From text to insights: understanding museum consumer behavior through text mining TripAdvisor reviews

Abstract

Purpose

This study aims to determine consumer satisfaction dimensions that lead to a willingness to share positive emotions through the study of TripAdvisor users’ reviews on St. Petersburg museums. The explorative study reveals the most significant factors that could predict museum visitors’ behavioral intentions.

Design/methodology/approach

The study is based on the theory of planned behavior and the “cognitive-affective-conative” model to analyze TripAdvisor reviews (n = 23020) and understand the relationship between the affective and the conative components of consumer behavior. Quantitative text-mining analysis allowed us to view every lemma of every review as a single factor for a deeper understanding of the phenomenon.

Findings

The research has enlarged the literature on museum consumer behavior. Behavioral intentions of museum visitors are affected by satisfaction dimensions, especially emotions felt; the esthetic dimension and museums’ surroundings affect consumers’ overall willingness to share positive emotions, while bad service quality and pricing policy make a visit to the museums less satisfying.

Practical implications

Managers can enhance their offerings and attract new consumers by identifying the satisfaction dimensions that influence their intentions to share positive emotions. The research findings can aid museums, tour agencies and government officials in developing targeted products and strategies to meet consumers’ expectations and promote urban tourism.

Originality/value

The research identified the dimensions that influence visitors’ willingness to share positive emotions through user-generated content in the context of museums. The study applies quantitative text analysis based on logit regression, which is a novel approach in the field of urban tourism research.

Citation

Burkov, I. and Gorgadze, A. (2023), "From text to insights: understanding museum consumer behavior through text mining TripAdvisor reviews", International Journal of Tourism Cities, Vol. 9 No. 3, pp. 712-728. https://doi.org/10.1108/IJTC-05-2023-0085