Year: 2024
Type: article
Abstract: Research background: Customer satisfaction is one of the key factors influencing businesses’ success and long-term profitability. Understanding customer needs and preferences is particularly important in the e-bike segment, which is experiencing dynamic growth. Satisfied customers make repeat purchases, and their recommendations help build a positive brand image, which is essential in a competitive market. Identifying the factors that most influence satisfaction enables companies to adapt their products and services to meet customer expectations better and thus secure a stable position in the market. Purpose of the article: The article aims to analyse customer satisfaction with e-bikes and identify its key factors. Based on the findings, it offers recommendations for dealers while also proposing a prediction model to understand customer needs better. Methods: Customer satisfaction with e-bikes was measured through an online survey in the form of a questionnaire. Data collection was conducted from January 2024 to the end of February 2024 and involved 388 respondents. The data collected will be used to create predictive models of customer satisfaction, the accuracy and quality of which will be subsequently compared. The analysis results will also be presented graphically, allowing for a better interpretation of the relationships and trends identified. Findings & Value added: Five models were created to predict customer satisfaction. The logistic regression method was the most effective based on overall accuracy and sensitivity. This method identified engine brand, battery capacity, and product purchase location as significant variables. This model allows sellers to tailor their offerings better and provide goods significantly contributing to higher customer satisfaction.
Source: Ekonomicko-manazerske spektrum
Authors: Lucia Svabova, Veronika Labosova, Martina Borovcova, Natalia Jarabicova
Institutions: University of Žilina, VSB - Technical University of Ostrava
Language: English
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W7081978006