Bibliographic record
Abstract
Purpose Allen and Meyer’s (1990) three component model of organizational commitment is now well accepted in the study of consumer–service provider relationships (Keiningham et al., 2015). Commitment profiling is a “person-centered approach” to commitment (Meyer et al., 2012) which examines groups of individuals who share similar commitment mindsets. The purpose of this paper is to apply commitment profile methodology to the analysis of customer–firm relationships in the context of financial services. Design/methodology/approach This method was applied with customer data collected as part of a nation-wide panel study of consumer financial service relationships in Canada. In total, 428 banking customers participated in this study. Findings This study identified five distinct bank customer commitment profiles (fully committed, affective commitment dominant, continuance commitment dominant, moderately committed and uncommitted) that varied in both size and behaviors and intentions. Research limitations/implications This is an exploration of commitment profiling as a technique to understand the ways in which consumers differ in terms of their commitment mindsets and behavior. It has application to a wide range of service relationships beyond financial services. Practical implications This has applications for market segmentation on the basis of customer commitment mindsets in many service sectors, but banking in particular. Since financial institutions have adopted various techniques to measure customer lifetime value (CLV), it would be appropriate to understand how various commitment profiles (segments) are linked to CLV. Originality/value While commitment profiling is a well-developed approach in understanding the nature of the firm–employee employment relationships, this is an early and exploratory attempt at applying this method in the context of a customer–financial service provider marketing relationship. This is a novel way of understanding bank customer segments in terms of their felt commitment to the financial institution with which they do business.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".