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The purpose of this study is to discuss how ethnicity and gender influence high-tech entrepreneurship in the Cambridge cluster emphasizing homophily and how and why bridging and bonding ties are created in moving individuals from the periphery to cross the threshold into acceptance.

The authors present findings on the micro-dynamics of ethnicity and gender in high-tech entrepreneurship underpinned by homophily. The authors discuss how ethnicity and gender influence who enters and stays in high-tech entrepreneurship university spinouts. Through an entrepreneurial narrative qualitative approach, the authors explore how and why bridging and bonding ties may be created and the challenges for those at the periphery to cross the threshold into acceptance. The study adopts an entrepreneurial narrative qualitative approach using interviews with individuals associated with the Cambridge cluster.

The authors add to the entrepreneurship literature by responding to the call for multiplexity within homophily, and the research indicates that homophily strongly influences who enters and who leaves the cluster based on bridging and bonding ties. The findings address the need for more focused understanding of entrepreneurial clusters and how mechanisms can be developed to create an environment to nurture both bridging and bonding ties. It is possible for an entrepreneurial cluster to be perceived as attractive and thriving while being homophilous. Ethnic individuals and women continue to struggle to gain acceptance in the Cambridge cluster.

Interviews were conducted by one person – an ethnic minority female – for continuity of all interviews. Yet as many of the participants were not minorities, it is possible that an interviewer who was not an ethnic minority may have elicited different narratives.

The study adds to the entrepreneurship literature by focusing on multiplexity within homophily in examining the dynamics of homophily in the context of the Cambridge cluster and the significance of nurturing bridging and bonding ties. The research comments on implications for practice among three interlinked but autonomous groups: first, the individual entrepreneurs; second, the networks she/he belongs to; third, the university as both fertile ground for entrepreneurship and an educational institution where entrepreneurship education is engaged in for encouraging and supporting spinouts.

The inclusion of LGBTQ + persons (lesbian, gay, bisexual, transgender, queer and having other sexual orientations and gender identities) is a crucial step in improving gender diversity in the workplace; however, till date, it remains a significant challenge for human resource management professionals. The current study critically examines this issue of an inclusive workplace for LGBTQ + people through a systematic review of the existing research that has empirically studied their experiences at the workplace. It also examines the resistance and challenges organizations face in LGBTQ + diversity training and provides future research avenues.

For systematically reviewing the literature, Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) model has been used. A total of 101 empirical studies have been reviewed.

The result shows that LGBTQ + people encounter multiple negative workplace experiences, including proximal (hiring discrimination and housing discrimination) and distal workplace discrimination (unsafe work climate, microaggressions and harassment). These aversive experiences lead to work stress while also mandating that people manage their sexual identity and style of dressing. This stress, in turn, impacts their work–family outcomes, job satisfaction and decision-making with regard to their careers.

The paper provides a holistic understanding of the aversive workplace experiences encountered by sexual minorities.

This study investigates whether the artificial neural network approach, when used on a large organizational soft HR performance dataset, results in a better (R²/RMSE) model compared to the linear regression. With the use of predictive modelling, a more informed base for managerial decision making within soft HR performance management is offered.

The study builds on a dataset (n > 43 k) stemming from an annual employee MNC survey. It covers several soft HR performance drivers and outcomes (such as engagement, satisfaction and others) that either have evidence of a dual-role nature or non-linear relationships. This study applies the framework for artificial neural network analysis in organization research (Scarborough and Somers, 2006).

The analysis reveals a substantial artificial neural network model performance (R² > 0.75) with an excellent fit statistic (nRMSE <0.10) and all drivers have the same relative importance (RMI [0.102; 0.125]). This predictive analysis revealed that the organization has to increase six of the drivers, keep two on the same level and decrease one.

Up to date, this study uses the largest dataset in soft HR performance management. Additionally, the predictive results reveal that specific target values lay below the current levels to achieve optimal performance.