Adopting Learning Analytics to Inform Postgraduate Curriculum Design: Recommendations and Research Agenda

Understanding student sentiment plays a vital role in understanding the changes that could or should be made in curriculum design at university. Learning Analytics (LA) has shown potential for improving student learning experiences and supporting teacher inquiry. Yet, there is limited research that reports on the adoption and actual use of LA to support teacher inquiry. This four-year longitudinal study captures sentiment of postgraduate students at a university in Ireland, by integrating LA with the steps of teacher inquiry. This study makes three important contributions to teaching and learning literature. First, it reports on the use of LA to support teacher inquiry over four one-year cycles of a Master of Science in Business Analytics programme between 2016 and 2020. Second, it provides evidence-based recommendations on how to optimise LA to support teacher inquiry, with specific attention as to how these can improve the assimilation of LA into the curriculum design and delivery. Third, the paper concludes with a research agenda to help improve the adoption and integration of LA in the future

Trends in mobile payments research: A literature review

Mobile payments (m-payments) are increasingly being adopted by organisations as a new way of doing business in the 21st century. During the last few years, the use of m-payments as a new payment channel has resulted in an increase in the volume of literature dedicated to the topic. For this reason, this paper presents the findings of a review of literature aimed at identifying the key research themes and methodologies researched. In order to uncover these trends the authors reviewed the top twenty cited papers since 1999 and the twenty most recently published papers on m-payments since August 2014

Artificial intelligence in information systems research: A systematic literature review and research agenda

AI has received increased attention from the information systems (IS) research community in recent years. There is, however, a growing concern that research on AI could experience a lack of cumulative building of knowledge, which has overshadowed IS research previously. This study addresses this concern, by conducting a systematic literature review of AI research in IS between 2005 and 2020. The search strategy resulted in 1877 studies, of which 98 were identified as primary studies and a synthesise of key themes that are pertinent to this study is presented. In doing so, this study makes important contributions, namely (i) an identification of the current reported business value and contributions of AI, (ii) research and practical implications on the use of AI and (iii) opportunities for future AI research in the form of a research agenda

Supply chain resilience in mindful humanitarian aid organizations: the role of big data analytics

PurposeThe purpose of this paper is to understand the nomological network of associations between collective mindfulness and big data analytics in fostering resilient humanitarian relief supply chains.Design/methodology/approachThe authors conceptualize a research model grounded in literature and test the hypotheses using survey data collected from informants at humanitarian aid organizations in Africa and Europe.FindingsThe findings demonstrate that organizational mindfulness is key to enabling resilient humanitarian relief supply chains, as opposed to just big data analytics.Originality/valueThis is the first study to examine organizational mindfulness and big data analytics in the context of humanitarian relief supply chains

Questioning Racial and Gender Bias in AI-based Recommendations: Do Espoused National Cultural Values Matter?

One realm of AI, recommender systems have attracted significant research attention due to concerns about its devastating effects to society’s most vulnerable and marginalised communities. Both media press and academic literature provide compelling evidence that AI-based recommendations help to perpetuate and exacerbate racial and gender biases. Yet, there is limited knowledge about the extent to which individuals might question AI-based recommendations when perceived as biased. To address this gap in knowledge, we investigate the effects of espoused national cultural values on AI questionability, by examining how individuals might question AI-based recommendations due to perceived racial or gender bias. Data collected from 387 survey respondents in the United States indicate that individuals with espoused national cultural values associated to collectivism, masculinity and uncertainty avoidance are more likely to question biased AI-based recommendations. This study advances understanding of how cultural values affect AI questionability due to perceived bias and it contributes to current academic discourse about the need to hold AI accountable

Learning Without Limits: Identifying the Barriers and Enablers to Equality, Diversity, and Inclusion in IS Education

Inclusion in Information Systems (IS) has received significant attention in recent years, but inclusion in IS curriculum design and delivery is comparatively underdeveloped. Understanding and working with diversity in IS student groups has implications for how we prepare students for a diverse workplace and the design and development of IS systems. Although progress has been made towards inclusive higher education, institutions have not transformed into multicultural diverse organizations. This paper showcases an initiative to apply principles of Universal Design in the particular context of an IS postgraduate programme in a leading Irish business school. This initiative is set within the context of two connected research projects seeking to identify barriers to inclusion experienced by students generally, and particularly by certain student groups, in the same school. The findings demonstrate the persistence of inclusion issues in higher education, including in IS, that Universal Design principles are effective in developing more inclusive teaching and learning practices, and that small actions can have a big impact in this regard. A set of key recommendations is provided; while not exhaustive, these contribute to the wider discourse on inclusion and offer practical suggestions to educators on the design and delivery of inclusive programmes at both undergraduate and postgraduate level

A Confirmation Bias View on Social Media Induced Polarisation During Covid-19

Social media has played a pivotal role in polarising views on politics, climate change, and more recently, the Covid-19 pandemic. Social media induced polarisation (SMIP) poses serious challenges to society as it could enable ‘digital wildfires’ that can wreak havoc worldwide. While the effects of SMIP have been extensively studied, there is limited understanding of the interplay between two key components of this phenomenon: confirmation bias (reinforcing one’s attitudes and beliefs) and echo chambers (i.e., hear their own voice). This paper addresses this knowledge deficit by exploring how manifestations of confirmation bias contributed to the development of ‘echo chambers’ at the height of the Covid-19 pandemic. Thematic analysis of data collected from 35 participants involved in supply chain information processing forms the basis of a conceptual model of SMIP and four key cross-cutting propositions emerging from the data that have implications for research and practice

Likelihood of Questioning AI-Based Recommendations Due to Perceived Racial/Gender Bias

Advances in artificial intelligence (AI) are giving rise to a multitude of AI-embedded technologies that are increasingly impacting all aspects of modern society. Yet, there is a paucity of rigorous research that advances understanding of when, and which type of, individuals are more likely to question AI-based recommendations due to perceived racial and gender bias. This study, which is part of a larger research stream contributes to knowledge by using a scenario-based survey that was issued to a sample of 387 U.S. participants. The findings suggest that considering perceived racial and gender bias, human resource (HR) recruitment and financial product/service procurement scenarios exhibit a higher questioning likelihood. Meanwhile, the healthcare scenario presents the lowest questioning likelihood. Furthermore, in the context of this study, U.S. participants tend to be more susceptible to questioning AI-based recommendations due to perceived racial bias rather than gender bias

Introduction to the Special Section: Digital Innovation for Social Development and Environmental Action

In 2022, we launched a call for papers for a special section on digital innovation for social development and environmental action. The call was motivated by the need for the information systems discipline to move beyond talking about sustainability to taking actions to address important challenges facing society and the planet. Many authors responded to the call and we are pleased to present the fruits of their labors. In this introduction to the special section, we discuss the motivations for the special section, explain how the special section came together, highlight key points of interest in the eight papers that make up the special section, and reflect on future directions for information systems research