A Theory of Interior Peaks : Activity Sequencing and Selection for Service Design
Problem Definition. Putting customer experience at the heart of service design has become a governing principle of today’s “experience economy.” Echoing this principle, our paper addresses a service designer’s problem of how to select and sequence activities in designing a service package.Academic/Practical Relevance. Empirical literature shows an ideal sequence often entails an interior peak; that is, the peak (i.e., highest-utility) activity is placed neither at the beginning nor the end of the package. Theoretic literature, by contrast, advocates placing the peak activity either at the beginning or the end. Our paper bridges this gap by developing a theory accounting for interior peaks. It also provides managerial implications for activity sequencing and selection. Methodology. We model the activity sequencing and selection problem as a nonlinear optimization problem and reformulate its objective as an additive function to generate structural insights. Results. We show heterogeneity in memory decay explains the phenomenon of interior peaks. The optimal sequence is in either an “IU” or “UI” shape. An interior peak is optimal when the memory-decay rate of the peak activity is neither too high nor too low.Managerial Implications. Our research sheds light on service sequencing by weighing the phenomenon of interior peaks. In the presence of an interior peak, we show it is optimal to schedule a low point immediately before or after the peak activity, creating a contrast in customer experience. In addition, interior peaks arise partly because the peak activity is more memorable than others. Guided by this logic, as the peak activity becomes even more memorable, one might be tempted to move it to an earlier slot; we show, counterintuitively, moving it to a later slot can be optimal. Our research also provides implications for activity selection by showing the optimal portfolio may consist of activities with the highest and lowest utility values, but not those with medium values
Year of publication: |
2020
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Authors: | Li, Yifu ; Dai, Tinglong ; Qi, Xiangtong |
Publisher: |
[S.l.] : SSRN |
Saved in:
freely available
Extent: | 1 Online-Ressource (29 p) |
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Type of publication: | Book / Working Paper |
Language: | English |
Notes: | In: Manufacturing & Service Operations Management Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments December 22, 2020 erstellt |
Other identifiers: | 10.2139/ssrn.3194758 [DOI] |
Classification: | C61 - Optimization Techniques; Programming Models; Dynamic Analysis ; D24 - Production; Capital and Total Factor Productivity; Capacity ; L21 - Business Objectives of the Firm ; M11 - Production Management ; z31 |
Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10014113329
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