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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.

Scholarly communications, particularly, questions and answers (Q&A) present on digital scholarly platforms provide a new avenue to gain knowledge. However, several studies have raised a concern about the content anomalies in these Q&A and suggested a proper validation before utilizing them in scholarly applications such as influence analysis and content-based recommendation systems. The content anomalies are referred as disinformation in this research. The purpose of this research is firstly, to assess scholarly communications in order to identify disinformation and secondly, to help scholarly platforms determine the scholars who probably disseminate such disinformation. These scholars are referred as the probable sources of disinformation.

To identify disinformation, the proposed model deduces (1) content redundancy and contextual redundancy in questions (2) contextual nonrelevance in answers with respect to the questions and (3) quality of answers with respect to the expertise of the answering scholars. Then, the model determines the probable sources of disinformation using the statistical analysis.

The model is evaluated on ResearchGate (RG) data. Results suggest that the model efficiently identifies disinformation from scholarly communications and accurately detects the probable sources of disinformation.

Different platforms with communication portals can use this model as a regulatory mechanism to restrict the prorogation of disinformation. Scholarly platforms can use this model to generate an accurate influence assessment mechanism and also relevant recommendations for their scholars.

The existing studies majorly deal with validating the answers using statistical measures. The proposed model focuses on questions as well as answers and performs a contextual analysis using an advanced word embedding technique.