Information Systems Frontiers最新文献

In the Internet of Things (IoT), interconnected smart things enable new products and services in cyber-physical systems. Yet, smart things not only inherit information technology (IT) security risks from their digital components, but they may also aggravate them through the use of technology platforms (TPs). In the context of the IoT, TPs describe a tangible (e.g., hardware) or intangible (e.g., software and standards) general-purpose technology that is shared between different models of smart things. While TPs are evolving rapidly owing to their functional and economic benefits, this is partly to the detriment of security, as several recent IoT security incidents demonstrate. We address this problem by formalizing the situation’s dynamics with an established risk quantification approach from platforms in the automotive industry, namely a Bernoulli mixture model. We outline and discuss the implications of relevant parameters for security risks of TP use in the IoT, i.e., correlation and heterogeneity, vulnerability probability and conformity costs, exploit probability and non-conformity costs, as well as TP connectivity. We argue that these parameters should be considered in IoT governance decisions and delineate prescriptive governance implications, identifying potential counter-measures at the individual, organizational, and regulatory levels.

Digital transformation and sustainability transformation are at the top of organizations’ agendas to remain competitive. While guidance on both transformations exists separately, even more research on integrating digital and sustainability transformation, namely twin transformation, is required. Specifically, deeper knowledge about relevant twin transformation capabilities and progress is needed for effective implementation. To enhance the understanding and provide corresponding guidance, we developed a twin transformation capability maturity model focusing on dynamic capabilities required to realize twin transformation based on a structured literature review and interviews with 13 experts. Further, we demonstrated its use with a technology service provider. Our contribution is twofold: First, accounting for organizations’ twin transformation starting points in terms of their digitalization and sustainability experience and expertise, we reveal three pathways to becoming a twin transformer. Second, our work provides an overview of 45 relevant twin transformation capabilities structured along six capability dimensions and four maturity stages. Our work also provides relevant practical implications supporting organizations in assessing their twin transformation maturity building the foundation for targeted capability development.

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.