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This study aims to observe and discuss an emerging disintermediation in transportation, finance and health care, and explain how these three key areas depend on intermediary institutions that are the fruit of modern corporate governance conditions that find their roots in classical sociological theory.

The authors review and incorporate a diversity of research literature to explain the likelihood for the development and continuation of disintermediation.

The authors map two sociological perspectives (Emile Durkheim’s theory of interdependence and Herbert Spencer’s theory of contracts) to two modern corporate governance theories (resource dependence theory and agency theory). The authors then discuss the challenging social situation resulting from modern corporate governance and show how these conditions create the potential for a continuum of disintermediation across the specific and crucial economic sectors of transportation, finance and health care.

The implications of this theoretical integration can help organizational leaders navigate complex social and strategic issues and prepare for the consequences that may result from the emerging disintermediation.

This study aims to synthesize and conceptualize the highly fragmented yet important literature on racial discrimination in entrepreneurship.

A bibliometric analysis and literature review were performed that involved 523 articles containing 26,926 references.

The bibliometric analysis identified three dominant research themes that comprehensively illustrate the state of research in this domain: strategic, sociocultural and individual-level perspectives. The synthesis of extant literature helped in formulating a holistic conceptual model that portrays the genuineness of racial discrimination in entrepreneurship. The sources, factors and impact of racial discrimination faced by entrepreneurs were identified. Based on the review and analysis of keywords, certain fruitful future research directions were formulated that will take the field forward.

This work is the first attempt to review the literature that narrows down the focus to racial discrimination in entrepreneurship (from other discriminations such as gender, cultural and religious discrimination) as one form of discrimination due to its unique origins and consequences.

The purpose of this study is to estimate the impact of conflict witnessed in Pakistan on the enrolment rates of boys and girls. Pakistan has the world’s second-highest number of out-of-school children, with an estimated 22.8 million children aged 5–16 years not attending school.

By merging data on violence with the data on enrolment rates, this paper finds that exposure to violence is correlated with a decline in overall district-level enrolment rates in the short run at primary-level schools and middle-level schools.

However, for boys, violence is also negatively correlated with enrolment rates at middle-level schools in the medium run. One possible mechanism tested in this paper is the potential substitution of boys into the labour market during a period of conflict.

To the best of the authors’ knowledge, this paper adds to the existing literature in several ways. Firstly, the effect of conflict on the labour market by impacting schooling for boys and girls is examined for the first time in Pakistan. Secondly, the district-level data set on enrolment rates used for this study is novel and has not been used before for this type of analysis. Thirdly, while this study strengthens the evidence that the short run effects of conflict are stronger than the long-run effects, it also confirms the negative effects of conflict do not fade away immediately. Fourthly, this study emphasizes that each conflict is unique in terms of its heterogeneous effects across different cohorts, such as gender, as these effects are dependent on the mechanism through which conflict impacts each individual.

The implementation of BIM in the construction industry requires the coevolution of the various aspects of the BIM ecosystem. The human dimension is a very important dimension of the ecosystem necessary for BIM implementation. It is imperative to study this aspect of the BIM ecosystem both from the employer perspective and employee availability to provide insights for stakeholders (job seekers, employers, students, researchers, policymakers, higher education institutions, career advisors and curriculum developers) interested in the labour market dynamics.

To understand the BIM actor roles through the employer lens and the actual BIM actors in the construction industry, this study employed data mining of job adverts from LinkedIn and Mncjobs website. Content analysis was employed to gain insights into the data collected. Also, through a quantitative approach, the existing BIM actor roles were identified.

The study identified the employers' expectations of BIM actors; however, it is noted that the BIM actor recruitment space is still a loose one as recruiters put out open advertisements to get a large pool of applicants. From the data analysed, it is concluded that the BIM actor role is not an entirely new profession. However, it simply exists as construction industry professionals with BIM tool skills. Also, the professional development route is not well defined yet.

This study presents a realistic angle to BIM actor roles hence enhancing BIM implementation from the human perspective. The findings present an insight into the preferred against the actual.

This study empirically examines the impact of climate change and agricultural research and development (R&D) as well as their interaction on agricultural productivity in 12 selected Asian and Pacific countries over the period of 1990–2018.

Various estimation methods for panel data, including Fixed Effects (FE), the Feasible Generalized Least Squares (FGLS) and two-step System Generalized Method of Moments (SGMM) were used.

Results show that both proxies of climate change – temperature and precipitation – have negative impacts on agricultural productivity. Notably, agricultural R&D investments not only increase agricultural productivity but also mitigate the detrimental impact of climate change proxied by temperature on agricultural productivity. Interestingly, climate change proxied by precipitation initially reduces agricultural productivity until a threshold of agricultural R&D beyond which precipitation increases agricultural productivity.

The findings imply useful policies to boost agricultural productivity by using R&D in the context of rising climate change in the vulnerable continent.

This study contributes to the literature in two ways. First, this study examines how climate change affects agricultural productivity in Asian and Pacific countries – those are most vulnerable to climate change. Second, this study assesses the role of R&D in improving agricultural productivity as well as its moderating effect in reducing the harmful impact of climate change on agricultural productivity.

This work analyzes female immigrants’ integration in the dimensions of education, labor market participation and fertility in 15 European countries, considering individual characteristics, including cultural background, host countries’ attitudes towards immigrants, the role of women in the family and country-specific integration policy. All these aspects taken together are crucial to understand the main patterns of integration focusing on gender differences.

We focus on second- and first-generation male and female immigrants between the age of 25 and 41, with a length of stay of at least ten years. Enrollment ratios for tertiary education in parents’ countries, the total fertility rate and the female labor force in the mother’s country represent ethnic background. Diversity in the destination regions is captured by local attitudes towards immigrants, the perceived role of women and national policies to integrate migrants [Migrant Integration Policy Index (MIPEX)]. The data are drawn from the European Social Survey (ESS) for 2010–2018. Our results are based on ordinary least squares (OLS) and logit estimates; multilevel analysis was conducted.

We find significant evidence of gender role transmission from mother to daughter; age at immigration seems to be crucial to examine the importance of the culture of origin among immigrants. However, females are responsive to attitudes toward immigrants and gender equality in receiving societies, while integration policies, by defining the set of opportunities, may contribute to both genders’ tertiary education and women’s probability of being in the labor force.

This work underlines that integration policies favoring equal rights as nationals may contribute to both women’s tertiary education and their probability of being in the labor force.

We explore female integration in Europe in the dimensions of education, labor market, fertility and the role of both immigrants’ cultural heritage and specific aspects of destination countries. Previous research, particularly in the USA, has generally focused on some of these features at the expense of a more comprehensive approach. This study builds upon the existing literature and contributes to it by taking a multifaceted approach to female integration in Western Europe, which presents not only an institutional context different from the USA but also some heterogeneity with respect to integration policies and socioeconomic factors.

Open innovation (OI), by now one of the major concepts for the analysis of innovation, is seen as a methodology for collaboratively designing and implementing solutions by engaging stakeholders in an iterative and inclusive service design process. This paper aims to empirically investigate OI capacities, defined as a cooperative, knowledge-sharing innovation ecosystem, and to explore how it can lead to improved performance of firms in Central and Eastern European (CEE) and Southeastern European (SEE) countries.

The study builds on the World Bank/European Bank for Reconstruction and Development (EBRD’s) Business Environment Enterprise Performance Survey (BEEPS) dataset for 2009, 2013 and 2019. Primarily, the research model was estimated using log-transformed ordinary least squares (OLS). Taking into consideration that this method might produce substantial bias, yielding misleading inferences, this study is fitting Poisson pseudo maximum likelihood estimators with robust standard errors and instrumental variable/generalized method of moments estimation (IV/GMM) approach for comparative results. Secondarily, the research model was tested using structural equation modelling (SEM) to investigate the relationship between five OI capacities and firm performance.

The findings indicate that there is a significant positive relationship between most OI capacities and firm performance, except for innovation, which did not show a statistically significant relationship with firm performance. Specifically, research and development (R&D), knowledge and coopetition are statistically significant and positively associated with firm performance, whereas transformation is statistically significant but negatively associated with firm performance. The IV/GMM estimations’ findings support the view that the firm performance is significantly affected by OI capacities, together with some control variables such as size, age, foreign ownership and year dummy to have a significant impact on firm performance.

This paper fills an identified gap in the literature by investigating the impact of OI on firm performance executed in the specific CEE and SEE country context.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.