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The purpose of this study is to examine the effect of bank-specific, industry-specific and macroeconomic determinants of bank profitability amongst domestic UK commercial banks.

This study used an empirically driven single equation framework that incorporates the traditional structure–conduct–performance (SCP) hypothesis. A generalised method of moments technique was applied to a panel of UK banks covering the period 1998–2018 to account for profit persistence.

The estimation results show that all bank-specific determinants, with the exception of credit risk, significantly affect bank profitability in the anticipated way. However, no evidence was found in support of the SCP hypothesis. Interest rates, especially longer-term interest rates, and the rate of inflation has a significant effect on bank profitability, with the business cycle having a symmetric insignificant effect once other variables have been accounted for. Profitability persists to a moderate extent within the UK banking market, indicating that there exists a departure from a perfectly competitive market structure.

The literature that examines the actual underlying determinants of UK domestic bank profitability is limited.

The purpose of this paper is to describe and theorize the type of hate-love relationship to performance measurement systems (PMSs) that individual researchers tend to develop in academia. To this end, the paper draws upon Foucault’s writings on neoliberalism to analyse PMSs as neoliberal technologies holding certain qualities that can be expected to elicit such ambivalent views.

The paper is based on a qualitative interview study of researchers from three Swedish universities, who were asked to reflect upon questions related to three overall themes, namely, what it means to be a researcher in contemporary academia, the existence and use of PMSs at their universities and if/how such PMSs affected them and their work as researchers.

The empirical findings show that the hate-love relationship can be understood in terms of how PMSs are involved in three central moments of governmentality, where each such moment of governmentality tends to elicit feelings of ambivalence among researchers due to how PMSs rely on: a restricted centrifugal mechanism, normalization rather than normation and a view of individual academics as entrepreneurs of themselves.

Existing literature has provided several important insights into how the introduction and use of PMSs in academia tend to result in both negative and positive experiences and reactions. The current paper adds to this literature through theorizing how and why PMSs may be expected to elicit such ambivalent experiences and reactions among individual researchers.

Cybersecurity vulnerabilities are often due to human users acting according to their own ethical priorities. With the goal of providing tailored training to cybersecurity professionals, the authors conducted a study to uncover profiles of human factors that influence which ethical principles are valued highest following exposure to ethical dilemmas presented in a cybersecurity game.

The authors’ game first sensitises players (cybersecurity trainees) to five cybersecurity ethical principles (beneficence, non-maleficence, justice, autonomy and explicability) and then allows the player to explore their application in multiple cybersecurity scenarios. After playing the game, players rank the five ethical principles in terms of importance. A total of 250 first-year cybersecurity students played the game. To develop profiles, the authors collected players' demographics, knowledge about ethics, personality, moral stance and values.

The authors built models to predict the importance of each of the five ethical principles. The analyses show that, generally, the main driver influencing the priority given to specific ethical principles is cultural background, followed by the personality traits of extraversion and conscientiousness. The importance of the ingroup was also a prominent factor.

Cybersecurity professionals need to understand the impact of users' ethical choices. To provide ethics training, the profiles uncovered will be used to build artificially intelligent (AI) non-player characters (NPCs) to expose the player to multiple viewpoints. The NPCs will adapt their training according to the predicted players’ viewpoint.