Unpacking the Drivers of Dissatisfaction and Satisfaction in a Fitness Mobile Application
This research investigates the factors influencing user satisfaction and dissatisfaction in fitness mobile applications. It employs Herzberg’s two-factor model through text mining to classify Fitbit mobile app attributes into satisfiers and dissatisfiers. The Fitbit app was chosen due to its prevale...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-09-01
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| Series: | Behavioral Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-328X/13/9/782 |
| _version_ | 1797581208656281600 |
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| author | Minseong Kim Sae-Mi Lee |
| author_facet | Minseong Kim Sae-Mi Lee |
| author_sort | Minseong Kim |
| collection | DOAJ |
| description | This research investigates the factors influencing user satisfaction and dissatisfaction in fitness mobile applications. It employs Herzberg’s two-factor model through text mining to classify Fitbit mobile app attributes into satisfiers and dissatisfiers. The Fitbit app was chosen due to its prevalence in the United States. The study analyzes 100,000 English reviews from the Fitbit app on the Google Play Store, categorizing attributes. It identifies three dissatisfying categories (functional, compatibility, paid services) and three satisfying categories (gratification, self-monitoring, self-regulation), comprising 25 sub-attributes. This classification offers in-depth insights into what drives user contentment or discontent with fitness apps. The findings contribute to the fitness app domain by applying text-mining and Herzberg’s model. Researchers can build upon this foundation, and practitioners can use it to enhance app experiences. However, this research relies on user reviews, often lacking comprehensive explanations. This limitation may hinder a profound understanding of the underlying psychological aspects in user sentiments. Nonetheless, this study takes strides toward optimizing fitness apps for users and developers. |
| first_indexed | 2024-03-10T23:02:03Z |
| format | Article |
| id | doaj.art-0ae407d4bce340bc9793f140ae96d37e |
| institution | Directory Open Access Journal |
| issn | 2076-328X |
| language | English |
| last_indexed | 2024-03-10T23:02:03Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Behavioral Sciences |
| spelling | doaj.art-0ae407d4bce340bc9793f140ae96d37e2023-11-19T09:35:41ZengMDPI AGBehavioral Sciences2076-328X2023-09-0113978210.3390/bs13090782Unpacking the Drivers of Dissatisfaction and Satisfaction in a Fitness Mobile ApplicationMinseong Kim0Sae-Mi Lee1Department of Management & Marketing, College of Business, Louisiana State University Shreveport, Shreveport, LA 71115, USASchool of Global Business, Kyungil University, 50 Gamasil-gil, Hayang-eup, Gyeongbuk, Gyeongsan-si 38428, Republic of KoreaThis research investigates the factors influencing user satisfaction and dissatisfaction in fitness mobile applications. It employs Herzberg’s two-factor model through text mining to classify Fitbit mobile app attributes into satisfiers and dissatisfiers. The Fitbit app was chosen due to its prevalence in the United States. The study analyzes 100,000 English reviews from the Fitbit app on the Google Play Store, categorizing attributes. It identifies three dissatisfying categories (functional, compatibility, paid services) and three satisfying categories (gratification, self-monitoring, self-regulation), comprising 25 sub-attributes. This classification offers in-depth insights into what drives user contentment or discontent with fitness apps. The findings contribute to the fitness app domain by applying text-mining and Herzberg’s model. Researchers can build upon this foundation, and practitioners can use it to enhance app experiences. However, this research relies on user reviews, often lacking comprehensive explanations. This limitation may hinder a profound understanding of the underlying psychological aspects in user sentiments. Nonetheless, this study takes strides toward optimizing fitness apps for users and developers.https://www.mdpi.com/2076-328X/13/9/782physical activityfitnesstechnologytwo-factor modelmobile application |
| spellingShingle | Minseong Kim Sae-Mi Lee Unpacking the Drivers of Dissatisfaction and Satisfaction in a Fitness Mobile Application Behavioral Sciences physical activity fitness technology two-factor model mobile application |
| title | Unpacking the Drivers of Dissatisfaction and Satisfaction in a Fitness Mobile Application |
| title_full | Unpacking the Drivers of Dissatisfaction and Satisfaction in a Fitness Mobile Application |
| title_fullStr | Unpacking the Drivers of Dissatisfaction and Satisfaction in a Fitness Mobile Application |
| title_full_unstemmed | Unpacking the Drivers of Dissatisfaction and Satisfaction in a Fitness Mobile Application |
| title_short | Unpacking the Drivers of Dissatisfaction and Satisfaction in a Fitness Mobile Application |
| title_sort | unpacking the drivers of dissatisfaction and satisfaction in a fitness mobile application |
| topic | physical activity fitness technology two-factor model mobile application |
| url | https://www.mdpi.com/2076-328X/13/9/782 |
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