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In this paper, the authors will examine Welch's legacy and aftermaths, both for GE and more broadly within management practice and academic thought. As a complex character, indeed a person of many contradictions, the authors try to avoid polemics in this, instead focusing on his accomplishments and the unanswered questions about his impact.

This paper is a historical case using secondary and published materials to assess the case of Jack Welch's leadership of General Electric over the period 1981–2001.

Welch's proponents suggest he emphasized controlling corporate destiny, being open to new ideas, pursuing quality and low cost, having confidence, a vision founded on reality, a global focus and possessing energy and enthusiasm. However, his short-termist perspective undermined the long-term success of the company and his “win at any cost” mantra predisposed some employees to cutting ethical or environmental corners. As the market capitalization gains evaporated that had been used to justify the “end justifies the means” rationale, little is left of his legacy.

The paper discusses the implications of the GE case for issues associated with corporate governance, financialization and human resource management.

This is a timely reconsideration of the Jack Welch legacy two years after his death. In avoiding polemics and seeking a considered assessment of his positive and negative outcomes, the paper is an important addition to the research on Welch and American management thought.

This study aims to identify the political alignment and political activity of the 11 Presidents of Britain’s most important scientific organisation, the Royal Society of London, in its early years 1662–1703, to determine whether or not the institution was politically aligned.

There is almost no information addressing the political alignment of the Royal Society or its Presidents available in the institution’s archives, or in the writings of historians specialising in its administration. Even reliable biographical sources, such as the Oxford Dictionary of National Biography provide very limited information. However, as 10 Presidents were elected Member of Parliament (MP), The History of Parliament: British Political, Social and Local History provides a wealth of accurate, in-depth data, revealing the alignment of both.

All Presidents held senior government offices, the first was a Royalist aristocrat; of the remaining 10, 8 were Royalist or Tory MPs, 2 of whom were falsely imprisoned by the House of Commons, 2 were Whig MPs, while 4 were elevated to the Lords. The institution was Royalist aligned 1662–1680, Tory aligned 1680–1695 and Whig aligned 1695–1703, which reflects changes in Parliament and State.

This study establishes that the early Royal Society was not an apolitical institution and that the political alignment of Presidents and institution continued in later eras. Furthermore, it demonstrates how the election or appointment of an organisation’s most senior officer can be used to signal its political alignment with government and other organisations to serve various ends.

The global financial crisis (GFC) has undermined the legitimacy of orthodox neo-classical economic assumptions, which nevertheless continue to frame the philosophical assumptions of teaching in business schools. The purpose of this paper is to make a case in favour of an expansion of the business school curriculum to incorporate behavioural economics. The paper will also contend that behavioural economics can be connected to social economics, as they are both heterodox in this study and analyse economic phenomenon outside of a neo-classical framework. The aim is to contribute to arguments for an expanded curriculum, beyond the framing assumptions of neo-classical rationalism. This paper will also support its case by reviewing behavioural economics to make the case that this literature can be connected to social economics. This assertion is based on shared connections, including the importance of Kantianism in behavioural economics and in social economics. These connections will be discussed as a common point of reference points, or ties that can serve to broker links between these two economic paradigms. Practical implications (if applicable) the GFC presents an opportunity to re-shape the business school curriculum to acknowledge the centrality of socio-economics and behavioural economics, and consequently to offer an alternative to the dominant ontological assumptions – taken from the economic understanding of rationality – that have previously underpinned business school pedagogy.

The paper presents an inter-disciplinary teaching case, which incorporates socio-economic and behavioural economics perspectives. The teaching case concerned a socio-economic understanding of corruption and white-collar crime. It was also inter-disciplinary to include inputs from business history and criminology. The teaching case developed an appreciation among students that corruption, white-collar crime and entrepreneurship can be analysed within a social economics and behavioural economics lens.

The teaching case example discussed an alternative socio-economic and behavioural economics understanding to core areas of the MBA curriculum with the potential to be included in other academic disciplines. This enabled students to apply a behavioural economic approach to white-collar crime. The findings derived from this case study are that behavioural economics has the potential to enhance the teaching of socio-economics.

The originality of this paper is to apply behavioural economics to a socio-economic teaching case, in core subject areas of the MBA curriculum.

The paper aims to review prior literature on the impact of audit quality and climate change reporting on corporate performance. It also aims to offer avenues for future research.

Based on the systematic literature review, bibliometric investigation and forest plot, the authors systematized the scientific knowledge from 183 papers.

Earlier studies either focused on audit quality and corporate performance or discussed the link between climate change and corporate performance. However, the way that audit quality and climate change can together influence corporate performance is yet to be examined. The authors fill the gap by examining the possible link between audit quality and climate change and establishing the influence of it on corporate performance from the existing literature.

Because of the immense importance of the company's contribution to climate change, the research findings will open up avenues for future research. In addition, findings will be useful for world policymakers in strengthening or modifying existing corporate responsibility policies.

This paper investigated whether a bank’s popularity and depositors' fear of Google search volume could affect bank deposits and credit.

The authors used two different quarterly data from Google Trends and banking data from 2012 Q1 to 2020 Q1. Based on available data, Google Trends data start from 2012. The authors exclude data after 2020 Q1 because the Covid-19 pandemic arguably increased the volume of Internet users due to shifting behavior to online activities. They merged and cleaned the data by winsorizing at 5 and 95 percentiles to avoid any outlier problems, reaching 74 banks in the sample. They used panel data estimation of quarterly data following Levy-Yeyati et al. (2010) and Trinugroho et al. (2020).

The results show that a higher search volume of a bank’s name leads to higher deposits. A higher search volume of depositor fear reduces deposits and credit. The authors also found that banks with high risk and a high search volume of their name have a significantly lower volume of deposits.

To the best of the authors’ knowledge, not many papers in banking and finance have used Google Trends data to gauge related issues regarding depositors' behavior. The authors have filled a gap in the literature by investigating whether the popularity of Google search and depositors' fear could impact deposits and credit. This study also attempted to establish whether Google Trends data could be a reliable source of information to predict depositors' behavior by using a Zscore to measure bank risk.

Given that existing retail brand research tends to treat each level of a retail brand as a separate concept, this paper aims to unveil the holistic nature of a multi-level retail brand, considering the distinctiveness of each level and the interrelationships between the images of different levels.

This study uses a scoping review approach that includes 478 retail brand articles. Subsequently, a thematic analysis method is applied.

The brand attributes that shape the distinct image of each retail brand level encompass diverse intrinsic and extrinsic attributes. Moreover, the holistic nature of a multi-level retail brand is formed by the interrelationships between the images of different levels, which are reflected in the presence of common extrinsic attributes and their interplay at attribute, benefit and attitude levels.

Theoretically, this review provides conceptual clarity by unveiling the multi-level yet holistic nature of a retail brand, helping researchers refine and extend existing theories in retail branding, while also providing new research opportunities in this field. Practically, the findings could guide retailers in implementing differentiated branding strategies at each level while achieving synergy across all levels.

This study aims to define, classify and interconnect the wide range of known entrepreneurial abilities with terms such as skills, capabilities and competencies, which have been used inconsistently within the entrepreneurial field.

This investigation is based on a systematic literature review and strengthened by a meta-analysis equipped with a bibliometric study to assist the generation of outcomes with a quantitative investigation.

This study proposes an evolving entrepreneurial ability model which interconnects genetic and acquired skill types, capabilities and competencies and is equipped with an Entrepreneurial Skills Map essential to operate in the 21st century.

The proposed model is specific to the entrepreneurial field.

This study supports universities and government agencies for the development of educational programs to prepare current and future entrepreneurs to match the changes in the new environment that has emerged with the COVID-19 pandemic.

This research contributes to the entrepreneurship research domain by shedding light on the inconsistent use of non-standardised terminologies and providing an entrepreneurial model and updated skills map to guide scholars to frame research in the post-COVID era with more clarity.

The field of broad-based employee ownership within corporations is a specific application of the foundational topic of property ownership. It is situated at the intersection of a broad range of scholarly disciplines including economics, law, finance and management. Each discipline contributes vocabulary and distinctions describing this field. That broad spectrum of disciplinary inquiry is a strength but it also lends a “ships passing in the night” quality to discussions of employee ownership. This paper attempts to unravel the narrative diversity surrounding this topic. Four meanings of ownership are introduced. Those meanings are in turn embedded within two abstract models of the corporation; the corporation as property and the corporation as social institution.

There is no experimental design The paper presents a conceptual overview and introduces a taxonomy of four meanings and two models of ownership.

Four meanings of ownership are introduced. The meanings are ownership as compensation, investment, retirement and membership. Those meanings are in turn embedded within two abstract models of the corporation; the corporation as property and the corporation as social institution.

No hypotheses are advanced. This is not a research paper. A conceptual overview that makes use of taxonomy of meanings and models is introduced to help clarify confusions abundant in the field of employee ownership. Readers may differ with the categories of meanings and models introduced in this conceptual overview.

The ambition of the paper is to describe the various meanings and models of employee ownership presently in use in both academic and applied settings. It is not necessary or desirable to assert the primacy of a single meaning or model in order to achieve progress. The analysis provided here surfaces a range of assumptions about ownership that have heretofore been implicit in both scholarship and in practice. Making those assumptions explicit should prove useful to both scholars and practitioners of employee ownership.

The concept of employee ownership enjoys a relatively broad appeal with the public. Among the academic disciplines that have trained their lights upon it, a more mixed reception prevails. Much of the academic and policy controversy derives from confusion about the nature and structure of employee ownership. This paper attempts to address that confusion by presenting a taxonomy of meanings and models that may prove useful for future research.

This study is one of the first efforts to comprehinsively map the various meanings and models of broad-based employee ownership.

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.

This study evaluated the impact of a faculty training program on student assessment using the Kirkpatrick model.

A self-reported survey assessed 111 Saudi and non-Saudi participants' satisfaction. Subjective and objective measures (self-reported measures, assessment literacy inventory and performance-based assessment tasks) gauged participants' learning level. Pre- and post-training data were collected from 2020 to 2022.

A highly significant effect on satisfaction (>80%) and learning levels was observed, as manifested by workplace practices of student assessment (>70%, the cut-off score). Pre- and post-training comparisons of participants' satisfaction and assessment literacy scores showed significant improvements following training. Multiple regression analyses showed no significant effects for gender and educational attainment but a substantial impact of academic cluster on participants' student assessment skills.

Long-term effects of training faculty on assessment practices and student achievement will be studied at the institutional level in future research.

The current study contributes to human capital investment via faculty training on student assessment, helping them comply with assessment best practices. This assures the quality, fairness and consistency of assessment processes across disciplines in higher education institutions, enhances assessment validity and trust in educational services and may support institutional accreditation.

This study provides opportunities for sharing best practices and helps establish a community of practice. It enhances learning outcomes achievement and empowers higher education graduates with attributes necessary to succeed in the labor market. The human capital investment may have a long-term impact on overall higher education quality.

This study contributes to the scarce literature investigating the impact of training faculty from different clusters on student assessment using subjective and objective measures. It provides developing and evaluating a long-term student assessment program following the Kirkpatrick model.