ABSTRACT. Information systems researchers have developed extensive domain knowledge about the ways in which digital technologies engage with organisations and society. The field has a growing tradition of presenting significant implications for practice from the research findings but is failing to engage with the development of digital policies not least because policy engagement involves more than just the dissemination of research findings.  Policy engagement is an important area of impact and engagement that information systems researchers are particularly well placed to engage in.  It directly relates to a key element of the UK’s Research Excellence Framework but also addresses emerging requirements from leading journals.  For example, in early 2023 both MISQ and ISR announced that all new submissions would need to include a “significance” or “contribution” statement that includes consideration of how the research could potentially influence practice, policy, or societal outcomes and when applicable, inform the broader discourse on the topic it addresses.  In this keynote I will reflect on my own experiences of policy engagement as well as make suggestions for how policy engagement can help shape society’s digital futures.

ABSTRACT. Since the introduction of Information and Communication Technologies, the virtual business meeting has become a popular platform for business networks, whose popularity increased massively during and post COVID-19. In this study, we focus on virtual business meetings and examine perceptions of presence in virtuality. We draw insights from a mixed methods study, and our findings suggest that prominent factors of technical, personal, and physical elements influence attendees’ perceptions of presence. We posit that by understanding attendees’ perception of social presence during virtual business meetings, we can better understand and explore the factors that contribute to virtual engagement and remote interaction. Based on this, we conclude our paper by suggesting specific interventions that can improve remote interaction during virtual meetings.

ABSTRACT. In recent decades, digital transformation proliferates and prevails among firms, profoundly affecting their operations, investments and information management. While applying digital technologies has pros and cons to business activities, its implications for future firm performance remain uncertain to stock market participants. This paper focuses on financial analysts, who play the role as information intermediaries in the stock market, and examines how their forecasts for firms are shaped by corporate digitalization. Based on a large sample of Chinese listed firms, we find that analysts covering firms with higher levels of digitalization are likely to make more accurate forecasts. This result is robust to using two-stage least squares regression, difference-in-differences regression, and firm-fixed-effects regression to mitigate potential endogeneity concerns. Our mediation analysis reveals that digital transformation improves firms’ operational efficiency, investment efficiency and information quality, and thereby increases analyst forecast accuracy. We also find that the association between corporate digital transformation and analyst forecast accuracy is more pronounced for analysts with greater work capabilities and more information resources, as well as for firms with lower levels of innovation. Our study provides new insights into the capital market implications of corporate digitalization through the lens of financial analysts.

ABSTRACT. Data marketplaces have emerged to facilitate data trading through secure engagement and collaboration among actors, providing mechanisms similar to online marketplaces, such as connecting buyers with sellers and facilitating financial exchanges. The inherent complexity of data coupled with challenges related to marketplace integration hinder the realization of effective data trading mechanisms on data marketplaces. Moreover, the fragmentation of the data marketplace ecosystem necessitates data assets matching capabilities to enable the federation of different marketplaces. In this paper, we explore various similarity metrics for data assets and conceptualize how matching of data assets can be performed in a data marketplace. This can enhance tasks such as pricing advisory and revenue allocation on data marketplaces. This study is a step towards creating a reliable and equitable data trading environment.

ABSTRACT. Innovation is vital for sustainable growth and the manufacturing sector’s survival. Digital technologies like blockchain and IoT have highlighted the potential of digital traceability to enhance supply chain understanding, yet its impact on technological innovation—product and process—remains unclear. This study develops a conceptual model, rooted in knowledge management, to examine how digital traceability influences innovation in manufacturing, with knowledge absorption as a mediator and market competition as a moderator. A survey of 296 manufacturing firms across four demonstration provinces revealed key insights: (1) Digital traceability significantly promotes product and process innovation. (2) Knowledge absorption mediates this relationship, fully for product innovation and partially for process innovation. (3) Market competition moderates the direct effect of chain traceability on product innovation and the mediation effects via knowledge absorption. These findings highlight knowledge absorption’s critical role and extend the literature on digital traceability’s role in fostering innovation in competitive markets.

ABSTRACT. Low visibility conditions significantly increase the risk of road accidents, as they hinder a driver's ability to perceive and react to obstacles on the road. Factors such as fog, mist, heavy rain, snow, or darkness can severely limit visibility and pose a danger to drivers. However, technology offers potential solutions to mitigate these risks through obstacle detection and Augmented Reality (AR) systems. Obstacle detection systems utilize various sensors, such as cameras, radar, and LiDAR, to detect objects and obstacles in the surrounding environment. These sensors can provide real-time data on the position, distance, and movement of objects, helping drivers anticipate and avoid potential collisions. Additionally, advanced algorithms and machine learning techniques enable the system to distinguish between different objects, such as vehicles, pedestrians, and stationary obstacles. Besides AR systems can enhance a driver's perception of the road by overlaying relevant information onto their field of view. The framework combines obstacle detection and AR technologies enabling drivers to receive immediate warnings and visual cues regarding potential hazards, even in low visibility conditions.

ABSTRACT. The increasing relevance of Artificial Intelligence (AI) in the Fourth Industrial Revolution has highlighted significant disparities in AI adoption among Small and Medium Enterprises (SMEs), particularly in developing regions such as Nigeria. This research builds on a Systematic Literature Review (SLR) that identified the socio-technical factors contributing to the AI divide, using empirical data from a survey of 144 Nigerian SMEs to deepen understanding of these challenges. Findings reveal that technical challenges, such as inadequate infrastructure and outdated technology, intersect with social issues, including resistance to change and low digital literacy, exacerbating the divide. Socio-technical barriers, such as skills shortages, ethical concerns, and regulatory gaps, hinder AI integration and equitable outcomes. The study highlights the need for targeted interventions, including policy support, infrastructure development, and capacity-building programs, to enable SMEs to harness AI’s transformative potential. It advocates for broader research to contextualise these findings and develop actionable strategies for addressing the AI divide in similar economies.

ABSTRACT. The notion of climate health refers to the multiplicity of health effects and risks posed by different dimensions of climate change. Climate health data are data that capture multiple dimensions of climate health, and that can be used, aggregated and mapped in order to tackle global health challenges. In this paper we use the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to conduct a literature review on climate health data, focusing on their actionability in relation to global health challenges. Our literature review identifies themes of (a) early warning and emergency response, (b) assessment of planning interventions, and (c) preventive and future planning as central to research on climate health data. With this literature review, we frame climate health data as an object of interest for the IS field, exploring ways in which IS research can uniquely contribute to knowledge on the global challenge of climate health.

ABSTRACT. This paper explores how energy consumption feedback can influence user decisions when cost is not an issue. Smart sockets can measure energy use and carbon emissions as well as indicating the source of the electricity coming into the socket via an LED traffic light system. Drawing on a study with university students for whom cost was not a consideration as it was included in the monthly rental fee, the paper investigates how users interact with these devices, focusing on three key areas: (i) drivers for and barriers to adoption, (ii) comprehension of feedback mechanisms, and (iii) potential for behaviour change aimed at reducing carbon emissions. Our findings indicate that participants appreciated the LED feedback, but were hindered by limitations in data granularity and the device’s size. Most users were open to adopting such technology, if the cost of the device was not significant, though some confusion around LED and graphical feedback highlighted the need for enhanced user guidance.

ABSTRACT. With the expansion of AI systems, both in scale and complexity, it becomes ever more urgent to bridge the gap between advancement and environmental, social, and economic sustainability. In our desire to offer a practical tool for fostering sustainable AI development, we introduce an overall framework in guiding developers in adopting sustainable practices throughout the AI development lifecycle: the Sustainable AI Development Card. From data management to model training and deployment, this framework offers practical strategies in the direction of energy consumption minimisation and carbon emissions reduction, each with social equity promotion. Interposed upon recent advances in energy-efficient algorithms, hardware optimisation, and ethical AI deployment, the paper concentrates on detailing specific strategies that make alignment of AI performance with attainable sustainability objectives possible. The above shows how developers can minimise the sustainable risks of AI systems with no significant reduction in accuracy or scalability.

ABSTRACT. Digital social media platforms are flooded with vast amounts of unknown and often unverified information, where users are constantly exposed to new content that can quickly influence their perspectives. The initial formation of opinions in this environment is critical, as first impressions shape immediate reactions and can significantly influence user behavior, political leanings, and consumption patterns. Understanding how these opinions form is essential for fostering responsible information sharing and combating misinformation. This research explores the interaction between cognitive processes and the digital identity of information sources on opinion formation. Study one, involving 320 participants, reveals that initial opinions begin to solidify after consuming five pieces of information. Study two, with 180 participants, investigates factors such as cognitive structures, personality traits, and socio-demographic characteristics, finding that users are more likely to trust information from profiles with unverified titles (e.g., Dr. or Doctor) than from verified expert influencers

ABSTRACT. This study explores how emotional responses and social identity influence the viewing and sharing of viral videos among football fans and non-fans. Grounded in social identity and social sharing of emotions theories, it suggests that identity and emotions drive the sharing behaviour in football fandom. According to social identity theory, fans align strongly with their own team (in-group) while often viewing rival teams and their supporters (out-group) less favourably. The social sharing of emotions theory further suggests that fans are more likely to share videos that elicit intense emotions. The study develops a methodological framework showing that, when exposed to a viral football video, fans demonstrate higher emotional intensity and are more inclined to share than non-fans. The findings highlight that the likelihood of sharing increases when viewers experience peak affective states, emphasizing the role of emotional intensity in the spread of football-related content.

ABSTRACT. Consumers engage in social media activities, such as liking, commenting, sharing, and creating content, often revealing their identities, even if it is an online persona. The rise of social commerce allows users to purchase brands directly through social media, but it raises privacy concerns. Brands can personalise messages to drive engagement, yet data monetisation has created tensions among technology, privacy, consumers, regulators, and firms. This paper explores the links between UK and Thai consumers’ social media activities, their privacy concerns, and perceptions of brand intrusiveness in relation to purchase intentions via social commerce. Results indicate limited differences in engagement levels between Thai and UK consumers, with active participation reducing feelings of intrusiveness and privacy concerns. However, some differences exist among those users who do have privacy concerns affecting their purchase intention, an interesting finding given that regulations surrounding online privacy are emerging at varying rates globally.

ABSTRACT. This paper explores the perceptions of UK university students on the use of generative artificial intelligence (Gen-AI) by lecturers in higher educational settings in the areas of teaching and instruction. It analyses the qualitative survey responses of 205 students majoring in different business fields to unearth a wide range of student opinion on Gen-AI integration into education by lecturers. The five key themes that emerged were: scepticism and concern about Gen-AI; value of human interaction and the personal touch; potential benefits with caution; negative impact on teaching quality and creativity; uncertainty. Students were concerned about how Gen-AI might further undermine educational quality, authenticity, and that very critical human interaction in teaching. They cited the value of human elements such as mentorship, tailored individual feedback, and inspirational teaching that Gen-AI would never in their view replace. While accepting the efficiency and support that could be brought about by Gen-AI, it was desired by respondents to have a balanced approach ensuring that Gen-AI complements rather than replaces human educators. This is a paper underscoring the need for lecturers to adopt Gen-AI with caution and consideration for its limitations and possible negative effects on creativity and engagement of students. These ambivalence feelings and uncertainty amongst students points to a continuous need for dialogue and further research into learners’ views of Gen-AI being used by educators. Some of the suggested future research directions include the conduct of longitudinal studies that trace changes in student perceptions over time, comparative studies across different disciplines and cultural contexts, and experimental studies of the actual impact of Gen-AI on learning outcomes and engagement. The research contributes to a heated area of Gen-AI in education by highlighting the under-researched area of the student perspectives of Gen-AI technology integration into the classroom and learning experience. It shows that the value that students put on human interaction and the personal touch should not be underestimated by practitioners when considering the potential gains that Gen-AI could bring.

ABSTRACT. This study examines the integration of Constructivist and Social Learning Theories within AI-enhanced collaborative community learning, with a focus on the implementation of Perusall, a social reading and peer annotation platform. Constructivist theory foregrounds active knowledge construction, while Social Learning Theory emphasises learning through social interaction and observation. Their convergence supports pedagogical approaches that promote exploration, problem-solving, and collective reflection. Drawing on three years of data from a postgraduate module at a UK university, this research compares student engagement and performance before and after the adoption of Perusall. Findings indicate that the platform enhances learner engagement and academic outcomes by fostering interactive and collaborative learning environments. The study offers empirical evidence on the pedagogical efficacy of AI-driven tools, contributing to the theoretical discourse on technology-mediated learning and providing practical implications for educators. It highlights the transformative potential of AI in fostering more engaging, effective, and inclusive educational practices.

ABSTRACT. This study explores the integration of Generative AI (GenAI) in Brazilian higher education, focusing on its responsible use and unique challenges in Brazil. Despite global advancements, Latin America lags in establishing GenAI policies, which may deepen educational inequalities. Through a multi-case analysis of both public and private institutions, the research proposed will examine GenAI’s roles in teaching, learning, and administration, assessing its benefits and risks. Findings will inform policy recommendations tailored to Brazil's educational landscape, aiming to balance accessibility, ethics, and effectiveness in AI adoption. It is anticipated that this research will provide insights for policy development to guide AI's role in education responsibly.

ABSTRACT. This study explores the application of the Technology Acceptance Model (TAM) to AI-powered digital innovations within a transnational governance framework. By integrating Latourian actor-network theory (ANT), this study examines how institutional motivations, regulatory compliance, and ethical and cultural acceptance drive organisations to develop and adopt AI innovations, enhancing their market acceptance and transnational accountability. We extend the TAM framework by incorporating regulatory, ethical, and socio-technical considerations as key social pressures shaping AI adoption. Recognizing that AI is embedded within complex actor-networks, we argue that accountability is co-constructed among organisations, regulators, and societal actors rather than being confined to individual developers or adopters. To address these challenges, we propose two key solutions: (1) internal resource reconfiguration, where organisations restructure their governance and compliance mechanisms to align with global standards; and (2) reshaping organisational boundaries through actor-network management, fostering engagement with external stakeholders, regulatory bodies, and transnational governance institutions. These approaches allow organisations to enhance AI accountability, foster ethical and regulatory alignment, and improve market acceptance on a global scale.

ABSTRACT. Blockchain applications are envisioned as transformative across sectors. Signals from practice, however, indicate obstacles in adopting such applications. We investigate the gap between the theoretical promise and practical adoption of blockchain applications in fields outside of finance. In doing so, we explore the cumulated body of knowledge on blockchain applications through the lens of prospective theorising and evaluate the speculative rigour of this field and its imagined futures. Through the meta-analysis of 126 review articles, we assess the state of blockchain adoption, academic sentiment and real-world pilot projects and initiatives. The anticipated findings of our study showcase that while the academic discourse maintains a sentiment of persistent optimism, real-world adoption remains limited, with discontinued projects exemplifying recurring obstacles. We emphasise the need for enhanced speculative rigour and inclusion of descriptive theorising in blockchain research to ground expectations in empirical evidence and delineate an agenda for future research on the topic.

ABSTRACT. Blockchain technology (BCT) has a disruptive impact on supply chain operations, offering significant potential to enhance efficiency and transparency. However, there is relatively limited research focused on exploring the critical success factors (CSFs) for BCT implementation in supply chains. This study addresses this gap by conducting a Delphi survey. Through a systematic literature review (SLR), 33 potential success factors were initially identified, of which 28 CSFs were selected in the first round of the Delphi study. In the second and third rounds, panel members ranked these CSFs and reached a consensus on their relative importance. This study serves as a valuable resource for supply chain stakeholders, providing managers with insights into the significance of each CSF. By ranking these factors, the study offers guidance for managers to optimize resource allocation in BCT implementation.

ABSTRACT. Generative Artificial Intelligence (GAI) present both transformative opportunities and complex ethical challenges in the evolving Higher Education (HE) landscape. This research explores the crucial aspect of trust among educators in HE regarding the ethical use of GAI, avital factor for its successful integration into teaching and learning environments. Through a survey research approach, this study combines quantitative and qualitative analysis to assess the levels of trust educators place in students’ ethical use of GAI. The research examines key constructs such as transparency, reliability, accountability, cultural contexts, trust, and ethical alignment through descriptive and thematic analysis. The findings reveal that trust is a critical lever in the adoption and effective use of GAI in HE. The research highlights how various dimensions of trust affect educators’ engagement with GAI. These insights pave the way for the development of targeted guidelines aimed at strengthening trust and promoting an ethical framework for GAI in HE.

ABSTRACT. Hundreds of millions of people interact and use Large Language Models (LLMs) on a weekly basis. Hence, it cannot be surprising that students and lecturers in higher education (HE) also use LLMs in educational contexts and for educational purposes, aiming both at the same goal, namely increasing the learning success (LS); hence, while students use LLMs for exam preparations, lecturers use it as a pedagogical tool to offer a more efficient learning environment. To achieve an overview of the current literature that analyses factors that either increase or decrease the LS of students due to the use of LLMs, we have conducted a systematic literature review. As such, our research question is as follows: How might the use of an LLM such as ChatGPT impact the teaching and the LS of students in HE? In this paper, we present and discuss the progressive and regressive factors that contribute to either an increase or decrease of LS.

ABSTRACT. Synthetic data (SD) is becoming an increasingly important tool in artificial intelligence (AI) research, particularly in domains where real-world data can be difficult or costly to obtain. In this research-in-progress paper, we explore the use of SD derived from brainwave and movement data to power an augmented reality (AR) episodic ballet experience. The goal of this WIP is to prompt discussions around the ethical use of body data in computing education through immersive technologies and to explore new technologies for teaching and learning within computing education. By leveraging SD rather than real user data, we aim to create an immersive AR experience that allows exploration of the relationship between physical movement, cognition, and artistic expression, while avoiding potential privacy and consent issues associated with the use of personal biometric data. Through this WIP, we investigate the technical challenges and opportunities in using SD to enable novel educational experiences, as well as the broader implications for the role of synthetic data in AI-powered educational research and applications. Our findings have the potential to inform best practices around the ethical development of data-driven educational technologies that respect individual privacy and autonomy.

ABSTRACT. Effective stock management is expected to solver the issue of overstocking and understock that may cause revenue losses. This research aims to present an integrated strong inventory management system using cutting-edge technology implementation applied on the publicly available market data. Raw sales data is pre-processed and fine-tuned, time series analysis and a range of machine learning strategies were applied on tuned dataset to produce accurate demand forecasts. Furthermore, we integrated Open AI for evaluating the product's generalized review to help with decision-making in the simplest and most efficient way. It is demonstrated that the simplest strategy to control stock levels to maximize profitability could be achieved by integrating the of projected sales and customer reviews.

ABSTRACT. The data quality of master data, and data governance, has been under researched despite the increase in importance and relevancy to technologies such as AI and for data-driven decision making. To understand why there is a disjoint between the need for accurate master data and the lack of attention that it has been given in research and organisations, a systematic literature review was conducted for relevant Information Systems papers over the last ten years. The Work Systems Framework was used to interpret possible causes of master data quality issues found from 56 relevant articles. Most of the causes were found to be related to information, and infrastructure. This may indicate that there are important causes of master data quality that may or may not be entirely in the control of the work system itself.

ABSTRACT. Migraine is a neurological condition that impairs quality of life, with diagnostic challenges, especially in resource-limited settings lacking specialised tools and expertise. While AI models for migraine classification have been explored in standard healthcare, limited research focuses on low-resource environments. To address this, we evaluate the efficacy of Machine Learning and Deep Learning models (SVM, KNN, DT, RF, and TabNet) for migraine classification, with a focus on computational efficiency and interpretability. Among the models, RF emerged as the best model, achieving 95.8% accuracy, precision, recall, and F1 score, while TabNet achieved slightly lower performance 91.1%, 91.8%, 91.1%, 90.7% respectively. RF demonstrated enhanced computational efficiency, with a training time of 0.9s and memory usage of 0.14 MB, compared to TabNet's 10.8s and higher memory usage. Furthermore, SHAP analysis supported RF’s interpretability, and we propose RF as a cost-effective, AI-driven diagnostic tool for migraine classification, improving access to healthcare in resource-limited regions.

ABSTRACT. The notion of design justice captures both the way injustice can be directly designed into technology, and the role of human agency in countering it by justice-informed design. This paper applies a design justice approach to an object, Electronic Medical Records (EMRs) in the obstetrician/gynaecological (OB/GYN) context, which closely concerns the production and visualisation of health data. Through qualitative research on OB/GYN EMRs in four Indian hospitals, we discover a reality where a “normal” patient is seen as the default, and deviations from it require substantial workarounds on the doctors’ side. Refusing digital health views originating in Western biomedical systems, the use of EMRs by doctors in our study points to three byways for design justice: working towards intersectionality-informed design; designing systems that allow users to inscribe their data preferences into digital health systems; and co-designing digital health solutions. All three byways, illustrated through our field data, offer implications for IS research on design justice.

ABSTRACT. Mobile Health (mHealth) has the potential to transform healthcare allowing for rapid diagnosis, care and public health surveillance. Our research investigates the usability and acceptability of an mHealth application, called UniHealth, used in parallel with a study-supplied multiplex antigen test for self-testing at home for symptomatic or asymptomatic respiratory infections (SARS-CoV-2, Adenovirus, Influenza A and B and RSV). We conducted an anonymous online survey informed by validated questions using a 5-point-Likert derived from Unified Theory of Acceptance and Use of Technology (UTAUT). The survey was sent to university students and staff engaged with the wider UniHealth study. After data cleaning, we analysed 62 completed records. Most participants surveyed strongly agreed/agreed that the UniHealth app and the supplied multiplex self-tests were easy to use. Our analysis confirmed the applicability of UTAUT in the context of mHealth and antigen self-testing in the home. The findings of this study will benefit software designers and developers, antigen test designers and manufacturers, policy makers, and healthcare providers.

ABSTRACT. Over the recent decade or so, the Chinese government implemented a commercial reform that features governmental application of digital technologies to acquire and process firm information. The core objective of commercial reform is to improve information transparency and monitoring on corporate commercial activities. To explore the economic effectiveness of the reform, we examine how it impacts firms’ stock price crash risk. We find robust evidence that the commercial reform that digitalizes government regulatory activities mitigates stock price crash risk and achieves so via enhancing information environment and monitoring for firms. This finding is more prominent for firms with higher levels of digitalization and innovation and those with weaker internal governance. Overall, our findings highlight a potential benefit of applying digital technologies to regulatory reform, encouraging governments to adopt digital tools to improve information environments and monitoring for firms, and thereby promoting a more stable and efficient capital market.

ABSTRACT. As digital images become fluid, mobile, and embedded within algorithmic infrastructures, this paper examines the politics of visibility in algorithmic governance, focusing on digital archives, data retention, and the governance of deletion. Using the Swedish police’s adoption of Clearview AI’s facial recognition technology as a case study, it explores how networked images resist erasure, challenging the enforcement of the Right to Be Forgotten (RTBF) under the EU’s General Data Protection Regulation (GDPR). By analyzing policy documents, institutional workflows, and bureaucratic decision-making, the paper asks: How do archival practices influence the ability to uphold the right to be forgotten within algorithmic governance? The findings reveal tensions between public and private data infrastructures, where automated decision-making, predictive analytics, and archival mechanisms entrench networked images in state operations. This study calls for a re-evaluation of privacy, digital archives, and algorithmic governance, as deletion remains a politically and technically contingent act within distributed systems.

ABSTRACT. While surveillance and transparency have each received extensive attention on their own, there is a paucity of research integrating these concepts to produce a more nuanced analysis of their effects when deployed through workplace analytics. This developmental paper proposes a conceptual framework that integrates surveillance and transparency as parallel effects of workplace analytics in order to produce new and deeper insights into their impact on employee experience, and specifically on intra-organisational trust dynamics and employee engagement. Guided by the proposed conceptual framework, a future empirical study will be undertaken to examine the interplay between surveillance and transparency and their subsequent impact on intra-organisational trust and employee engagement.

ABSTRACT. As political pressures threaten DEI and sustainability research in parts of the world, UK and European IS scholars have a unique opportunity to lead. This keynote challenges us to rethink what impact means in the age of AI—and asks: Impact for whom? Inclusion on whose terms? It’s not just a conversation about inclusion—it’s a call to reimagine the kind of research our field needs now.