The Autonomy Tussle: AI Technology and Employee Job Crafting Responses
Bibliographic record
Abstract
With the development of artificial intelligence (AI) and its applications, such as learning algorithms, it seems likely that work and organization will be profoundly reshaped. While this subject has been debated in broad terms (Arntz et al., 2016; Brynjolfsson & McAfee, 2014; Faraj et al., 2018), little has been written specifically from the perspective of employees (Phan et al., 2017). Little is known about the impact of AI on their work experiences and how they may respond. In a qualitative study of 27 bank employees, we investigated how learning algorithms shaped working conditions, how they affected autonomy and the meaning of work and how these constructs changed over time. The employees responded to the changes through job crafting behaviours (Wrzesniewski & Dutton, 2001). By considering the effects of the learning algorithms on the employees’ work experiences from their perspective, we offer a novel application of job crafting theory to AI technology. The employees responded to AI by changing task and relationship boundaries, and cognitively reframed their jobs. Their job crafting behaviours can be interpreted broadly as attempts to rebalance their levels of autonomy (which were initially reduced by the introduction of AI), to move toward closer personal relationships with customers and to reposition their meaning of work. In general, employees’ job crafting also had implications for employees’ managers, customers, and their work context in terms of the meaning of the AI tools and how they were used. Employees’ concerted response across the three job crafting dimensions underlines the importance of synergy across job crafting dimensions if they are to be successful in altering employees’ experience of work and enhancing the human value of their services.AbstractIn this qualitative study of 27 bank employees, we investigated how learning algorithms affected their working conditions, their autonomy and the meaning of their work. We show that employees responded to the AI-induced changes through job crafting behaviours (Wrzesniewski & Dutton, 2001). Employees reshaped their task and relationship boundaries, and cognitively reframed their jobs, to maintain their autonomy, their desired social relationships and the meaning of their work. By considering the effects of learning algorithms on the employees’ work experience from their perspective, we provide a novel application of job crafting theory. Employees’ concerted response across the three job crafting dimensions underlines the importance of synergy across job crafting dimensions if they are to be successful in altering employees’ experience of work and enhancing the human value of their services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".