Information Systems Frontiers最新文献

Despite considerable research on the factors influencing the use of e-government, citizens are apprehensive of e-government services due to the concerns primarily related to trust, risk, security and privacy. This study presents a meta-analytic structural equation modeling (MASEM) analysis of the findings reported by 68 prior empirical studies on e-government adoption. Specifically, the model examined the direct effects of trust in government, trust in internet, perceived risk, and perceived privacy and security on e-government trust, and its impact on users’ behavioral intention to use e-government. The findings bear significant theoretical and practical implications.

Time and temporal constraints are implicit in most databases. To facilitate data analysis and quality assessment, a database should provide explicit operations to identify the violation of temporal constraints. Against this background, the purpose of this paper is threefold: (1) we identify and provide a formal definition of five common anomalies in temporal databases, (2) we propose two new relational operations that allow, respectively, to label anomalous tuples in and to retrieve the anomalous tuples from a dataset, and (3) we provide three different SQL implementations of these operations for current relational database management systems. The healthcare domain is used to illustrate the usage and utility of the temporal anomalies. Finally, an experimental evaluation on real-world and synthetic data analyses the performance of the different implementations of the anomaly operators.

The objective of this paper is to investigate the factors that influence the adoption of Big Data Analytics (BDA) in manufacturing companies and examine the impact of BDA adoption on performance, while also considering the moderating effect of data-driven culture. An online questionnaire survey was conducted with medium and large manufacturing companies in Malaysia, resulting in a total of 267 responses collected through non-probability purposive sampling. The results show that technology complexity, perceived relative advantage, top management support, IT infrastructure and capabilities, normative pressure, and mimetic pressure are significant determinants of BDA adoption. Moreover, the adoption of BDA has a positive impact on financial and market performance, with data-driven culture moderating the relationship between BDA adoption and financial performance. This study highlights the critical factors that contribute to BDA adoption and its outcomes, providing manufacturing companies with awareness on this topic.

There are many concerns at the global level about environmental performance. The United Nations has created a framework for measuring national development goals that enable environmental sustainability. This paper examines the relationships between technological and social factors as enablers of environmental performance and draws from technological determinism and human agency paradigms. It fills an important gap in the literature by empirically examining the hypothesized relationships. The specific examined factors are online service (maturity and quality), IT infrastructure, e-participation, corruption-free, and human capital. Environmental performance is the dependent variable. These factors are relevant to ten of the seventeen goals that the United Nations set in their SDG framework. The hypotheses are tested and validated using secondary data collected by reputable global institutions and PLS-SEM analytical procedures. The results indicate that technology can enable environmental performance directly and indirectly through e-participation. We also found that e-participation influences corruption-free and human capital that positively impact environmental performance. This paper provides significant implications for research and practice.

The significance of metaverse platforms is growing in both research and practical applications. To utilize the chances and opportunities metaverse platforms offer, research and practice must understand how these platforms create value, which has not been adequately explored. Our research explores the characteristics of metaverse platforms that facilitate value creation for organizations in both B2B and B2C sectors. Employing a qualitative inductive approach, we conducted 15 interviews with decision-makers from international corporations active in the metaverse. We identified 26 metaverse platform characteristics, which we categorized into six dimensions based on the DeLone and McLean Information Systems success model. Subsequently, we provide examples to illustrate the application of these identified characteristics within metaverse platforms. This study contributes to the academic discourse by uncovering the characteristics that shape the competitive landscape of emerging metaverse platforms. Leveraging these characteristics may offer metaverse providers a competitive edge in attracting complementary organizations to their platforms.

This study elucidates the nature of information technology (IT) capabilities by developing an integrated framework that expounds upon the hierarchy inherent within IT capabilities. This research uses qualitative interviews with 64 IT professionals grounded in the resource-based theory to delineate three layers of IT capabilities. At the foundational level, IT capabilities reflect firms’ IT-related assets, encompassing IT infrastructure, informational, and enabled assets that are valuable, rare, and inimitable. Higher up, firms’ IT capabilities manifest through competence in organising these IT-related assets effectively. Operational IT competence is instrumental in mobilising and deploying each IT-related asset, while dynamic IT capabilities represent firms’ capacity to reconfigure and assimilate various operational IT competencies. This research contributes to the field by providing an integrative theoretical understanding of how IT capabilities are formed. The proposed model addresses fragmentation in the existing literature, facilitating the development of more cohesive, evidence-based strategies for generating business value from IT.

Rating can be obligatory for many tasks, such as film recommendation, hotel rating, and product evaluation. Aggregating ratings given by numerous raters is a necessary and effective way to obtain comprehensive evaluation of the objects. While the awareness of potential distortion for some of the targeted objects, has attracted substantial attention of researchers and motivated the designing of the robust rating aggregation method to overcome the impact of disturbance from ignorant/malicious raters in practice. In this paper, we focus on rating aggregation with collusive disturbance, which is hard to be eliminated and invalidate traditional rating aggregation methods. Therefore, we will introduce the idea of detecting collusive group into rating aggregation to develop a new method, called robust rating aggregation method based on rater group trustworthiness (RGT), which obtains four main modules: Graph Mapping, Rater Group Detection, Group Trustworthiness Calculating, and Rating Aggregation. Experimental results and analyses demonstrate that our method is more robust to collusive disturbance than other traditional methods.

This paper explores the convergence of development economics, regulatory policies, and public health considerations within the field of Information Systems (IS) research, focusing specifically on 5G and 6G mobile technologies. Despite the widespread deployment of these technologies and their potential health implications, there is a limited understanding in IS literature on why countries adopt varying thresholds for radiation regulation. Our study, analyzing data from 124 countries, uncovered an inverted U-shaped relationship between digital literacy and the rigidity of radiation regulation. This finding reveals that nations with lower digital literacy levels tend to enforce stricter regulations, whereas those with higher literacy levels adopt more relaxed policies. By highlighting how digital literacy, a critical aspect of the digital divide, significantly influences regulatory frameworks in telecommunications, this study contributes to filling the gap in IS research. This underscores the necessity of informed and transparent regulatory decision making, especially in countries with diverse levels of digital literacy. Calling for a multidisciplinary approach to policy formulation, our work enriches the broader discourse in IS research, underlining the pivotal role of digital literacy in shaping both the access and regulatory landscapes of emerging technologies.

Advances in science and technology act as the gatekeepers of a sustainable future where a stable environment helps generate the power for innovation. Supply chains are the messengers of this euphoric future. However, when the messengers and the gatekeepers are not in sync, the flow of information is bound to stop and bring about a chaotic turn of events, the repercussions of which can be felt through the years. The same was the case with the COVID-19 pandemic, where the lack of man–machine collaboration in Industry 4.0 and the inability of firms to advance their supply chains technologically left them exposed and vulnerable to the disruptions created by the pandemic. It was an eye-opener for companies worldwide as the supply chains collapsed and production reached a standstill. Thus, a stance arises to re-evaluate the resilience capabilities of the supply chains and rethink the priorities for achieving sustainable and resilient supply chain practices. We also suggest injecting industry 5.0 technologies to meet the re-assessed priorities. For this, we have identified the criteria and CSFs of supply chain resilience using the PRISMA 2020 statement and subsequently analyzed them using PF-AHP (for finding criteria weights), m-TISM (to interpret the interrelationships of the CSFs), PF-CoCoSo (to rank the CSFs) and sensitivity analysis (to check the robustness). The results suggest cost-effectiveness as the top weighted criteria and disruption awareness as the highest priority CSF for achieving supply chain resilience.

Three levels of fear of heights were detected in subjects with different severities of acrophobia, based on the electroencephalographic (EEG) and electrocardiographic (ECG) signals. The study aims to demonstrate the feasibility of a data-fusion-based method for real-time assessment of the fear of heights intensity to integrate into adaptive Virtual Reality Exposure Therapy for acrophobia. The generalization performance of classification tasks on fear states is improved by exploiting both trait-based clustering and Domain Adaptation methods. Participants were gradually exposed to increasing height levels through a Virtual Reality (VR) scenario representing a canyon. The initial severity of fear of heights, the level of distress at each height, and the anxiety level before and after the exposure were assessed through the Acrophobia Questionnaire, the Subjective Unit of Distress, and the State and Trait Anxiety Inventory, respectively. The Simulator Sickness Questionnaire was administered to exclude possible motion sickness interference in the experiment. The EEG and ECG signals were acquired through a 32-channel headset and 1 Lead ECG derivation during the exposure to the eliciting VR scenario. Four classifiers (i.e. Support Vector Machines, Deep Neural Networks, Random Forests, and k-Nearest Neighbors) were adopted in the experimental environment. Preliminary tests were performed in a within-subject experiment, achieving the best classification accuracy of (87.1 % pm 7.8 %) with a Deep Neural Network. As the cross-subject approach is concerned, three strategies, namely Domain Adaptation (DA), data fusion (combining EEG with ECG), and participant clustering (based on the acrophobia severity), were evaluated. DA resulted in the most effective strategies by determining an improvement of more than 20 % in classification accuracy. Random Forest performed the best classification accuracy for the severe acrophobia cluster with a mean of (63.6 %) and a standard deviation of ( 13.4 %) over three classes by exploiting Stratified Normalization.