Penalizing Neural Network and Autoencoder for the Analysis of Marketing Measurement Scales in Service Marketing Applications

Toshikuni Sato ()
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Toshikuni Sato: Ishinomaki Senshu University

A chapter in AI and Analytics for Smart Cities and Service Systems, 2021, pp 31-42 from Springer

Abstract: 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.

Keywords: Stable neural network; Identifiability; Interpretability; Latent variable model; Construct measurements (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-030-90275-9_3

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DOI: 10.1007/978-3-030-90275-9_3

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