Information Systems Frontiers最新文献

With the widespread use of computers and the internet in the workplace, computer use for personal reasons during work time, or cyberloafing, has become quite common. Without a clear understanding of the consequences of cyberloafing, practitioners cannot properly design an IT policy aimed at managing employees' cyberloafing. This study aims to develop and test a model of the relationship between cyberloafing and task performance. Specifically, we attempt to demonstrate how performance-based monetary incentives and time change the role of cyberloafing in task performance. Drawing on the theory of goal setting and the capacity theory of attention, we developed research hypotheses on how cyberloafing interacts with incentives and time to influence task performance. To test the hypotheses, we conducted five 2 × 2 experiments repeatedly on 189 subjects. The results of hierarchical linear modeling showed that although cyberloafing generally worsened task performance, this relationship varied with performance-based monetary incentives. Incentives significantly diminished the negative effect of cyberloafing on task performance. However, as our theory predicted, the moderating effect of incentives decreased over time. More specifically, we found that the two-way interaction between cyberloafing and incentives was in effect during earlier phases but gradually disappeared over time. This study contributes to IS research and practice by providing valuable insights into the role of cyberloafing in task performance and how this relationship changes over time with the option of performance-based monetary incentives.

Digitizing healthcare is a major aim of healthcare policy, with efforts aimed at increasing adoption of electronic health records (EHRs). We study the capability use for EHRs through the lens of normalisation process theory to assess whether these barriers to adoption also remain barriers to sustained use. We focus on health information exchange (HIE), which is one of the most challenging capabilities identified in the literature. We analyse the National Electronic Health Records Survey data, in which physicians were asked whether known HIE adoption barriers remain in place, and how frequently they use HIE capabilities. Though we expect that adoption barriers reported to be less problematic will be associated with greater capability use, we found that adoption barriers perceived to be more (less) problematic were not necessarily those that predicted less (greater) capability use. This study contributes through a critical examination of the process of normalization of EHR capabilities.

Digitalization of the healthcare industry is a major trend and focus worldwide. It has the capability to improve the quality of care, reduce costs, and increase accessibility. India’s Healthcare Vision 2030 serves as a driving force compelling healthcare organization in India to embrace digitalization in their operations and services. We surveyed Indian healthcare employees to provide a comprehensive understanding of how external factors impact an organization's internal resources towards successful adoption of healthcare digitalization. The integration of three theoretical perspectives Institutional Theory (IP), Resource-Based View (RBV), and Absorptive Capacity Theory (ACT)) enables a more holistic and intricacies view. Our results emphasize that healthcare digital transformation requires more than just investment and time. Neglecting to respond to external pressures can lead to limited outcomes in digitalization efforts. It necessitates the presence of an appropriate organizational culture, accompanied by strong belief and support from top management.

Short video represents a novel form of social media with rich vividness and sociability, facilitating social media influencers’ (SMIs) self-presentations and endorsements. While SMIs become primary information sources through short videos, they also face challenges such as high return rates and consumer distrust. This research investigates how SMIs can effectively achieve authenticity through the design of self-presentation strategies, specifically focusing on credibility and attractiveness from a source-effect perspective. Across three studies, this research demonstrates that: (1) both credibility and attractiveness positively increase SMIs’ authenticity perception, mediated by para-social interaction; (2) credibility and attractiveness exhibit a negative interactive relationship; (3) the substitutability of credibility and attractiveness varies depending on the type of SMIs (informative vs. entertainment). This research contributes to the literature on short-video information processing and consumer attitudes toward SMIs based on authenticity building.

As emphasized in multiple United Nations (UN) reports, sustainable agriculture, a key goal in the UN Sustainable Development Goals (SDGs), calls for dedicated efforts and innovative solutions. In this study, greenhouse gas (GHG) emissions in the groundnut supply chain from the region of Diourbel & Niakhar, Senegal, to the port of Dakar are investigated. The groundnut supply chain is divided into three steps: cultivation, harvesting, and processing/shipping. This work adheres to UN guidelines, addressing the imperative for sustainable agriculture by applying machine learning-based predictive modeling (MLPMs) utilizing the FAOSTAT and EDGAR databases. Additionally, it provides a novel approach using blockchain-enabled off-chain machine learning through smart contracts built on Hyperledger Fabric to secure GHG emissions storage and machine learning’s predictive analytics from fraud and enhance transparency and data security. This study also develops a decision-making dashboard to provide actionable insights for GHG emissions reduction strategies across the groundnut supply chain.

Due to the growing volume of e-waste in the world and its environmental impact, it is important to understand how to extend the useful life of electronic items. In this paper, we examine the remanufacturing process of end-of-life laptops for third-party remanufacturers and consider their pricing problem, which involves issues like a lack of reliable datasets, fluctuating costs of new components, and difficulties in benchmarking laptop prices, to name a few. We develop a unique approach that uses machine learning algorithms to help price remanufactured laptops. Our methodology involves a variety of techniques, which include an additive model, CART analysis, Random Forest, and Polynomial Regression. We consider depreciation and discount factors to account for the varying ages and conditions of laptops when estimating remanufactured laptop prices. Finally, we also compare our estimated prices to traditional prices. In summary, we leverage data-driven decision-making and develop a robust methodology for pricing remanufactured laptops to extend their lifespan.

Unforeseen events (e.g., COVID-19, the Russia-Ukraine conflict) create significant challenges for accurately predicting CO2 emissions in the airline industry. These events severely disrupt air travel by grounding planes and creating unpredictable, ad hoc flight schedules. This leads to many missing data points and data quality issues in the emission datasets, hampering accurate prediction. To address this issue, we develop a predictive analytics method to forecast CO2 emissions using a unique dataset of monthly emissions from 29,707 aircraft. Our approach outperforms prominent machine learning techniques in both accuracy and computational time. This paper contributes to theoretical knowledge in three ways: 1) advancing predictive analytics theory, 2) illustrating the organisational benefits of using analytics for decision-making, and 3) contributing to the growing focus on aviation in information systems literature. From a practical standpoint, our industry partner adopted our forecasting approach under an evaluation licence into their client-facing CO2 emissions platform.

Effective management of colorectal cancer (CRC) necessitates precise prognostication and informed decision-making, yet existing literature often lacks emphasis on parsimonious variable selection and conveying complex interdependencies among factors to medical practitioners. To address this gap, we propose a decision support system integrating Elastic Net (EN) and Simulated Annealing (SA) algorithms for variable selection, followed by Tree Augmented Naive Bayes (TAN) modeling to elucidate conditional relationships. Through k-fold cross-validation, we identify optimal TAN models with varying variable sets and explore interdependency structures. Our approach acknowledges the challenge of conveying intricate relationships among numerous variables to medical practitioners and aims to enhance patient-physician communication. The stage of cancer emerges as a robust predictor, with its significance amplified by the number of metastatic lymph nodes. Moreover, the impact of metastatic lymph nodes on survival prediction varies with the age of diagnosis, with diminished relevance observed in older patients. Age itself emerges as a crucial determinant of survival, yet its effect is modulated by marital status. Leveraging these insights, we develop a web-based tool to facilitate physician–patient communication, mitigate clinical inertia, and enhance decision-making in CRC treatment. This research contributes to a parsimonious model with superior predictive capabilities while uncovering hidden conditional relationships, fostering more meaningful discussions between physicians and patients without compromising patient satisfaction with healthcare provision.

The COVID-19 pandemic has highlighted the critical need for advanced technology in healthcare. Clinical Decision Support Systems (CDSS) utilizing Artificial Intelligence (AI) have emerged as one of the most promising technologies for improving patient outcomes. This study’s focus on developing a deep state-space model (DSSM) is of utmost importance, as it addresses the current limitations of AI predictive models in handling high-dimensional and longitudinal electronic health records (EHRs). The DSSM’s ability to capture time-varying information from unstructured medical notes, combined with label-dependent attention for interpretability, will allow for more accurate risk prediction for patients. As we move into a post-COVID-19 era, the importance of CDSS in precision medicine cannot be ignored. This study’s contribution to the development of DSSM for unstructured medical notes has the potential to greatly improve patient care and outcomes in the future.

Recommendation systems are becoming an invaluable assistant not only for users, who may be disoriented in the presence of a huge number of different alternatives, but also for service providers or sellers, who would like to be able to guide the choice of customers toward particular items with specific characteristics. This influence capability can be particularly useful in the tourism domain, where the need to manage the industry in a more sustainable way and the ability to predict and control the level of crowding of PoIs (Points of Interest) have become more pressing in recent years. In this paper, we study the role of contextual information in determining both PoI occupations and user preferences, and we explore how machine learning and deep learning techniques can help produce good recommendations for users by enriching historical information with its contextual counterpart. As a result, we propose the architecture of ARTEMIS, a context-Aware Recommender sysTEM wIth crowding forecaSting, able to learn and forecast user preferences and occupation levels based on historical contextual features. Throughout the paper, we refer to a real-world application scenario regarding the tourist visits performed in Verona, a municipality in Northern Italy, between 2014 and 2019.