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This paper aims to introduce International Digital Collaborative Autoethnographical Psychobiography (IDCAP).

This paper describes how IDCAP was developed to answer research questions about what it takes and what it means to recover from mental illness. During its development, IDCAP combined the diverse and intersectional experiences, knowledge and interests of an Anglo-Swedish research team with what could be found in different publications concerning the experiences and the mental illnesses of the musicians Syd Barrett, Peter Green and Brian Wilson.

IDCAP combines features of autoethnography and psychobiography to offer a novel qualitative research method.

Whilst IDCAP was created to focus on recovery from mental illness and musicians, it can be applied to other areas of research. It shares the same limitations as autoethnography and psychobiography, although some of the features of IDCAP may go some way to mitigate against these.

IDCAP is a novel research method that is offered to other researchers to develop and enhance further through application.

IDCAP is a collaborative research method that encourages the involvement of a wide range of researchers from different countries and cultures. It can be used to give voice to marginalised groups and to counter discrimination and prejudice. Recovery from mental illness is a topic of great personal and social value.

IDCAP is a novel research method that, to the best of the authors’ knowledge, has not been explicitly used before.

Every business company deals with the dilemma of how much to invest in long-term (LT) versus short-term (ST) problem (LTvST problem). LT operations increase the reputation of the company, and revenue is rewarded in the future. In contrast, ST operations result in immediate rewards. Thus, every organization faces the dilemma of how much to invest in LT versus ST activities. The former deals with the “what” or effectiveness, and the latter deals with the “how” or efficiency. The role of managers is to solve this dilemma; however, they often fail to do so, mainly because of a lack of knowledge. This study aims to propose a dynamic optimal control model that formulates and solves the LTvST problem.

This study proposes a dynamic optimal control model that formulates and solves the dilemma whether to invest in short- or LT operations.

This model is illustrated as an example of an academic institute that wants to maximize its reputation. Investing in effectiveness in the academy translates into investing in research, whereas investing in efficiency translates into investing in teaching. Universities and colleges with a good reputation attract stronger candidates and benefit from higher tuition fees. Steady-state conditions and insightful observations were obtained by studying the optimal solution and performing a sensitivity analysis.

To the best of the authors’ knowledge, this paper is the first one to explore the optimal strategy when trying to maximize the short and LT activities of a company and solve the LTvST problem. Furthermore, it is applied on universities where teaching is the ST activity and research the LT activity. The insights gleaned from the application are relevant to many different fields. The authors believe that the paper makes a significant contribution to academic literature and to business managers.

Designing and developing responsible business practices can create various tensions for service organizations. The purpose of this research is to develop a deeper understanding of the relationship between customer engagement (CE) and responsible business practices (e.g. environmental, social and/or governance [ESG], corporate social responsibility [CSR] and diversity, equity, and inclusion [DEI]) and explore customer engagement tensions that service organizations may face.

This research develops a list of CE-related responsible business practice tensions and empirically explores their relevance through in-depth interviews with nine ESG professionals.

This paper makes three important contributions. First, we find support for nine distinct but related tensions with implications for CE that organizations must navigate when pursuing responsible business practices. Second, interview participants provide some suggestions for tackling these tensions, which we support with relevant theories. Finally, we develop a conceptual framework that may stimulate future service research and inform the implementation of ESG strategies.

To the best of the authors’ knowledge, this research is the first to conceptualize and empirically explore the tensions that emerge between responsible business practices and CE. The authors develop a novel analysis of the CE-related tensions that emerge when pursuing an ESG strategy.

The findings are based on a small sample of ESG professionals. Future research may take a quantitative approach to further evaluate the role that these tensions play in engaging customers.

This research provides a conceptual framework that may guide ESG professionals in understanding, framing and navigating CE-related tensions when pursuing responsible business practices.

A social benefit may be found when service organizations are better able to successfully navigate CE-related tensions when pursuing responsible business practices.

This study aims to present a comprehensive investigation into users’ behavioural intentions to use e-pharmacies through the lens of the privacy calculus model. The present study also investigates the effects of perceived benefit, perceived privacy risk, timeliness and perceived app quality on e-pharmacy usage through the mediating role of trustworthiness.

The study used a deductive approach and collected data from 338 respondents using the purposive sampling technique. partial least squares structural equation modelling was applied to analyse the data.

The findings of the study indicate that perceived benefit, perceived privacy risk, timeliness and perceived app quality do not directly impact users’ behavioural intentions towards e-pharmacy adoption. Instead, it demonstrated that perceived benefit, perceived privacy risk, timeliness and perceived app quality influenced behavioural intention indirectly through the mediating role of trustworthiness

This study offers valuable insights to entrepreneurs, marketers and policymakers, enabling them to develop regulations, guidelines and policies that cultivate trust, safeguard privacy, ensure prompt services and create an enabling environment for the adoption of e-pharmacies. The present study also contributes to the existing literature by extending the privacy calculus model with the integration of timeliness and perceived app quality to explain users’ adoption behaviour towards e-pharmacy.

The purpose of this paper is to consider the ethical and environmental implications of allowing space resource extraction to disrupt existing fuel economies, including how companies can be held accountable for ensuring the responsible use of their space assets. It will also briefly consider how such assets should be taxed, and the cost/benefit analyses required to justify the considerable expense of supporting this emerging space industry.

This paper adopts theoretical bioethics methodologies to explore issues of normative ethics and the formulation of moral rules to govern individual, collective and institutional behaviour. Specifically, it considers social justice and social contract theory, consequentialist and deontological accounts of ethical evaluation. It also draws on sociological and organisational literature to discuss Dowling and Pfeffer’s (1975) and Suchman’s (1995) theories of pragmatic, cognitive and moral legitimacy as they may be applied to off-world mining regulations and the handling of space assets.

The findings of this conceptual paper indicate there is both a growing appetite for tighter resource extraction regulations to address climate change and wealth concentration globally, and an opportunity to establish and legitimise new ethical norms for commercial activity in space that can avoid some of the challenges currently facing fossil fuel divestment movements on Earth.

By adopting methodologies from theoretical bioethics, sociology and business studies, including applying a legitimacy lens to the issue of off-world mining, this paper synthesises existing knowledges from these fields and brings them to the new context of the future space resource economy.

Evidence-based practice is a professional standard for occupational therapists, but limited time, resources and knowledge challenge its implementation. This study aims to identify what free evidence summary sources (FESS) can be found through a simple online search, related to child/youth interventions surrounding cerebral palsy (CP), autism spectrum disorder (ASD), developmental coordination disorder (DCD), mental health or attention-deficit/hyperactivity disorder (MH/ADHD). Evidence summaries share research in concise, time-efficient manners.

An internet-based scoping review was conducted between February 2022 and July 2022, using Google, and known evidence summary producers. Evidence summaries meeting the inclusion criteria were located and catalogued. Type of agency, target audiences, purpose and distribution of evidence summaries related to diagnosis were identified for each FESS.

Ten FESS were found, which produced 113 intervention-focused evidence summaries within the past 10 years. These FESS were aimed at a variety of target audiences: service providers, service users, parents/families, researchers and commissioners, and were produced primarily by non-profit/charity organisations (6 of 10) who were trying to fill a gap in evidence. Forty-eight evidence summaries were related to ASD, 34 to CP, 29 to MH/ADHD and two to DCD.

A catalogue of FESS that exist online was produced, to support evidence-based practice for paediatric occupational therapists with limited resources, and may support improved health promotion and informed decision-making for service users. No consistent framework for FESS evidence summaries exists at this time.

With a historic lack of attention to synthesis methods such as systematic review and meta-analysis and a lack of randomised controlled trials, the evidence base for behavioural interventions for children and adults who are autistic or are diagnosed with developmental disabilities is patchy. The Sharland Foundation Developmental Disabilities Applied Behavioural Research and Impact Network (SF-DDARIN), a network of like-minded researchers and practitioners across the UK, aims to address this. The purpose of this paper is to describe the network’s work and provide the context for the remaining articles in the special issue that exemplify network projects.

In this case study paper, the authors describe how the SF-DDARIN works and is resourced, detailing the process used to maximise research opportunities by facilitating network members working together. The authors outline the progressive research steps that the SF-DDARIN has identified are needed to develop and improve the evidence base for behavioural interventions systematically and, with examples, describe how the network delivers these steps.

Since its establishment in 2016, the SF-DDARIN network members have collectively worked on more than 53 projects involving over 50 researchers, had over 120 special schools contribute to projects and have recruited over 500 participants. This has been achieved through funding from the Sharland Foundation, primarily to cover the staffing costs of a small support team and internship partnerships with external organisations. Some projects have attracted external funding.

SF-DDARIN may provide an innovative, effective and resource-efficient model for other groups seeking to develop and extend their evidence base in developmental disability research.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

PhD graduates are entering an increasing range of careers, but past research has highlighted a lack of preparation for these careers. This study aims to explore the reflections of PhD graduates from science and humanities and social science disciplines regarding support for career development (CD) during their study.

The authors used an explanatory sequential mixed-methods design and collected 136 survey responses and interviewed 21 PhD graduates from two US and one New Zealand universities to investigate their career readiness. Using the lens of Cognitive Information Processing theory, the authors explored the development of self-knowledge and career options-knowledge, and how support at the macro (institutional), meso (departmental) and micro (supervisors) levels influenced CD.

During doctoral study, there was very poor engagement with CD activities. Graduates displayed limited self-knowledge and poor knowledge about career options. Graduates reported drawing mainly on their departments and supervisors for career guidance. Although there were pockets of good practice, some departments were perceived as promoting academia as the only successful outcome, neglecting to support other possible pathways. Some graduates reported excellent supervisor support for CD, but others described disinterest or a damaging response if students said they were not wanting to pursue academia.

The enabling aspects for developing self- and options-knowledge are collated into a conceptual model, which identifies key factors at institutional, departmental and supervisor levels, as well as for PhD students themselves.

While extant research does mention performance management systems as antecedent to a management accountant’s role, and that there is tension between both, there is little detailed research. Thus, this paper aims to investigate the extent to which a performance management system interacts with the role of a management accountant.

The study is a cross-sectional field study, using interviews with paired management accountants and operative managers in 16 multinational organisations in Germany. The perspectives of both management accountants and operative managers are analysed separately. The role episode model theoretically informs the study.

The findings reveal management accountants distinguish between three roles of scorekeeping, controlling and business support, similar to prior literature. By contrast, operating managers are concerned with the value-adding and non-value-adding character of activities and thus support a dichotomy of management accountants’ roles. Drawing upon the role episode model, this study elucidates the interplay between performance management systems and the roles of management accountants, which encompass both role-taking and role-making dynamics. Additionally, this study contributes to management control literature by operationalising the components of a performance management system framework and linking them to the role of management accountants, as proposed by role antecedents in previous literature. The study also uncovers factors influencing role-taking and role-making, alongside examining the repercussions of role consensus or conflict based on the interaction with the operating manager.

This paper is subject to the normal limitations of case study research and generalisation. The findings may also be influenced by the cultural context of the study.

An updated role episode model is presented, highlighting further performance management systems’ components. The study also reveals factors enabling and/or inhibiting the management accountants’ business support role and the impact of role consensus/conflict.