Information Systems Frontiers最新文献

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.

This special issue focuses on the application of eXtended Reality (XR) technologies—comprising Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)—and Artificial Intelligence (AI) in the fields of medicine and rehabilitation. AR provides support in minimally invasive surgery, where it visualises internal anatomical structures on the patient’s body and provides real-time feedback to improve accuracy, keep the surgeon’s attention and reduce the risk of errors. Furthermore, XR technologies can be used to develop applications for pre-operative planning or for training surgeons through serious games. AI finds applications both in medical image processing, for the recognition of anatomical structures and the reconstruction of 3D models, and in the analysis of biological data for patient monitoring and disease diagnosis. In rehabilitation, XR and AI can enable personalised therapy plans, increase patient engagement through immersive environments and provide real-time feedback to improve recovery outcomes. The papers in this special issue deal with rehabilitation through serious games, AI-enhanced XR applications for healthcare, digital twins and the analysis of bio/neuro-adaptive signals.

The valuation of artwork is a fundamental issue in cultural economics. This study examines the impact of visual elements on a painting’s price. Several characteristics are evaluated such as its intricacy, points of interest, segmentation-based features, and local color features. The study also employs theories by Itten and Kandinsky, and applies mixed-effects models to assess how these characteristics impact the painting’s price. The influence of color is examined in the context of abstractionism, a highly complex art style, where the selection of color is crucial. Itten’s theory, the most acclaimed color theory in the art world, is used as a basis for this analysis since it has spawned various sub-theories and is the basis for teaching artists. A unique dataset of 3,885 paintings from Christie’s and Sotheby’s is used, and it is found that Itten’s color harmony has a low predicting power, color complexity metrics are inconsequential, and color diversity is a better predictor of the price of abstract art.

Explainable artificial intelligence (XAI) and other digital technologies are altering the nature of social entrepreneurship, marketing, and other service activities. The structures and strategies of entrepreneurs undergo radical change as a result of the impact of XAI on marketing and innovation. Despite the increased interest in business to business (B2B) literature, there are limitations on how and what circumstances the activities of B2B marketing on social entrepreneurship. Therefore, this study outlines how XAI will impact B2B services by building resilience during and after crisis events such as the COVID-19 pandemic. To develop an in-depth understanding on the theories of social entrepreneurship, B2B marketing, and emerging technologies, this study set apart and conceptualize relevant factors and linkages. The result shows that based on a survey of 295 samples of B2B services entrepreneurial businesses, XAI enhances the establishment of a sustainable resilience for B2B marketing activities and contribute to building social entrepreneurial strategies for B2B marketing innovation.

Cybersecurity incidents damage not only the organizations attacked, but also society in general, harming customers and stakeholders. Through the text mining of the incident database, we observed that the impact of cybersecurity incident trends became more outward-oriented causing increased risks associated with social responsibility. Thus, this study aims to validate the potential effect of cybersecurity incidents on social responsibility risks and stock price drops. To derive meaningful factors from the description of incidents, we mined the texts to extract the features of the severity of incidents and their direction of impact whether inward or outward. The severity score is derived from sentiment analysis and the impact direction by topic modeling and machine learning models including SVM, LSTM, and BERT. The effects of these incident features are studied through regression models with social responsibility risk and stock price drops as dependent variables. To conduct this study, we collected incident texts from the Privacy Rights Clearinghouse database, and social responsibility risk indices from the Privacy and Data Security index and Cyber Risk Rating scores. The subsequent short-term stock price drops are measured by Cumulative Abnormal Returns and their variations. Our analysis revealed a profound impact of cybersecurity incidents on social responsibility risk indices and stock price drops with the moderating effect of outward impact in both models. However, we recognize the incompatibility between an annual index of social responsibility risk and short-term stock price drops. Therefore, we propose a short-term social responsibility risk index for cybersecurity which can be derived from the disclosed incidents. All these scenarios support the premise that cybersecurity incidents significantly impact the social responsibility risk and may lead to potential stock price drops.