Earticle

초록

영어

The proliferation of mobile applications and the unique characteristics of the mobile environment have attracted significant research interest in understanding customers’ purchasing behaviors in mobile commerce. In this study, we extend customer value theory by combining the predictors of product performance with customer value framework to investigate how in-store information creates value for customers and influences mobile application downloads. Using a data set collected from the Google Application Store, we find that customers value both text and non-text information when they make downloading decisions. We apply latent semantic analysis techniques to analyze customer reviews and product descriptions in the mobile application store and determine the embedded valuable information. Results show that, for mobile applications, price, number of raters, and helpful information in customer reviews and product descriptions significantly affect the number of downloads. Conversely, average rating does not work in the mobile environment. This study contributes to the literature by revealing the role of in-store information in mobile application downloads and by providing application developers with useful guidance about increasing application downloads by improving in-store information management.

목차

ABSTRACT
 I. Introduction
 Ⅱ. Conceptual Background
  2.1. Customer Value Theory
  2.2. Predictors of Product Performance
 Ⅲ. Hypothesis Development
  3.1. Functional Value
  3.2. Experiential Value
  3.3. Symbolic Value
  3.4. Cost Value
 Ⅳ. Methodology
  4.1. Data Collection
  4.2. Text Mining
  4.3. Empirical Model Specification
 Ⅴ. Results and Discussion
 Ⅵ. Conclusion and Limitations
 

저자

• Ying Wang [ Ph.D. candidate, Information Systems and Quantitative Sciences, Rawls College of Business Administration, Texas Tech University, USA ]
• Miguel Aguirre-Urreta [ Assistant Professor, Information Systems and Quantitative Sciences, Rawls College of Business Administration, Texas Tech University, USA ]
• Jaeki Song [ Professor, Information Systems and Quantitative Sciences, Rawls College of Business Administration, Texas Tech University, USA ] Corresponding author

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