Aim/Purpose: This study investigates the relationship between Electronic Word-of-Mouth (EWOM), Information Adoption, and the stock investment of Vietnamese investors. Background: Misinformation spreads online, and a lack of strong information analysis skills can lead Vietnamese investors to make poor stock choices. By understanding how online conversations and information processing influence investment decisions, this research can help investors avoid these pitfalls. Methodology: This study applies Structural Equation Modelling (SEM) to investigate how non-professional investors react to online information and which information factors influence their investment decisions. The final sample includes 512 investors from 18 to 65 years old from various professional backgrounds (including finance, technology, education, etc.). We conducted a combined online and offline survey using a convenience sampling method from August to November 2023. Contribution: This study contributes to the growing literature on Electronic Word-of-Mouth (EWOM) and its impact on investment decisions. While prior research has explored EWOM in various contexts, we focus on Vietnamese investors, which can offer valuable insights into its role within a developing nation’s stock market. Investors, particularly those who are new or less experienced, are often susceptible to the influence of EWOM. By examining EWOM’s influence in Vietnam, this study sheds light on a crucial factor impacting investment behavior in this emerging market. Findings: The results show that EWOM has a moderate impact on the Information Adoption and investment decisions of Vietnamese stock investors. Information Quality (QL) is the factor that has the strongest impact on Information Adoption (IA), followed by Information Credibility (IC) and Attitude Towards Information (AT). Needs for Information (NI) only have a small impact on Information Adoption (IA). Finally, Information Adoption (IA) has a limited influence on investor decisions in stock investment. We also find that investors need to verify information through official sites before making investment decisions based on posts in social media groups. Recommendations for Practitioners: The findings suggest that state management and media agencies need to coordinate to improve the quality of EWOM information to protect investors and promote the healthy development of the stock market. Social media platform managers need to moderate content, remove false information, prioritize displaying authentic information, cooperate with experts, provide complete information, and personalize the experience to enhance investor trust and positive attitude. Securities companies need to provide complete, accurate, and updated information about the market and investment products. They can enhance investor trust and positive attitude by developing news channels, interacting with investors, and providing auxiliary services. Listed companies need to take the initiative to improve the quality of information disclosure and ensure clarity, comprehensibility, and regular updates. Use diverse communication channels and improve corporate governance capacity to increase investor trust and positive attitude. Investors need to seek information from reliable sources, compare information from multiple sources, and carefully check the source and author of the information. They should improve their investment knowledge and skills, consult experts, define investment goals, and build a suitable investment portfolio. Recommendation for Researchers: This study synthesized previous research on EWOM, but there is still a gap in the field of securities because each nation has its laws, regulations, and policies. The relationships between the factors in the model are not yet clear, and there is a need to develop a model with more interactive factors. The research results need to be further verified, and more research can be conducted on the influence of investor psychology, investment experience, etc. Impact on Society: This study finds that online word-of-mouth (EWOM) can influence Vietnamese investors’ stock decisions, but information quality is more important. Policymakers should regulate EWOM accuracy, fund managers should use social media to reach investors, and investors should diversify their information sources. Future Research: This study focuses solely on the stock market, while individual investors in Vietnam may engage in various other investment forms such as gold, real estate, or cryptocurrencies. Therefore, future research could expand the scope to include other investment types to gain a more comprehensive understanding of how individual investors in Vietnam utilize electronic word-of-mouth (EWOM) and adopt information in their investment decision-making process. Furthermore, while these findings may apply to other emerging markets with similar levels of financial literacy as Vietnam, they may not fully extend to countries with higher financial literacy rates. Hence, further studies could be conducted in developed countries to examine the generalizability of these findings. Finally, future research could see how EWOM’s impact changes over a longer period. Additionally, a more nuanced understanding of the information adoption process could be achieved by developing a research model with additional factors.

Aim/Purpose: This study explored teachers’ data-driven decision-making processes during routine and emergency remote teaching, as experienced during the COVID-19 pandemic. Background: Decision-making is essential in teaching, with informed decisions promoting student learning and teachers’ professional development most effectively. However, obstacles to the use of data have been identified in many studies. Methodology: Using a qualitative methodology (N=20), we studied how teachers make decisions, what data is available, and what data they would like to have to improve their decision-making. We used an inductive approach (bottom-up), utilizing teachers’ statements related to decision-making as the unit of analysis. Contribution: Our findings shed an important light on teachers’ Data-Driven Decision-Making (DDDM), highlighting the differences between routine and Emergency Remote Teaching (ERT). Findings: Overall, we found that teachers make teaching decisions in three main areas: pedagogy, discipline-related issues, and appearance and behavior. They shift between making decisions based on data and making decisions based on intuition. Academic-related decisions are the most prominent in routine teaching, and during ERT, they were almost the only area in which teachers’ decisions were made. Teachers reported collecting data about students’ academic achievements and emotional state and considered the organizational culture, consultation with colleagues, and parents’ involvement before decision-making. Recommendations for Practitioners: Promote a culture of data-driven decision-making across the education system; Make diverse and rich data of different types accessible to teachers; Increase professional and emotional support for teachers. Recommendation for Researchers: Researchers have the potential to expand the scope of this study by conducting research using other methodologies and in different countries. Impact on Society: This study highlights the importance of teachers’ data-driven decision-making in improving teaching practices and promoting students’ achievement. Future Research: Additional research is required to examine data-driven decision-making in diverse circumstances.

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 research aims to develop a smart agricultural knowledge management framework to empower emergent farmers and extension officers (advisors to farmers) in developing countries as part of a smart farming lab (SFL). The framework utilizes knowledge objects (KOs) to capture information and knowledge of different forms, including indigenous knowledge. It builds upon a foundation of established agricultural knowledge management (AKM) models and serves as the cornerstone for an envisioned SFL. This framework facilitates optimal decision support by fostering linkages between these KOs and relevant organizations, knowledge holders, and knowledge seekers within the SFL environment. Background: Emergent farmers and extension officers encounter numerous obstacles in their knowledge operations and decision-making. This includes limited access to agricultural information and difficulties in applying it effectively. Many lack reliable sources of support, and even when information is available, understanding and applying it to specific situations can be challenging. Additionally, extension offices struggle with operational decisions and knowledge management due to agricultural organizations operating isolated in silos, hindering their access to necessary knowledge. This research introduces an SFL with a proposed AKM process model aimed at transforming emergent farmers into smart, innovative entities by addressing these challenges. Methodology: This study is presented as a theory-concept paper and utilizes a literature review to evaluate and synthesize three distinct AKM models using several approaches. The results of the analysis are used to design a new AKM process model. Contribution: This research culminates in a new AKM process framework that incorporates the strengths of various existing AKM models and supports emergent farmers and extension officers to become smart, innovative entities. One main difference between the three models analyzed, and the one proposed in this research, is the deployment and use of knowledge assets in the form of KOs. The proposed framework also incorporates metadata and annotations to enhance knowledge discoverability and enable AI-powered applications to leverage captured knowledge effectively. In practical terms, it contributes by further motivating the use of KOs to enable the transfer and the capturing of organizational knowledge. Findings: A model for an SFL that incorporates the proposed agricultural knowledge management framework is presented. This model is part of a larger knowledge factory (KF). It includes feedback loops, KOs, and mechanisms to facilitate intelligent decision-making. The significance of fostering interconnected communities is emphasized through the creation of linkages. These communities consist of knowledge seekers and bearers, with information disseminated through social media and other communication integration platforms. Recommendations for Practitioners: Practitioners and other scholars should consider implementing the proposed AKM process model as part of a larger SFL to support emergent farmers and extension officers in making operational decisions and applying knowledge management strategies. Recommendation for Researchers: The AKM process model is only presented in conceptual form. Therefore, researchers can practically test and assess the new framework in an agricultural setting. They can also further explore the potential of social media integration platforms to connect knowledge seekers with knowledge holders. Impact on Society: The proposed AKM process model has the potential to support emergent farmers and extension officers in becoming smart, innovative entities, leading to improved agricultural practices and potentially contributing to food security. Future Research: This paper discusses the AKM process model in an agrarian setting, but it can also be applied in other domains, such as education and the healthcare sector. Future research can evaluate the model’s effectiveness and explore and further investigate the semantic web and social media integration.

Aim/Purpose: This study aims to investigate the influence of e-government public services on public trust at the local government level, addressing the pressing need to understand the factors shaping citizen perceptions and trust in government institutions. Background: With the proliferation of e-government initiatives worldwide, governments are increasingly turning to digital solutions to enhance public service delivery and promote transparency. However, despite the potential benefits, there remains a gap in understanding how these initiatives impact public trust in government institutions, particularly at the local level. This study seeks to address this gap by examining the relationship between e-government service quality, individual perceptions, and public trust, providing valuable insights into the complexities of citizen-government interactions in the digital age. Methodology: Employing a quantitative approach, this study utilises surveys distributed to users of e-government services in one of the regencies in Indonesia. The sample consists of 278 individuals. Data analysis is conducted using Partial Least Squares Structural Equation Modelling, allowing for the exploration of relationships among variables and their influence on public trust. Contribution: This study provides insights into the factors influencing public trust in e-government services at the local government level, offering a nuanced understanding of the relationship between service quality, individual perceptions, and public trust. Findings: This study emphasises information quality and service quality in e-government-based public services as crucial determinants of individual perception in rural areas. Interestingly, system quality in e-government services has no influence on individual perception. In the individual perception, perceived security and privacy emerge as the strongest antecedent of public trust, highlighting the need to guarantee secure and private services for citizens in rural areas. These findings emphasise the importance of prioritising high-quality information, excellent service delivery, and robust security measures to foster and sustain public trust in e-government services. Recommendations for Practitioners: Practitioners must prioritise enhancing the quality of e-government services due to their significant impact on individual perception, leading to higher public trust. Government agencies must ensure reliability, responsiveness, and the effective fulfilment of user needs. Additionally, upholding high standards of information quality in e-government services by delivering accurate, relevant, and timely information remains crucial. Strengthening security measures through robust protocols such as data encryption and secure authentication becomes essential for protecting user data. With that in mind, the authors believe that public trust in government would escalate. Recommendation for Researchers: Researchers could investigate the relation between system quality in e-government services and individual perception in different rural settings. Longitudinal studies could also elucidate how evolving service quality, information quality, and security measures impact user satisfaction and trust over time. Comparative studies across regions or countries can reveal cultural and contextual differences in individual perceptions, identifying both universal principles and region-specific strategies for e-government platforms. Analysing user behaviour and preferences across various demographic groups can inform targeted interventions. Furthermore, examining the potential of emerging technologies such as blockchain or artificial intelligence in enhancing e-government service delivery, security, and user engagement remains an interesting topic. Impact on Society: This study’s findings have significant implications for fostering public trust in government institutions, ultimately strengthening democracy and citizen-government relations. By understanding how e-government initiatives influence public trust, policymakers can make informed decisions to improve service delivery, enhance citizen engagement, and promote transparency, thus contributing to more resilient and accountable governance structures. Future Research: Future research could opt for longitudinal studies to evaluate the long-term effects of enhancements in service quality, information quality, and security. Cross-cultural investigations can uncover universal principles and contextual differences in user experiences, supporting global e-government strategies in rural areas. Future research could also improve the research model by adding more variables, such as risk aversion or fear of job loss, to gauge individual perceptions.

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 focuses on two targeting strategies of out-store Location-Based Mobile Advertising (LBMA): the geo-fencing strategy (i.e., targeting customers who are near the focal store) and the geo-conquesting strategy (i.e., targeting those who are near competitors’ stores to visit the focal store). To the authors’ knowledge, no previous studies have compared the perceived intrusiveness of advertisements (ads) in geo-fencing and geo-conquesting settings, despite the accumulating literature on out-store LBMA. Hence, the aim of this study is to determine which targeting strategy is more effective in terms of reducing the perception of ads’ intrusiveness and increasing positive customers’ attitudes and purchase intention. Background: The intrusive nature of LBMA is perceived negatively by some customers, impacting their attitudes toward the ad, purchase intention, and even their perception of the brand. Therefore, identifying the targeting strategy under which ads are perceived as less intrusive is essential. Additionally, brick-and-mortar clothing stores in Jordan are facing challenges due to the rise of online shopping and increased competition from nearby stores. Thus, examining geo-fencing and geo-conquesting might tackle these challenges and encourage local clothing retailers to adopt these strategies. Methodology: A quantitative method was used in this study. A between-subjects experimental design was used to collect the data using a scenario-based survey distributed to Jordanians aged 18 to 45. A total of 531 responses were collected. After excluding those who do not belong to the targeted age group and those who did not pass the manipulation check, 406 responses were analyzed using the Statistical Package for the Social Sciences (SPSS) software version 28 and the Analysis of Moment Structures (AMOS) software version 26 to conduct Structural Equation Modeling (SEM). Contribution: This work offers valuable contributions by investigating the impact of the perceived intrusiveness of ads on purchase intention in the contexts of geo-fencing and geo-conquesting, which has not been studied before. Additionally, it fills a gap by examining this phenomenon in Jordan, a developing country in which attitudes toward LBMA have not been previously explored. Findings: The results revealed that location-based mobile ads sent under a geo-fencing strategy are perceived as less intrusive than those sent under a geo-conquesting strategy. In addition, customers’ attitudes fully mediate the relationship between intrusiveness and purchase intention only under the geo-fencing strategy. Ultimately, neither of the strategies is more effective in terms of increasing positive customer attitudes and purchase intentions in the context of clothing retail stores in Jordan. Recommendations for Practitioners: Clothing retailers in Jordan should consider adopting geo-fencing and geo-conquesting strategies to boost purchase intentions and tackle industry challenges. Additionally, to increase purchase intentions with geo-fencing, practitioners should focus on fostering positive customer attitudes toward ads, as simply perceiving them as less intrusive is not sufficient to drive purchase intention without the mediating effect of positive attitudes. Recommendation for Researchers: This research is crucial for academics and researchers as geolocation technology and LBMA are expected to advance significantly in the future. Researchers can investigate this topic through a randomized field experiment, followed by a research questionnaire to collect data from a real-world setting. Impact on Society: Utilizing LBMA is essential for local clothing retail stores that are trying to effectively reach and connect with their customers because searching the Internet for local goods and services is done primarily on mobile devices. Indeed, this study revealed that customers in both settings (i.e., geo-fencing and geo-conquesting) reported a high intention to visit the promoting store and to purchase from the advertised product category. Future Research: Future research can apply this topic to different industries and cultural contexts, as the results may vary across industries and regions. Moreover, future research could build on this study by investigating additional constructs, such as product category involvement, customization, and content type of the message (e.g., informative, entertaining).

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: The objective of this study is to summarize the challenges of Agile Requirements Engineering (Agile RE) in the public sector in republican and constitutional monarchy nations. Additionally, it offers recommendations to address these challenges. Background: Failure of IT projects in the public sector results in financial losses for the state and loss of public trust, often attributed to issues in requirements engineering such as prioritization of user needs and excessive scope of requirements. IT projects can have a higher success rate with Agile RE, but there are also drawbacks. Therefore, this study holds significance by presenting a thorough framework designed to pinpoint and overcome the challenges associated with Agile RE to increase the success rate of IT projects. Methodology: This study employs a Systematic Literature Review (SLR) protocol in the field of software engineering or related domains, which consists of three main phases: planning the review, conducting the review with a snowballing approach, and reporting the review. Furthermore, the authors perform open coding to categorize challenges based on the Agile methodologies adoption factor model and axial coding to map potential solutions. Contribution: The authors assert that this research enriches the existing literature on Agile RE, specifically within the public sector context, by mapping out challenges and possible solutions that contribute to creating a foundation for future studies to conduct a more in-depth analysis of Agile adoption in the public sector. Furthermore, it compares the barriers of Agile RE in the public sector with the general context, leading to the discovery of new theories specifically for this field. Findings: Most challenges related to Agile RE in the public sector are found in the people and process aspects. Project and organizational-related are subsequent aspects. Therefore, handling people and processes proficiently is imperative within Agile RE to prevent project failure. Recommendations for Practitioners: Our findings offer a comprehensive view of Agile RE in the public sector in republican and constitutional monarchy nations. This study maps the challenges encountered by the public sector and provides potential solutions. The authors encourage practitioners to consider our findings as a foundation for adopting Agile methodology in the public sector. Furthermore, this study can assist practitioners in identifying existing barriers related to Agile RE, pinpointing elements that contribute to overcoming those challenges, and developing strategies based on the specific needs of the organizations. Recommendation for Researchers: Researchers have the potential to expand the scope of this study by conducting research in other countries, especially African countries, as this study has not yet encompassed this geographic region. Additionally, they can strengthen the evidence linking Agile RE challenges to the risk of Agile project failure by performing empirical validation in a specific country. Impact on Society: This research conducts a comprehensive exploration of Agile RE within the public sector, serving as a foundation for the successful adoption of Agile methodology by overcoming obstacles related to Agile RE. This study highlights the importance of managing people, processes, projects, and organizational elements to increase the success of Agile adoption in the public sector. Future Research: In the future, researchers should work towards resolving the limitations identified in this study. This study has not provided a clear prioritization of challenges and solutions according to their significance. Therefore, future researchers can perform a Fuzzy Analytical Hierarchical Process (F-AHP) to prioritize the proposed solutions.

Aim/Purpose: This study examines the adoption and continued use of m-learning in Iraqi universities amidst an unstable environment by extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and Expectation-Confirmation Model (ECM) models. The primary goal is to address the specific challenges and opportunities in Iraq’s higher education institutions (HEIs) due to geopolitical instability and understand their impact on student acceptance, satisfaction, and continued m-learning usage. Background: The research builds on the growing importance of m-learning, especially in HEIs, and recognizes the unique challenges faced by institutions in Iraq, given the region’s instability. It identifies gaps in existing models and proposes extensions, introducing the variable “civil conflicts” to account for the volatile context. The study aims to contribute to a deeper understanding of m-learning acceptance in conflict-affected regions and provide insights for improving m-learning initiatives in Iraqi HEIs. Methodology: To achieve its objectives, this research employed a quantitative survey to collect data from 399 students in five Iraqi universities. PLS-SEM is used for the analysis of quantitative data, testing the extended UTAUT2 and ECM models. Contribution: The study’s findings are expected to contribute to the development of a nuanced understanding of m-learning adoption and continued usage in conflict-affected regions, particularly in the Iraqi HEI context. Findings: The study’s findings may inform strategies to enhance the effectiveness of m-learning initiatives in Iraqi HEIs and offer insights into how education can be supported in regions characterized by instability. Recommendations for Practitioners: Educators and policymakers can benefit from the research by making informed decisions to support education continuity and quality, particularly in conflict-affected areas. Recommendation for Researchers: Researchers can build upon this study by further exploring the adoption and usage of m-learning in unstable environments and evaluating the effectiveness of the proposed model extensions. Impact on Society: The research has the potential to positively impact society by improving access to quality education in regions affected by conflict and instability. Future Research: Future research can expand upon this study by examining the extended model’s applicability in different conflict-affected regions and assessing the long-term impact of m-learning initiatives on students’ educational outcomes.

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.

Aim/Purpose: This study investigates the drivers of user satisfaction in e-government services and its influence on continued use intention and citizen trust in government. It employs the integration of the Technology Acceptance Model (TAM) and the Information System Success Model (ISSM). Background: Electronic government, transforming citizen-state interactions, has gained momentum worldwide, including in India, where the aim is to leverage technology to improve citizen services, streamline administration, and engage the public. While prior research has explored factors influencing citizen satisfaction with e-government services globally, this area of study has been relatively unexplored in India, particularly in the post-COVID era. Challenges to widespread e-government adoption in India include a large and diverse population, limited digital infrastructure in rural areas, low digital literacy, and weak data protection regulations. Additionally, global declines in citizen trust, attributed to economic concerns, corruption, and information disclosures, further complicate the scenario. This study seeks to investigate the influence of various factors on user satisfaction and continuance usage of e-government services in India. It also aims to understand how these services contribute to building citizens’ trust in government. Methodology: The data were collected by utilizing survey items on drivers of e-government services, user satisfaction, citizen trust, and continuance use intention derived from existing literature on information systems and e-government. Responses from 501 Indian participants, collected using an online questionnaire, were analyzed using PLS-SEM. Contribution: This study makes a dual contribution to the e-government domain. First, it introduces a comprehensive research model that examines factors influencing users’ satisfaction and continuance intention with e-government services. The proposed model integrates the TAM and ISSM. Combining these models allows for a comprehensive examination of e-government satisfaction and continued intention. By analyzing the impact of user satisfaction on continuance intention and citizen trust through an integrated model, researchers and practitioners gain insights into the complex dynamics involved. Second, the study uncovers the effects of residential status on user satisfaction, trust, and continuance intention regarding e-government services. Findings reveal disparities in the influence of system and service quality on user satisfaction across different user segments. Researchers and policymakers should consider these insights when designing e-government services to ensure user satisfaction, continuance intention, and the building of citizen trust. Findings: The findings indicate that the quality of information, service, system, and perceived usefulness play important roles in user satisfaction with e-government services. All hypothesized paths were significant, except for perceived ease of use. Furthermore, the study highlights that user satisfaction significantly impacts citizen trust and continuance use intention. Recommendations for Practitioners: The findings suggest that government authorities should focus on delivering accurate, comprehensive, and timely information in a secure, glitch-free, and user-friendly digital environment. Implementing an interactive and accessible interface, ensuring compatibility across devices, and implementing swift query resolution mechanisms collectively contribute to improving users’ satisfaction. Conducting awareness and training initiatives, providing 24×7 access to online tutorials, helpdesks, technical support, clear FAQs, and integrating AI-driven customer service support can further ensure a seamless user experience. Government institutions should leverage social influence, community engagement, and social media campaigns to enhance user trust. Promotional campaigns, incentive programs, endorsements, and user testimonials should be used to improve users’ satisfaction and continuance intention. Recommendation for Researchers: An integrated model combining TAM and ISSM offers a robust approach for thoroughly analyzing the diverse factors influencing user satisfaction and continuance intention in the evolving digitalization landscape of e-government services. This expansion, aligning with ISSM’s perspective, enhances the literature by demonstrating how user satisfaction impacts continuance usage intention and citizen trust in e-government services in India and other emerging economies. Impact on Society: Examining the factors influencing user satisfaction and continuance intention in e-government services and their subsequent impact on citizen trust carries significant societal implications. The findings can contribute to the establishment of transparent and accountable governance practices, fostering a stronger connection between governments and their citizens. Future Research: There are several promising avenues to explore to enhance future research. Expanding the scope by incorporating a larger sample size could enable a more thorough analysis. Alternatively, delving into the performance of specific e-government services would offer greater precision, considering that this study treats e-government services generically. Additionally, incorporating in-depth interviews and longitudinal studies would yield a more comprehensive understanding of the dynamic evolution of digitalization.

Table of Contents for Volume 19, 2024, of the Interdisciplinary Journal of Information, Knowledge, and Management

In the context of asthma severity prediction, this study delves into the feature importance of various symptoms and demographic attributes. Leveraging a comprehensive dataset encompassing symptom occurrences across varying severity levels, this investigation employs visualisation techniques, such as stacked bar plots, to illustrate the distribution of symptomatology within different severity categories. Additionally, correlation coefficient analysis is applied to quantify the relationships between individual attributes and severity levels. Moreover, the study harnesses the power of random forest and the Gini importance methodology, essential tools in feature importance analytics, to discern the most influential predictors in asthma severity prediction. The experimental results bring to light compelling associations between certain symptoms, notably 'runny-nose' and 'nasal-congestion', and specific severity levels, elucidating their potential significance as pivotal predictive indicators. Conversely, demographic factors, encompassing age groups and gender, exhibit comparatively weaker correlations with symptomatology. These findings underscore the pivotal role of individual symptoms in characterising asthma severity, reinforcing the potential for feature importance analysis to enhance predictive models in the realm of asthma management and bioinformatics.

Alzheimer's disease (AD), a leading cause of dementia and mortality, presents a growing concern due to its irreversible progression and the rising costs of care. Early detection is crucial for managing AD, which begins with memory deterioration caused by the damage to neurons involved in cognitive functions. Although incurable, treatments can manage its symptoms. This study introduces a hybrid AlexNet+ResNet-50 model for AD diagnosis, utilising a pre-trained convolutional neural network (CNN) through transfer learning to analyse MRI scans. This method classifies MRI images into Alzheimer's disease (AD), moderate cognitive impairment (MCI), and normal control (NC), enhancing model efficiency without starting from scratch. Incorporating transfer learning allows for refining the CNN to categorise these conditions accurately. Our previous work also explored atlas-based segmentation combined with a U-Net model for segmentation, further supporting our findings. The hybrid model demonstrates superior performance, achieving 94.21% accuracy in identifying AD cases, indicating its potential as a highly effective tool for early AD diagnosis and contributing to efforts in managing the disease's impact.

Epidemiologists recommended contact-tracing as an effective control measure for the global infection like COVID-19 pandemic. Despite its effectiveness in infection containment, it has many limitations such as labour-intensive process, prone to human errors and most importantly, user privacy concerns. To address these shortcomings, we proposed location-aware blockchain-based hierarchical contact-tracing framework for anonymised data collection and processing. This infectious disease control framework serves both the infected users with localised alerts as well as stakeholders such as city officials and health workers with health statistics. Our proposed solution uses hierarchical network design that offloads individual infection block data to create hospital and city-level 'chains' for generating macro-level infection statistics. Results demonstrate that our system can represent the dynamic complexities of contract tracing in highly infection situations. Overall, our design emphasises on data processing and verification mechanism for large volume of infection data over a significant period of time for active risk assessment.

There is a need to emphasise and educate future business leaders on emerging technologies' disruptive and transformative impact on business processes. Allen (2020) suggests the need for a digital mindset and tech literacy in business management education. In our study, we define cyber literacy and cyber ethical mindset emphasising the importance of informing future leaders in business schools about the ethical dilemmas arising while using these emerging technologies. Additionally, we highlight various ethical pitfalls of using technologies enabling disruption (TED). Further, we contribute to the understanding of cyber literacy, cyber ethics and business ethics, how to incorporate cyber ethics into the management curriculum, and why there is a need to integrate cyber ethics into management education.

The world's economies are attempting to transform themselves to have a greater focus on developing knowledge as a commodity through innovation. Innovation starts with a creative activity that yields an invention but is augmented through a systematic value driven knowledge management system to yield new knowledge that can create a competitive advantage. To succeed in such an economy, organisations must have or develop the talent that can produce and use information effectively, they must have an ambidextrous organisational structure that allows them to innovate and produce simultaneously, and they must have an innovation management system to sustain effective innovation. In this paper we show how to augment existing university courses to simultaneously develop subject matter and innovation skills in students. We also suggest the incorporation of the new Innovations Management System Standard Series ISO 56000 into business curricula to better prepare students to function in the knowledge economy.

In the context of US universities where student evaluations of teaching play an important role in the retention and promotion of faculty, it is important to understand what a student expects in the classroom. This study took the perspective of Perry's cognitive development scheme with the following research question: what is the Perry level of cognitive development of business students? An established survey was used at two different universities. It was found that the median was position 3, and that there was large variation in three dimensions. First is the variation across program levels. Second, there was variation across universities. This becomes an issue when instructors move to a different university and questions the possibility to transfer 'best practices'. Third, variation was found within a specific program level. This means that instructors are faced with students who, from a cognitive perspective, have different demands which are unlikely to be simultaneously met.

In this paper, two hypothetical frameworks are proposed through the application of quality function deployment (QFD) to integrate the current institutional level and program level student learning focus areas with the relevant institutional and program specific stakeholder expectations. A generic skillset proficiency expected of all the graduating students at the institutional level by the stakeholders is considered in the first QFD application example and a program specific knowledge proficiency expected at the program level by the stakeholders is considered in the second QFD application example. Operations management major/option is considered for illustration purposes at the program level. In addition, an assurance of learning based approach rooted in continuous improvement philosophy is proposed to align the stakeholder expectations with the relevant student learning outcomes at different learning tiers.

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.

This study investigates cross-border commercial air passenger traffic in Africa, focusing on the development of the 15 busiest country pairs during the period 1989 to 2015. It explores dimensions not previously studied by using ICAO's 'Traffic by Flight Stage' (TFS) and data from the CEPII Gravity Dataset. The spatial results show on an uneven geographical distribution of country pairs with the centre of gravity to South, East and North-East Africa, with one long-distance corridor between Egypt and South Africa. Countries in North and West Africa have rather few linkages, except for Egypt. Central African countries are not represented among the 15 country pairs. Although the number of passengers and the rank among the countries have shifted, South Africa and Egypt stand out, as having most country pair connections. Factors such as changing economic, diplomatic and political relations have had an influence on changing country pair connections throughout the period. A number of variables were selected to investigate how they correlated with Africa's commercial passenger traffic. Of the seven variables selected, five did show on a correlation and two did partly so. In that view, Africa's air traffic follows rather typical patterns.

Unmanned stores are one of the latest conceptual developments in retail and have received much attention, especially in the context of COVID-19-related social restrictions and the associated changes in consumer behaviour. The concept considers the latest technological developments and promises to offer various benefits to consumers and retailers based on artificial intelligence and automation. Using a German sample, this paper aims to evaluate consumers' acceptance of and intention to use the most prominent innovative solutions in unmanned stores. A modified technology acceptance model (TAM) as a theoretical framework was applied to the study. The results of the structural equation modelling make two contributions to the existing literature: First, the acceptance criteria for unmanned stores have not been analysed previously. Second, the modified TAM could be confirmed in this study. We provide empirical evidence suggesting that significant numbers of consumers accept unmanned stores, especially if the stores are strategically located and when individuals have a high innovation affinity.