A Framework To Measure the Trustworthiness of the User Feedback in Mobile Application Stores

Abstract: Context: Mobile application stores like Google Play, Apple store, Windows store have over 3 million apps. Users download the applications from their respective stores and they generally prefer the apps with the highest ratings. In response to the present situation, application stores provided the categories like editor’s choice or top charts, providing better visibility for the applications. Customer reviews play such critical role in the development of the application and the organization, in such case there might be flawed reviews or biased opinions about the application due to many factors. The biased opinions and flawed reviews are likely to cause user review untrustworthiness. The reviews or ratings in the mobile application stores are used by the organizations to make the applications more efficient and more adaptable to the user. The context leads to importance of the user’s review trustworthiness and managing the trustworthiness in the user feedback by knowing the causes of mistrust. Hence, there is a need for a framework to understand the trustworthiness in the user given feedback. Objectives: In the following study the author aims for the accomplishment of the following objectives, firstly, exploring the causes of untrustworthiness in user feedback for an application in the mobile application stores such as google play store. Secondly, Exploring the effects of trustworthiness on the users and developers. Finally, the aim is to propose a framework for managing the trustworthiness in the feedback. Methods: To accomplish the objectives, author used qualitative research method. The data collection method is an interview-based survey that was conducted with 13 participants, to find out the causes of untrustworthiness in the user feedback from user’s perspective and developer’s perspective. Author follows thematic coding for qualitative data analysis. Results:Author identifies 11 codes from the description of the transcripts and explores the relationship among the trustworthiness with the causes. 11 codes were put into 4 themes, and a thematic network is created between the themes. The relations were then analyzed with cost-effect analysis. Conclusions: We conclude that 11 causes effect the trustworthiness according to user’s perspective and 9 causes effect the trustworthiness according to the developer’s perspective, from the analysis. Segregating the trustworthy feedback from the untrustworthy feedback is important for the developers, as the next releases should be planned based on that. Finally, an inclusion and exclusion criteria to help developers manage trustworthy user feedback is defined.