1Research Scholar, Department of Management Studies, Central University of Haryana
2Professor, Department of Management Studies, Central University of Haryana
This paper proposes the Occupational-Motivational Engagement Model (OMEM), a conceptual framework for thinking about how occupational identity shapes motivational experience under algorithmic management on digital work platforms.
The paper is conceptual. It draws on integrative theorising, bringing together Self-Determination Theory, Herzberg’s Two-Factor Theory, and occupational identity research, and reading these alongside contemporary work-design scholarship (Parker & Knight, 2024) and recent empirical evidence from platform-work studies. Following a problematisation logic, we identify a gap in current frameworks and propose three psychological mechanisms (identity-based need prioritisation, cognitive framing of platform features, and occupationspecific social comparison) that, acting together, produce systematic differences in how white-collar and blue-collar platform workers experience algorithmic management.
Seven propositions set out the occupational differences. Whitecollar workers tend to prioritise method autonomy, read algorithmic control as an encroachment on professional discretion, and anchor their comparisons on skill development and client quality. Blue-collar workers tend to prioritise temporal autonomy, treat algorithms as a source of predictability, and compare on earnings and performance metrics.
OMEM points HRM toward occupationally informed algorithmic design. Platforms should protect method autonomy and invest in qualitative feedback for white-collar workers. For blue-collar workers, predictable workflows and legible quantitative metrics matter more.Originality/value: OMEM is, to our knowledge, the first mechanismbased framework that connects occupational identity, algorithmic job design, and motivational outcomes in digital labour. It responds to gaps flagged in recent systematic reviews of platform work (Kadolkar et al., 2024; Masta & Kaushiva, 2024) by explaining why workers from different occupational backgrounds read the same algorithmic management differently, and offers a theoretical base for occupationally sensitive HRM design.
Digital platform work, Algorithmic management, Occupational identity, Worker motivation, Job design, Self-Determination Theory
