The Autonomy Tussle: AI Technology and Employee Job Crafting Responses
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,006 | 0,007 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».