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The purpose of the study is to examine the psychological impact of COVID-19 on health workers' career satisfaction and intention to leave the health profession, with neurotic personality type as a moderator.

A total of 277 health workers in two public hospitals in Ghana were included in this study. Purposive and convenience sampling techniques were adopted for the study, focusing on eight departments that were involved in the management of COVID-19 cases. Validated instruments were used to measure burnout, intention to leave, neurotic personality and career satisfaction. Using AMOS and partial least squares structural equation modeling (PLS-SEM), various techniques were employed to analyze mediating and moderating mechanisms.

The departments had staff sizes ranging from 19 to 40, with 67% female and 33% male, with an average age of 31. Nurses accounted for the majority of responses (67.8%), followed by physicians (13.9%), sonographers (0.9%), lab technicians (0.9%) and other respondents (16.5%). The study found that health workers’ level of burnout during COVID-19 had a positive effect on their intention to leave the health profession. Career satisfaction does not mediate this relationship; however, career satisfaction negatively influences the intention to leave the health profession. A neurotic personality does not moderate this relationship.

This study provides validation of burnout and intention to leave among health workers in Ghana during COVID-19 and supports the proposition that threats to resources (burnout) and having a resource (career satisfaction) have effects on the intention to leave one’s profession.

The purpose of the study is to perform a scientific mapping and detect the evolution pattern of two emerging fields, organizational capabilities and sustainable supply chain management (SSCM), to detect and visualize the existing conceptual domains and identify less-explored areas.

This study uses a methodological combination involving systematic literature review and bibliometric analysis. The methodology was implemented in the following order: definition and selection of the material using an electronic database, descriptive analysis of the material, category selection using bibliographic coupling analysis by VOSviewer (clusterization), material evaluation and content analysis.

The research results clarify the intellectual structure within the academic field. The authors’ identified three main clusters: (1) sustainable capabilities and practices in supply chain management (SCM), (2) green SCM and performance and (3) information technology and innovation. The findings reveal that there is a rich field to be explored, especially regarding issues involving sustainable technological capabilities, sustainable initiatives and key resource development.

This study facilitates researchers’ and practitioners’ understanding and their ability to map the different paths and evolution of SSCM and organizational capabilities. It can encourage managers and policymakers alike to conceive new approaches to engage in the adoption of SSCM.

This work employs a singular approach to identify the intellectual knowledge and topics related to the implementation of SSCM by adopting the theoretical approach of sustainable organizational capacity. It contributes to the debate on distinguishing specific sustainable organizational capabilities from traditional capabilities.

This study aims to focus on assessing the psychometric properties necessary to validate the internal structure of the TOURQUAL scale.

A quantitative research study was conducted in collaboration with the Brazilian Network of Tourism Observatories, comprising 927 respondents surveyed between October 2021 and May 2022. The data analysis involved the application of descriptive statistics and exploratory factor analysis, in alignment with the principles outlined in the Standards for Educational and Psychological Testing 2014 to validate the scale.

The findings of this study validate the TOURQUAL scale as a robust tool for assessing the perceived quality of tourist services, with results demonstrating one-dimensionality and replicability.

To the best of the authors’ knowledge, this study is the first to assess the psychometric properties for validating the internal structure of the TOURQUAL scale.

Digital technologies over time are becoming increasingly pervasive and relatively affordable, finding a large diffusion in Small and Medium Enterprises (SMEs) also for internationalization purposes. However, less is known about the specific mechanisms by which this can be achieved. Specifically, we focus on how SMEs can face the international environment, leveraging digital technologies and thanks to their intellectual capital (IC).

We analyze the relationship between digital technologies and the internationalization of SMEs, exploring the mediating role of IC in its three dimensions: human, relational and innovation capital, and assessing the possible moderating effects posed by international institutional conditions, specifically the Sino-US trade frictions. The relationships are tested using a sample of companies listed on China’s A-share Growth Enterprise Market (GEM) from 2010 to 2021.

Digital technologies help to internationalize SMEs. However, this positive relationship is affected (mediated) by the presence of an already consolidated IC. In addition, the institutional conditions of the international market, such as the Sino-US trade friction, moderate the components of IC differently. Specifically, the overall mediating effect of human and relational capital is boosted, while this does not happen for innovation capital.

First, this study contributes to the literature on organizational resilience, especially digital resilience, confirming its validity in the context of internationalization and, in particular, those processes adopted by SMEs. Second, we clarify the mechanisms through which digital technologies exert their impact on the process of internationalization and in particular the prominent necessity of having IC. Third, our conclusions enrich the understanding of how IC components react to turbulence in international markets.

Universities strategically organize themselves around sustainability, including transformative goals in teaching and learning. Simultaneously, the role of online education has become more prominent. This study aims to better understand the purpose and process of creating online sustainability education (OSE) and to identify challenges and opportunities for implementing these courses and programs to achieve universities’ broader transformative sustainability goals.

This study uses a multiple case study design to research three universities in diverse geographical and institutional contexts (Finland, the USA and Colombia). Qualitative data was collected by interviewing program-related experts (n = 31) and reviews of universities’ strategic documents.

The findings suggest that despite important advances, further attention is merited regarding aligning the purpose of OSE with student learning outcomes, clarifying the values underlying the process of online program/course creation and developing transformative and process-oriented approaches and pedagogies to implement OSE. The authors also highlight emerging challenges and opportunities in online environments for sustainability education in different institutional contexts, including reaping the benefits of multilocality and diverse student experiences regarding sustainability issues.

There is a paucity of studies on university-level sustainability education in relation to online environments. This research expands on the existing literature by exploring three different geographical and institutional contexts and shedding light on the relationship between the practical implementation of OSE and universities’ broader sustainability goals.

The purpose of this study is to investigate the extent to which TQM has influenced the legal system in Australia, an area seldom investigated in the quality or legal literature.

Documentary and policy analysis of legislation, rules and rulemaking documentation based on a partial application of historical-policy analysis (HPA). Textual analysis was based on Dean and Bowen's (1994) definition of TQM and Vinni's (2007) review of new public management and Swiss (1992) “reformed TQM” concepts.

Australia's Tertiary Education Quality and Standards Agency Act of 2011 and supporting legal documents such as Guidance Notes include language reflective of TQM principles, providing evidence that present-day administrative law schemes include TQM practices and tools to undergird procedures of regulatory expectations (sometimes in the form of standards), monitoring and general operations. Oftentimes, it is the supporting legal documentation where TQM practices are found and operationalized.

This is a proof-of-concept research study to determine the feasibility to identify TQM concepts within the existing language of legal statutes and supporting regulatory documentation. As such this study worked out the preliminary research challenges in performing this type of analysis.

Understanding TQM's impact on legal systems expands the system's perspective of organizations that do not always factor in the influence government policy has on organizational behaviours and outlooks. More specifically, understanding TQM's influence sheds insight on regulatory requirements imposed on a sector and the normative aspects of regulatory compliance that impact the operations and strategic planning of organizations.

The article provides an example of how legal administrative rulemaking influences organizational operational and strategic activities to remain viable in the organization's business or industrial sector.

There are few research papers or literature reviews pertaining to the subject of TQM concepts embedded in laws and regulations, most of which date from the 1980s through early 2000s.

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.

Most UK ambulance services undertake remote assessments of 999 calls with nurses and paramedics to manage demand and reduce inappropriate hospital admissions. However, little is known about the differences in the types of cases managed by the two professions comparatively, their clinical outcomes, and the quality and safety they offer.

The retrospective descriptive study analysed data collected at Welsh Ambulance Services University NHS Trust (WAST) from prioritisation, triage, and audit tools between the 17th May 2022 to 8th November 2022. A total of 21,076 cases and 728 audits were included for review.

There was little difference in the type and frequency of the presenting complaints assessed, and clinical outcomes reached in percentage terms. Whilst paramedics had more highly compliant call audits and fewer non-compliant call audits, there was, again, little difference in percentage terms between the two, indicating positive levels of safety across the two professional groups.

There continues to be a substantial difference between UK paramedics to those in the Middle East, the United States, and some African nations, which may limit the applicability of findings. This study also looked at a six-month window from only one UK service using one type of prioritisation and triage tool. Future research could explore longer periods from multiple services using various tools. It is important to note that this study did not link outcome data with primary, secondary or tertiary care settings. Thus, it is impossible to determine if the level of care aligned closely with the final diagnosis.

The practical implications of this work include better workforce planning for agencies who have perhaps only employed one type of clinician or a reaffirmation to those who have employed both. The authors suggest that the training and education of both sets of clinicians could remain general in nature, with no overt requirement for specificity based on professional registration alone. Commissioners and stakeholders in the wider health economy should consider ensuring equitable access to alternative pathways for patients assessed by both nurses and paramedics.

It has been posited that UK nurses and paramedics are, by virtue of their consistency in education, skill set, licensure, and general experience, both able to achieve safe and effective remote outcomes in 999 settings. This study provides evidence to support that hypothesis. These results say more about the two professions' ability to work together rather than just the professions themselves. The multidisciplinary team approach is well-established in acute care settings, and is broadly considered to improve communication, coordination decision making, adherence to up-to-date treatment recommendations, and be positive for shared learning and development for younger colleagues.

Most UK services use a mix of nurses and paramedics; however, little is known about the differences in the types of cases managed by the two professions comparatively, their clinical outcomes, and the quality and safety they each offer. The most recent studies of this nature were published in 2003 and 2004 and looked only at low-acuity 999 calls when remote assessment was not even an established role for UK paramedics. This study updates the literature, identifies areas for future research, and applies to the international setting for the most part.

When a crisis occurs, do corporate social responsibility (CSR) activities protect organizational reputation by buffering negative effects or do CSR activities intensify negative effects, potentially leading to a worse reputation compared to if the organization had no prior CSR engagement? The authors hypothesize that if a crisis emerges in a domain aligned with an organization’s CSR initiatives (crisis-congruent CSR) backfire effects would arise, adversely affecting the organization’s reputation. Conversely, in cases of incongruence, where the crisis emerges in a domain not aligned with an organization’s previous CSR involvement, a buffering effect would manifest, protecting the organization’s reputation.

The authors conducted an experiment with a 3 (crisis-congruent, crisis-incongruent, and no CSR activities) × 2 (repeated measures) mixed factorial design. In the first scenario, no information was provided concerning a company’s social commitment. Alternatively, participants were exposed to an article illustrating the company’s dedication either to healthcare (crisis-incongruent commitment) or to combating sexism (crisis-congruent commitment). Afterward, participants were presented with a newspaper article addressing allegations of sexism against the company’s CEO.

The findings demonstrate that prior CSR activities have the potential both to serve as a buffer and to cause backfire effects in times of crisis. Domain congruence is the decisive moderator of these effects: Crisis-incongruent CSR activities acted as a buffer, crisis-congruent CSR activities “backfired” and led to more negative perceptions of the company’s reputation.

The study directly contributes to the understanding of CSR effects in crisis communication, while also addressing the often paradoxical and contradictory findings of prior studies.

This article introduces the Responsible AI for Service Excellence (RAISE) framework. RAISE is a strategic framework for responsibly integrating AI into service industries. It emphasizes collaborative AI design and deployment that aligns with the evolving global standards and societal well-being while promoting business success and sustainable development.

This multidisciplinary conceptual article draws upon the United Nations' Sustainable Development Goals (SDGs) and AI ethics guidelines to lay out three principles for practicing RAISE: (1) Embrace AI to serve the greater good, (2) Design and deploy responsible AI and (3) Practice transformative collaboration with different service organizations to implement responsible AI.

By acknowledging the potential risks and challenges associated with AI usage, this article provides practical recommendations for service entities (i.e. service organizations, policymakers, AI developers, customers and researchers) to strengthen their commitment to responsible and sustainable service practices.

This is the first service research article to discuss and provide specific practices for leveraging responsible AI for service excellence.