Aim/Purpose: This study investigated a model of mobile esports acceptance among urban citizens based on an extended Technology Acceptance Model (TAM). Background: Currently, esports are increasingly popular and in demand by the public. Supported by the widespread development of mobile devices, it has become an interactive market trend to play games in a new model, mobile esports. Methodology: This study collected data from 400 respondents and analyzed it using partial least squares-structural equation modeling (PLS-SEM). Contribution: This study addresses two research gaps. The first gap is limited esports information systems studies, particularly in mobile esports acceptance studies. The second gap is limited exploration of external variables in online gaming acceptance studies. Thus, this study proposed a TAM extended model by integrating the TAM native variables with other external variables such as visual appeal, enjoyment, social interaction, and competition to explore mobile esports acceptance by urban citizens. Findings: Nine hypotheses were accepted, and four were rejected. The visual appeal did not affect the acceptance. Meanwhile, social interaction and enjoyment significantly affected both perceived ease of use and usefulness. However, perceived ease of use surprisingly had an insignificant effect on attitude toward using mobile esports. Moreover, competition significantly affected the acceptance, particularly on perceived usefulness. Recommendations for Practitioners: Fresh and innovative features, such as new game items or themes, should be frequently introduced to enhance players’ continued enjoyment. Moreover, mobile esports providers should offer a solid platform to excite players’ interactions to increase the likelihood that users feel content. On the other hand, the national sports ministry/agency or responsible authorities should organize many esports competitions, big or small, to search for new talents. Recommendation for Researchers: Visual appeal in this study did not influence the perceived ease of use or usefulness. However, it could affect enjoyment. Thus, it would be worth revisiting the relationship between visual appeal and enjoyment. At the same time, perceived ease of use is a strong driver for the continued use of most online games, but not in this study. It could indicate significant differences between mobile esports and typical online games, one of which is the different purposes. Users might play online games for recreational intention, but players would use mobile esports to compete, win, or even get monetary rewards. Therefore, although users might find mobile esports challenging and hard to use, they tend to keep playing it. Thus, monetary rewards could be considered a determinant of the continuation of use. Impact on Society: Nowadays, users are being paid for playing games. It also would be an excel-lent job if they become professional esports athletes. This study investigated factors that could affect the continued use of mobile esports. Like other jobs, playing games professionally in the long term could make the players tedious and tired. Therefore, responsible parties, like mobile esports providers or governments, could use the recommendations of this study to promote positive behavior among the players. They will not feel like working and still con-sider playing mobile esports a hobby if they happily do the job. In the long run, the players could also make a nation’s society proud if they can be a champion in prestigious competitions. Future Research: A larger sample size will be needed to generalize the results, such as for a nation. It is also preferable if the sample is randomized systematically. Future works should also investigate whether the same results are acquired in other mobile esports. Furthermore, to extend our knowledge and deepen our understanding of the variables that influence mobile esports adoption, the subsequent research could look at other mobile esports acceptability based on characteristics of system functionality and moderator effects. Finally, longitudinal data-collecting approaches are suggested for future studies since behavior can change over time.

Aim/Purpose: This study identifies the elements that influence intentions to purchase from the most popular Arabic online classifieds platform, OpenSooq.com. Background: Online purchasing has become popular among consumers in the past two decades, with perceived risk and trust playing key roles in consumers’ intention to purchase online. Methodology: A questionnaire survey was conducted of Internet users from three Jordanian districts to investigate how they used the OpenSooq platform in their e-commerce activities. In total, 202 usable responses were collected, and the data were analyzed with PLS-SEM for hypothesis testing and model validation. Contribution: Though online trading is increasingly popular, the factors that impact the behavior of consumers when purchasing high-value products have not been adequately investigated. Therefore, this study examined the factors affecting perceived risk, and the potential impact of privacy concerns on the perceived risk of online smartphone buyers. The study framework can help explore online behavior in various situations to ascertain similarities and differences and probe other aspects of online buying. Findings: Perceived risk negatively correlates with online purchasing behavior and trust. However, privacy concern and perceived risk, transaction security and trust, and trust and online purchasing behavior exhibited positive correlations. Recommendations for Practitioners: Customers can complete and retain online purchases in a range of settings illuminated in this study’s methods and procedures. Moreover, businesses can manage their IT arrangements to make Internet shopping more convenient and build processes for online shopping that allow for engagement, training, and ease of use, thus improving their customers’ online purchasing behavior. Recommendation for Researchers: Given the insight into the understanding and integration of variables including perceived risk, privacy issues, trust, transaction security, and online purchasing behavior, academics can build on the groundwork of this research paradigm to investigate underdeveloped countries, particularly Jordan, further. Impact on Society: Understanding the characteristics that influence online purchasing behavior can help countries realize the full potential of online shopping, particularly the benefits of safe, fast, and low-cost financial transactions without the need for an intermediary. Future Research: Future research can examine the link between online purchase intent, perceived risk, privacy concerns, trust, and transaction security to see if the findings of this study in Jordan can be applied to a broader context in other countries.

Aim/Purpose: This research aims to analyze the effect of human resource management on humanitarian operations performance, using humanitarian organizations in Malaysia as a case. Background: Humanitarian organizations need to develop and continue effective on-the-job human resource management, such as training and development and managing employee performance to enhance the performance of their humanitarian operations. Methodology: The sampling technique that was conducted is probability sampling. In particular, the technique is called stratified sampling. This technique is chosen because it is involving the division of a population into a smaller group, called “strata”. The questionnaire survey was distributed to humanitarian organizations in Malaysia to collect research data, and PLS-SEM analysis was conducted to validate the conceptual model. Contribution: This research focuses on the effect of human resource management on humanitarian operations performance in humanitarian organizations with consistent training to ensure successful humanitarian operations. Findings: The results of PLS-SEM analysis confirmed that Training and Employee Development, Recruitment and Employee Selection, and Communicative Management Style are significantly correlated with humanitarian operations performance, giving 75.7% variations which means that these human resource management are critical factors for increasing humanitarian operations performance in Malaysian humanitarian organizations. Recommendations for Practitioners: This research will enhance humanitarian operations performance for humanitarian organizations, in-line policies outlined under the Malaysia National Security Council Directive No. 20, and benefit the field of disaster management. Recommendation for Researchers: This research can be used by the authorized individual involved in humanitarian operations to satisfy the needs of the victims, which ultimately contributes to the performance of these humanitarian organizations. Impact on Society: This research highlighted the human resource management that is vital for humanitarian organizations, which will increase humanitarian operations performance in an organization. Future Research: This study is conducted in the context of humanitarian organizations in Malaysia. It is unclear whether the key findings of this study can be generalized. Therefore, it is suggested that, in future research, the current research model should be extended to include different countries for validation.

Aim/Purpose: This paper identified and explored the factors influencing Bitcoin adoption and use in South Africa. Background: Since its introduction in 2008, the value and popularity of Bitcoin has risen exponentially. Captivating the eyes of the world, from regulators to economists, Bitcoin promises to revolutionize the digital currency space. Despite being over 10 years old, the concept of cryptocurrency is fairly new in South Africa, a developing country. South African’s interest in Bitcoin continues to grow with the country constantly ranking within the top 10 in online searches for “Bitcoin” and “cryptocurrency” on Google. The primary objective of this research was to identify adoption factors amongst South African citizens, an area that has not received much research focus in the past. In addition to this, the study aimed to identify how Bitcoin is primarily used in South Africa. Methodology: A survey-based questionnaire was utilized to obtain responses from adopters of Bitcoin in South Africa. The quantitative survey was completed by 204 respondents. Contribution: This research contributes to the body of knowledge relating to Bitcoin adoption, specifically from a developing country. Adoption factors are identified that can be utilized by businesses that intend to adopt cryptocurrency, to strategically prepare for the potential risks or opportunities brought about by Bitcoin and cryptocurrency in general. Findings: The findings of this study indicate that while perceived usefulness, perceived ease of use, subjective norms, and facilitating conditions positively influence intention to adopt Bitcoin, trust was the only construct that is statistically significant and hence is the greatest driver of adoption in South Africa. In terms of its primary use in South Africa, the study revealed that Bitcoin is used as a speculative instrument for short-term trading in South Africa followed by being used as a long-term investment in the crypto-asset class. No respondent indicated that they utilize Bitcoin as a payment method in South Africa. Recommendations for Practitioners: When developing crypto-based investment products, custodians of assets must ensure that a minimum-security protocol is followed to safeguard these assets. This will enhance the trust that potential investors and customers have in their systems and products. Recommendation for Researchers: This study focused on adoption factors for South African citizens. Future studies should be conducted to identify adoption factors by businesses in South Africa. Impact on Society: Bitcoin offers an alternate trading instrument and investment option, with the possibility of large gains over a relatively short period. Bitcoin also presents the possibility of cross-border transactions at a significantly lower cost compared to traditional cross-border transfers of funds. Future Research: Studies should be conducted to explore the factors influencing the adoption of altcoins to determine if the technological differences influence the adoption of one currency over the other. Research should also be conducted comparing the taxation of cryptocurrency in various countries around the world.

Aim/Purpose: Acquisitions play a pivotal role in the growth strategy of a firm. Extensive resources and time are dedicated by a firm toward the identification of prospective acquisition candidates. The Indian manufacturing sector is currently experiencing significant growth, organically and inorganically, through acquisitions. The principal aim of this study is to explore models that can predict acquisitions and compare their performance in the Indian manufacturing sector. Background: Mergers and Acquisitions (M&A) have been integral to a firm’s growth strategy. Over the years, academic research has investigated multiple models for predicting acquisitions. In the context of the Indian manufacturing industry, the research is limited to prediction models. This research paper explores three models, namely Logistic Regression, Decision Tree, and Multilayer Perceptron, to predict acquisitions. Methodology: The methodology includes defining the accounting variables to be used in the model which have been selected based on strong theoretical foundations. The Indian manufacturing industry was selected as the focus, specifically, data for firms listed in the Bombay Stock Exchange (BSE) between 2010 and 2022 from the Prowess database. There were multiple techniques, such as data transformation and data scrubbing, that were used to mitigate bias and enhance the data reliability. The dataset was split into 70% training and 30% test data. The performance of the three models was compared using standard metrics. Contribution: The research contributes to the existing body of knowledge in multiple dimensions. First, a prediction model customized to the Indian manufacturing sector has been developed. Second, there are accounting variables identified specific to the Indian manufacturing sector. Third, the paper contributes to prediction modeling in the Indian manufacturing sector where there is limited research. Findings: The study found significant supporting evidence for four of the proposed hypotheses indicating that accounting variables can be used to predict acquisitions. It has been ascertained that statistically significant variables influence acquisition likelihood: Quick Ratio, Equity Turnover, Pretax Margin, and Total Sales. These variables are intrinsically linked with the theories of liquidity, growth-resource mismatch, profitability, and firm size. Furthermore, comparing performance metrics reveals that the Decision Tree model exhibits the highest accuracy rate of 62.3%, specificity rate of 66.4%, and the lowest false positive ratio of 33.6%. In contrast, the Multilayer Perceptron model exhibits the highest precision rate of 61.4% and recall rate of 64.3%. Recommendations for Practitioners: The study findings can help practitioners build custom prediction models for their firms. The model can be developed as a live reference model, which is continually updated based on a firm’s results. In addition, there is an opportunity for industry practitioners to establish a benchmark score that provides a reference for acquisitions. Recommendation for Researchers: Researchers can expand the scope of research by including additional classification modeling techniques. The data quality can be enhanced by cross-validation with other databases. Textual commentary about the target firms, including management and analyst quotes, provides additional insight that can enhance the predictive power of the models. Impact on Society: The research provides insights into leveraging emerging technologies to predict acquisitions. The theoretical basis and modeling attributes provide a foundation that can be further expanded to suit specific industries and firms. Future Research: There are opportunities to expand the scope of research in various dimensions by comparing acquisition prediction models across industries and cross-border and domestic acquisitions. Additionally, it is plausible to explore further research by incorporating non-financial data, such as management commentary, to augment the acquisition prediction model.

Aim/Purpose: To predict the change-proneness of software from the continuous evolution using machine learning methods. To identify when software changes become statistically significant and how metrics change. Background: Software evolution is the most time-consuming activity after a software release. Understanding evolution patterns aids in understanding post-release software activities. Many methodologies have been proposed to comprehend software evolution and growth. As a result, change prediction is critical for future software maintenance. Methodology: I propose using machine learning methods to predict change-prone classes. Classes that are expected to change in future releases were defined as change-prone. The previous release was only considered by the researchers to define change-proneness. In this study, I use the evolution of software to redefine change-proneness. Many snapshots of software were studied to determine when changes became statistically significant, and snapshots were taken biweekly. The research was validated by looking at the evolution of five large open-source systems. Contribution: In this study, I use the evolution of software to redefine change-proneness. The research was validated by looking at the evolution of five large open-source systems. Findings: Software metrics can measure the significance of evolution in software. In addition, metric values change within different periods and the significance of change should be considered for each metric separately. For five classifiers, change-proneness prediction models were trained on one snapshot and tested on the next. In most snapshots, the prediction performance was excellent. For example, for Eclipse, the F-measure values were between 80 and 94. For other systems, the F-measure values were higher than 75 for most snapshots. Recommendations for Practitioners: Software change happens frequently in the evolution of software; however, the significance of change happens over a considerable length of time and this time should be considered when evaluating the quality of software. Recommendation for Researchers: Researchers should consider the significance of change when studying software evolution. Software changes should be taken from different perspectives besides the size or length of the code. Impact on Society: Software quality management is affected by the continuous evolution of projects. Knowing the appropriate time for software maintenance reduces the costs and impacts of software changes. Future Research: Studying the significance of software evolution for software refactoring helps improve the internal quality of software code.

Aim/Purpose: This study utilized a comprehensive framework to investigate the adoption of Business Analytics (BA) and its effects on performance in commercial banks in Jordan. The framework integrated the Technological-Organizational-Environmental (TOE) model, the Diffusion of Innovation (DOI) theory, and the Resource-Based View (RBV). Background: The recent trend of utilizing data for business operations and decision-making has positively impacted organizations. Business analytics (BA) is a leading technique that generates valuable insights from data. It has gained considerable attention from scholars and practitioners across various industries. However, guidance is lacking for organizations to implement BA effectively specific to their business contexts. This research aims to evaluate factors influencing BA adoption by Jordanian commercial banks and examine how its implementation impacts bank performance. The goal is to provide needed empirical evidence surrounding BA adoption and outcomes in the Jordanian banking sector. Methodology: The study gathered empirical data by conducting an online questionnaire survey with senior and middle managers from 13 commercial banks in Jordan. The participants were purposefully selected, and the questionnaire was designed based on relevant and well-established literature. A total of 307 valid questionnaires were collected and considered for data analysis. Contribution: This study makes a dual contribution to the BA domain. Firstly, it introduces a research model that comprehensively examines the factors that influence the adoption of BA. The proposed model integrates the TOE framework, DOI theory, and RBV theory. Combining these frameworks allows for a comprehensive examination of BA adoption in the banking industry. By analyzing the technological, organizational, and environmental factors through the TOE framework, understanding the diffusion process through the DOI theory, and assessing the role of resources and capabilities through the RBV theory, researchers and practitioners can better understand the complex dynamics involved. This integrated approach enables a more nuanced assessment of the factors that shape BA adoption and its subsequent impact on business performance within the banking industry. Secondly, it uncovers the effects of BA adoption on business performance. These noteworthy findings stem from a rigorous analysis of primary data collected from commercial banks in Jordan. By presenting a holistic model and delving into the implications for business performance, this research offers valuable insights to researchers and practitioners alike in the field of BA. Findings: The findings revealed that various technological (data quality, complexity, compatibility, relative advantage), organizational (top management support, organizational readiness), and environmental (external support) factors are crucial in shaping the decision to adopt BA. Furthermore, the study findings demonstrated a positive relationship between BA adoption and performance outcomes in Jordanian commercial banks. Recommendations for Practitioners: The findings suggest that Jordanian commercial banks should enforce data quality practices, provide clear standards, invest in data quality tools and technologies, and conduct regular data audits. Top management support is crucial for fostering a data-driven decision-making culture. Organizational readiness involves having the necessary resources and skilled personnel, as well as promoting continuous learning and improvement. Highlighting the benefits of BA helps overcome resistance to technological innovation and encourages adoption by demonstrating improved decision-making processes and operational efficiency. Furthermore, external support is crucial for banks to adopt Business Analytics (BA). Banks should partner with experienced vendors to gain expertise and incorporate best practices. Vendors also provide training and technical support to overcome technological barriers. Compatibility is essential for optimal performance, requiring managers to modify workflows and IT infrastructure. Complexity, including data, organizational, and technical complexities, is a major obstacle to BA adoption. Banks should take a holistic approach, focusing on people, processes, and technology, and prioritize data quality and governance. Building a skilled team, fostering a data-driven culture, and investing in technology and infrastructure are essential. Recommendation for Researchers: The integration of the TOE framework, the DOI theory, and the RBV theory can prove to be a powerful approach for comprehensively analyzing the various factors that influence BA adoption within the dynamic banking industry. Furthermore, this combined framework enables us to gain deeper insights into the subsequent impact of BA adoption on overall business performance. Impact on Society: Examining the factors influencing BA adoption in the banking industry and its subsequent impact on business performance can have wide-ranging societal implications. It can promote data-driven decision-making, enhance customer experiences, strengthen fraud detection, foster financial inclusion, contribute to economic growth, and trigger discussions on ethical considerations. Future Research: To further advance future research, there are several avenues to consider. One option is to broaden the scope by including a larger sample size, allowing for a more comprehensive analysis. Another possibility is to investigate the impact of BA adoption on various performance indicators beyond the ones already examined. Additionally, incorporating qualitative research methods would provide a more holistic understanding of the organizational dynamics and challenges associated with the adoption of BA in Jordanian commercial banks.

Aim/Purpose: Blockchain technology (BCT) has emerged as a potential catalyst for transforming government institutions and services, yet the adoption of blockchain in governments faces various challenges, for which previous studies have yet to provide practical solutions. Background: This study aims to identify and analyse barriers, potential solutions, and their relations in implementing BC for governments through a systematic literature review (SLR). The authors grouped the challenges based on the Technology-Organisation-Environment (TOE) framework while exercising a thematic grouping for the solutions, followed by a comprehensive mapping to unveil the relationship between challenges and solutions. Methodology: This study employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 methodology, combined with the tollgate method, to improve the quality of selected articles. The authors further administer a three-level approach (open coding, axial coding, and selective coding) to analyse the challenges and solutions from the articles. Contribution: The authors argue that this study enriches the existing literature on BC adoption, particularly in the government context, by providing a comprehensive framework to analyse and address the unique challenges and solutions, thus contributing to the development of new theories and models for future research in BC adoption in government settings and fostering deeper exploration in the field. Findings: The authors have unveiled 40 adoption challenges categorised using the TOE framework. The most prevalent technological challenges include security concerns and integration & interoperability, while cultural resistance, lack of support and involvement, and employees’ capability hinder adoption at the organisational level. Notably, the environmental dimension lacks legal and standard frameworks. The study further unveils 28 potential solutions, encompassing legal frameworks, security and privacy measures, collaboration and governance, technological readiness and infrastructure, and strategic planning and adoption. The authors of the study have further mapped the solutions to the identified challenges, revealing that the establishment of legal frameworks stands out as the most comprehensive solution. Recommendations for Practitioners: Our findings provide a big picture regarding BC adoption for governments around the globe. This study charts the problems commonly encountered by government agencies and presents proven solutions in their wake. The authors endeavour practitioners, particularly those in governments, to embrace our findings as the cornerstone of BC/BCT adoption. These insights can aid practitioners in identifying existing or potential obstacles in adopting BC, pinpointing success factors, and formulating strategies tailored to their organisations. Recommendation for Researchers: Researchers could extend this study by making an in-depth analysis of challenges or solutions in specific types of countries, such as developed and developing countries, as the authors believe this approach would yield more insights. Researchers could also test, validate, and verify the mapping in this study to improve the quality of the study further and thus can be a great aid for governments to adopt BC/BCT fully. Impact on Society: This study provides a comprehensive exploration of BC adoption in the government context, offering detailed explanations and valuable insights that hold significant value for government policymakers and decision-makers, serving as a bedrock for successful implementation by addressing roadblocks and emphasising the importance of establishing a supportive culture and structure, engaging stakeholders, and addressing security and privacy concerns, ultimately enhancing the efficiency and effectiveness of BC adoption in government institutions and services. Future Research: Future research should address the limitations identified in this study by expanding the scope of the literature search to include previously inaccessible sources and exploring alternative frameworks to capture dynamic changes and contextual factors in BC adoption. Additionally, rigorous scrutiny, review, and testing are essential to establish the practical and theoretical validity of the identified solutions, while in-depth analyses of country-specific and regional challenges will provide valuable insights into the unique considerations faced by different governments.

Aim/Purpose: Based on the stimulus-organism-response (SOR) model, the current study investigated social commerce functions as an innovative retailing technological support by selecting the three most appropriate features for the Chinese online shopping environment with respective value co-creation intentions. Background: Social commerce is the customers’ online shopping touchpoint in the latest retail era, which serves as a corporate technological tool to extend specific customer services. Although social commerce is a relatively novel platform, limited theoretical attention was provided to determine retailers’ approaches in employing relevant functions to improve consumer experience and value co-creation. Methodology: A questionnaire was distributed to Chinese customers, with 408 valid questionnaires being returned and analyzed through Structural Equation Modeling (SEM). Contribution: The current study investigated the new retail concept and value co-creation from the consumer’s perspective by developing a theoretical model encompassing new retail traits and consumer value, which contributed to an alternative theoretical understanding of value creation, marketing, and consumer behaviour in the new retail business model. Findings: The results demonstrated that value co-creation intention was determined by customer experience, hedonic experience, and trust. Simultaneously, the three factors were significantly influenced by interactivity, personalisation, and sociability features. Specifically, customers’ perceptions of the new retail idea and the consumer co-creation value were examined. Resultantly, this study constructed a model bridging new retail characteristics with consumer value. Recommendations for Practitioners: Nonetheless, past new retail management practice studies mainly focused on superficial happiness in the process of human-computer interaction, which engendered a computer system design solely satisfying consumers’ sensory stimulation and experience while neglecting consumers’ hidden value demands. As such, a shift from the subjective perspective to the realisation perspective is required to express and further understand the actual meaning and depth of consumer happiness. Recommendation for Researchers: New retailers could incorporate social characteristics on social commerce platforms to improve the effectiveness of marketing strategies while increasing user trust to generate higher profitability. Impact on Society: The new retail enterprises should prioritise consumers’ acquisition of happiness meaning and deep experience through self-realisation, cognitive improvement, identity identification, and other aspects of consumer experiences and purchase processes. By accurately revealing and matching consumers’ fundamental perspectives, new retailers could continuously satisfy consumer requirements in optimally obtaining happiness. Future Research: Future comparative studies could be conducted on diverse companies within the same industry for comprehensive findings. Moreover, other underlying factors with significant influences, such as social convenience, group cognitive ability, individual family environment, and other external stimuli were not included in the present study examinations.

Aim/Purpose: The major challenges for firms to initiate corporate social responsibility (CSR) arise from resource constraints, complexity, and uncertainty. Consuming considerable financial and human resources is the main difficulty for smaller firms or those operating in less profitable industries, and the lack of immediate outputs from CSR initiatives poses a challenge for firms in prioritizing and assessing their effectiveness. Background: To better integrate CSR management into overall business strategy and decision-making processes, Blockchain technology (BCT) could potentially offer a feasible and optimal alternative to CSR reports. Methodology: This study uses the fixed effects regression by way of the Least Squares Dummy Variable (LSDV) approach in STATA to analyze the direct effect of CSR management on business performance and the moderating effect of BCT adoption on this relationship with a panel data set of 5810 observations collected from the 874 listed companies in 2015 in Taiwan Stock Exchange through 2021. Contribution: This study contributes to the literature by shedding light on the organizational factors that influence BCT adoption. Findings: The findings show that firms with high levels of CSR management have better business performance. Additionally, the adoption of BCT strengthens the positive relationship between CSR management and business performance, but it cannot replace the fundamental principles of CSR. Finally, firm size does not significantly affect BCT adoption, indicating that companies of all sizes have an equal opportunity to adopt BCT, which can help to level the playing field in terms of resources available to different firms. Recommendations for Practitioners: This study suggests that firms managing CSR practices have better business performance, and the adoption of BCTs further enhances this positive relationship. However, BCT adoption does not have the same positive effect on business performance as CSR practices. Additionally, this research can help to inform public policy related to BCT adoption and diffusion. Recommendation for Researchers: By exploring the factors that influence BCT adoption, future researchers can provide insights into the key challenges and opportunities faced by organizations of different sizes and help to develop strategies for promoting the effective adoption of BCT. Impact on Society: Given the limitations of current CSR reporting, the understanding gained from BCT applications can provide companies with an alternative mechanism to foster progress in CSR implementation. Future Research: Firstly, while the fixed-effects model might have dampened the power of explanation because it only captures within-unit variation and ignores between-unit variation, the explanatory power is further limited due to only integrating two independent variables in this model. Because of limited data availability, this study only utilizes CSR_Report and firm_size as independent variables. Future studies can consider more key factors and may lead to different results. Additionally, panel data is collected from Taiwan and, therefore, may not be representative of the broader population. Future researchers integrating the Stock Exchange of different countries are recommended.

Aim/Purpose: The primary purpose of this study is to investigate the factors that impact the behavioral intention to use social media (SM) for knowledge sharing (KS) in the disaster relief (DR) context. Background: With the continuing growth of SM for KS in the DR environment, disaster relief organizations across the globe have started to realize its importance in streamlining their processes in the post-implementation phase. However, SM-based KS depends on the willingness of members to share their knowledge with others, which is affected by several technological, social, and organizational factors. Methodology: A survey was conducted in Somalia to gather primary data from DR practitioners, using purposive sampling as the technique. The survey collected 214 valid responses, which were then analyzed with the PLS-SEM approach. Contribution: The study contributes to an understanding of the real-life hurdles faced by disaster relief organizations by expanding on the C-TAM-TPB model with the inclusion of top management support, organizational rewards, enjoyment in helping others, knowledge self-efficacy, and interpersonal trust factors. Additionally, it provides useful recommendations to managers of disaster relief organizations on the key factors to consider. Findings: The findings recorded that perceived usefulness, ease of use, top management support, enjoyment in helping others, knowledge self-efficacy, and interpersonal trust were critical factors in determining behavioral intention (BI) to use SM-based KS in the DR context. Furthermore, the mediator variables were attitude, subjective norms, and perceived behavioral control. Recommendations for Practitioners: Based on the research findings, it was determined that management should create different discussion forums among the disaster relief teams to ensure the long-term use of SM-based KS within DR organizations. They should also become involved in the discussions for disaster-related knowledge such as food supplies, shelter, or medical relief that disaster victims need. Disaster relief managers should consider effective and adequate training to enhance individual knowledge and self-efficacy since a lack of training may increase barriers and difficulties in using SM for KS during a DR process. Recommendation for Researchers: The conceptual model, further empirically investigated, can be employed by other developing countries in fostering acceptance of SM for KS during disaster relief operations. Impact on Society: Disaster relief operations can be facilitated using social media by considering the challenges DR practitioners face during emergencies. Future Research: In generalizing this study’s findings, other national or global disaster relief organizations should consider, when applying and testing, the research instruments and proposed model. The researchers may extend this study by collecting data from managers or administrators since they are different types of users of the SM-based KS system.

Aim/Purpose: Given the increasingly important role of knowledge and human resources for firms in developing and emerging countries to pursue innovation, this paper aims to study and explore the potential intermediating roles of knowledge donation and collection in linking high-involvement human resource management (HRM) practice and innovation capability. The paper also explores possible moderators of market turbulence in fostering the influences of knowledge-sharing (KS) behaviors on innovation competence in terms of incremental and radical innovation. Background: The fitness of HRM practice is critical for organizations to foster knowledge capital and internal resources for improving innovation and sustaining competitive advantage. Methodology: The study sample is 309 respondents and Structural Equation Model (SEM) was used for the analysis of the data obtained through a questionnaire survey with the aid of AMOS version 22. Contribution: This paper increases the understanding of the precursor role of high-involvement HRM practices, intermediating mechanism of KS activities, and the regulating influence of market turbulence in predicting and fostering innovation capability, thereby pushing forward the theory of HRM and innovation management. Findings: The empirical findings support the proposed hypotheses relating to the intermediating role of KS in the HRM practices-innovation relationship. It spotlights the crucial character of market turbulence in driving the domination of knowledge-sharing behaviors on incremental innovation. Recommendations for Practitioners: The proposed research model can be applied by leaders and directors to foster their organizational innovation competence. Recommendation for Researchers: Researchers are recommended to explore the influence of different models of HRM practices on innovation to identify the most effective pathway leading to innovation for firms in developing and emerging nations. Impact on Society: This paper provides valuable initiatives for firms in developing and emerging markets on how to leverage the strategic and internal resources of an organization for enhancing innovation. Future Research: Future studies should investigate the influence of HRM practices and knowledge resources to promote frugal innovation models for dealing with resource scarcity.

Aim/Purpose: This study aimed to increase our understanding of how the stages of the customer purchase journey, privacy trade-offs, and information sensitivity of different business service sectors affect consumers’ privacy concerns. Background: The study investigated young consumers’ willingness to provide consent to use their personal data at different phases of the customer journey. This study also examined their readiness to provide consent if they receive personal benefits, and how information sensitivity varied between different individuals and business sectors. Methodology: Data was collected by a quantitative survey (n=309) and analyzed with R using the Bayesian linear mixed effect modeling approach. The sample consisted of university students in Finland, who represented a group of young and digitally native consumers. The questionnaire was designed for this study and included constructs with primarily Likert-scale items. Contribution: The study contributed to data privacy and consent management research in information sensitivity, privacy trade-off, and the customer journey. The study underlined the need for a stronger user experience focus and contextuality. Findings: The results showed that readiness to disclose personal data varied at different phases of the customer journey as privacy concerns did not decrease in a linear fashion throughout the purchase process. Perceived benefits affected the willingness to provide consent for data usage, but concerned consumers would be less trade-off oriented. Self-benefit was the most relevant reason for sharing, while customization was the least. There is a connection between the information sensitivity of different business sector information and privacy concerns. No support for gender differences was found, but age affected benefits and business sector variables. Recommendations for Practitioners: The study recommends approaching consumers’ data privacy concerns from a customer journey perspective while trying to motivate consumers to share their personal data with relevant perceived benefits. The self-benefit was the most relevant benefit for willingness to provide consent, while customization was the least. Recommendation for Researchers: The study shows that individual preference for privacy was a major factor directly and via interaction for all three models. This study also showed that consumers’ subjective decision-making in privacy issues is both a situational and a contextual factor. Impact on Society: This study could encourage policymakers and societies to develop guidelines on how to develop privacy practices and consent management to be more user centric as individuals are increasingly concerned about their online privacy. Future Research: This study encourages examining consumers’ motivational factors to provide digital consent for companies with experimental research settings. This study also calls to explore perceived benefits in all age groups from the perspective of different information in various business sectors. This study shows that privacy concern is a contextual and situational factor.

Aim/Purpose: Using the Agile Adoption Framework (AAF), this study aims to examine the agile potential of software development companies in Nepal based on their agile maturity level. In addition, this study also examines the impact of various basic agile practices in determining the maturity level of the agile processes being implemented in the software industry of Nepal. Background: Even if most organizations in the software sector utilize agile development strategies, it is essential to evaluate their performance. Nepal’s software industry did not adopt agile techniques till 2014. The Nepalese industry must always adapt to new developments and discover ways to make software development more efficient and beneficial. The population of the study consists of 1,500 and 2,000 employees of software companies in Nepal implementing agile techniques. Methodology: The sample size considered was 150 employees working in software companies in Nepal. However, only 106 respondents responded after three follow-ups. The sample was collected with purposive sampling. A questionnaire was developed to gain information on Customer Adaptive, Customer Collaboration, Continuous Delivery, Human Centric, and Technical Excellence related to agile practices along with the Agile Maturity Level. Contribution: This research contributes to the understanding of agile practices adopted in software companies in developing countries like Nepal. It also reveals the determinants of the agility of software companies in developing countries. Findings: The results suggest that some of the basic principles of agile have a very significant role in Agile Maturity Level in the Nepali context. In the context of Nepal, human-centered practices have a very high level of correlation, which plays a vital role as a major predictor of the agile maturity level. In addition, Technical Excellence is the variable that has the highest level of association with the Agile Maturity Level, making it the most significant predictor of this quality. Recommendations for Practitioners: As Nepali software companies are mostly offshore or serve outsourcing companies, there is a very thin probability of Nepali developers being able to interact with actual clients and this might be one of the reasons for the Nepali industry not relying on Customer Adaptation and Collaboration as major factors of the Agile methodologies. Continuous Delivery, on the other hand, has a significant degree of correlation with Agile Maturity Level. Human-centric practices have a very high level of correlation as well as being a major predictor in determining the Agile Maturity Level in the context of Nepal. Technical Excellence is the most significant predictor and the variable which has the highest level of correlation with Agile Maturity Level. Practitioners should mainly focus on technical excellence as well as human-centric practices to achieve a higher level of Agile Maturity. Recommendation for Researchers: There has not been any such research in the Nepali context that anyone could rely on, to deep dive into their organizational concerns regarding agile strategies and plans. Researchers will need to focus on a more statistical approach with data-driven solutions to the issues related to people and processes. Researchers will need to cover freelancers as well as academics to get a different perspective on what can be the better practices to achieve a higher level of agile maturity. Impact on Society: This study on Agile work is accessible not only to the software industry but also to the general public. The Agile technique has had a huge impact on society’s project management. It has revolutionized how teams approach project planning, development, and execution. The paper’s findings will further information regarding the Agile methodology, which emphasizes collaboration and communication, fosters teamwork and higher quality work, and promotes the exchange of knowledge, ideas, and the pursuit of common goals. Future Research: Owing to the limitations of this study, it is necessary to analyze agile practices in the Nepalese software sector using additional factors that influence agile maturity. The conclusion that years of agile experience do not serve as a balancing factor for both agile practices and the Agile Maturity Level requires additional research. Whether a software outsourcing firm or not, the organization type had no bearing on the degree of maturity of agile methods; this leaves space for further research.

Aim/Purpose: Social commerce is an emergent topic widely used for product and service sourcing. It helps companies to have frequent interaction with their customers and strive to achieve a competitive advantage. Yet there is only little empirical evidence focusing on social commerce and its adoption in SMEs to date. This study investigates the key factors affecting social commerce adoption in SMEs. This research designed a theoretical model using the Technology, Organization, and Environment (TOE) Model Background: Despite its rapid growth and usage, social commerce is still in its evolution phase and its current conception is vague and restricted. Therefore, considering the benefits of social commerce for consumers and businesses, it is important to explore the concept of social commerce. Methodology: The research floated a self-administered questionnaire and surveyed 218 Jordanian SME businesses. The data was analyzed using smart PLS and the results were drawn that covers the detail of the characteristics of respondents, study descriptive, results of regressions assumptions, e.g., data normality, reliability, validity, common method biases, and description of the measurement model, followed by the findings of hypothesis analysis. Contribution: This study has many significant contributions to the existing studies on firms’ adoption of social commerce. It indicates that organizational readiness from the organizational perspective and consumer pressure from the environmental dimension of the TOE model are significant influential elements in the adoption of social commerce in business, followed by high-level management support and trading partner pressure, respectively. This shows that organizational readiness to adopt social commerce and consumer pressure has a vital role in Jordanian SMEs’ adopting social commerce. Findings: The results were drawn from a survey of 218 Jordanian SMEs, indicating that organizational readiness from an organizational dimension and consumer pressure environmental perspective, followed by top management’s support and trading partner pressure, significantly predicts the adoption intentions of social commerce. However, perceived usefulness and security concerns from a technological context do not significantly impact behavioral intentions to utilize social commerce. Recommendations for Practitioners: Lack of awareness about new technology and its potential benefits are not well diffused in the Jordanian context. In short, both organizational and environmental dimensions of the TOE framework significantly influence the behavioral intentions for social commerce adoption in the Jordanian context whereas the third-dimension technological factors do not affect the behavioral intentions of SMEs to adopt social commerce. In the technological context, SMEs need to invest in technology and must spread awareness among Jordanian consumers about the potential benefits of technology and must encourage them to use social commerce platforms to interact because of the high significance of social commerce for businesses as it facilitates the quick completion of tasks, enhances the productivity, and improves the chances of high profitability. Recommendation for Researchers: First, the study is limited in scope as it discusses the direct links between the TOE framework, behavioral intentions to use social commerce, and the actual usage of social commerce in the Jordanian context rather than testing the mediation, and moderation. Future research may examine the mediators and moderators in the conceptual model. Second, the research examined the behavioral intentions of SMEs rather than consumers to adopt social commerce. Further research might consider the consumer perspective on social commerce. Impact on Society: This research aims to identify the key factor that impact the behavioral intentions of SME businesses to practice social commerce. The theoretical underpinning of the study lies in the TOE model, as using its basic assumptions the conceptual grounds and hypothesis of the study are developed. Future Research: The study findings are not generalizable in different contexts as it was specifically conducted by gathering data from the Jordanian population. However future studies may consider different contexts, sectors, cultures, or countries to examine the model. Lastly, the research collected data using convenience sampling from 218 SMEs in Jordan, which may create difficulty in the generalizability of the research, so needs to examine a larger sample in future studies.

Aim/Purpose: This study aims to develop a theoretical model based on the SOR (Stimulus – Organism – Response) framework and GETAMEL, which cover environmental, personal, and learning quality aspects to identify factors influencing students’ acceptance of the use of LMS in high schools, especially after COVID-19 pandemic. Background: After the COVID-19 pandemic, many high schools reopened for in-person classes, which led to a decreased reliance on e-learning. The shift from online to traditional face-to-face learning has influenced students’ perceptions of the importance of e-learning in their academic activities. Consequently, high schools are facing the challenge of ensuring that LMS can still be integrated into the teaching-learning process even after the pandemic ends. Therefore, this study proposes a model to investigate the factors that affect students’ actual use of LMS in the high school environment. Methodology: This study used 890 high school students to validate the theoretical model using Structural Equation Modeling (SEM) analysis to deliver direct, indirect, and moderating effect analysis. Contribution: This study combines SOR and acceptance theory to provide a model to explain high school students’ intention to use technology. The involvement of direct, indirect, and moderating effects analysis offers an alternative result and discussion and is considered another contribution of this study from a technical perspective. Findings: The findings show that perceived satisfaction is the most influential factor affecting the use of LMS, followed by perceived usefulness. Meanwhile, from indirect effect analysis, subjective norms and computer self-efficacy were found to indirectly affect actual use through perceived usefulness as a mediator. Content quality was also an indirect predictor of the actual use of LMS through perceived satisfaction. Further, the moderating effect of age influenced perceived satisfaction’s direct effect on actual use. Recommendations for Practitioners: This study provides practical recommendations that can be useful to high schools and other stakeholders in improving the use of LMS in educational environments. Specifically, exploring the implementation of LMS in high schools prior to and following the COVID-19 outbreak can offer valuable insights into the changing educational environment. Recommendation for Researchers: The results of this study present a significant theoretical contribution by employing a comprehensive approach to explain the adoption of LMS among high school students after the COVID-19 pandemic. This contribution extends the GETAMEL framework by incorporating environmental, personal, and learning quality aspects while also analyzing both direct and indirect effects, which have not been previously explored in this context. Impact on Society: This study provides knowledge to high schools for improving the use of LMS in educational environments post-COVID-19, leading to an enhanced teaching-learning process. Future Research: This study, however, is limited to collecting responses exclusively from Indonesian respondents. Therefore, the replication of the finding needs to consider the characteristics and culture similar to Indonesian students, which is regarded as the limitation of this study.

Aim/Purpose: This study aims to develop a solution for personalized tourism recommendations that addresses information overload, data sparsity, and the cold-start problem. It focuses on enabling tourists to choose the most suitable tourism-related facilities, such as restaurants and hotels, that match their individual needs and preferences. Background: The tourism industry is experiencing a significant shift towards digitalization due to the increasing use of online platforms and the abundance of user data. Travelers now heavily rely on online resources to explore destinations and associated options like hotels, restaurants, attractions, transportation, and events. In this dynamic landscape, personalized recommendation systems play a crucial role in enhancing user experience and ensuring customer satisfaction. However, existing recommendation systems encounter major challenges in precisely understanding the complexities of user preferences within the tourism domain. Traditional approaches often rely solely on user ratings, neglecting the complex nature of travel choices. Data sparsity further complicates the issue, as users might have limited interactions with the system or incomplete preference profiles. This sparsity can hinder the effectiveness of these systems, leading to inaccurate or irrelevant recommendations. The cold-start problem presents another challenge, particularly with new users who lack a substantial interaction history within the system, thereby complicating the task of recommending relevant options. These limitations can greatly hinder the performance of recommendation systems and ultimately reduce user satisfaction with the overall experience. Methodology: The proposed User-based Multi-Criteria Trust-aware Collaborative Filtering (UMCTCF) approach exploits two key aspects to enhance both the accuracy and coverage of recommendations within tourism recommender systems: multi-criteria user preferences and implicit trust networks. Multi-criteria ratings capture the various factors that influence user preferences for specific tourism items, such as restaurants or hotels. These factors surpass a simple one-star rating and take into account the complex nature of travel choices. Implicit trust relationships refer to connections between users that are established through shared interests and past interactions without the need for explicit trust declarations. By integrating these elements, UMCTCF aims to provide more accurate and reliable recommendations, especially when data sparsity limits the ability to accurately predict user preferences, particularly for new users. Furthermore, the approach employs a switch hybridization scheme, which combines predictions from different components within UMCTCF. This scheme leads to a more robust recommendation strategy by leveraging diverse sources of information. Extensive experiments were conducted using real-world tourism datasets encompassing restaurants and hotels to evaluate the effectiveness of UMCTCF. The performance of UMCTCF was then compared against baseline methods to assess its prediction accuracy and coverage. Contribution: This study introduces a novel and effective recommendation approach, UMCTCF, which addresses the limitations of existing methods in personalized tourism recommendations by offering several key contributions. First, it transcends simple item preferences by incorporating multi-criteria user preferences. This allows UMCTCF to consider the various factors that users prioritize when making tourism decisions, leading to a more comprehensive understanding of user choices and, ultimately, more accurate recommendations. Second, UMCTCF leverages the collective wisdom of users by incorporating an implicit trust network into the recommendation process. By incorporating these trust relationships into the recommendation process, UMCTCF enhances its effectiveness, particularly in scenarios with data sparsity or new users with limited interaction history. Finally, UMCTCF demonstrates robustness towards data sparsity and the cold-start problem. This resilience in situations with limited data or incomplete user profiles makes UMCTCF particularly suitable for real-world applications in the tourism domain. Findings: The results consistently demonstrated UMCTCF’s superiority in key metrics, effectively addressing the challenges of data sparsity and new users while enhancing both prediction accuracy and coverage. In terms of prediction accuracy, UMCTCF yielded significantly more accurate predictions of user preferences for tourism items compared to baseline methods. Furthermore, UMCTCF achieved superior coverage compared to baseline methods, signifying its ability to recommend a wider range of tourism items, particularly for new users who might have limited interaction history within the system. This increased coverage has the potential to enhance user satisfaction by offering a more diverse and enriching set of recommendations. These findings collectively highlight the effectiveness of UMCTCF in addressing the challenges of personalized tourism recommendations, paving the way for improved user satisfaction and decision-making within the tourism domain. Recommendations for Practitioners: The proposed UMCTCF approach offers a potential opportunity for tourism recommendation systems, enabling practitioners to create solutions that prioritize the needs and preferences of users. By incorporating UMCTCF into online tourism platforms, tourists can utilize its capabilities to make well-informed decisions when selecting tourism-related facilities. Furthermore, UMCTCF’s robust design allows it to function effectively even in scenarios with data sparsity or new users with limited interaction history. This characteristic makes UMCTCF particularly valuable for real-world applications, especially in scenarios where these limitations are common obstacles. Recommendation for Researchers: The success of UMCTCF can open up new avenues in personalized recommendation research. One promising direction lies in exploring the integration of additional contextual information, such as temporal (time-based) or location-based information. By incorporating these elements, the model could be further improved, allowing for even more personalized recommendations. Furthermore, exploring the potential of UMCTCF in domains other than tourism has considerable significance. By exploring its effectiveness in other e-commerce domains, researchers can broaden the impact of UMCTCF and contribute to the advancement of personalized recommendation systems across various industries. Impact on Society: UMCTCF has the potential to make a positive impact on society in various ways. By delivering accurate and diverse recommendations that are tailored to individual user preferences, UMCTCF fosters a more positive and rewarding user experience with tourism recommendation systems. This can lead to increased user engagement with tourism platforms, ultimately enhancing overall satisfaction with travel planning. Furthermore, UMCTCF enables users to make more informed decisions through broader and more accurate recommendations, potentially reducing planning stress and leading to more fulfilling travel experiences. Future Research: Expanding upon the success of UMCTCF, future research activities can explore several promising paths. Enriching UMCTCF with various contextual data, such as spatial or location-based data, to enhance recommendation accuracy and relevance. Leveraging user-generated content, like reviews and social media posts, could provide deeper insights into user preferences and sentiments, improving personalization. Additionally, applying UMCTCF in various e-commerce domains beyond tourism, such as online shopping, entertainment, and healthcare, could yield valuable insights and enhance recommendation systems. Finally, exploring the integration of optimization algorithms could improve both recommendation accuracy and efficiency.

Aim/Purpose: This paper intends to answer the question regarding the extent to which political postings with value differences/similarities will influence the level of implicit knowledge sharing (KS) among work colleagues in organizations. More specifically, the study assesses contributors’ responses to a workmate’s publicity about politics on social media platforms (SMP) and their eagerness to implement implicit KS to the co-worker. Background: Previously published articles have confirmed an association between publicity about politics and the reactions from workfellows in the organization. Moreover, prior work confirmed that workers’ social media postings about politics may create unfavorable responses, such as being disliked and distrusted by workfellows. This may obstruct the KS because interpersonal relations are among the KS’s essential components. Therefore, it is imperative to assess whether the workfellows’ relationship affected by political publicity would impede the KS in the office. Methodology: Data was gathered using the vignette technique and online survey. A total of 510 online and offline questionnaires were distributed to respondents in Indonesian Halal firms who have implemented knowledge-sharing practices and have been at work for no less than twelve months in the present role. Next, the 317 completed questionnaires were examined with partial least squares structural equation modeling (PLS-SEM). Contribution: Postings about politics on SMP can either facilitate or impede the level of KS in organizations, and this research topic is relatively scarce in the knowledge management discipline. While previously published articles have concentrated on public organizations, this research centers on private firms. Moreover, this work empirically examines private companies in Indonesia, which is also understudied in the existing literature. Findings: The outcomes confirm that perceived political value similarity (PPV) in a co-worker’s social-media publicity has a significant and indirect influence on contributors’ eagerness to perform implicit/tacit KS. Further, colleague likability and trustworthiness significantly influence the level of KS among respondents. As PPV significantly forms colleague likability, likability strongly and positively shapes trustworthiness. Recommendations for Practitioners: The study shows that political publicity significantly affects implicit knowledge sharing (KS). As a result, managers and leaders, particularly those in private firms, are strengthened to instruct their staff about the ramifications of publicity embedded in employees’ SMP postings, particularly about political topics, as it may result in either negative or positive perceptions amongst the staff towards the workmate who posts. Recommendation for Researchers: As this study focuses on examining KS behavior in a large context, i.e., Indonesia Halal firms that dominate the Indonesian economy, and the fact that much polarization research focuses on society at large and less on specific sectors of life, it is important and interesting for researchers to conduct similar studies in a specific workplace as political agreements and disagreements become so important and consequential in everyday lives. Impact on Society: This article makes the implication that a person’s personality can influence how they react to political posts on SMP. It is difficult for the exposers to know the personality of each viewer of publicity in daily life. Workers’ newfound knowledge can motivate them to use SMP responsibly and lessen the probability that they will disclose information that might make their co-workers feel or perceive anything unfavorably. Future Research: There is a need for further studies to examine if the results can be applied to different locations and organizations, as individuals’ behaviors may vary according to the cultures of society and firms. Furthermore, future research can take into account the individual characteristics of workers, such as hospitability, self-confidence, and psychological strength, which may be well-matched with future work models. Future research may potentially employ a qualitative technique to offer deeper insights into the same topic.

Aim/Purpose: This study aims to evaluate the success of deep learning in building user profiles for personalized information access. Background: To better express document content and information during the matching phase of the information retrieval (IR) process, deep learning architectures could potentially offer a feasible and optimal alternative to user profile building for personalized information access. Methodology: This study uses deep learning-based models to deduce the domain of the document deemed implicitly relevant by a user that corresponds to their center of interest, and then used predicted domain by the best given architecture with user’s characteristics to predict other centers of interest. Contribution: This study contributes to the literature by considering the difference in vocabulary used to express document content and information needs. Users are integrated into all research phases in order to provide them with relevant information adapted to their context and their preferences meeting their precise needs. To better express document content and information during this phase, deep learning models are employed to learn complex representations of documents and queries. These models can capture hierarchical, sequential, or attention-based patterns in textual data. Findings: The results show that deep learning models were highly effective for building user profiles for personalized information access since they leveraged the power of neural networks in analyzing and understanding complex patterns in user behavior, preferences, and user interactions. Recommendations for Practitioners: Building effective user profiles for personalized information access is an ongoing process that requires a combination of technology, user engagement, and a commitment to privacy and security. Recommendation for Researchers: Researchers involved in building user profiles for personalized information access play a crucial role in advancing the field and developing more innovative deep-based networks solutions by exploring novel data sources, such as biometric data, sentiment analysis, or physiological signals, to enhance user profiles. They can investigate the integration of multimodal data for a more comprehensive understanding of user preferences. Impact on Society: The proposed models can provide companies with an alternative and sophisticated recommendation system to foster progress in building user profiles by analyzing complex user behavior, preferences, and interactions, leading to more effective and dynamic content suggestions. Future Research: The development of user profile evolution models and their integration into a personalized information search system may be confronted with other problems such as the interpretability and transparency of the learning-based models. Developing interpretable machine learning techniques and visualization tools to explain how user profiles are constructed and used for personalized information access seems necessary to us as a future extension of our work.

Aim/Purpose: This study aims to analyze the factors that influence user addiction to AR face filters in social network applications and their impact on the online social anxiety of users in Indonesia. Background: To date, social media users have started to use augmented reality (AR) face filters. However, AR face filters have the potential to create positive and negative effects for social media users. The study combines the Big Five Model (BFM), Sense of Virtual Community (SVOC), and Stimuli, Organism, and Response (SOR) frameworks. We adopted the SOR theory by involving the personality factors and SOVC factors as stimuli, addiction as an organism, and social anxiety as a response. BFM is the most significant theory related to personality. Methodology: We used a quantitative approach for this study by using an online survey. We conducted research on 903 Indonesian respondents who have used an AR face filter feature at least once. The respondents were grouped into three categories: overall, new users, and old users. In this study, group classification was carried out based on the development timeline of the AR face filter in the social network application. This grouping was carried out to facilitate data analysis as well as to determine and compare the different effects of the factors in each group. The data were analyzed using the covariance-based structural equation model through the AMOS 26 program. Contribution: This research fills the gap in previous research which did not discuss much about the impact of addiction in using AR face filters on online social anxiety of users of social network applications. Findings: The results of this study indicated neuroticism, membership, and immersion influence AR face filter addiction in all test groups. In addition, ARA has a significant effect on online social anxiety. Recommendations for Practitioners: The findings are expected to be valuable to social network service providers and AR creators in improving their services and to ensure policies related to the list of AR face filters that are appropriate for use by their users as a form of preventing addictive behavior of that feature. Recommendation for Researchers: This study suggested other researchers consider other negative impacts of AR face filters on aspects such as depression, life satisfaction, and academic performance. Impact on Society: AR face filter users may experience changes in their self-awareness in using face filters and avoid the latter’s negative impacts. Future Research: Future research might explore other impacts from AR face filter addiction behavior, such as depression, life satisfaction, and so on. Apart from that, future research might investigate the positive impact of AR face filters to gain a better understanding of the impact of AR face filters.

Aim/Purpose: This study examined the use of Social Media Applications (SMAs) for accessing health-related information within a heterogeneous population in Jordan. The objective of this study was therefore threefold: (i) to investigate the usage of SMAs, including WhatsApp, Twitter, YouTube, Snapchat, Instagram, and Facebook, for accessing health-related information; (ii) to examine potential variations in the use of SMAs based on demographic and behavioral characteristics; and (iii) to identify the factors that can predict the use of SMAs. Background: There has been limited focus on investigating the behavior of laypeople in Jordan when it comes to seeking health information from SMAs. Methodology: A cross-sectional study was conducted among the general population in Jordan using an online questionnaire administered to 207 users. A purposive sampling technique was employed, wherein all the participants actively sought online health information. Descriptive statistics, t-tests, and regression analyses were utilized to analyze the collected data. Contribution: This study adds to the existing body of research on health information seeking from SMAs in developing countries, with a specific focus on Jordan. Moreover, laypeople, often disregarded by researchers and health information providers, are the most vulnerable individuals who warrant greater attention. Findings: The findings indicated that individuals often utilized YouTube as a platform to acquire health-related information, whereas their usage of Facebook for this purpose was less frequent. Participants rarely utilized Instagram and WhatsApp to obtain health information, while Twitter and Snapchat were very seldom used for this purpose. The variable of sex demonstrated a notable positive correlation with the utilization of YouTube and Twitter for the purpose of finding health-related information. Conversely, the variable of nationality exhibited a substantial positive correlation with the utilization of Facebook, Instagram, and Twitter. Consulting medical professionals regarding information obtained from the Internet was a strong indicator of using Instagram to search for health-related information. Recommendations for Practitioners: Based on the empirical results, this study provides feasible recommendations for the government, healthcare providers, and developers of SMAs. Recommendation for Researchers: Researchers should conduct separate investigations for each application specifically pertaining to the acquisition of health-related information. Additionally, it is advisable to investigate additional variables that may serve as predictors for the utilization of SMAs. Impact on Society: The objective of this study is to enhance the inclination of the general public in Jordan to utilize SMAs for health-related information while also maximizing the societal benefits of these applications. Future Research: Additional research is required to examine social media’s usability (regarding ease of use) and utility (comparing advantages to risks) in facilitating effective positive change and impact in healthcare.

Scientific experiments, simulations, and modern applications generate large amounts of data. Analysing resources required to process such big datasets is essential to identify application running costs for cloud or in-house deployments. Researchers have proposed keeping data in raw formats to avoid upfront utilisation of resources. However, it poses reparsing issues for frequently accessed data. The paper discusses detailed comparative analysis of resources required by in-situ engines and traditional database management systems to process a real-world scientific dataset. A resource monitoring framework has been developed and incorporated into the raw data query processing framework to achieve this goal. The work identified different query types best suited to a given data processing tool in terms of data to result time and resource requirements. The analysis of resource utilisation patterns has led to the development of query complexity aware (QCA) and resource utilisation aware (RUA) data partitioning techniques to process big raw data efficiently. Resource utilisation data have been analysed to estimate the data processing capacity of a given machine.

Can questions sent to Open-Educational-Resource (OER) websites such as Ask-An-Expert serve as indicators for students’ interest in science? This issue was examined using an online questionnaire which included an equal number of questions about the topics “space” and “nutrition” randomly selected from three different sources: a 5th-grade science textbook, the “Ask-An-Expert” website, and questions collected from other students in the same age group. A sample of 113 5th-graders from two elementary schools were asked to rate their interest level in finding out the answer to these questions without knowledge of their source. Significant differences in students’ interest level were found between questions: textbook questions were ranked lowest for both subjects, and questions from the open-resource were ranked high. This finding suggests that questions sent to an open-resource could be used as an indicator of students’ interest in science. In addition, the high correlation of interests expressed by students from the two schools may point to a potential generalization of the findings. This study contributes by highlighting OER as a new and promising indicator of student interest, which may help bring “student voices” into mainstream science teaching to increase student interest in science.

Self-efficacy is essential to learning but what happens when learning is done as a result of a collective process? What is the role of individual self-efficacy in collective problem solving? This research examines the manifestation of self-efficacy in prediction markets that are configured as collective problem-solving platforms and whether self-efficacy of traders affects the collective outcome. Prediction markets are collective-intelligence platforms that use a financial markets mechanism to combine knowledge and opinions of a group of people. Traders express their opinions or knowledge by buying and selling “stocks” related to questions or events. The collective outcome is derived from the final price of the stocks. Self-efficacy, one’s belief in his or her ability to act in a manner that leads to success, is known to affect personal performance in many domains. To date, its manifestation in computer-mediated collaborative environments and its effect on the collective outcome has not been studied. In a controlled experiment, 632 participants in 47 markets traded a solution to a complex problem, a naïve framing of the knapsack problem. Contrary to earlier research, we find that technical and functional self-efficacy perceptions are indistinguishable, probably due to a focus on outcomes rather than on resources. Further, results demonstrate that prediction markets are an effective collective problem-solving platform that correctly aggregates individual knowledge and is resilient to traders’ self-efficacy.

In recent years, schools (as well as universities) have added cyber security to their computer science curricula. This topic is still new for most of the current teachers, who would normally have a standard computer science background. Therefore the teachers are trained and then teaching their students what they have just learned. In order to explore differences in both populations’ learning, we compared measures of software quality and security between high-school teachers and students. We collected 109 source files, written in Python by 18 teachers and 31 students, and engineered 32 features, based on common standards for software quality (PEP 8) and security (derived from CERT Secure Coding Standards). We use a multi-view, data-driven approach, by (a) using hierarchical clustering to bottom-up partition the population into groups based on their code-related features and (b) building a decision tree model that predicts whether a student or a teacher wrote a given code (resulting with a LOOCV kappa of 0.751). Overall, our findings suggest that the teachers’ codes have a better quality than the students’ – with a sub-group of the teachers, mostly males, demonstrate better coding than their peers and the students – and that the students’ codes are slightly better secured than the teachers’ codes (although both populations show very low security levels). The findings imply that teachers might benefit from their prior knowledge and experience, but also emphasize the lack of continuous involvement of some of the teachers with code-writing. Therefore, findings shed light on computer science teachers as lifelong learners. Findings also highlight the difference between quality and security in today’s programming paradigms. Implications for these findings are discussed.