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In this paper, we revisit this well‐established linear relationship of person‐organisation, demands‐abilities, and needs‐supply fit with job satisfaction, commitment, and OCBs, and propose that this relationship may be linear for affective work outcomes but curvilinear for behavioural ones. We test this idea in a two‐wave sample of 212 employees, with measures taken 4 weeks apart. The results support the idea that the relationship between fit and behavioural outcomes can, indeed, be curvilinear. Overall, this study contributes to a better understanding of the nature of the relationship between fit and work outcomes by challenging the long‐held 'more fit is better' logic that pervades much of the PE fit research to date. [ABSTRACT FROM AUTHOR]

Background: Big data research requires public support. It has been argued that this can be achieved by public involvement and engagement to ensure that public views are at the centre of research projects. Researchers should aim to include diverse communities, including seldom‐heard voices, to ensure that a range of voices are heard and that research is meaningful to them. Objective: We explored how researchers involve and engage seldom‐heard communities around big data research. Methods: This is a qualitative study. Researchers who had experience of involving or engaging seldom‐heard communities in big data research were recruited. They were based in England (n = 5), Scotland (n = 4), Belgium (n = 2) and Canada (n = 1). Twelve semistructured interviews were conducted on Zoom. All interviews were audio‐recorded and transcribed, and we used reflexive thematic analysis to analyse participants' experiences. Results: The analysis highlighted the complexity of involving and engaging seldom‐heard communities around big data research. Four themes were developed to represent participants' experiences: (1) abstraction and complexity of big data, (2) one size does not fit all, (3) working in partnership and (4) empowering the public contribution. Conclusion: The study offers researchers a better understanding of how to involve and engage seldom‐heard communities in a meaningful way around big data research. There is no one right approach, with involvement and engagement activities required to be project‐specific and dependent on the public contributors, researchers' needs, resources and time available. Patient and Public Involvement: Two public contributors are authors of the paper and they were involved in the study design, analysis and writing. [ABSTRACT FROM AUTHOR]