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The architectural profession faces an increasingly complex ethical landscape. In recent decades, the ethical foundations for architecture – formally defined in professional codes of conduct and reflected in regulatory frameworks – have expanded to incorporate imperatives derived from environmental and social responsibilities. The present research examines how these expanding ethical expectations are reflected in, and supported by, recent research about architectural ethics.

Analysis is based on a systematic review of recent research (2002–2023) focussing on ethical values and associated behaviours in the architectural profession. The review identifies 37 research articles (from a starting set of 2,483 articles), which are analysed empirically and then qualitatively to draw out views around three types of ethical behaviour. The paper then discusses how these findings align thematically with the evolving ethical needs of architectural practitioners (as defined in the professional codes of ethics and conduct of the RIBA in the UK, AIA in Australia and the AIA in the USA).

While business ethics remains the focus of past research, there has been a rise in research about ethics and social and environmental factors. The content of professional codes reflects this shift, setting the conditions for architects to aspire to increased expectations of environmental and social responsibility.

This article undertakes the first systematic review of recent research about architectural ethics and its alignment with the content of contemporary professional codes. Organised thematically, recent research about ethical values and associated behaviours can offer guidance for the evolving ethical needs of architects.

Understanding ways to foster wellbeing in postgraduate-research students (PGRs) requires focus especially with respect to positive relationship formation with supervisory teams. Hence, the purpose of this study was to explore six different ways of nurturing wellbeing; perceptions of positive relationships with supervisory teams; and interactions between these factors.

In total, 155 PGRs completed questionnaires at baseline and six months. The predictor variables were six ways of nurturing wellbeing; the outcome variable was psychological wellbeing appraisals overall; and the moderator variables were positive perceptions of relationships with key staff.

The most effective ways of nurturing wellbeing were Noticing and Being Aware; Discovering and Learning; Connecting with Others; and Being Healthy and Safe. Over time, Noticing and Being Aware predicted psychological wellbeing appraisals overall. Positive relationships with supervisors, co-supervisors and work peers were associated with wellbeing appraisals. Furthermore, positive relationships with co-supervisors most convincingly strengthened the relationships between wellbeing appraisals and Noticing and Being Healthy cross-sectionally, and Giving longitudinally.

It is concerning that PGRs are often overlooked when developing policies and strategies to combat mental health problems. Rather than simply focusing on diminishing mental health problems, the current work evidences ways of optimizing positive aspects of PGR experiences by actively nurturing wellbeing in tandem with enhancing relationships with supervisory team members. However, such initiatives have to be an investment at institutional, as well as individual levels.

Examining the interactions between nurturing positive wellbeing in PGRs and positive relationships with supervisory team members is under-researched.

This paper aims to managerial decision styles, following the General Decision-Making Style Inventory, as potential predictors of individual bias awareness and bias blind spots, with a focus on the rational decision style.

The research is based on a survey of 50°C-1 level managers within Forbes 2000 companies. It explores their decision styles and their assessments of their own and others’ decision behavior.

The results show that the awareness of one’s own susceptibility to biases and bias blind spots is highly dependent on an individual’s (self-declared) decision style and type of cognitive bias; decision-makers with a strong tendency toward a rational or spontaneous decision style see themselves as less vulnerable to cognitive biases but also show a much stronger bias blind spot than those with a tendency toward other decision styles. Meanwhile, decision-makers with a strong tendency toward an intuitive decision style tend to recognize their own vulnerability to cognitive biases and even show a negative blind spot, thus seeing themselves as more affected by cognitive biases than others.

To date, decision styles have not been used as a lens through which to view susceptibility to cognitive biases and bias blind spots in managerial decision-making. As demonstrated in this article, decision styles can serve as predictors of individual awareness and susceptibility to cognitive biases and bias blind spots for managers.

The purpose of the study was to examine graduate student perspectives on the common and unique roles peer mentors and supervisors play in supporting student success and wellbeing during their program.

A qualitative research design involving semistructured interviews with 62 thesis-based masters and doctoral students from four professional faculties, Education, Medicine, Nursing and Social Work, at a large public research-intensive university in Canada.

Findings transcend the four disciplines of study. Communities of support are described that involve both supervisors and peers in combination, clusters of meaning by supervisory paradigm are identified and original findings presented that expand upon the learning alliance framework by explicitly considering the role of peer mentors in graduate student success.

While supervisors bear primary responsibility for fostering effective research-based relationships, this study’s findings strengthen the argument that mentoring and advising of graduate students is most effective when conducted within a collaborative community of support that involves learning alliances among faculty, peers, program staff and academic leaders across the institution.

A four-pronged approach to graduate education that emphasizes the collective responsibility of institutions, programs, supervisors and students in creating a supportive ecosystem for holistic graduate student academic success and wellbeing is recommended.

Key argument that it is essential to embrace a collaborative and community of support mindset, where multiple stakeholders actively contribute to the wellbeing and academic development of graduate students throughout their programs.

A cross-disciplinary perspective is offered on the importance of both supervisors and peers in assisting thesis-based graduate students to successfully navigate academic, social and personal journeys through graduate school.

The current gap between the required and available artificial intelligence (AI) professionals poses significant challenges for organisations and academia. Organisations are challenged to identify and secure the appropriate AI competencies. Simultaneously, academia is challenged to design, offer and quickly scale academic programmes in line with industry needs and train new generations of AI professionals. Therefore, identifying and structuring AI competencies is necessary to effectively overcome the AI competence shortage.

A probabilistic topic model was applied to explore the AI competence categories empirically. The authors analysed 1159 AI-related online job ads published on LinkedIn.

The authors identified five predominant competence categories: (1) Data Science, (2) AI Software Development, (3) AI Product Development and Management, (4) AI Client Servicing, and (5) AI Research. These five competence categories were summarised under the developed AI competence framework.

The AI competence framework contributes to clarifying and structuring the diverse AI landscape. These findings have the potential to aid various stakeholders involved in the process of training, recruiting and selecting AI professionals. They may guide organisations in constructing a complementary portfolio of AI competencies by helping users match the right competence requirements with an organisation's needs and business objectives. Similarly, they can support academia in designing academic programmes aligned with industry needs. Furthermore, while focusing on AI, this study contributes to the research stream of information technology (IT) competencies.