Scan it, buy it, pay it – customers' psychological contracts as partial employees in retail
Bibliographic record
Abstract
Purpose Self-service technologies (SST) have become more and more pervasive in retail to facilitate autonomous checkout. In this context, customers play an active role and, as such, can be considered as “partial employees.” Partial employees have to perform a wide range of tasks, get rewarded for their work and need to understand the terms of the exchange, all without being subject to a formalized contract. In this research, the authors suggest that partial employees go through a process of organizational socialization that allows them to define the psychological contract they hold with the organization. Design/methodology/approach In order to investigate the psychological contracts of partial employees, 324 Canadian customers using SST completed an online questionnaire, in which their SST use, psychological contract fulfillment and organizational socialization were measured. Findings Descriptive analyses highlight that customers as partial employees build a psychological contract with their most frequent retailer, as they perceive not only retailer inducements but also their own contributions. Multiple linear regressions suggest that organizational socialization favors psychological contract fulfillment, but that specific dimensions of organizational socialization are important for employer inducements vs. employee contributions. Moreover, results suggest that the frequency of use of SST as well as the patronage positively predicts psychological contract fulfillment. Originality/value This research investigates a specific situation of unconventional employment – that of customers as partial employees with organizations. It contributes to the literature on the psychological contract by broadening its application to new relations and to the literature on customer management by reemphasizing the relevance of the psychological contract in this domain.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".