Information Systems Frontiers最新文献

The continuing, explosive developments in generative artificial intelligence (GenAI), built on large language models and related algorithms, has led to much excitement and speculation about the potential impact of this new technology. Claims include artificial intelligence (AI) being poised to revolutionize business and society and dramatically change personal life. However, it is not clear how this technology, with its significantly distinct features from past AI technologies, has transformative potential or how researchers in information systems should react to it. In this paper, we consider the evolving and emerging trends of AI in order to examine its present and predict its future impacts. Many existing papers on GenAI are either too technical for most information systems researchers or lack the depth needed to appreciate the potential impacts of GenAI. We, therefore, attempt to bridge the technical and organizational communities of GenAI from a system-oriented sociotechnical perspective. Specifically, we explore the unique features of GenAI, which are rooted in the continued change from symbolism to connectionism, and the deep systemic and inherent properties of human-AI ecosystems. We retrace the evolution of AI that proceeded the level of adoption, adaption, and use found today, in order to propose future research on various impacts of GenAI in both business and society within the context of information systems research. Our efforts are intended to contribute to the creation of a well-structured research agenda in the information systems community to support innovative strategies and operations enabled by this new wave of AI.

We propose a method for the dynamic evaluation of the output provided by any Real Time Object Detection Algorithm. This work focuses on single object detection from video streams and the main objective is the enhancement of the process with regard to its so-called trustworthiness based on the spatial consideration of the sequence of video frames that are fed as inputs on a Convolutional Neural Network (CNN). To this end, we propose a method that systematically tests the differences between the consecutive values returned by the employed neural network. The process identifies patterns that flag potential false positive predictions based on classic similarity metrics and evaluates the quality of the CNN results in a methodologically agnostic fashion. An extended computational illustration demonstrates the effectiveness and the potentials of the proposed approach.

The paper posits that in the increasingly connected digital landscape, there is a growing need to examine the scale and scope of responsible cybersecurity. In an exploratory study that involved qualitative interviews with senior cybersecurity professionals, we identify different layers of responsible cybersecurity that span across techno-centric, human-centric, organizational (intra and inter) and societal centric perspectives. We present these in an onion-shaped framework and show that collectively these diverse perspectives highlight the linked responsibilities of different stakeholders both within and beyond the organization. The study also finds that senior leadership plays a crucial role in fostering responsible cybersecurity across the different layers. Implications for research and practice are discussed. 

The Beyond 5G (B5G) network promotes the development of all sectors of society and greatly changes our lives. To provide subscribers with better security and privacy protection, the 3rd Generation Partnership Project (3GPP) has enhanced the Non-Access Stratum (NAS) protocol for B5G. It is crucial to analyze the security of NAS protocol and confirm whether it achieves security goals. However, previous work mainly considered the issues in B5G standard, while overlooking the fact that 4G and B5G networks coexist in actual mobile network operators. In this paper, we provide the first systematic security analysis model for B5G NAS protocol under the assumption of network coexistence. We identified 9 protocol vulnerabilities, including one never reported before. This new vulnerability could be exploited to track the target user. We have reported the novel vulnerability to the GSM Association (GSMA) and obtained a tracking number CVD-2022-0058.

The proliferation of generative artificial intelligence (GenAI) has disrupted academic institutions across the world, presenting transformative challenges for decision makers, and leading to questions around existing methods and practices within higher education (HE). The widespread adoption of GenAI tools and processes highlights an ongoing change to existing perceptions of the role of humans and machines. Academics have expressed concerns relating to: academic integrity, undermining critical thinking, lowering of academic standards and the threat to existing academic models. This study presents a mixed methods approach to developing valuable insight to the key underlying challenges impacting GenAI adoption within HE. The results highlight many of the key challenges impacting decision makers in the formation of policy and strategic direction. The findings identify significant interdependencies between the key underlying challenges associated with GenAI adoption in HE. We further discuss the implications in the findings of the high levels of driving power of the factors: (i) perceived risks from Large Language Model training and learning; (ii) the reliability of GenAI outputs in the context of impact on creativity and decision making; (iii) the impact from poor levels of GenAI platform regulation. We posit this research as offering new insight and perspective on the changing landscape of HE through the widespread adoption of GenAI.

The symmetry of complex networks is a global property that has recently gained attention since MacArthur et al. 2008 showed that many real-world networks contain a considerable number of symmetries. These authors work with a very strict symmetry definition based on the network’s automorphism detecting mostly local symmetries in complex networks. The potential problem with this approach is that even a slight change in the graph’s structure can remove or create some symmetry. Recently, Liu (2020) proposed to use an approximate automorphism instead of strict automorphism. This method can discover symmetries in the network while accepting some minor imperfections in their structure. The proposed numerical method, however, exhibits some performance problems and has some limitations while it assumes the absence of fixed points and thus concentrates only on global symmetries. In this work, we exploit alternative approaches recently developed for treating the Graph Matching Problem and propose a method, which we will refer to as Quadratic Symmetry Approximator (QSA), to address the aforementioned shortcomings. To test our method, we propose a set of random graph models suitable for assessing a wide family of approximate symmetry algorithms. Although our modified method can potentially be applied to all types of symmetries, in the current work we perform optimization and testing oriented towards more global symmetries motivated by testing on the human brain.

This study delves into the dynamics of pragmatic interoperability, focusing on the case of a digital ecosystem in India —the eKrishi platform—which combines of industry 4.0 technologies with human-centric principles. Through qualitative analysis, we unveil the motivations shaping system and business-level interoperability alignment. We found that three categories of sustainability metrics—socio-economic, socio-ecological, and eco-efficiency— are driven by diverse pragmatic views. Furthermore, we found that system-level alignment is driven by actors’ defensive strategy for compliance and standardization, while business level interoperability is underpinned by actors’ offensive strategy for social and economic innovation. The study introduces a 2 × 2 alignment framework—corporate citizenship, regulatory stewardship, corporate stewardship, and value chain stewardship—offering nuanced insights. By aligning systems and business motives for pragmatic interoperability, we contribute towards theory building on interoperability and provide practical implications for guiding stakeholder alignment in Industry 4.0 initiatives.

Mobile cloud computing apps have become the dominant type of mobile app, providing users with many benefits but also causing privacy concerns related to data being uploaded to the cloud. Since many mobile cloud computing apps have billions of current users around the world, the role of culture in privacy after adoption is pertinent to researchers, users, and developers. This study investigates how culture affects privacy considerations of mobile cloud app users in the post-adoption phase and how it shapes their response to developers’ institutional privacy assurances such as privacy policies and ISO 27018 certification. Based on surveys of current mobile cloud computing app users across three countries: the US (n = 1,045), the UK (n = 183), and India (n = 1,189), we find that users from different cultures differ in their considerations of privacy and in perceptions of institutional privacy assurance. The results show that cultural dimensions moderate the effects of value and risk of transferring to the cloud on continued use. We also find counterintuitive results for the direction in which uncertainty avoidance and power distance shape users’ reactions to institutional privacy assurances. Our findings suggest that MCC app developers need to be consider users’ cultures when designing and communicating their institutional privacy assurances.

The spread and impact of decision support systems (DSS) have continued to gain intensity with applications in medical diagnosis, control systems, air traffic control, security systems and executive dashboards that help in strategic decision-making. As the field of machine learning (ML) continues to develop, DSS researchers have been incorporating ML techniques into DSS artifacts and this trend is growing. Though researchers have been talking about intelligent decision support systems for about three decades now, there has not been any recent attempt to provide a comprehensive framework to guide researchers and developers in creating DSS that use machine learning techniques. In this paper we examine the progress that has been made in applying ML techniques for developing DSS, based on a literature analysis of 2093 journal papers published from 2014 – 2024, and propose a framework for future development of intelligent DSS.