Abstract
This paper discusses penalized neural networks to establish a stable neural network model for survey data measured by traditional marketing scales. Interpreting estimated hidden units and weights in a neural network is often challenging because of its non-identifiability. Factor models in social science are a traditional non-identifiable model for analyzing questionnaire measurements. Hence, many studies have proposed identification conditions. Accordingly, we propose penalty functions that represent the equivalent identification conditions in standard factor models to reduce the non-identifiability and instability of neural networks. We apply these penalty functions in the empirical analysis of autoencoders with e-service quality scale data. The proposed method provides an explainable result that is theoretically reasonable in that e-service quality scale. While comparing the penalized autoencoder with traditional factor models, we discuss potential applications and tasks of the proposed method in service marketing research for further exploration.
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Notes
- 1.
Note: Figure 2 displays only the relationship between observed variables and factor scores.Additionally, the intercept is assumed to be equal to zero in the usual case.
- 2.
Note: Although the data were gathered from three categories (food and drink, fashion, and daily necessities), we used only one category data (food and drink) due to limitations of space.
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Acknowledgments
The author gratefully acknowledges the partial support of JSPS KAKENHI Grant Number 20K22125. The author would also like to thank Enago (www.enago.jp) for the English language review. Additionally, the two reviewers involved provided helpful and insightful comments that improved the overall quality of the manuscript, for which the author is thankful.
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Sato, T. (2021). Penalizing Neural Network and Autoencoder for the Analysis of Marketing Measurement Scales in Service Marketing Applications. In: Qiu, R., Lyons, K., Chen, W. (eds) AI and Analytics for Smart Cities and Service Systems. ICSS 2021. Lecture Notes in Operations Research. Springer, Cham. https://doi.org/10.1007/978-3-030-90275-9_3
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