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: Previous research has generally concentrated on identifying the variables that most significantly influence customer churn or has used customer segmentation to identify a subset of potential consumers, excluding its effects on forecast accuracy. Consequently, there are two primary research goals in this work. The initial goal was to examine the impact of customer segmentation on the accuracy of customer churn prediction in the banking sector using machine learning models. The second objective is to experiment, contrast, and assess which machine learning approaches are most effective in predicting customer churn. Background: This paper reviews the theoretical basis of customer churn, and customer segmentation, and suggests using supervised machine-learning techniques for customer attrition prediction. Methodology: In this study, we use different machine learning models such as k-means clustering to segment customers, k-nearest neighbors, logistic regression, decision tree, random forest, and support vector machine to apply to the dataset to predict customer churn. Contribution: The results demonstrate that the dataset performs well with the random forest model, with an accuracy of about 97%, and that, following customer segmentation, the mean accuracy of each model performed well, with logistic regression having the lowest accuracy (87.27%) and random forest having the best (97.25%). Findings: Customer segmentation does not have much impact on the precision of predictions. It is dependent on the dataset and the models we choose. Recommendations for Practitioners: The practitioners can apply the proposed solutions to build a predictive system or apply them in other fields such as education, tourism, marketing, and human resources. Recommendation for Researchers: The research paradigm is also applicable in other areas such as artificial intelligence, machine learning, and churn prediction. Impact on Society: Customer churn will cause the value flowing from customers to enterprises to decrease. If customer churn continues to occur, the enterprise will gradually lose its competitive advantage. Future Research: Build a real-time or near real-time application to provide close information to make good decisions. Furthermore, handle the imbalanced data using new techniques.

Aim/Purpose: The use of digital technology, such as an electronic records management system (ERMS), has prompted widespread changes across organizations. The organization needs to support its operations with an automation system to improve production performance. This study investigates ERMS’s potentiality to enhance organizational performance in the oil and gas industry. Background: Oil and gas organizations generate enormous electronic records that lead to difficulties in managing them without any system or digitalization procedure. The need to use a system to manage big data and records affects information security and creates several problems. This study supports decision-makers in oil and gas organizations to use ERMS to enhance organizational performance. Methodology: We used a quantitative method by integrating the typical partial least squares (SEM-PLS) approach, including measurement items, respondents’ demographics, sampling and collection of data, and data analysis. The SEM-PLS approach uses a measurement and structural model assessment to analyze data. Contribution: This study contributes significantly to theory and practice by providing advancements in identity theory in the context of big data management and electronic records management. This study is a foundation for further research on the role of ERMS in operations performance and Big Data Management (BDM). This research makes a theoretical contribution by studying a theory-driven framework that may serve as an essential lens to evaluate the role of ERMS in performance and increase its potentiality in the future. This research also evaluated the combined impacts of general technology acceptance theory elements and identity theory in the context of ERMS to support data management. Findings: This study provides an empirically tested model that helps organizations to adopt ERMS based on the influence of big data management. The current study’s findings looked at the concerns of oil and gas organizations about integrating new technologies to support organizational performance. The results demonstrated that individual characteristics of users in oil and gas organizations, in conjunction with administrative features, are robust predictors of ERMS. The results show that ERMS potentiality significantly influences the organizational performance of oil and gas organizations. The research results fit the big ideas about how big data management and ERMS affect respondents to adopt new technologies. Recommendations for Practitioners: This study contributes significantly to the theory and practice of ERMS potentiality and BDM by developing and validating a new framework for adopting ERMS to support the performance and production of oil and gas organizations. The current study adds a new framework to identity theory in the context of ERMS and BDM. It increases the perceived benefits of using ERMS in protecting the credibility and authenticity of electronic records in oil and gas organizations. Recommendation for Researchers: This study serves as a foundation for future research into the function and influence of big data management on ERMS that support the organizational performance. Researchers can examine the framework of this study in other nations in the future, and they will be able to analyze this research framework to compare various results in other countries and expand ERMS generalizability and efficacy. Impact on Society: ERMS and its impact on BDM is still a developing field, and readers of this article can assist in gaining a better understanding of the literature’s dissemination of ERMS adoption in the oil and gas industry. This study presents an experimentally validated model of ERMS adoption with the effect of BDM in the oil and gas industry. Future Research: In the future, researchers may be able to examine the impact of BDM and user technology fit as critical factors in adopting ERMS by using different theories or locations. Furthermore, researchers may include the moderating impact of demographical parameters such as age, gender, wealth, and experience into this study model to make it even more robust and comprehensive. In addition, future research may examine the significant direct correlations between human traits, organizational features, and individual perceptions of BDM that are directly related to ERMS potentiality and operational performance in the future.

Aim/Purpose: This research utilized a learn-to-rank algorithm to provide medical recommendations to prescribers. The algorithm has been utilized in other domains, such as information retrieval and recommender systems. Background: Ranking the possible medical treatments according to diagnoses of the medical cases is very beneficial for doctors, especially during the coding process. Methodology: We developed two deep learning pointwise learn-to-rank models within one prediction pipeline: one for predicting the top possible active ingredients from disease features, the other for ranking actual medicines codes from diseases and the ingredients features. Contribution: A new learn-to-rank deep learning model has been developed to rank medical procedures based on datasets collected from insurance companies. Findings: We ran 18 cross-validation trials on a confidential dataset from an insurance company. We obtained an average normalized discounted cumulative gain (NDCG@8) of 74% with a 5% standard deviation as a result of all 18 experiments. Our approach outperformed a known approach used in the information retrieval domain in which data is represented in LibSVM format. Then, we ran the same trials using three learn-to-rank models – pointwise, pairwise, and listwise – which yielded average NDCG@8 of 71%, 72%, and 72%, respectively. Recommendations for Practitioners: The proposed model provides an insightful approach to helping to manage the patient’s treatment process. Recommendation for Researchers: This research lays the groundwork for exploring various applications of data science techniques and machine learning algorithms in the medical field. Future studies should focus on the significant potential of learn-to-rank algorithms across different medical domains, including their use in cost-effectiveness models. Emphasizing these algorithms could enhance decision-making processes and optimize resource allocation in healthcare settings. Impact on Society: This will help insurance companies and end users reduce the cost associated with patient treatment. It also helps doctors to choose the best procedure and medicines for their patients. Future Research: Future research is required to investigate the impact of medicine data at a granular level.

Aim/Purpose: This study investigated factors influencing the intention to engage in green innovation among small and medium enterprises (SMEs) in the tourism sector, using an integrated approach from the pro-environmental planned behavior (PEPB) and technology organization environment (TOE) models. Background: Green innovation is a long-term strategy aimed at addressing environmental challenges in the Indonesian tourism sector, especially those related to SMEs in culinary, accommodation, transportation, and creative industries. While prior research primarily focused on innovation characteristics and various behavioral intentions towards new technologies, this study pioneered an approach to understanding green innovation practices among SMEs by examining behavioral intention and the influence of internal organizational and external environmental factors. This was achieved through the PEPB model, which extends the theory of planned behavior (TPB) by incorporating perceived authority support and perceived environmental concern and integrating it with the TOE model. This comprehensive approach was crucial for understanding SME motivations, needs, and challenges in adopting green innovation, thereby supporting environmental sustainability. Methodology: Data were collected through offline and online questionnaires and interviews with 405 SMEs that had implemented green innovation as respondents. The theoretical model was tested using partial least squares structural equation modeling (PLS-SEM) with top-level constructs. Contribution: This research contributed to the development and validation of an integrated model for green innovation in SMEs, offering insights and recommendations for all stakeholders in the tourism sector to formulate effective green innovation strategies. Findings: This research revealed that the integrated model of pro-environmental planned behavior and technology organization environment successfully explained 71% of the factors influencing the intention to engage in green innovation for SMEs in the tourism sector. Perceived authority support emerged as the strongest factor, while perceived behavioral control was identified as a weaker factor. Recommendations for Practitioners: The research findings recommended that SMEs in the tourism sector focus on customer satisfaction and operational efficiency and optimize the recruitment and training processes of resources to maximize success in adopting environmentally friendly innovations. Meanwhile, for the government, providing support, incentives, and stringent environmental regulations could encourage sustainable business practices. Recommendation for Researchers: The research findings recommended that SMEs in the tourism sector focus on customer satisfaction and operational efficiency and optimize the recruitment and training processes of resources to maximize success in adopting environmentally friendly innovations. Meanwhile, for the government, providing support, incentives, and stringent environmental regulations could encourage sustainable business practices. Impact on Society: Examining the factors influencing the intention to engage in green innovation among SMEs in the tourism sector carried significant social implications. The findings contributed to recommending strategies for businesses and stakeholders such as the government, investors, and tourists to collectively strive to minimize environmental damage in tourist areas through the implementation of green innovation. Future Research: There are several promising avenues to explore to enhance future research. Expanding the scope to include diverse regions and industries and using additional approaches, such as leadership theory and management commitment theories, can increase the R-squared value. Additionally, broadening the profile of interviewees to obtain a more comprehensive understanding of the intention to engage in green innovation should be considered.

Aim/Purpose: The last decade has witnessed a tremendous progression in mobile penetration across the world and, most importantly, in developing countries like India. This research aims to investigate and analyze the factors influencing the adoption of mobile payments (M-payments) in the Indian rural population. This, in turn, would bring about positive changes in the lives of people in these countries. Background: A conceptual framework was worked upon using UTAUT as a foundation, which included constructs, namely, facilitating conditions, social influences, performance expectancy, and effort expectancy. The model was further extended by incorporating the awareness construct of m-payments to make it more comprehensive and to understand behavioral intentions and usage behavior for m-payments in rural India. Methodology: A questionnaire-based study was conducted to collect primary data from 410 respondents residing in rural areas in the state of Punjab. Convenience sampling was conducted to collect the data. Structural equation modeling was used to conduct statistical analysis, including exploratory and confirmatory factor analyses. Contribution: A new conceptual model for M-payments adoption in rural India was developed based on the study’s findings. Using the findings of the study, marketers, policymakers, and academicians can gain insight into the factors that motivate the rural population to use M-payments. Findings: The study has found that M-payment Awareness (AW) is the strongest factor within the proposed model for deeper diffusion of M-payments in rural areas in the state of Punjab. Performance expectancy (PE), effort expectancy (EE), social influences (SI), and facilitating conditions (FC) are also positively and significantly related to behavioral intentions for using M-payments among the Indian rural population in the state of Punjab. Recommendations for Practitioners: M-payments are emerging as a new mode of transactions among the Indian masses. The government needs to play a pivotal role in advocating the benefits linked with the usage of M-payments by planning financial literacy and awareness campaigns, promoting transparency and accountability of the intermediaries, and reducing transaction costs of using M-payments. Mobile manufacturing companies should come up with devices that are easy to use and incorporate multilanguage mobile applications, especially for rural areas, as India is a multi-lingual country. A robust regulatory framework will not only shape consumer trust but also prevent privacy breaches. Recommendation for Researchers: It is recommended that a comparative study among different M-payment platforms be conducted by exploring constructs such as usefulness and ease of use. However, the vulnerability of data leakage may result in insecurity and skepticism about its adoption. Impact on Society: India’s rural areas have immense potential for adoption of M-payments. Appropriate policies, awareness drives, and necessary infrastructure will boost faster and smoother adoption of M-payments in rural India to thrive in the digital economy. Future Research: The adapted model can be further tested with moderating factors like age, gender, occupation, and education to understand better the complexities of M-payments, especially in rural areas of India. Additionally, cross-sectional studies could be conducted to evaluate the behavioral intentions of different sections of society.

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 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 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 research investigated the determinant factors influencing the adoption intentions of Chatsicum, a Knowledge-Based Chatbot (KBC) aimed at enhancing the species literacy of biodiversity students. Background: This research was conducted to bridge the gap between technology, education, and biodiversity conservation. Innovative solutions are needed to empower individuals with knowledge, particularly species knowledge, in preserving the natural world. Methodology: The study employed a quantitative approach using the Partial Least Square Structural Equation Modeling (PLS-SEM) and sampled 145 university students as respondents. The research model combined the Task-Technology Fit (TTF) framework with elements from the Diffusion of Innovation (DOI), including relative advantage, compatibility, complexity, and observability. Also, the model introduced perceived trust as an independent variable. The primary dependent variable under examination was the intention to use the KBC. Contribution: The findings of this research contribute to a deeper understanding of the critical factors affecting the adoption of the KBC in biodiversity education and outreach, as studies in this context are limited. This study provides valuable insights for developers, educators, and policymakers interested in promoting species literacy and leveraging innovative technologies by analyzing the interplay of TTF and DOI constructs alongside perceived trust. Ultimately, this research aims to foster more effective and accessible biodiversity education strategies. Findings: TTF influenced all DOI variables, such as relative advantage, compatibility, observability, and trust positively and complexity negatively. In conclusion, TTF strongly affected usage intention indirectly. However, relative advantage, complexity, and observability insignificantly influenced the intention to use. Meanwhile, compatibility and trust strongly affected the use intention. Recommendations for Practitioners: Developers should prioritize building and maintaining chatbots that are aligned with the tasks, needs, and goals of the target users, as well as establishing trust through the assurance of information accuracy. Educators could develop tailored educational interventions that resonate with the values and preferences of diverse learners and are aligned closely with students’ learning needs, preferences, and curriculum while ensuring seamless integration with the existing educational context. Conservation organizations and policymakers could also utilize the findings of this study to enhance their outreach strategies, as the KBC is intended for students and biodiversity laypeople. Recommendation for Researchers: Researchers should explore the nuances of relationships between TTF and DOI, as well as trust, and consider the potential influence of mediating and moderating variables to advance the field of technology adoption in educational contexts. Researchers could also explore why relative advantage, complexity, and observability did not significantly impact the usage intention and whether specific user segments or contextual factors influence these relationships. Impact on Society: This research has significant societal impacts by improving species literacy, advancing technology in education, and promoting conservation efforts. Species knowledge could raise awareness regarding biodiversity and the importance of conservation, thereby leading to more informed and responsible citizens. Future Research: Future works should address the challenges and opportunities presented by KBCs in the context of species literacy enhancement, for example, interventions or experiments to influence the non-significant factors. Furthermore, longitudinal studies should investigate whether user behavior evolves. Ultimately, examining the correlation between species literacy, specifically when augmented by chatbots, and tangible conservation practices is an imperative domain in the future. It may entail evaluating the extent to which enhanced knowledge leads to concrete measures promoting biodiversity preservation.

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 identify the characteristics of YouTube video reviews that consumers utilize to evaluate review helpfulness and explores how they process such information. This study aims to investigate the effect of argument quality, review popularity, number of likes, and source credibility on consumers’ perception of YouTube’s video review helpfulness. Background: Video reviews posted on YouTube are an emerging form of online reviews, which have the potential to be more helpful than textual reviews due to their visual and audible cues that deliver more vivid information about product features and specifications. With the availability of an enormous number of video reviews with unpredictable quality, it becomes challenging for consumers to find helpful reviews without consuming significant time and effort. In addition, YouTube does not provide a specific feature that indicates a review helpfulness similar to the one found on e-commerce websites. Consequently, consumers have to examine the characteristics of video reviews that are readily available on YouTube, evaluate them, and form a perception of whether a review is helpful or not. Despite the increasing popularity of YouTube’s video reviews, video reviews’ helpfulness received inadequate attention in the literature. The antecedents of the helpfulness of online video reviews are still underinvestigated, and more research is needed to identify the characteristics that consumers depend upon to assess video review helpfulness. Furthermore, it is important to understand how consumers process the information they gain from these characteristics to form a perception of their helpfulness. Methodology: Following an extended investigation of the relevant literature, we identified four key video characteristics that consumers presumably utilize to evaluate review helpfulness on YouTube (i.e., review popularity, number of likes, source credibility, and argument quality). By employing the Elaboration Likelihood Model (ELM), we classified these characteristics along the central and peripheral routes. The central route characteristics require a high cognitive effort by consumers to process the review’s message and reach a logical decision. In contrast, the peripheral route assumes that consumers judge the review’s message based on superficial qualities without substantial cognitive effort. A research model is introduced to investigate the effect of central and peripheral cues and their corresponding video review characteristics on review helpfulness. Accordingly, argument quality is proposed in the central route of the model, while review popularity, number of likes, and source credibility are proposed in the peripheral route. Furthermore, the study investigates how consumers process the information they obtain from these routes jointly or independently. To empirically test the proposed model, a convenient sample of 361 YouTube users was obtained through an online survey. The partial least squares method was used to investigate the effect of the proposed characteristics on video review helpfulness. Contribution: This study contributes to the literature in several ways. First, it is one of the few studies that investigate online video reviews’ helpfulness. Second, this study identifies several unique characteristics of YouTube’s video reviews that span peripheral and central routes, which potentially contribute to review helpfulness. Third, this study proposes a conceptual model based on the ELM to explore the effect of central and peripheral cues and their corresponding review characteristics on review helpfulness. Fourth, the research findings provide implications for research and practice that advance the theoretical understanding of video reviews’ helpfulness and serve as guidelines to create more helpful video reviews by better understanding the consumer’s cognitive processes. Findings: The results show that among the four characteristics proposed in the research model, argument quality in the central route is the strongest determinant factor affecting video review helpfulness. Results also show that review popularity, source credibility, and the number of likes in the peripheral route have significant effects on video review helpfulness. Altogether, our results show that the effect of the peripheral route adds up to 0.463 compared to 0.430, which is the impact magnitude of the argument quality construct in the central route. Based on the comparable effect magnitude of the central and peripheral routes of the model on video review helpfulness, our results indicate that both peripheral and central cues significantly affect consumers’ perception of video review helpfulness. The two routes are not mutually exclusive, and their cues can be processed in parallel or consecutive ways. Recommendations for Practitioners: The study recommends creating a dedicated category for reviews on YouTube with a specific feature for consumers to indicate the helpfulness of a video review, similar to the helpful vote button in textual reviews. The study also recommends that reviewers deliver more appealing and convincing argument quality, work toward improving their credibility, and understand the factors that contribute to video popularity. Impact on Society: Identifying the characteristics that affect video review helpfulness on YouTube helps consumers access helpful reviews more efficiently and improves their purchase decisions. Future Research: Future research could look into different types of data that could be extracted from YouTube to investigate the helpfulness of online video reviews. Future studies could employ machine learning and sentiment analysis techniques to reach more insights. Future research could also investigate the effect of product types in the context of online video reviews.

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.

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.

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.

This paper aims to share the author's vision on possible research directions for big knowledge-based AI. A renewed definition of big knowledge (BK) and big knowledge systems (BKS) is first introduced. Then the first BKS model, called cloud knowledge social intelligence (CKEI) is provided with a hierarchy of knowledge as a service (KAAS). At last, a new semantics, the big-and-broad step axiomatic structural operational semantics (BBASOS) for applications on BKS is introduced and discussed with a practical distributed BKS model knowledge graph network KGN and a mini example.

Large graphs or automata have their data that cannot fit in a single machine, or may take unreasonable time to be processed. We implement with MapReduce and Giraph two algorithms for intersecting and minimising large and distributed automata. We provide some comparative analysis, and the experiment results are depicted in figures. Our work experimentally validates our propositions as long as it shows that our choice, in comparison with MapReduce one, is not only more suitable for graph-oriented algorithms, but also speeds the executions up. This work is one of the first steps of a long-term goal that consists in a high level distributed graph processing language.

Prediction of route based on historical trip observation of users is widely employed in location-based services. This work concentrates on building a route prediction system using Lempel-Ziv technique applied to a historical corpus of user travel data. Huge continuous logs of historical GPS traces representing the user's location in past are decomposed into smaller logical units known as trips. User trips are converted into sequences of road network edges using a process known as map matching. Lempel-Ziv is applied on road network edges to build the prediction model that captures the user's travel pattern in the past. A two-phased model is proposed using a map reduce framework without losing accuracy and efficiency. Model is then used to predict the user's end-to-end route given a partial route travelled by the user at any point in time. The objective of the proposed work is to build a Route Prediction system in which model building and prediction both are horizontally scalable.

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.

Significant work in service domain has focused on customer journey within e-service delivery system process (e-SDSP). Few studies have focused on process-centric approach to customer journey during delivery of e-services. This study aims to investigate the performance assessment of three online retailers (alternatives) using perceived service process during different stages of e-SDSP as a criterion for decision-making. TOPSIS is used in this paper to rate and evaluate multiple online retailers. Based on perceived service process as the criterion, results show that online retailer-2 outperforms other two online retailers. This study is one of the first to rate online retailers by utilising customer-perceived service process (latent variables) as a decision-making criterion throughout e-SDSP. The finding suggests that perceived searching process is the most essential criterion for decision-making, followed by the perceived after-sales service process, the perceived agreement process, and the perceived fulfilment process. Implications, limitations, and future scope are also discussed.