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Because family businesses are highly complex enterprises, researchers need appropriate theoretical and methodological tools to study them. The neoconfigurational perspective and its accompanying method, qualitative comparative analysis, are particularly well suited to phenomena characterized by complex causality, but their uptake in family business research has been slow and fragmented. To remedy this, the authors highlight their unique ability to address research questions for which other approaches are not well suited and discuss how they might be applied to family business phenomena.

The authors introduce the core tenets of the neoconfigurational perspective and how its set-theoretic epistemology differs from traditional approaches to theorizing and analysis. The authors then use a dataset of family firms to present a primer on conducting qualitative comparative analysis and interpreting the results.

The authors find that family firm resources can be combined in multiple ways to affect business survival, suggesting that resources are substitutable and complementary. The authors discuss how the unique features of the neoconfigurational approach, namely equifinality, conjunctural causation and causal asymmetry, can be fruitfully applied to break new ground in scholarly understanding of family businesses.

This article allows family business researchers to apply the neoconfigurational approach without first having to consult multiple and disparate sources often written for other disciplines. This article explicates how to leverage the theoretical and empirical advantages of the neoconfigurational approach in the context of family businesses, supporting a more widespread adoption of the neoconfigurational perspective in family business research.

The authors seek an answer to the research question: how do the disclosure of the intended use of initial public offering (IPO) proceeds and firm characteristics jointly influence IPO performance?

Data on the use of proceeds, firm age, size, high- or low-tech industry, and the length of the use of proceeds section were collected from 341 IPOs in the USA, UK, and Hong Kong. Fuzzy-set Qualitative Comparative Analysis was used to predict which configurations of IPO use of proceeds and firm characteristics consistently led to above-average IPO performance.

Ten configurations of causal factors were found to lead to above-average IPO performance. Disclosure of IPO proceeds use matters for IPO performance but is contingent on firm characteristics. Whether a firm is in a high- or low-technology industry along with its size and age have distinct effects on which intended uses of proceeds are beneficial and how long their intended proceeds section must be to lead to above-average IPO performance.

These findings contribute to a multidimensional view of IPO performance. The authors use information processing and a management perspective to see how the use of proceeds sections help frame an IPO’s equity story. The use of a configurational methodology and a management perspective shows how IPOs can be viewed as a bundle of attributes.

Sustainable Family Business Theory states that human, social, and financial capital are important for new family venture growth, yet there may be multiple combinations that could be beneficial. The purpose of this paper is to examine whether all three types of resources are always needed for growth.

Fuzzy-set Qualitative Comparative Analysis, a configurational method, is used to investigate which combinations of human, social, and financial capital consistently lead to new family venture growth.

Multiple distinct combinations of resources – usually containing some form of human capital along with either social or financial capital – were sufficient for new family ventures to grow.

The findings contribute to a more accurate Sustainable Family Business Theory in terms of the resource bundles needed to achieve growth. Not all three primary resources are needed at founding for the venture to grow. Results suggest a need for renewed focus on human capital in family venture research, as well as further investigations of the resource configurations uncovered here and their effects on family firm outcomes.

Given the costs associated with acquiring resources, the findings can inform family entrepreneurs and other stakeholders purposed with assisting new family ventures regarding optimal avenues of achieving growth.

This study advances theory by demonstrating which combinations of primary resources lead to new family venture growth. The findings shed light on how human, social, and financial capital may substitute for each other, as well as how the value of each depends on the presence or absence of the others.

Strategic transformations are likely necessary for all organizations at some point in their existence, but the role of external stakeholders in committing resources to support transformations has been largely overlooked. This paper aims to begin to fill this gap by developing a theoretical model detailing which factors increase the likelihood that financial stakeholders will commit resources to strategic transformation.

Neo-institutional and stakeholder theories are applied to the strategic transformation phenomenon to develop six propositions regarding financial stakeholders’ resource commitment to strategic transformation.

Moral legitimacy, pragmatic legitimacy and unfamiliarity with the firm directly affect the likelihood that financial stakeholders will commit resources to strategic transformation. Cognitive legitimacy or familiarity amplifies the positive effect of pragmatic legitimacy on resource commitment, and pragmatic legitimacy lessens the negative effect of unfamiliarity with the firm on resource commitment.

This paper lays out a clear conceptual model of the antecedents of financial stakeholders’ resource commitment to strategic transformation, aiding practitioners in securing critical stakeholder support and filling an important gap in strategic transformation/stakeholder literature.

The purpose of this paper is to analyse how the transition into entrepreneurship is constructed in the context of the boundaryless career. The paper focuses on a particular type of career transition driven by dismissal or unemployment.

The research material is collected in life‐story interviews with three Finnish female owner‐managers. In the study, a narrative analysis of the career move into entrepreneurship is conducted.

The results demonstrate how the concept of the boundaryless career and career discourse as such, and the personal career history and the larger employment setting in particular, are applied in making sense of the transition into entrepreneurship. The significance of dismissal or unemployment is not uniform, but is dependent on the participant's interpretation of the boundaryless career and work‐based security. Entrepreneurship is constructed both as a gender‐neutral and gendered process.

The career perspective could be more widely applicable to research into entrepreneurship, and perceiving entrepreneurship as work could offer an interesting avenue for future interdisciplinary study within career research.

By portraying the transition from unemployment/redundancy into entrepreneurship as a normal career shift, entrepreneurship is potentially made accessible to a broader group of people including unemployed women. Narratives have potential to be applied as career management tools.

The paper provides a contextualised view of the transition into entrepreneurship after unemployment and demonstrates how the entrepreneurship process is connected to the individual work history, employment setting, and gender.

The literature on precarious and insecure work rarely examines how workers with jobs in large bureaucratic firms experience insecurity. Current theories suggest two approaches. First, workers might focus on their individual occupation and detach their commitment from firms that no longer reciprocate long-term commitments. Second, employees might respond with increased organizational commitment because leaving an employer creates risks of uncertainty. Based on in-depth interviews with 22 financial services professionals, this paper refines our understanding of when workers focus on intra-organizational career development. This happens when large firms offer opportunities for advancement and foster loyalty. I develop the terms spiral staircase and serial monogamy career. A spiral staircase career results when workers take entrepreneurial approaches to advancement that include lateral job changes and vertical promotions within a firm. When the local labor market has multiple firms in their sector, career advancement may take an intermediate form, in which workers spend medium-to-long-term stints with multiple organizations. I call this the serial monogamy career. My research shows how sector characteristics and geography can impact worker commitment and mobility in insecure environments.

Begins by reviewing relevant literature to identify some of the features that are said to appear exclusively in e‐mails. Also highlights the main issues in the debate between those who embrace the new style of writing generated by e‐mail, and those who feel it is detrimental to communication. A total of 300 examples of e‐mails were obtained from a wide range of donors. Features counted included emoticons, acronyms and creative spelling. The lengths of texts and of sentences within them were amongst other calculations made. The data was grouped according to purpose: social, business personal and business impersonal. Users of e‐mail appear to be generally unconcerned with formalities. Although the sample of e‐mail texts was lucid, writers often dispense with traditions when opening their e‐mail, and their closings are informal. Social e‐mails involved the most creative sort of communication. Argues that the increased informality and lack of consistency of e‐mail texts is because e‐mail is a new medium where no clear guidelines exist. A standard for e‐mail communication might usefully be established, but such a standard should remain flexible.

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.

Changing career patterns and the erosion of job security have led to a growing emphasis on employability as a basis for career and employment success. The written and psychological contracts between employer and employer have become more transactional and less relational, and loyalty is no longer a guarantee of ongoing employment. Individuals are thus expected to take primary responsibility for their own employability rather than relying on the organisation to direct and maintain their careers. The purpose of this paper is to identify and examine the assumptions underpinning the concept of employability and evaluate the extent to which employability has been adopted as a new covenant in the employment relationship.

Through a review of relevant literature the paper discusses current research on careers and employability and examines the available evidence regarding its adoption as a basis for contemporary employment relationships.

The paper finds that the transfer of responsibility for employability from organisation to individual has not been widespread. There is still an expectation that organisations will manage careers through job‐specific training and development. Employability has primarily benefited employees with highly developed or high‐demand skills. Employability is not a guarantee of finding suitable employment.

Employers can assist their employees by clarifying changes to the psychological contract, highlighting the benefits of career self‐management, and providing training and development in generic employability skills.

The paper questions underlying assumptions about employability and explores issues of relevance to human resource managers, policy‐makers, employers and employees.

The purpose of this paper is to provide a review of the literature with respect to traumatic stress in a firefighting context. The goal was to provide a clear and concise review intended for use by both researchers and practitioners. Firefighters are an under-researched group in the academic literature and updated review articles are necessary to advance this body of work.

Searches of the English language literature on firefighters and traumatic stress were completed and supplemented with a review of clinical information related to post-traumatic stress disorder (PTSD). The search was comprehensive but was not guided by systematic review guidelines.

Research regarding firefighters and traumatic stress is limited and inconsistent in outcome. Much of the available literature supports a link between fire service work and increased post-traumatic symptomatology; however, some research has neglected to demonstrate a relationship for these factors. Some efforts such as the training of coping skills and team building may improve firefighters’ resilience to PTSD, while therapeutic and psychopharmacological treatments may be effective in reducing PTSD progression.

To the authors’ knowledge, no previous paper is available that has specifically intended to address firefighters and traumatic stress in a review format for researchers and practitioners. In the authors’ experience, fire service members are eager for literature addressing their profession and presented in a manner accessible for both non-academic and academic audiences.