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: The rise of modern artificial intelligence (AI), in particular, machine learning (ML), has provided new opportunities and directions for knowledge management (KM). A central question for the future of KM is whether it will be dominated by an automation strategy that replaces knowledge work or whether it will support a knowledge-enablement strategy that enhances knowledge work and uplifts knowledge workers. This paper addresses this question by re-examining and updating a critical argument against KM by the sociologist of science Steve Fuller (2002), who held that KM was extractive and exploitative from its origins. Background: This paper re-examines Fuller’s argument in light of current developments in artificial intelligence and knowledge management technologies. It reviews Fuller’s arguments in its original context wherein expert systems and knowledge engineering were influential paradigms in KM, and it then considers how the arguments put forward are given new life in light of current developments in AI and efforts to incorporate AI in the KM technical stack. The paper shows that conceptions of tacit knowledge play a key role in answering the question of whether an automating or enabling strategy will dominate. It shows that a better understanding of tacit knowledge, as reflected in more recent literature, supports an enabling vision. Methodology: The paper uses a conceptual analysis methodology grounded in epistemology and knowledge studies. It reviews a set of historically important works in the field of knowledge management and identifies and analyzes their core concepts and conceptual structure. Contribution: The paper shows that KM has had a faulty conception of tacit knowledge from its origins and that this conception lends credibility to an extractive vision supportive of replacement automation strategies. The paper then shows that recent scholarship on tacit knowledge and related forms of reasoning, in particular, abduction, provide a more theoretically robust conception of tacit knowledge that supports the centrality of human knowledge and knowledge workers against replacement automation strategies. The paper provides new insights into tacit knowledge and human reasoning vis-à-vis knowledge work. It lays the foundation for KM as a field with an independent, ethically defensible approach to technology-based business strategies that can leverage AI without becoming a merely supporting field for AI. Findings: Fuller’s argument is forceful when updated with examples from current AI technologies such as deep learning (DL) (e.g., image recognition algorithms) and large language models (LLMs) such as ChatGPT. Fuller’s view that KM presupposed a specific epistemology in which knowledge can be extracted into embodied (computerized) but disembedded (decontextualized) information applies to current forms of AI, such as machine learning, as much as it does to expert systems. Fuller’s concept of expertise is narrower than necessary for the context of KM but can be expanded to other forms of knowledge work. His account of the social dynamics of expertise as professionalism can be expanded as well and fits more plausibly in corporate contexts. The concept of tacit knowledge that has dominated the KM literature from its origins is overly simplistic and outdated. As such, it supports an extractive view of KM. More recent scholarship on tacit knowledge shows it is a complex and variegated concept. In particular, current work on tacit knowledge is developing a more theoretically robust and detailed conception of human knowledge that shows its centrality in organizations as a driver of innovation and higher-order thinking. These new understandings of tacit knowledge support a non-extractive, human enabling view of KM in relation to AI. Recommendations for Practitioners: Practitioners can use the findings of the paper to consider ways to implement KM technologies in ways that do not neglect the importance of tacit knowledge in automation projects (which neglect often leads to failure). They should also consider how to enhance and fully leverage tacit knowledge through AI technologies and augment human knowledge. Recommendation for Researchers: Researchers can use these findings as a conceptual framework in research concerning the impact of AI on knowledge work. In particular, the distinction between replacement and enabling technologies, and the analysis of tacit knowledge as a structural concept, can be used to categorize and analyze AI technologies relative to KM research objectives. Impact on Society: The potential of AI on employment in the knowledge economy is a major issue in the ethics of AI literature and is widely recognized in the popular press as one of the pressing societal risks created by AI and specific types such as generative AI. This paper shows that KM, as a field of research and practice, does not need to and should not add to the risks created by automation-replacement strategies. Rather, KM has the conceptual resources to pursue a (human) knowledge enablement approach that can stand as a viable alternative to the automation-replacement vision. Future Research: The findings of the paper suggest a number of research trajectories. They include: Further study of tacit knowledge and its underlying cognitive mechanisms and structures in relation to knowledge work and KM objectives. Research into different types of knowledge work and knowledge processes and the role that tacit and explicit knowledge play. Research into the relation between KM and automation in terms of KM’s history and current technical developments. Research into how AI arguments knowledge works and how KM can provide an enabling framework.

Aim/Purpose: While existing literature has extensively explored factors influencing the success of big data projects and proposed big data maturity models, no study has harnessed machine learning to predict project success and identify the critical features contributing significantly to that success. The purpose of this paper is to offer fresh insights into the realm of big data projects by leveraging machine-learning algorithms. Background: Previously, we introduced the Global Big Data Maturity Model (GBDMM), which encompassed various domains inspired by the success factors of big data projects. In this paper, we transformed these maturity domains into a survey and collected feedback from 90 big data experts across the Middle East, Gulf, Africa, and Turkey regions regarding their own projects. This approach aims to gather firsthand insights from practitioners and experts in the field. Methodology: To analyze the feedback obtained from the survey, we applied several algorithms suitable for small datasets and categorical features. Our approach included cross-validation and feature selection techniques to mitigate overfitting and enhance model performance. Notably, the best-performing algorithms in our study were the Decision Tree (achieving an F1 score of 67%) and the Cat Boost classifier (also achieving an F1 score of 67%). Contribution: This research makes a significant contribution to the field of big data projects. By utilizing machine-learning techniques, we predict the success or failure of such projects and identify the key features that significantly contribute to their success. This provides companies with a valuable model for predicting their own big data project outcomes. Findings: Our analysis revealed that the domains of strategy and data have the most influential impact on the success of big data projects. Therefore, companies should prioritize these domains when undertaking such projects. Furthermore, we now have an initial model capable of predicting project success or failure, which can be invaluable for companies. Recommendations for Practitioners: Based on our findings, we recommend that practitioners concentrate on developing robust strategies and prioritize data management to enhance the outcomes of their big data projects. Additionally, practitioners can leverage machine-learning techniques to predict the success rate of these projects. Recommendation for Researchers: For further research in this field, we suggest exploring additional algorithms and techniques and refining existing models to enhance the accuracy and reliability of predicting the success of big data projects. Researchers may also investigate further into the interplay between strategy, data, and the success of such projects. Impact on Society: By improving the success rate of big data projects, our findings enable organizations to create more efficient and impactful data-driven solutions across various sectors. This, in turn, facilitates informed decision-making, effective resource allocation, improved operational efficiency, and overall performance enhancement. Future Research: In the future, gathering additional feedback from a broader range of big data experts will be valuable and help refine the prediction algorithm. Conducting longitudinal studies to analyze the long-term success and outcomes of Big Data projects would be beneficial. Furthermore, exploring the applicability of our model across different regions and industries will provide further insights into the field.

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 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 paper investigates the user acceptability of blockchain technology in the healthcare sector, with a specific focus on healthcare professionals in Jordan. Background: The study seeks to identify the factors that affect healthcare professionals’ use and acceptance of blockchain technology in Jordan. Methodology: The study’s research framework integrates factors from the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). A questionnaire was distributed to collect data from 372 healthcare professionals in Jordan, and the results were analyzed using structural equation modeling based on the Partial Least Square (PLS) technique. Contribution: While only a few previous studies have explored blockchain technology acceptance in the healthcare sector using either the TAM or the UTAUT, this study uniquely integrates elements from both models, offering a novel approach that provides a comprehensive understanding of the factors that influence the acceptance of blockchain technology among healthcare professionals in Jordan. The findings can assist decision-makers in developing strategies to enhance the adoption rate of blockchain technology in the Jordanian healthcare sector. Findings: The study revealed that usability, convenience, privacy and security, cost, and trust significantly impact the perceived usefulness of blockchain technology. The findings also suggest that healthcare professionals are more likely to have a positive attitude towards blockchain-based healthcare systems if they perceive them as useful and easy to use. Attitude, social influence, and facilitating conditions were found to significantly impact behavioral intention to use. Recommendations for Practitioners: Stakeholders should focus on developing blockchain-based healthcare systems that are easy to use, convenient, efficient, and effort-free. Recommendation for Researchers: Researchers may compare the acceptance of blockchain technology in the healthcare sector with other industries to identify industry-specific factors that may influence adoption. This comparative analysis can contribute to a broader understanding of technology acceptance. Impact on Society: Successful adoption of blockchain technology in the healthcare sector can lead to improved efficiency, enhanced protection of healthcare data, and reduced administrative burdens. This, in turn, can positively impact patient care and lead to cost savings, which contributes to more sustainable and accessible healthcare services. Future Research: Future research may explore integrating blockchain technology with other emerging technologies, such as artificial intelligence and sidechain, to create more comprehensive and innovative healthcare solutions.

Aim/Purpose: This paper aims to investigate and understand the intentions of management undergraduate students in Hangzhou, China, regarding the adoption of Artificial Intelligence (AI) technologies in their education. It addresses the need to explore the factors influencing AI adoption in the educational context and contribute to the ongoing discourse on technology integration in higher education. Background: The paper addresses the problem by conducting a comprehensive investigation into the perceptions of management undergraduate students in Hangzhou, China, regarding the adoption of AI in education. The study explores various factors, including Perceived Relative Advantage and Trialability, to shed light on the nuanced dynamics influencing AI technology adoption in the context of higher education. Methodology: The study employs a quantitative research approach, utilizing the Confirmatory Tetrad Analysis (CTA) and Partial Least Squares Structural Equation Modeling (PLS-SEM) methodologies. The research sample consists of management undergraduate students in Hangzhou, China, and the methods include data screening, principal component analysis, confirmatory tetrad analysis, and evaluation of the measurement and structural models. We used a random sampling method to distribute 420 online, self-administered questionnaires among management students aged 18 to 21 at universities in Hangzhou. Contribution: This paper explores how management students in Hangzhou, China, perceive the adoption of AI in education. It identifies factors that influence AI adoption intention. Furthermore, the study emphasizes the complex nature of technology adoption in the changing educational technology landscape. It offers a thorough comprehension of this process while challenging and expanding the existing literature by revealing the insignificant impacts of certain factors. This highlights the need for an approach to AI integration in education that is context-specific and culturally sensitive. Findings: The study highlights students’ positive attitudes toward integrating AI in educational settings. Perceived relative advantage and trialability were found to impact AI adoption intention significantly. AI adoption is influenced by social and cultural contexts rather than factors like compatibility, complexity, and observability. Peer influence, instructor guidance, and the university environment were identified as pivotal in shaping students’ attitudes toward AI technologies. Recommendations for Practitioners: To promote the use of AI among management students in Hangzhou, practitioners should highlight the benefits and the ease of testing these technologies. It is essential to create communication strategies tailored to the student’s needs, consider cultural differences, and utilize the influence of peers and instructors. Establishing a supportive environment within the university that encourages innovation through policies and regulations is vital. Additionally, it is recommended that students’ attitudes towards AI be monitored constantly, and strategies adjusted accordingly to keep up with the changing technological landscape. Recommendation for Researchers: Researchers should conduct cross-disciplinary and cross-cultural studies with qualitative and longitudinal research designs to understand factors affecting AI adoption in education. It is essential to investigate compatibility, complexity, observability, individual attitudes, prior experience, and the evolving role of peers and instructors. Impact on Society: The study’s insights into the positive attitudes of management students in Hangzhou, China, toward AI adoption in education have broader societal implications. It reflects a readiness for transformative educational experiences in a region known for technological advancements. However, the study also underscores the importance of cautious integration, considering associated risks like data privacy and biases to ensure equitable benefits and uphold educational values. Future Research: Future research should delve into AI adoption in various academic disciplines and regions, employing longitudinal designs and qualitative methods to understand cultural influences and the roles of peers and instructors. Investigating moderating factors influencing specific factors’ relationship with AI adoption intention is essential for a comprehensive understanding.

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 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 research aims to evaluate models from meta-learning techniques, such as Riemannian Model Agnostic Meta-Learning (RMAML), Model-Agnostic Meta-Learning (MAML), and Reptile meta-learning, to obtain high-quality metadata. The goal is to utilize this metadata to increase accuracy and efficiency in identifying chili varieties in smart agriculture. Background: The identification of chili varieties in smart agriculture is a complex process that requires a multi-faceted approach. One challenge in chili variety identification is the lack of a large and diverse dataset. This can be addressed using meta-learning techniques, which allow the model to leverage knowledge learned from other related tasks or artificially expand the dataset by applying transformations to existing data. Another challenge is the variation in growing conditions, which can affect the appearance of chili varieties. Meta-learning techniques can help address this challenge by allowing the model to adapt to variations in growing conditions with task-specific embeddings and optimizations. With the help of meta-learning techniques, such as data augmentation, data characterization, selection of datasets, and performance estimation, quality metadata for accurate identification of chili varieties can be achieved even in the presence of limited data and variations in growing conditions. Furthermore, the use of meta-learning techniques in chili variety identification can also assist in addressing challenges related to the computational complexity of the task. Methodology: The research approach employed is quantitative, specifically comparing three models from meta-learning techniques to determine which model is most suitable for our dataset. Data was collected from the variety assembly garden in the form of images of chili leaves using a mobile device. The research successfully gathered 1,974 images of chili leaves, with 697 images of large red chilies, 649 images of curly red chilies, and 628 images of cayenne peppers. These chili leaf images were then processed using augmentation techniques. The results of image data augmentation were categorized based on leaf characteristics (such as oval, lancet, elliptical, serrated leaf edges, and flat leaf edges). Subsequently, training and validation utilized three models from meta-learning techniques. The final stage involved model evaluation using 2-way and 3-way classification, as well as 5-shot and 10-shot learning scenarios to select the dataset with the best performance. Contribution: Improving classification accuracy, with a focus on ensuring high-quality data, allows for more precise identification and classification of chili varieties. Enhancing model training through an emphasis on data quality ensures that the models receive reliable and representative input, leading to improved generalization and performance in identifying chili varieties. Findings: With small collections of datasets, the authors have used data augmentation and meta-learning techniques to overcome the challenges of limited data and variations in growing conditions. Recommendations for Practitioners: By leveraging the knowledge and adaptability gained from meta-learning, accurate identification of chili varieties can be achieved even with limited data and variations in growing conditions. The use of meta-learning techniques in chili variety identification can greatly improve the accuracy and reliability of the identification process. Recommendation for Researchers: Using meta-learning techniques, such as transfer learning and parameter optimization, researchers can overcome challenges related to limited data and variations in growing conditions in chili variety identification. Impact on Society: The findings from this research can help identify superior chili seeds, thereby motivating farmers to cultivate high-quality chilies and achieve bountiful harvests. Future Research: We intend to verify our approach on a more extensive array of datasets and explore the implementation of more resilient regularization techniques, going beyond image augmentation, within the meta-learning techniques. Furthermore, our goal is to expand our research to encompass the automatic learning of parameters during training and tackle issues associated with noisy labels. Building on the insights gained from our observed outcomes, a future objective is to enhance the refinement of model-agnostic meta-learning techniques that can effectively adapt to intricate task distributions with substantial domain gaps between tasks. To realize this aim, our proposal involves devising model-agnostic meta-learning techniques specifically designed for multi-modal scenarios.

Aim/Purpose: The research aims to investigate the factors that influence the continuous use of mobile banking applications to complete banking monetary transactions. Background: Despite a significant increase in the use of mobile banking applications, particularly during the COVID-19 pandemic, new evidence indicates that the use rate of mobile banking applications for operating banking monetary transactions has declined. Methodology: The study proposed an integrated model based mainly on the process virtualization theory (PVT) with other novel factors such as mobile banking application anthropomorphism and virtual process failure risk. The study model was empirically validated using structural equation modeling analysis on quantitative data from 484 mobile banking application users from Jordan. Contribution: The study focuses on continuing use or post-adoption behavior rather than pre-adoption behavior. This is important since the maximum and long-term viability, as well as the financial investment in mobile banking applications, depend on regular usage rather than first-time use or initial experience. Findings: The results indicate that process virtualizable and anthropomorphism have a strong positive impact on bank customers’ decisions to continue using mobile banking applications to complete banking monetary transactions. Meanwhile, the negative impact of virtualization process failure risk on continuous use has been discovered. The found factors explain 67.5% of the variance in continuous use. Recommendations for Practitioners: The study identified novel, significant factors that affect bank customers’ decisions to use mobile banking applications frequently, and these factors should be examined, matched, satisfied, or addressed when redesigning or upgrading mobile applications. Banks should provide users with clear directions, processes, or tutorials on how to complete monetary transactions effectively. They should also embrace Artificial Intelligence (AI) technology to improve their applications and products with anthropomorphic features like speech synthesizers, Chatbots, and AI-powered virtual bank assistants. This is expected to help bank customers conduct various banking services conveniently and securely, just as if interacting with real people. The study further recommends that banks create and publish clear norms and procedures, as well as promote tolerance and protect consumers’ rights when the process fails or mistakes occur. Recommendation for Researchers: The study provides measurement items that were specifically built for the context of mobile banking applications based on PVT notions. Researchers are invited to reuse, test, and modify existing measurement items, as well as submit new ones if necessary. The study model does not consider psychological aspects like trust and satisfaction, which would provide additional insight into factors affecting continuing use. Researchers could potentially take a different approach by focusing on user resistance and non-adoption. Impact on Society: Financial inclusion is problematic, particularly in underdeveloped nations. According to financial inclusion research, Jordanians rarely utilize mobile banking apps. Continuous usage of mobile banking applications will be extremely beneficial in closing the financial inclusion gap, particularly among women. Furthermore, it could help the country’s efforts to transition to a digital society. Future Research: The majority of study participants are from urban areas. Future studies should focus on consumers who live in rural areas. It was also suggested that the elderly be targeted because they may have different views/perspectives on the continued use of mobile banking applications.

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.

The proposed work aims to discover the international technology competition and development of human-artificial intelligence (AI) collaboration on content creation in the film and animation industries to support the strategic planning, decision-making of R&D, and soft innovation. The study demonstrates a hybrid approach that combines technology life cycle (TLC) and latent Dirichlet allocation (LDA) topic modelling. We analyse 1,982 patents of AI collaborating on creating film and animation in the primary patent application countries (i.e., patents applied to the intellectual property offices of the USA, China, Korea, Japan, and European Patent Office, EPO) from 2010 to 2020. The TLC results show growing trends in the international technology competition. The major topic trends corresponding to TLC phases denote strong potential or future stagnation signals in different countries. The study provides the future R&D signals and suggests stimulating soft innovation with human-AI collaboration to face growing competition.

This conceptual research aims to connect aspects of learning activities of continuing education for professionals (CPE). The objective is to provide conclusions about modes of professional learning within diversity, equity, inclusion, and belonging (DEIB) training. This interpretation is placed in context relating to the process of professional learning objectives. A CPE DEIB training plan is presented as an example of how to provide continuing professional education to adult learners within a DEIB curriculum (El-Amin, 2020). The purpose of incorporating the foundations of CPE into DEIB training permits organisations to strengthening organisational development and productivity. By connecting the foundations of curriculum design, alignment, assessment and mapping, and research-informed innovation, CPE aims to enhance the effectiveness of organisational DEIB initiatives. A CPE DEIB training plan emphasises the importance of accountability, employee involvement, and effective training to drive DEIB initiatives.

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.

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.

This study aims to identify and understand the perspectives of automotive aftersales stakeholders regarding current challenges posed by decarbonisation strategies. It examines potential responses that the automotive aftersales business could undertake to address these challenges. Semi-structured interviews were undertaken with automotive industry experts from Europe and Latin America. This paper focuses primarily on impacts of decarbonisation upon automotive aftersales and the potential role of data in that business. Results show that investment in technology will be a condition for businesses that want to remain active in the industry. Furthermore, experts agree that incumbent manufacturers are not filling the technology gap that the energy transition is creating in the automotive sector, a consequence of which will be the entrance of new players from other sectors. The current aftersales businesses will potentially lose bargaining control. Moreover, policy makers are seen as unreliable leaders of the transition agenda.