One Algorithm, Two Responses: Occupational Identity and Motivation in White and Blue Collar Platform Work

Purpose: 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. Design/methodology/approach: 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 occupation-specific social comparison) that, acting together, produce systematic differences in how white-collar and blue-collar platform workers experience algorithmic management. Findings: Seven propositions set out the occupational differences. White-collar 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. Practical implications: 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 mechanism-based framework that connects occupational identity, algorithmic job design, and motivational experience to explain occupational variation in platform work.