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This study aims to identify integrated marketing communication (IMC) antecedents and the consequences of planned brand identity in the context of higher education, and empirically test a number of hypotheses related to the constructs of these antecedents and consequences.

A model of the IMC antecedents and consequences of planned brand identity was tested in a survey conducted among stakeholders in two London-based universities. Structural equation modelling (SEM) was applied to gain insight into the various influences and relationships.

The study identifies and confirms key constructs in planned brand identity. IMC antecedents of planned brand identity, such as brand elements, service attributes, public relations and place/country of origin, were found to positively influence the planned brand identity consequences of awareness, image and reputation. However, websites, social media, advertising and direct marketing were not found to have significant influence.

The focus on two UK universities limits the generalisability of the findings. Future research should be conducted in other country settings to test the relationships identified in the present study. Also, future research may build on the study’s findings by investigating the attitudinal and behavioural consequences of brand identification in the higher education context.

Professionals responsible for universities’ promotional and branding activities need to evaluate the relative contributions of the IMC antecedents of planned brand identity. Brand elements such as design, colour and name, for example, should be reviewed to determine whether modifications are required in different international markets. The increasing prevalence of social media, one of the key antecedents of brand awareness, offers opportunities for universities to engage in brand co-creation by interacting with past, present and future students on relevant digital platforms. Finally, the place/country-of-origin cue is of particular relevance to institutions of higher education given the increasing numbers of students at both undergraduate and postgraduate levels who are choosing to study abroad (Melewar and Akel, 2005). The attraction of the UK as a country to study in, or the appeal of individual cities such as London, should be fully integrated into universities’ IMC strategies.

The study makes two main contributions. First is the theoretical contribution by identifying the core IMC antecedents and consequences of planned brand identity for universities and from this extrapolate key directions for future research. Second it is indicated that a number of managerial implications are designed to assist in the formulation of improved professional practice.

This paper aims to identify ethical challenges of using artificial intelligence (AI)-based accounting systems for decision-making and discusses its findings based on Rest's four-component model of antecedents for ethical decision-making. This study derives implications for accounting and auditing scholars and practitioners.

This research is rooted in the hermeneutics tradition of interpretative accounting research, in which the reader and the texts engage in a form of dialogue. To substantiate this dialogue, the authors conduct a theoretically informed, narrative (semi-systematic) literature review spanning the years 2015–2020. This review's narrative is driven by the depicted contexts and the accounting/auditing practices found in selected articles are used as sample instead of the research or methods.

In the thematic coding of the selected papers the authors identify five major ethical challenges of AI-based decision-making in accounting: objectivity, privacy, transparency, accountability and trustworthiness. Using Rest's component model of antecedents for ethical decision-making as a stable framework for our structure, the authors critically discuss the challenges and their relevance for a future human–machine collaboration within varying agency between humans and AI.

This paper contributes to the literature on accounting as a subjectivising as well as mediating practice in a socio-material context. It does so by providing a solid base of arguments that AI alone, despite its enabling and mediating role in accounting, cannot make ethical accounting decisions because it lacks the necessary preconditions in terms of Rest's model of antecedents. What is more, as AI is bound to pre-set goals and subjected to human made conditions despite its autonomous learning and adaptive practices, it lacks true agency. As a consequence, accountability needs to be shared between humans and AI. The authors suggest that related governance as well as internal and external auditing processes need to be adapted in terms of skills and awareness to ensure an ethical AI-based decision-making.