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Through a feminist lens, the study explores women’s experiences of entrepreneurial leadership in the UK and how the women manage competing and contrary patriarchal and individualism discourses and associated discursive paradox.

This study follows social constructionism and Feminist Standpoint Research approach, providing space for women to voice and contextualise lived experiences from multiple standpoints. The study comprises five cases of women entrepreneurs in IT, law, construction, beauty and childcare, using a two-stage semi-structured interview process analysed through discourse analysis.

This study provides new insights into the entrenched patriarchal socio-cultural context for women entrepreneurial leaders in the UK. The competing discourses provoke a discursive paradox, which dominates and oppresses women. This is managed through a process of discursive blending, blurring and merging contrary discursive expectations. The women use the individualism discourse to obscure patriarchy’s domination and as a resource to resist patriarchal gender power relations. To blend the discourses, the women use particular tactics: engaging in patriarchal bargains, such as “dressing not to impress”; can sidestep and manoeuvre these bargains and can utilise “patriarchal advantages”, turning gender oppression into benefits by “working it positively”.

This study addresses the lack of research interrogating patriarchy in the Global North and the absence of understandings of how women entrepreneurial leaders manage the competing and contrary discourses of patriarchy and individualism, which actively shape their experiences. The study illuminates the significance and increasing requirement for feminism to disrupt the ever-increasing power of patriarchy in entrepreneurship.

The use of generative artificial intelligence (genAi) language models such as ChatGPT to write assignment text is well established. This paper aims to assess to what extent genAi can be used to obtain guidance on how to avoid detection when commissioning and submitting contract-written assignments and how workable the offered solutions are.

Although ChatGPT is programmed not to provide answers that are unethical or that may cause harm to people, ChatGPT’s can be prompted to answer with inverted moral valence, thereby supplying unethical answers. The authors tasked ChatGPT to generate 30 essays that discussed the benefits of submitting contract-written undergraduate assignments and outline the best ways of avoiding detection. The authors scored the likelihood that ChatGPT’s suggestions would be successful in avoiding detection by markers when submitting contract-written work.

While the majority of suggested strategies had a low chance of escaping detection, recommendations related to obscuring plagiarism and content blending as well as techniques related to distraction have a higher probability of remaining undetected. The authors conclude that ChatGPT can be used with success as a brainstorming tool to provide cheating advice, but that its success depends on the vigilance of the assignment markers and the cheating student’s ability to distinguish between genuinely viable options and those that appear to be workable but are not.

This paper is a novel application of making ChatGPT answer with inverted moral valence, simulating queries by students who may be intent on escaping detection when committing academic misconduct.

This research, titled ‘AI in Digital Marketing: The Ethical Implications of Digital Influence on Markets and Consumers,’ conducts a comprehensive examination of the nuanced role played by artificial intelligence (AI) in shaping consumer behaviour and influencing decision-making processes. While the incorporation of AI in marketing offers commendable advantages, such as providing personalised content and optimising strategies to enhance customer experiences and market efficiency, it concurrently introduces ethical considerations. This study meticulously scrutinises the latent potential for market grooming, wherein AI subtly guides consumers towards decisions that may not align with their best interests. By delving into instances of data privacy breaches, algorithmic biases and the unintended consequences of hyper-personalisation, this research contributes substantively to the ongoing discourse on the responsible utilisation of AI. The study underscores the imperative need for regulatory frameworks aimed at ensuring ethical practices in the dynamically shifting digital landscape. It endeavours to strike an equitable balance between the constructive contributions and potential pitfalls of AI in the realm of marketing. Through this research, we aim to shed light on the ethical dimensions associated with the digital manipulation of markets and consumers, providing insights that can inform industry practices, policymaking and public awareness.

There has been a paradigm shift within research exploring autistic experience in recent decades towards greater participation, agency and voice for autistic researchers and the autistic community more generally (Fletcher-Watson et al., 2019). This approach has shown a greater focus on research oriented towards the priorities or preferences of the autistic community (Fletcher-Watson & Happé, 2019; Pellicano et al., 2014), curtails concerns regarding epistemic injustice and has influenced understandings of autistic ontology and neurology. Co-produced research, characterised by the inclusion of diverse stakeholders, builds trust between participants. Nonetheless, co-production in research requires careful planning and support (Stark et al., 2021), sometimes proving ‘turbulent’ and ‘challenging’ (Worsley et al., 2021). This chapter explores the experiences and reflections of a team of autistic and non-autistic researchers conducting co-produced research amid the global COVID-19 pandemic. With research practices and systems altered due to increased remote work, online communication and limited in-person interaction, this topic is especially pertinent. With the increasing emphasis on involving members of the autistic community in research at all levels of development, the impact of the pandemic on how participatory research is carried out may be complex. This chapter has implications for planning and conducting co-produced research in our new reality, considering both the opportunities and obstacles it presents.

Aged-care work has become an extreme form of work. Anti-violence HRM, comprising practices to combat workplace violence, is important in an industry with widespread violence. In this paper, we employ social exchange theory to better understand the effect of anti-violence HRM and trust in the manager on perceived nurse and PCA cynicism working in Australian aged care facilities and their subsequent intention to leave.

This study used a mixed method with two stages. Stage 1 comprised semi-structured interviews with 10 managers and 50 nurses and PCAs working in Australian aged care facilities. Stage 2 comprised a survey of nurses and PCAs with a total of 254 completed responses in Time 1 (first wave) and 225 completed responses in Time 2 (second wave).

We tested three hypotheses and reported that interestingly anti-violence HRM was positively associated with organisational cynicism. Organisational cynicism mediated the relationship between anti-violence HRM and intention to leave. Worker trust in the manager moderated the relationship between anti-violence HRM practices and organisational cynicism, such that high levels of trust in the manager increased the effect of anti-violence HRM practices to reduce organisational cynicism and subsequently reduce intention to leave.

We find evidence that in aged care, workers' trust in their managers is critical for effectual anti-violence HRM. We argue that implementation of HRM practices may be more complex in extreme work settings. It is crucial to study HRM in situ and understand the root of social exchange(s) as a foundation for HRM to influence employee attitudes and behaviour.

This study aims to uncover the constraints of automation and the affordances of augmentation related to implementing artificial intelligence (AI)-powered systems across different task types: mechanical, thinking and feeling.

Qualitative study involving 45 interviews with various stakeholders in artistic gymnastics, for which AI-powered systems for the judging process are currently developed and tested. Stakeholders include judges, gymnasts, coaches and a technology vendor.

We identify perceived constraints of automation, such as too much mechanization, preciseness and inability of the system to evaluate artistry or to provide human interaction. Moreover, we find that the complexity and impreciseness of the rules prevent automation. In addition, we identify affordances of augmentation such as speedier, fault-less, more accurate and objective evaluation. Moreover, augmentation affords to provide an explanation, which in turn may decrease the number of decision disputes.

While the unique context of our study is revealing, the generalizability of our specific findings still needs to be established. However, the approach of considering task types is readily applicable in other contexts.

Our research provides useful insights for organizations that consider implementing AI for evaluation in terms of possible constraints, risks and implications of automation for the organizational practices and human agents while suggesting augmented AI-human work as a more beneficial approach in the long term.

Our granular approach provides a novel point of view on AI implementation, as our findings challenge the notion of full automation of mechanical and partial automation of thinking tasks. Therefore, we put forward augmentation as the most viable AI implementation approach. In addition, we developed a rich understanding of the perception of various stakeholders with a similar institutional background, which responds to recent calls in socio-technical research.

The purpose of this study was to examine how two socio-historical contexts within the United States, the Movement for Black Lives and the COVID-19 pandemic, informed approaches to improving racial climate in science, technology, mathematics and engineering (STEM) graduate education.

The authors used a general qualitative inquiry research study design to conduct focus groups (n = 121) with graduate students, postdoctoral fellows and faculty members from across STEM disciplines as well as administrators whose work involves STEM graduate students. Participants were from two US institutions involved in a National Science Foundation networked improvement community seeking to create inclusive environments for STEM graduate students.

This study demonstrates how these socio-historical contexts illuminated and amplified on-going efforts to address racial climate for graduate students in US-based graduate education. In response to these events, STEM faculty devoted time that otherwise might have gone to purely technical or scientific endeavors to addressing racial climate. However, some faculty members remain hesitant to address racial climate and efforts appear to have further waned over time. While diversity, inclusion and equity efforts came to the forefront of the collective consciousness during this time, participants worry that these efforts are not sustainable, particularly without support from faculty and administrators.

The findings from this study will inform efforts to improve racial climate in STEM graduate programs.

This study fills an identified need to capture how socio-historical contexts, like the US Movement for Black Lives and the COVID-19 pandemic, have influenced approaches to improving racial climate in STEM graduate programs.

Distinguishing between autism characteristics and trauma-related symptoms may be clinically challenging, particularly in individuals who have experienced early traumatisation. Previous studies have described a risk that trauma-related symptoms are misinterpreted and/or misattributed to autism. This study aims to describe and explore assessment strategies to distinguish autism and early traumatisation in the case of a young woman with mild intellectual disability.

A clinical case study outlining assessment strategies, diagnostic decision-making and initial intervention.

A multi-informant interdisciplinary assessment using multiple assessment tools, together with a comprehensive review of records from previous assessments and contacts with various services, was helpful in distinguishing between autism and trauma. This included specific assessment tools for autism and trauma. Autism characteristics and trauma-related symptoms appeared to interact, not merely co-occur.

The current case demonstrates that diagnostic overshadowing may occur for autism in the context of early trauma. The case further highlights the importance of not ascribing trauma-related symptoms to autism, as service provision and treatment need to take account of both. Overlooking autism in individuals who have experienced early traumatisation may result in a risk that intervention and care are not appropriately adapted, which may involve a risk of exacerbating trauma symptoms.