Artificial Intelligence Generated Content (AIGC) has rapidly emerged with the capability to generate different forms of content, including text, images, videos, and other modalities, which can achieve a quality similar to content created by humans. As a result, AIGC is now widely applied across various domains such as digital marketing, education, and public health, and has shown promising results by enhancing content creation efficiency and improving information delivery. However, there are few studies that explore the latest progress and emerging challenges of AIGC across different domains. To bridge this gap, this paper brings together 16 scholars from multiple disciplines to provide a cross-domain perspective on the trends and challenges of AIGC. Specifically, the contributions of this paper are threefold: (1) It first provides a broader overview of AIGC, spanning the training techniques of Generative AI, detection methods, and both the spread and use of AI-generated content across digital platforms. (2) It then introduces the societal impacts of AIGC across diverse domains, along with a review of existing methods employed in these contexts. (3) Finally, it discusses the key technical challenges and presents research propositions to guide future work. Through these contributions, this vision paper seeks to offer readers a cross-domain perspective on AIGC, providing insights into its current research trends, ongoing challenges, and future directions.

Implementing effective social entrepreneurship can result in a better redistribution of wealth and well-being in society. Social mediating technologies are digital transformation tools that can fulfill specific goals and the success of social entrepreneurship. This paper aims to answer the following research question: What makes social entrepreneurship successful and what is its relevance to social mediating technologies? This paper utilizes a meta-analysis method through a review of the literature, data analyses, and inductive reasoning to reach two research propositions: (1) social mediating technologies support the achievement of social entrepreneurship success and social network capabilities, and (2) social mediating technologies and social network capabilities support the realization of social entrepreneurship success. These two propositions have been further validated through industrial data analyses. A conceptual framework has been created and analyzed to portray the research question. The framework has created three constructs, including social mediating technologies, social network capabilities, and social entrepreneurship success. This study contributed to the social entrepreneurship field by adopting technological success factors and establishing the inter-relationships among these three constructs in the framework.

The implementation of green information technology (Green IT) contributes to the accomplishment of environmental sustainability. Corporate social responsibility (CSR) discipline demonstrates the responsibility of enterprises that impacts society. CSR has a strong effect to the economic and societal development enforced by corporate actions. The purpose of this study is to deliberate the emergent effect of environmental sustainability and the possibility of reaching sustainability in society through the joint effort of corporate social responsibility and Green IT. This study proposes a value model that combines the operational procedures of turning CSR and Green IT into the desired value outcome. Two corporate experience analyses are used to illustrate this model’s four stages value creation process.

The growth of a country is heavily depending on its investment over science and technology. In fact, the progress in computer science research serves as a vital factor in the development of a country. Interestingly, despite that the Association of Southeast Asian Nations (ASEAN) is located near each other, they do not share a similar level of ICT development, which leads to a digital divide among them. Thus, it is crucial to bibliometrically evaluate and compare the computer science research output over the latest decade (i.e., 2009 to 2018) among these countries. With the data gathered from the Web of Science, this study seeks to uncover the research focus of all ASEAN countries over the latest decade by analyzing the author keywords. Majority of the ASEAN was having a different research focus, explaining the varied levels of ICT development. Practical and theoretical implications are then offered accordingly.

Cyberbullying on social media has become a serious matter and is the most rampant form of bullying among students nowadays. Accordingly, this study examines mobile social cyberbullying perpetration, a new form of cyberbullying perpetration that is conducted over mobile social media, through the theoretical lenses of Barlett and Gentile Cyberbullying Model (BGCM) and Mobile Technology Acceptance Model (MTAM). The Partial Least Squares Structural Equation Modeling analysis was performed on the 489 samples gathered from university students in order to obtain inferential statistics. Perceived anonymity and belief in the irrelevance of muscularity from the BGCM are positively related to mobile usefulness and mobile ease of use from the MTAM, while all of them are reported to have a positive association with the positive cyberbullying attitudes, which ultimately explain mobile social cyberbullying perpetration. From the results, it is ascertained that the well-founded integrated model has made a novel contribution toward the current literature of cyberbullying perpetration, particularly the rarely studied mobile social cyberbullying perpetration. Several suggestions on reducing the mobile social cyberbullying perpetration are then made to the practitioners, especially the government policymakers and mobile social media service providers.

The emergences of Web 2.0 and cloud computing have contributed greatly to the prevalence of electronic Word-of-Mouth (eWoM). Unlike most of the existing studies which have used linear models, nonlinear relationships were discovered in hotel booking intention. So far, the effects of elaboration-likelihood model (ELM) and demographics have been largely overlooked, though studies have shown that ELM may explain consumers’ perception, behavior, and IS acceptance. Data were gathered from 497 patrons of 10 hotels in Golden Triangle, Kuala Lumpur, Malaysia. Using artificial neural network (ANN), we found that user involvement, positive eWoM, user expertise, perceived credibility, education, negative eWoM, and income are among the important predictors explaining 81% of variance in booking intention. The theoretical implications may further advance eWoM and ELM literatures, while the managerial implications may provide novel understandings to hotel operators, advertisers, and relevant hospitality policy makers in formulating effective decision making.

Considering that privacy paradox is currently an underexplored research area, this study proposes a privacy paradox model to explain the reasons behind such phenomenon. Built upon the theoretical link of belief-intention-behaviour and the idea of multi-mediation, this study seeks to discover the relationship between concern for social media information privacy and usage intention, and the potential mediators that stand between such linkage. The results suggest that perceived mobility and social support do mediate the said linkage, and the model could reasonably explain usage intention, which surprisingly has a negative association with usage behaviour. Theoretical and practical implications are subsequently offered.

This study aims to examine the direct and mediating relationships between team diversity, individual and team learning, and individual and team innovation performance. Using a sample of 266 white-collar professionals in Hong Kong, we found that team diversity plays a substantial role in improving both individual and team learning. Individual learning and team learning are independent and positively related to individual and team innovation performance. Individual learning partially mediates the relationship between team diversity and team learning. Individual innovation performance is found to be a partial mediator of team learning and team innovation performance. This study contributes in understanding the unexplored mediating effects of individual learning on team learning, and the individual innovation performance on team innovation performance. Team diversity is found to be the main enabler in the overall model. It is hoped that the research paradigm of this article will generate further development of information systems and technology literature.

Despite the influence of electronic word-of-mouth (eWOM) messages in decision-making processes, few studies have tested the determinants of persuasive eWOM messages among social media users. Although most of the researches focused on western and eastern cultural convergence or divergence of online communications, little attention has been paid on the validity and applicability of cultural orientations in countries with perceived inherent similar values. This study examines how Chinese and Malaysian users process eWOM messages and decide on continuing their overseas study. The significance of the study is to identify critical factors that influence Chinese and Malaysian users’ attitudes and behavior when processing persuasive eWOM messages. Results revealed that Facebook is the most used social networking site (SNS) for Malaysian users, while QQ Qzone for Chinese counterparts. This study found that argument quality, source credibility, source attractiveness, source perception, and source style exerted varying influences on Chinese and Malaysian users’ attitudes and intentions to continue their study abroad.

Electronic word-of-mouth (eWOM) has become one of the most influential communication tools. Few studies have identified what makes certain online reviews more influential than others. The objective of this study is to develop a better understanding of the impact of online reviews on consumer attitude and behavioral intention through a conceptual framework built from a series of theories and models. Combined experiment and survey methods contribute to the literature. Research findings revealed that online review antecedents of eWOM like review quality, valence, credibility, and quantity exerted impact on consumers’ attitude toward products. Personal involvement was a predominant predictor of consumers’ attitude. This study identified emotional strength’s mediating role in enhancing online review credibility and inducing a favorable attitude toward products.

MOTIVATION: Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately fatal or cause irreparable damage to the heart sustained over long periods of time. Therefore, the ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. In this article, classification by using associative Petri net (APN) for personalized ECG-arrhythmia-pattern identification is proposed for the first time in literature. RESULTS: A rule-based classification model and reasoning algorithm of APN are created for ECG arrhythmias classification. The performance evaluation using MIT-BIH arrhythmia database shows that our approach compares well with other reported studies.

This paper developed a method to assess information security risks in e-healthcare. Specifically, it first developed a static E-Healthcare Information Security Risk (EHISR) model to present thirty-three security risk factors by identifying information security threats and their sources in e-healthcare. Second, a dynamic E-Healthcare Information Flow (EHIF) model was developed to logically link these information risk factors in the EHISR model. Pattern analysis showed that information security risks could be classified into two levels, and versatility analysis showed that the overall security risks for eight information flows were close with a range from 55% to 86%. Third, one quantifiable approach based on a relative-weighted assessment model was developed to demonstrate how to assess the information security risks in e-healthcare. This quantitative security risk measurement establishes a reference point for assessing e-healthcare security risks and assists managers in selecting a reliable information flow infrastructure with a lower security risk level.

With the increase in the number senior citizens and chronic diseases, the number of elderly patients who need constant assistance has increased. One key point of all critical care for elderly patient is the continuous monitoring of their vital signs. Among these, the ECG signal is used for noninvasive diagnosis of cardiovascular diseases. Also, there is a pressing need to have a proper system in place for patient identification. Errors in patient identification, and hence improper administration of medication can lead to disastrous results. This paper proposes a novel embedded mobile ECG reasoning system that integrates ECG signal reasoning and RF identification together to monitor an elderly patient. As a result, our proposed method has a good accuracy in heart beat recognition, and enables continuous monitoring and identification of the elderly patient when alone. Moreover, in order to examine and validate our proposed system, we propose a managerial research model to test whether it can be implemented in a medical organization. The results prove that the mobility, usability, and performance of our proposed system have impacts on the user's attitude, and there is a significant positive relation between the user's attitude and the intent to use our proposed system.

Although Bayesian networks (BNs) are increasingly being used to solve real-world risk problems, their use is still constrained by the difficulty of constructing the node probability tables (NPTs). A key challenge is to construct relevant NPTs using the minimal amount of expert elicitation, recognizing that it is rarely cost effective to elicit complete sets of probability values. We describe a simple approach to defining NPTs for a large class of commonly occurring nodes (called ranked nodes). The approach is based on the doubly truncated normal distribution with a central tendency that is invariably a type of weighted function of the parent nodes. In extensive real-world case studies, we have found that this approach is sufficient for generating the NPTs of a very large class of nodes. We describe one such case study for validation purposes. The approach has been fully automated in a commercial tool, called AgenaRisk, and is thus accessible to all types of domain experts. We believe that this work represents a useful contribution to the BN research and technology, since its application makes the difference between being able to build realistic BN models and not.

This research proposes an intelligent cache mechanism for a data warehouse system in a mobile environment. Because mobile devices can often be disconnected from the host server and due to the low bandwidth of wireless networks, it is more efficient to store query results from a mobile device in the cache. For more personal use of mobile devices, we use a data mining technique to determine the pattern from a record of previous queries. Then the data, which will be retrieved by the user, are prefetched and stored in the cache, thus, improving the query efficiency. We demonstrate the feasibility of the proposed approach with experiments using simulation. Comparison of our approach with a standard approach indicates that there is a significant advantage to using mobile data warehouse systems.

Medical data registries serve an important role in the support, investigation and application of emerging treatments and technologies in health care, and in the study of rare or unusual diseases or treatments. By consolidating information of interest into databases designed to support a specialized role, they seek to improve the delivery of health care to specialized groups. By enabling exchange of information, and interrogation of large databases, health care workers can probe for knowledge not available in traditional medical record systems. The Extracorporeal Life Support Organization maintains a registry of patients treated with an extraordinary therapy for life-threatening cardiopulmonary failure, known as extracorporeal life support (ECLS). The registry is a database on patients from 120 domestic and international centers. Its initial implementation as a localized relational database imposed significant limits on dissemination of information for benchmarking, and for ad hoc queries to support on-going patient care. This paper reviews the legacy implementation of the database, and the development and transformation of the database from a limited localized database into a global information management and decision support tool to assist domestic U.S. and international centers in their application of this dynamic technology.

Health care changes are occurring rapidly. How successfully a health care institution keeps up with these changes depends on information technology and the evaluation of outcomes of care. There have been many studies investigating these factors during the integration of information technology for various applications. This article examines findings from the related literature and derives some lessons on how they can be utilized in the integration of information technology with outcomes management in the health care environment.