria while assessing the information. People with socio-economic status use various and different channels to obtain information, thus, there is a positive and significant relationship between the use of search engines and meta-search engines, internal and external databases, conference papers, library RSS, specialized social networks, consultation with librarians and technical blogs, and their socio-economic status.Conclusion: The social and economic status explains and predicts the information-seeking behavior of the staff and the results confirmed the theory of knowledge gap. Prediction of the facilities required for searching and seeking information in organizations and making them accessible to all human resources can help provide fair access to information for the better part of society and reduce the knowledge gap.

Diseases like diabetes are chronic and require long-term management. Inadequate production of insulin results in high blood sugar levels. Such diseases lead to serious health issues such as heart ailments, blood vessel complaints, eye ailments, kidney function disorders, and nerve ailments. Hence, accurate assessment and management of risk factors are crucial for the onset of diabetes. Our proposed approach combines fuzzy logic & machine learning algorithms for diabetes risk prediction. Three machine learning models were trained to classify patients into two categories of diabetes (Type-I and Type-II) based on their clinical dataset collected from Katihar Medical College & Hospital and Suvadhan Lab. The polynomial regression algorithm achieved a score of 0.947, while the support vector regression algorithm with the rbf kernel achieved a score of 0.954, with a linear kernel achieved a score of 0.73. Our proposed approach performed well with respect to the conventional approaches with improved accuracy by identifying the patients at diabetes risk. In future work, we further analyze the relationship between other ignored factors which contribute to diabetes risk.

An Artificial Intelligence (AI) technique plays the most crucial factor to consider in energy utilization in a wireless sensor network (WSN). AI transforms industrial operations by optimizing the energy consumption in sensor nodes. As a result, it is crucial for improving sensor node location accuracy, particularly in unbalanced or Adhoc environments. Because of this, the purpose of this research is to improve the accuracy of the localization process in locations where sensor nodes encounter barriers or obstacles on a regular basis. The Bees Swarm Optimization (BSO) algorithm is used to segment sensor nodes in order to increase the accuracy of the Direction of Arrival (DoA) estimate between the anchor and unknown node pairs. Even in the presence of unbalanced conditions, the proposed DoA- BSO involving three separate bee colonies can identify plausible anchor nodes as well as segment nodes arranged in clusters. In order to obtain the intended result, the objective function is designed to take into consideration the hops, energy, and transmission distance of the anchor and unknown node pairs, among other factors. The studies are carried out in a large-scale WSN using sensor node pairs in order to determine the precision with which the DoA-BSO can be located. When comparing DoA-BSO to conventional approaches, the findings of the meta-heuristic algorithm show that it improves the accuracy and segmentation of nodes significantly

The military and political leadership of Ukraine considers the domestic military-industrial complex as an important component of the country's national security and defense strategy and pays special attention to increasing the efficiency of production and scientific and technical activities of defense industry enterprises and organizations. The study represents directions for improving the state regulation for the further development of the military-industrial complex in Ukraine under the conditions of the transition to modern information technologies. Proposals have been made for the formation of the organizational and economic mechanism for state regulation development of the military-industrial complex, aimed at ensuring its innovativeness, stimulating scientific and technical activity, and implementing modern information technologies systematically during the production of weapons, ammunition and military goods.