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While the economic literature mostly tackled discrimination looking at labour costs, this work focuses on its relation to labour productivity, arguing that discrimination may worsen the performance of female employees. In this view, it represents a source of allocative inefficiency, which contributes to reducing output.

Female discrimination is both a social and an economic problem. In social terms, consolidated gender stereotypes impose constraints on women’s behaviour, worsening their overall well-being. In economic terms, women face generally worse labour market conditions. Using long-run Italian data spanning from 1861 to 2009, the authors propose a novel measure of female discrimination based on the observed frequency of discriminating epithets. Following social capital theory, the authors distinguish between structural and voluntary discrimination, and use Data Envelopment Analysis for time series data to assess the extent of inefficiency that each component of discrimination induces in the production process.

The results draw the trajectory of female discrimination in Italy and provide evidence in favour of the idea that female discrimination reduces productive efficiency. In particular, the structural component of female discrimination, although less sizeable than the voluntary component, plays a major role, especially in recent years, where more stringent beauty standards fuel looks-based discrimination.

The contribution of this work is twofold. First, based on contributions from social sciences different from economics, it proposes a novel theoretical framework that explores the effect of discriminatory language on labour productivity. Second, it introduces a novel and direct measure of female discrimination at the country level, based on the bidirectional link between language and culture. The indicator is easily understood by policymakers and may be used to evaluate the effectiveness of anti-discrimination policies.

We conceptualized the novel phenomenon of COVID-induced virtual teams and its implications and provided researchers with the required information on how to conduct a phenomenon-based study for conceptualizing novel phenomena in relevant ways.

This article stems from phenomenon-based and, thus, theory-building and grounded qualitative research in the German industrial sector. We conducted 47 problem-centered interviews in two phases (February–July 2021 and February–July 2022) to understand how team members and team leaders experienced COVID-induced virtual teamwork and its subsequent developments.

Empirically, we found COVID-induced virtual teams to be characterized by a high relevance of shaping positive team dynamics via steering internal moderators; crisis is a novel external moderator and transformation becomes the key output factor to be leveraged. Work-from-home leads to specific configuration needs and interrelations between work-from-home and on-site introduce additional dynamics. Methodologically, the phenomenon-based approach is found to be highly suitable for studying the effects of such novel phenomena.

This article is explorative. Thus, we advocate further research on related novel phenomena, such as post-COVID-hybrid and work-from-home teams. A model of how to encourage positive dynamics in post-COVID-hybrid teams is developed and lays the groundwork for further studies on post-COVID teamwork. Concerning methodology, researchers are provided with information on how to conduct phenomenon-based research on novel phenomena, such as the COVID-induced virtual teams that we studied.

Companies receive advice on how to encourage positive dynamics in post-COVID teamwork, e.g. on identifying best practices and resilient individuals.

In a country such as Germany that faces labor shortages, our insights might facilitate better labor-market integration for those with care-work obligations and international workers.

We offer a first conceptualization of a relevant novel phenomenon, namely COVID-induced virtual teams. We exemplify the phenomenon-based approach as a suitable methodology that serves to build relevant theory using active categorization.

Small business owners require objective solutions to deal with threats of labour unionisation. This study aims to centre on a novel exploration for improving the leadership acumen of small business owners to address labour unionisation. Specifically, small business owners need a theoretical framework that uses best practices from human resource management (HRM) and industrial relations to provide solutions.

The design, methodology and approach reflect post-modernist epistemological and ontological perspectives for conducting systematic literature reviews. A systematic literature review revealed the relationship between small business owners’ leadership, positive industrial relations and HRM policies and procedures. To identify relevant studies in the review, the utilisation of several databases (EBSCO Database, including PsycINFO and Psych studies; Web of Science) and a mix of ranked journals from entrepreneurship, human resources, leadership and organisational behaviour.

The findings and results in this paper reflect the purpose, methodology and literature analysis culminating in 162,132,000 peer-reviewed studies. A total of 142 peer-reviewed studies met criterion for review. For example, the purpose of this review focused on labour unionisation mitigation for small businesses and HRM solutions. In doing so, the methodology allowed for identification of a novel research topic (i.e. how small business owners mitigate labour unionisation) worthy of further investigation. The sparse findings on labour unionisation mitigation represent small business owners' reticence in creating HRM policies and procedures.

This study contributes research implications for theory and practice by offering small business owners a theoretical framework to address labour unionisation. The framework, centring on HRM solutions, is grounded in social exchange theory to address the novel topic of labour unionisation mitigation in small businesses. This study results suggest that HRM solutions for small business owners provide competitive advantages for employee job satisfaction through a holistic industrial relations approach. In future research, examining other issues discussed in this study can influence the understanding of the relationship between small business owners and industrial relations to increase employee job satisfaction.

The introduction of robots as value-adding “workers” on the shop floor triggers complex changes to manufacturing work. Such changes involve highly entangled relationships between technology, organisation and people. Understanding such entanglements requires a holistic assessment of contemporary robotised manufacturing work, to anticipate the dynamically emerging opportunities and risks of robotised work.

A systematic literature review of 87 papers was conducted to capture relevant themes of change in robotised manufacturing work. The literature was analysed using a thematic analysis approach, with Checkland’s soft systems thinking as an analytical framework.

Based on the literature analysis, the authors present a systemic conceptualisation of robotised manufacturing work. Specifically, the conceptualisation highlights four entangled themes of change: work, organisation of labour, workers’ (experiences) and the firm’s environment. Moreover, the authors discuss the complex patterns of interactions between these objects as relationships that defy straightforward cause–effect models.

The findings draw attention to complex interactions between robotisation and manufacturing work. It can, therefore, inform strategic decisions and support projects for robotisation from a holistic perspective.

The authors present a novel approach to studying and designing robotised manufacturing work as a conceptual system. In particular, the paper shifts the focus towards crucial properties of the system, which are subject to complex changes alongside the introduction of robot technology in manufacturing. Soft systems thinking enables new research avenues to explain complex phenomena at the intersection of robotisation and manufacturing work.

The study is aimed to compare the prediction success of basic machine learning and ensemble machine learning models and accordingly create novel prediction models by combining machine learning models to increase the prediction success in construction labor productivity prediction models.

Categorical and numerical data used in prediction models in many studies in the literature for the prediction of construction labor productivity were made ready for analysis by preprocessing. The Python programming language was used to develop machine learning models. As a result of many variation trials, the models were combined and the proposed novel voting and stacking meta-ensemble machine learning models were constituted. Finally, the models were compared to Target and Taylor diagram.

Meta-ensemble models have been developed for labor productivity prediction by combining machine learning models. Voting ensemble by combining et, gbm, xgboost, lightgbm, catboost and mlp models and stacking ensemble by combining et, gbm, xgboost, catboost and mlp models were created and finally the Et model as meta-learner was selected. Considering the prediction success, it has been determined that the voting and stacking meta-ensemble algorithms have higher prediction success than other machine learning algorithms. Model evaluation metrics, namely MAE, MSE, RMSE and R², were selected to measure the prediction success. For the voting meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R² are 0.0499, 0.0045, 0.0671 and 0.7886, respectively. For the stacking meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R² are 0.0469, 0.0043, 0.0658 and 0.7967, respectively.

The study shows the comparison between machine learning algorithms and created novel meta-ensemble machine learning algorithms to predict the labor productivity of construction formwork activity. The practitioners and project planners can use this model as reliable and accurate tool for predicting the labor productivity of construction formwork activity prior to construction planning.

The study provides insight into the application of ensemble machine learning algorithms in predicting construction labor productivity. Additionally, novel meta-ensemble algorithms have been used and proposed. Therefore, it is hoped that predicting the labor productivity of construction formwork activity with high accuracy will make a great contribution to construction project management.

The authors provide a novel interpretation of the relationship between skill demand and labour market concentration based on the training rationale.

The authors use a novel data set on Italian online job vacancies during 2013–2018 to analyse the relationship between labour market concentration and employers' skill demand. The authors construct measures of market concentration and skill intensity in the local labour market. The authors regress the measures of skill demand on market concentration, controlling for sector, occupations and other features of the labour market. The authors also use the Hausman–Nevo instrument for market concentration.

The authors show that employers in a highly concentrated labour market demand competencies associated with the ability of workers to learn faster (e.g. social skills) rather than actual knowledge. They also require less experience but higher education. These results are consistent with the hypothesis that employers in more concentrated labour markets are more prone to train their employees. Instead of looking for workers who already have job-specific skills, they look for workers who can acquire them faster and efficiently. The authors provide a theoretical framework within which to analyse these aspects as well as providing a test for the relevant hypotheses.

In addition to cross-countries differences in labour market regulations, the authors' findings suggest that policy authorities should consider the local labour market structure when studying workforce development programmes aimed at bridging the skill gap of displaced workers. Moreover, the authors show that market concentration can have relevant implications for human resource (HR) managers by affecting their recruitment behaviour through the demand for skills. In fact, concentrated markets tend to favour firms' collusion and anti-competitive behaviour that could strongly affect HR management practices.

The authors' paper innovates on the literature in a number of ways. First, the authors provide evidence of local labour market concentration in Italy. Second, the authors provide evidence of skill demand at the local level using a detailed skill taxonomy that goes beyond the classical distinction between high and low skills. Third, and most importantly, the authors provide evidence of the relationship between skill demand and labour market concentration. By analysing detailed skills and competencies, the authors take one step beyond understanding the features of labour demand in monopsonistic markets.

Though widely recognized as essential for improving work performance across various domains, self-efficacy’s specific role in managing construction workforces remains understudied. This knowledge gap restricts our ability to uncover new factors that enhance workforce management effectiveness and ultimately boost construction labor productivity (CLP). To address this, our study proposes and tests a novel model. This model explores the impact mechanism of self-efficacy on CLP by investigating the mediating role of work motivation. By delving into this crucial yet underexplored area, we aim to provide valuable insights for construction project managers and researchers alike, paving the way for more effective workforce management strategies and consequently, improved CLP.

This study utilizes a mixed-method approach, incorporating both qualitative and quantitative methodologies. Data from 112 rebar workers at five construction sites in Vietnam underwent analysis using Cronbach’s alpha, exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and structural equation modeling (SEM) to examine the novel research model.

The results indicate a positive and significant association between self-efficacy and CLP. Additionally, work motivation emerged as a full mediator in the relationship between self-efficacy and CLP. Specifically, individuals with higher self-efficacy set ambitious goals and invest more effort in their pursuit, leading to increased work motivation and, ultimately, heightened productivity levels.

The significant implications of the current study extend to construction managers and policymakers alike. Construction managers can leverage the findings to devise targeted interventions aimed at enhancing the self-efficacy and work motivation of their workforce, potentially resulting in noteworthy enhancements in CLP. Policymakers, too, can benefit from these findings by formulating policies that actively support the cultivation of self-efficacy and work motivation among construction workers. Such policies have the potential to foster a more productive and efficient construction industry, aligning with the broader goals of workforce development and industry enhancement.

This study expands existing knowledge by identifying the important role of self-efficacy in work performance enhancement and the mediating role of work motivation in terms of these relationships.

Four novel trends: water scarcity, income inequality, labor shortage and gentrification, are receiving ever greater attention because of the devastating effects they having on the well-being of humanity. The purpose of this paper is to briefly describe each trend, discuss its effect on tourism and offer support from research as to how tourism can mitigate the effects of these trends.

The article draws on a literature review to identify comparatively new events, patterns and trends that are likely to impede the development of tourism in the coming years.

This study documents the negative implications these trends could have for the future of tourism if they are not well managed. Guidance on how destination managers and travel industry managers can mitigate each of these trends is provided.

Given the damage to humanity these trends have created and the potential future damage they will create, there is a call to researchers to both develop and document ways to mitigate the negative effects of these trends.

Suggested actions on how managers can reduce or eliminate the negative impacts of these trends are provided.

This is one of the first studies to link these novel trends to the implications they have for tourism.

This study aims to analyze the factors driving Syrian refugees into the informal labor market in Türkiye despite the existence of regulations and programs to facilitate their integration into the formal labor market.

This study presents results from a literature review of secondary sources and primary data collection through semi-structured interviews with key stakeholders and Syrian refugees.

The study shows that the implementation of policies and programs to boost formal employment among refugees has yielded limited results. Many refugees continue to operate within the informal economy. This informality is due to various socio-economic challenges, including anti-refugee sentiments, geographical restrictions and economic crises. The 2023 twin earthquakes have further exacerbated the vulnerable situation of refugees, intensifying the difficulty of achieving self-reliance.

The study’s drawbacks include a small sample size. This implies that the sample is not representative; therefore, results may lack generalizability.

The study’s findings could stimulate greater engagement in public policy, facilitate the management of public perceptions regarding refugees and provide support to the private sector, all to enhance the integration of Syrian refugees into the formal labor market.

This study addresses crucial areas previously unexplored, including the impact of economic and natural disaster crises on the labor market integration of refugees. To the best of the author’s knowledge, by investigating these factors for the first time, this study offers novel insights into their influence on refugees’ labor market integration.

This paper aims to introduce a novel structure for the physical internet (PI)–enabled sustainable supplier selection and inventory management problem under uncertain environments.

To address hybrid uncertainty both in the objective function and constraints, a novel interactive hybrid multi-objective optimization solution approach combining Me-based fuzzy possibilistic programming and interval programming approaches is tailored.

Various numerical experiments are introduced to validate the feasibility of the established model and the proposed solution method.

Due to its interconnectedness, the PI has the opportunity to support firms in addressing sustainability challenges and reducing initial impact. The sustainable supplier selection and inventory management have become critical operational challenges in PI-enabled supply chain problems. This is the first attempt on this issue, which uses the presented novel interactive possibilistic programming method.