Abstract
Artificial intelligence (AI) and digital transformation are widely framed as strategic imperatives. Yet many transformation initiatives fail to realise anticipated organisational value. Increasing evidence suggests that these failures stem less from technological capability than from how organisations manage human adaptation, shifting expectations, and evolving role boundaries. This paper examines AI transformation through the lens of psychological contract theory and argues that technological disruption is best conceptualised as multi-domain psychological contract reconfiguration rather than as isolated breach events.
Drawing on qualitative interviews with 30 UK academics conducted during pandemic-induced digital transition, the study explores how abrupt technology-enabled change recalibrates reciprocal obligations between employees and organisations. Using a Gioia-informed interpretive methodology, first-order themes were iteratively clustered into second-order dimensions corresponding to psychological contract domains. The analysis identifies reconfiguration across Meaning-making (sensemaking and appraisal processes), Ideological, Relational, and Transactional contract content, alongside Attitudinal and Behavioural Effects, as articulated within the MIRTE framework (Meaning-making, Ideological, Relational, Transactional contracts and their Effects). MIRTE conceptualises transformation as a dynamic system in which sensemaking mediates contract evaluation and attitudinal-behavioural effects recursively shape subsequent appraisals.
Findings show that transformation outcomes hinge on how employees interpret organisational actions under conditions of uncertainty and opacity. Identical institutional decisions were construed either as fulfilment-promoting or breach-inducing, depending on meaning-making processes. Attitudinal and behavioural outcomes did not merely follow these dynamics but recursively shaped subsequent interpretations of organisational actions. Although grounded in pandemic-induced disruption, the mechanisms identified are structurally analogous to AI-enabled transformation contexts characterised by algorithmic opacity and redistributed discretion.
The paper contributes by shifting psychological contract theorising toward structural multi-domain recalibration and by offering a practice-relevant framework for diagnosing people-side transformation risk. Conceptualising AI transformation as reciprocal obligation management provides a coherent basis for more sustainable and trust-preserving implementation strategies in future-of-work contexts.