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This research explores the development of trust and trustworthiness between buyers and sellers in mobile app markets. Trust is critical for online transactions, yet its establishment is challenged by the unique environment of mobile markets, where low costs and limited filtering mechanisms leave buyers vulnerable. We aim to identify key trust factors for users, including integrity, ability, and benevolence, while also examining sellers' strategies to foster customer trust. Our analysis utilizes user comments from a diverse array of apps to gain insights into these dynamics.
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The Development of Trustand Trustworthiness between Buyers and Sellers on Mobile App Market Mengxue Zheng mzheng11@illinois.edu Wei Yang weiyang3@illinois.edu CS598-KGK: Social Spaces on the Internet Fall 2013 University of Illinois at Urbana Champaign
Motivation • Trust is important to the online store in general. • Low degrees of online consumer trust prevent consumers from purchasing and even from just window-shopping; (Gefen,2000) • The less verifiable and less controllable business environment of the Web. (Reichheld & Schefter, 2000) • The buyers’ and sellers’ behaviors on mobile app market could be different from those on traditional online store. • The low cost for sellers to cheat. • The app market doesn't set high standard for buyers like traditional shopping website such as eBay or Amazon. Lack of filtering/punishing mechanism for app market. • The cost for buyers to buy is different • Buyers pay lower price to get the product. A large portion of apps on market are free. • Buyers could be more vulnerable to the apps for security perspective. Malicious app could endanger the security and privacy of buyers.
Research Objectives • Objective1: Study the main factors that buyers form the trust • Objective2: Study the approaches that sellers take to gain customer's trust.
Definitions • Trust is the product of a set of trustworthiness beliefs. (Jarvenpaa et al., 1998; Mayer & Davis, 1999) • The beliefs include: • Integrity • The belief that the trusted party adheres to accepted rules of conduct, such as honesty and keeping promises. • Ability • The beliefs about the skills and competence of the trusted party. • Benevolence • The belief that the trusted party, aside from wanting to make a legitimate profit, wants to do good to the customer
Definitions in the case of mobile app market • Integrity • The belief that the purchased apps’ functionalities will be consistent with the functionalities in the descriptions. • Ability • The belief that the developers will write apps with fine quality or performance. • Benevolence • The belief that the purchased apps are not malwares or potentially unwanted applications (PUAs, e.g., Spyware,Trackware, Adware).
Research Questions • RQ1: For each category of apps, what are the weight of integrity, ability, and benevolence for user to form the trust? Which are the buyers main focus? • RQ1.1 If the main focus is ability, or benevolence, which aspects of ability or benevolence that users will most concern? • RQ2: For each category of apps, what are the approaches that sellers have taken to gain trust from the users?
Metrics • RQ1: The percentages of comments related to integrity, ability, and benevolence respectively in all buyers’ comments. • RQ1.1 If the main focus is ability, or benevolence, the percentages of comments for each aspects of ability or benevolence among all comments related to ability or benevolence ? • RQ2: The actions that sellers have taken to make the buyers change their ratings.
Methodologies • Data Extraction • Comments from top apps of both free and nonfree in each category • We pick top 6 apps from both free and nonfree list for each category. • In total of 300 Apps from 25 categories • Comments from all hybrid mobile apps using phonegap • To compensate the potential bias in popular apps. • Crawling 881 AppID from phonegap website, 581 of them have user comments. • Data Preprocessing • Data Analysis
Methodologies • Data Extraction • Data Preprocessing • Reduce words amount using a list of stop words (i.e., delete words without any meaning, e.g., a, an, we). • http://www.ranks.nl/resources/stopwords.html • Data Analysis
Methodologies • Data Extraction • Data Preprocessing • Data Analysis • Count the frequency for each word from the comments. We get a list of words and their frequency after that. • Identify the words that representing integrity, ability, and benevolence respectively, and manually verify the accuracy, then calculate their percentages. • Searching the updated comments after sellers’ changes. Find out their concerns and incentives to update the comments.
Result • General Findings • A large portion of positive comments do not reflect any users’ preferences. (Simple Comments) • Generally User focus more on ability. But the weights of each beliefs vary across different categories. • In each aspects of ability, compatibility, usability and stability are common user concerns. • The benevolence belief have more weights in GAME category.
