Information Systems Frontiers最新文献

With the expansion of business activities around the world and the importance of sustainability in various fields, corporate sustainability has become a strategic imperative for management plans and investment decision. Therefore, this study focuses on examining the contribution of sustainability variables, i.e., economic, social, and environmental (ESG), to corporates profitability at 5936 companies distributed globally in an industry sectors using the data mining methods. The data extracted from Thomson Reuters database (ASSET4 ESG) for the period of 2002–2017 was used for modelling. Different algorithms, such as decision tree, support vector machine, and Naïve Bayes, were used for modelling. Since the current study uses a multi-class classification, the Kappa criterion was used to assess the quality of the classification algorithm. The results of the study confirmed that none of the sustainability dimensions had a negative impact on corporate profitability.

Smart home technology (SHT) offers numerous economic, social, and environmental benefits, positioning it as a sustainable option for individuals and families seeking eco-friendly living solutions. Despite these advantages, adoption rates for SHT remain paradoxically low. Recognizing the ecological potential of SHT, this study investigates the psychological processes that influence the perceived sustainable value of SHT offerings within website content and how these perceptions affect adoption behavior. By integrating innovation diffusion theory with perceived value theory, this research provides a comprehensive framework for understanding the adoption of complex innovations like SHT. Empirical findings reveal that imagery processing during the online purchasing experience significantly enhances the perception of sustainable benefits and reduces the perceived sacrifices associated with adopting SHT, highlighting the importance of visual content in promoting sustainable technology adoption.

Contactless methods are widely used to measure vital signs from recorded or live videos using remote photoplethysmography (rPPG), which takes advantage of the slight skin color variation that occurs periodically on specific body regions with each blood pulse. However, existing rPPG-based solutions are typically expensive and not suitable for daily use at home for personal healthcare. To address this issue, we have recently developed a low-cost device that allows for the real-time estimation of vital signs using rPPG and can be easily integrated into any common home environment. The device consists of a smart mirror equipped with a camera that captures facial videos and extracts rPPG signals by processing video frames. One major limitation of this solution was its high sensitivity to abrupt head movements during video acquisition. This paper presents some advancements in the development of our smart device aimed at obtaining a more robust measurement of vital signs. Experimental results on live videos show that the new version of our system overcomes the limitations of the previous version, offering a more stable performance. Moreover, the new methodology shows improved performance compared to other state-of-the-art rPPG algorithms when tested on pre-recorded in-house videos from the UBFC-RPPG database.

Building on the perspectives of the uses & gratification (U&G) theory and stimulus-organism-response (S–O-R) model, this article develops and tests an integrative framework to examine the underlying factors influencing customers’ experiences with chatbots as a form of virtual conversational agent (VCA) in the UK and Vietnam. In addition to utilitarian and hedonic factors, anthropomorphism and social presence are also investigated, which are considered important experiential dimensions in a customer-machine relationship. We also explore how stimuli such as functionality, communication style similarity, and aesthetics indirectly affect outcomes like customer satisfaction and reuse intention, mediated by four types of customer experiences. Data collected from a sample of 417 and 359 participants in the UK and Vietnam respectively revealed that, in general, perceived informativeness, credibility, enjoyment, functionality, and communication style similarity are crucial for customer satisfaction in both countries. Interesting differences in the effects of customer experience between developed and developing countries were observed. For instance, the effects of anthropomorphism and social presence on satisfaction are only effective for customers from developed country, while those from developing country only need information provided by chatbots be transparent. Our findings offer a novel way to understand customer experience with chatbots and provide important theoretical and managerial implications.

Credit risk assessment has drawn great interests from both researcher studies and financial institutions. In fact, classifying an applicant as defaulter or non-defaulter customer helps banks to make a reasonable decision. The classification of applicants is based on a set of historical information of past loans. Data sets for analysis may include different features, many of which may be irrelevant to the decision making process. Keeping irrelevant features or leaving out relevant ones may be harmful, causing generation of poor quality patterns that may lead to confusion decision. Determining an appropriate set of predictors is an important challenge in credit risk prediction research which guarantees better decision-making. It is the task of searching the smallest subset of features that provide the highest accuracy and comprehensibility. Thus, this study proposes feature selection-based classification model on credit risk assessment. To this end, five algorithms are applied, Speed-constrained Multi-objective PSO (SMPSO), Non-dominated Sorting Algorithm (NSGA-II), Sequential Forward Selection (SFS), Sequential Forward Floating Selection (SFFS), and Random Subset Feature Selection (RSFS). The selected subset is evaluated based on three classifiers K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Artificial Neural Network (ANN). Our proposed model is validated using three real-world credit datasets. The obtained results confirm the efficiency of SMPSO-KNN model to select the most significant features and provide the highest classification accuracy compared to existing models.

Online health communities (OHCs) offer emotional and informational support to their users. However, past research has primarily treated these supports as separate, but they coexist in messages, making it essential to consider the emotional valence of text to understand the support being provided. This study examines how aligning questions and responses in OHCs reduces information gaps, and enhances support quality and perceived helpfulness. We use a labeled data set of question-response pairs to develop multimodal machine learning models to predict support interactions. Using explainable AI, we reveal the emotions within support exchanges, underscoring how emotional valence in the text determines informational support in OHCs and provide insight into the interaction between emotional and informational support. This study refines social support theory and establishes a foundation for decision aids and emotion-sensitive AI systems to deliver personalized social support tailored to users’ informational and emotional needs.

As per agenda-setting theory, political agenda is concerned with the government’s agenda, including politicians and political parties. Political actors utilize various channels to set their political agenda, including social media platforms such as Twitter (now X). Political agenda-setting can be influenced by anonymous user-generated content following the Bright Internet. This is why speech acts, experts, users with affiliations and parties through annotated Tweets were analyzed in this study. In doing so, the agenda formation during the 2019 European Parliament Election in Germany based on the agenda-setting theory as our theoretical framework, was analyzed. A prediction model was trained to predict users’ voting tendencies based on three feature categories: social, network, and text. By combining features from all categories logistical regression leads to the best predictions matching the election results. The contribution to theory is an approach to identify agenda formation based on our novel variables. For practice, a novel approach is presented to forecast the winner of events.

Network slicing is a 5G concept that virtualizes the physical network infrastructure to accommodate multiple service requirements on the same network, where each slice manages diverse needs and ensures their coexistence. In this work, we leverage blockchain technology to strengthen the security of handover authentication (HA) processes in network slicing systems.The proposed system addresses the challenge of reducing latency during handovers by incorporating a hybrid on-chain/off-chain model, optimizing the balance between security and speed. It employs the Raft consensus mechanism, which offers lower latency compared to more traditional consensus protocols such as PBFT. It establishes a decentralized registry for recording transfer events, streamlining user equipment (UE) identification verification, and improving HA efficiency. Moreover, we also introduce a three-component model: network slicing, user environments, and a Hyperledger Fabric (HLF) blockchain for authentication and authorization, which enhances the user experience by minimizing delays, ensuring data privacy, and providing scalability. By leveraging edge computing in conjunction with network slicing, the system further reduces latency, making it more efficient for real-time applications in dynamic mobile environments. Performance experiments indicate satisfactory scalability and maintained service quality under increasing throughput, affirming the suitability of the HLF-based system for managing network scenarios. Furthermore, the system’s modular design ensures compatibility with existing authentication protocols, such as AKA and EAP, enabling seamless integration with legacy systems. Consequently, this work enhances network security and service quality, especially in network slicing, HA, and employing HLF for privacy and security solutions. As 5G networks continue to evolve toward 6G, this system’s scalability and flexibility offer a promising approach to addressing future challenges in secure and efficient handover authentication.

With the rapid development of information technology, the gig labor marketplace is fast growing, with digital platform-based instant messaging (IM) playing an important role in raising freelancers’ orders, serving the intention for the crowdsourcing platforms to increase capacity to balance supply and demand. Using a large-scale field experiment on a crowdsourcing freight platform, this study investigates the impact of IM on freelancers’ response rate of orders. Our findings suggest the effects of IM depend on its content and information richness level. Task-relevant information in IM increases the freelancers’ response rate, especially for the priority commitment information, compared with order price information. In addition, although adding task-irrelevant information in IM decreases the freelancers’ response rate, it does not mean the less task-irrelevant information results in a weaker negative IM effect. Rather than that, including task-irrelevant information with a medium information richness level in IM harms the freelancers’ response to the most significant extent. Moreover, our findings reveal crowdsourcing platforms’ actions of IM to increase freelancers’ response rate are consistent with the actions to improve the order acceptance rate, thus demonstrating the critical role of increasing freelancers’ response rate in raising their interest in the final acceptance of the order serving. Our findings guide crowdsourcing platforms to design effective digital platform-based IMs to communicate with freelancers to arouse their response and interest in serving the orders. The capacity of crowdsourcing platforms thus can be dynamically adjusted and expanded to benefit their profitability.

This paper provides a comprehensive examination of the ongoing debate surrounding Artificial Intelligence (AI) and its societal implications, with a particular focus on job displacement. The release of generative AI tools for public use, particularly ChatGPT, has created numerous concerns on how these tools will be used and adverse impacts on society. Augmented Intelligence has been introduced as a concept utilizing AI to enhance human capabilities but its distinction as an assistive role is ill-defined. This research provides insights into the reconceptualization of AI as Augmented Intelligence examining their differences in terms of knowledge development, decision-making, and outcomes. Through three case studies, we demonstrate the assistive role of Augmented Intelligence and how it can serve as a catalyst for job creation and cognitive enhancement. We also explore the impact of AI and IA tools as a sociotechnical system and their effect on human cognitive abilities through the theoretical lens of the Dunning Kruger Effect. We conclude with a research agenda to stimulate future directions of research.