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Critical realism is an increasingly popular “lens” through which complex events, entities and phenomena can be studied. Yet detailed operationalisations of critical realism are at present relatively scarce. This study's objective here is built on existing debates by developing an open systems model of reality, a basis for designing appropriate, internally consistent methodologies.

The authors use a qualitative case study examining changing practices for client contact management in professional services firms during restrictions imposed by the COVID-19 crisis to show how the model can be operationalised across all stages of a research study.

This study contributes to the literature on qualitative applications of critical realism by providing a detailed example of how the research paradigm influenced choices at every stage of the case study process.

More importantly, this model of reality as an open system provides a tool for other researchers to use in their own operationalisation of critical realism in a variety of different settings.

This study aims to synthesize existing findings in the gig worker training literature and identify the training rationales adopted by these studies, using a synthesized framework of organizational training rationales. This study seeks to delineate the rationales behind gig worker training and highlight unaddressed training needs within digital platforms, ultimately proposing a research agenda for future studies in this area.

A systematic review methodology is adopted to synthesize and analyze empirical, peer-reviewed studies on gig worker training.

The systematic review reveals that competency and economic rationales are predominantly adopted in gig worker training studies, with the relationship rationale, common in traditional training, notably absent. This study also outlines seven future research directions to highlight identified challenges and unaddressed training needs.

To the best of the author’s knowledge, this study is the first work that systematically reviews existing findings on gig worker training.