ABSTRACT
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.
ABSTRACT
Artificial Intelligence (AI) and Big Data Analytics (BDA) have the potential to significantly improve resilience of supply chains and to facilitate more effective management of supply chain resources. Despite such potential benefits and the increase in popularity of AI and BDA in the context of supply chains, research to date is dispersed into research streams that is largely based on the publication outlet. We curate and synthesise this dispersed knowledge by conducting a systematic literature review of AI and BDA research in supply chain resilience that have been published in the Chartered Association of Business School (CABS) ranked journals between 2011 and 2021. The search strategy resulted in 522 studies, of which 23 were identified as primary papers relevant to this research. The findings advance knowledge by (i) assessing the current state of AI and BDA in supply chain literature, (ii) identifying the phases of supply chain resilience (readiness, response, recovery, adaptability) that AI and BDA have been reported to improve, and (iii) synthesising the reported benefits of AI and BDA in the context of supply chain resilience.
ABSTRACT
Anecdotal evidence suggests that artificial intelligence (AI) technologies are highly effective in digital marketing and rapidly growing in popularity in the context of business-to-business (B2B) marketing. Yet empirical research on AI-powered B2B marketing, and particularly on the socio-technical aspects of its use, is sparse. This study uses Activity Theory (AT) as a theoretical lens to examine AI-powered B2B marketing as a collective activity system, and to illuminate the contradictions that emerge when adopting and implementing AI into traditional B2B marketing practices. AT is appropriate in the context of this study, as it shows how contradictions act as a motor for change and lead to transformational changes, rather than viewing tensions as a threat to prematurely abandon the adoption and implementation of AI in B2B marketing. Based on eighteen interviews with industry and academic experts, the study identifies contradictions with which marketing researchers and practitioners must contend. We show that these contradictions can be culturally or politically challenging to confront, and even when resolved, can have both intended and unintended consequences.
ABSTRACT
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.
ABSTRACT
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.
