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Teaching excellence remains a contested term in English higher education (HE). This paper begins by reflecting on its complex and sometimes blurred meaning, charting the divergence between academic interests in the complexity and contextual questions relating to practice development and organisational and sectoral shifts which have been driven by managerialism, accountability and “top-down” ideas of change. The authors argue that this divergence, epitomised in the development of the teaching excellence framework, has led to a confused, if ubiquitous, use of excellence to identify organisational and sector-led ideas of what it means to deliver quality teaching. However, these frameworks have become progressively detached from the complexity of practice investigated by those interested in pedagogy. The paper aims to discuss this issue.

This is a conceptual paper which brings together literature from teaching excellence, organisational science, time and HE to develop an alternative approach to pedagogic development.

Based on a critique of the current, confused conceptualisation of teaching excellence, the authors offer a different narrative which demonstrates how a reconsideration of the factors is important in developing critical and challenging teaching opportunities. Based on a “bottom-up” system focusing on dialogue, sustainability and “unhasty” time, the authors argue for a re-establishing of a holistic approach in HE providers based on emergent pedagogies as opposed to teaching excellence.

This paper demonstrates why teaching excellence has become conceptually fractured in an English context, and why a new approach to pedagogic development needs to be considered to establish a more positive and critical approach at both the institutional and sectoral levels. This paper outlines a possible approach to developing such renewal.

Recent technological advances have created volumes of data such that, unless some effective methods are used to analyse them, they will be either wasted or under‐examined for their useful information content. Scientific data visualization is an attempt to use graphical and numerical tools to extract information contained in data and hence to allow its analysis. This paper seeks to present a systematic approach to the development of tools for scientific data visualization.

It is shown that the approach to implement these tools involves four major steps: description of a reference model, validation of the data process, description of the software component and the design and implementation of the visualization tool.

This approach is substantiated by defining conditions suitable for scientific data visualization processes, in a relaxed manner. These conditions are subsequently refined more formally. Definitions and theorems of the proofs are succinctly discussed.

The mathematical description of the visualization process is necessary to understand and maintain some significant reduction in errors in scientific visualization processes.

Pipeline Equipment Ltd., Greatness Road, Sevenoaks, Kent TN14 5BY, announce the development of their Split‐o‐mat waste water cleaning machine to de‐contaminate wash water from paint washing plants. This new treatment system is described as a low cost method of removing paint pigment from wash water, allowing the water to be re‐used or discharged to drain. It is claimed that paint companies who are currently paying excessive charges for effluent disposal can benefit substantially from the Split‐o‐mat.

Studies on the coronavirus disease 2019 (COVID-19) tracing apps have mostly focused on how to optimize adoption and continuous use, but did not consider potential long-term effects of their introduction. This study aims to analyse whether the characteristics of the recent introduction of tracing apps may negatively impact individuals' attitudes and intentions to adopt future tracking technology.

In an online experiment across three countries (Australia, Germany, UK), the authors measured how perceived benefits of COVID-19 tracing apps as well as specific government and campaign-related factors affect privacy concerns, attitude towards future tracking apps and intention to adopt. The authors manipulated the type of provider (governmental vs private) and the type of beneficiaries of the future tracking technology app (the individual alone or also the public) as determinants of adoption.

The authors find that privacy concerns towards the COVID-19 tracing apps negatively impact attitude and intention to adopt future tracking apps. Future adoption is more likely if the app is provided by the government, whereas additional benefits to the public do not positively stimulate adoption. Second, the study analyzed different factors, including perceptions on governments and the app introduction, as well as perceived benefits.

Taking the introduction of COVID-19 apps in different countries as a basis, the authors link both perceived benefits and contextual factors to privacy concerns, attitudes towards and intention to adopt the related technology in the future. The authors hereby clarify the responsibility of governmental actors who conduct large-scale technology introductions for the future diffusion of related technologies.

This chapter starts by interrogating the notion of teaching excellence. It then moves on to discussing some of the data sources currently used in Higher Education Institutions (HEIs) to monitor and measure the quality of teaching. What do these sources actually reveal about teaching excellence and how might we make better use of them? From large-scale national censuses like the National Student Survey (NSS) to institutional data sets such as teaching observations, the contribution that each source makes to our understanding of the quality of HE teaching is underexplored and contested. It is argued that there is a need for more transparent debate across HEIs and the sector as a whole about the benefits and limitations of such data as well as greater acknowledgement of the role of collaboration over competition. The chapter concludes that teaching excellence is a marketised misconception of the complex reality of the reciprocal relationship between teaching and learning. Contrary to policy rhetoric and far from encouraging an environment of collegial improvement, it introduces an unhelpful ethos of contrived competition into what is essentially an interdependent relationship underpinned by collective collaboration. It is by focusing attention on the latter where the real gains and insights are likely to be made.

This paper reports the panel discussion on the topic of artificial intelligence (AI) and robots in our lives. This discussion was held at the Digitization of the Individual (DOTI) workshop at the International Conference on Information Systems in 2019. Three scholars (in alphabetical order: Ting-Peng Liang, Lionel Robert and Suprateek Sarker) who have done AI- and robot-related research (to varying degrees) were invited to participate in the panel discussion. The panel was moderated by Manuel Trenz.

This paper introduces the topic, chronicles the responses of the three panelists to the questions the workshop chairs posed and summarizes their responses, such that readers can have an overview of research on AI and robots in individuals' lives and insights about future research directions.

The panelists discussed four questions with regard to their research experiences on AI- and robot-related topics. They expressed their viewpoints on the underlying nature, potential and effects of AI in work and personal life domains. They also commented on the ethical dilemmas for research and practice and provided their outlook for future research in these emerging fields.

This paper aggregates the panelists' viewpoints, as expressed at the DOTI workshop. Crucial ethical and theoretical issues related to AI and robots in both work and personal life domains are addressed. Promising research directions to these cutting-edge research fields are also proposed.

The purpose of this study is to define and investigate the governance requirements of supply chain finance (SCF).

A qualitative analysis of 849 news articles published in UK newspapers (2000–2022) using the Gioia method.

SCF governance relies on developing capacities for reflexive scrutiny at two stages: (1) prior to entering into an SCF relationship and (2) during its operation. Based on the notion of SCF as a complex adaptive system, we theorise SCF governance requirements as a dual-layered semipermeable boundary. The semipermeability of the two layers allows for a dynamic exchange between the SCF system and its environment. The first layer is the capacity to selectively enable or control the entry and access of certain actors and practices into the SCF system. The second layer is a capacity for ongoing scrutiny of the SCF operation and its development. Further, we identify five aspects of governance to be enabled, i.e. enhancing adaptability, building confidence, improving efficiency, advancing technology and promoting transparency; and four aspects to be controlled, i.e. preventing abuse of power, curbing fraud risk, constraining operational risk and restricting risky extensions to SCF practices.

Our dynamic framework can guide supply chain (SC) members in making decisions about whether to participate, or continue to operate, in an SCF relationship. Moreover, the findings have implications for policymakers and authorities who oversee entry/access and the involvement of SCF providers, particularly, fintech firms.

The study contributes to both the SC and governance literature by providing a systematic analysis of what SCF governance has to accomplish. Our novel contribution lies in its analysis of SCF governance based on a complex adaptive system approach, which expands the existing literature where SCF is described in rather static terms. More specifically, it suggests a need for a dynamic duality of SCF governance through the semipermeable boundary that selectively enables and controls certain SCF actors and practices.