The impact of service bundles on the mechanism through which functional value and price value affect WOM intent
Bibliographic record
Abstract
Purpose The purpose of this paper is to contribute toward the current limited understanding of service bundles by investigating how purchasers of combined product-service bundles (bundle customers) differ from those purchasing a product and associated service separately (non-bundle customers). Design/methodology/approach The hypothesized effects were tested on a representative sample of mobile phone subscribers in Finland, through a multi-group moderated analysis using variance-based structural equation modeling. Findings While functional value had a stronger effect on attitude for bundle customers, price value is a stronger determinant of attitude for non-bundle customers. There was no difference between the groups in terms of how attitude determines the word-of-mouth (WOM) intent. The total influence of functional value on positive WOM intent was stronger for bundle customers vs non-bundle customers; in contrast, the total influence of price value on positive WOM was weaker for the bundle customers. Research limitations/implications Two interrelated frameworks, prospect theory and mental accounting theory, are used to analyze customer response to service bundles. The results demonstrate that bundles play a powerful role in determining engagement behaviors critical to firms. Purchasing a service bundle vs a non-bundle influences how price value and functional value determine attitude and WOM intent in fundamentally different ways. Practical implications In devising communication strategies to maximize positive WOM, managers need to emphasize functional benefits for bundle purchasers and price benefits for non-bundle customers. The results also demonstrate that it is more important for firms to track perceived value, as value and not attitude differentiates WOM generation in the two groups. Originality/value This is the first study to demonstrate how bundle and non-bundle customers determine value, and how functional value and price value determine WOM generation and attitude toward service provider in fundamentally different ways. The comparison of the bundle group where the firm acts as the main resource integrator to a non-bundle group where the customer is the main resource integrator in creating value helps demonstrate the need for firms to treat the two groups in distinct ways.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".