Platform adoption by mobile application developers: A multimethodological approach

Song, Jaeki; Baker, Jeff; Wang, Ying; Choi, Hyoung-Yong; Bhattacherjee, Anol

doi:10.1016/j.dss.2017.12.013

Cited by 27 publications

(11 citation statements)

References 101 publications

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“…In the original model of Venkatesh et al [8] and the extended model UTAUT 2 [9], social influence measures the extent to which users are influenced by what others (friends, family) think about the use of technology. Some previous papers in the same online purchase context show that social influence is a very strong antecedent of behavioral intention [20,[34][35][36] and adoption of an IT platform [37]. Thus, we formed a new hypothesis: Hypothesis 3 (H3).…”

Section: Theoretical Background and Hypothesesmentioning

confidence: 94%

Online Recommendation Systems: Factors Influencing Use in E-Commerce

et al. 2020

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The increasing use of artificial intelligence (AI) to understand purchasing behavior has led to the development of recommendation systems in e-commerce platforms used as an influential element in the purchase decision process. This paper intends to ascertain what factors affect consumers’ adoption and use of online purchases recommendation systems. In order to achieve this objective, the Unified Theory of Adoption and Use of Technology (UTAUT 2) is extended with two variables that act as an inhibiting or positive influence on intention to use: technology fear and trust. The structural model was assessed using partial least squares (PLS) with an adequate global adjustment on a sample of 448 users of online recommendation systems. Among the results, it’s highlighted the importance of the inhibiting role of technology fear and the importance that users attach to the level of perceived trust in the recommendation system are highlighted. The performance expectancy and hedonic motivations have the greatest influence on intention to use these systems. Based on the results, this work provides a relevant recommendation to companies for the design of their e-commerce platforms and the implementation of online purchase recommendation systems.

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Section: Theoretical Background and Hypothesesmentioning

confidence: 94%

Online Recommendation Systems: Factors Influencing Use in E-Commerce

et al. 2020

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“…However, the “adoption” process and users’ behavioral factors that influence this process are substantially under-investigated ( Harris et al, 2016 ). Interestingly, expanding the perspective on the literature on apps to include the adoption of more general information and communication technology (ICTs), the number of studies on their adoption increases ( Palau-Saumell, Forgas-Coll, Sánchez-García, & Robres, 2019 ; Song, Baker, Wang, Choi, & Bhattacherjee, 2018 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Studies investigating the adoption of ICTs have primarily applied different theoretical models and theories ( Palau-Saumell et al, 2019 ), such as the theory of reasoned action (TRA), the theory of planned behavior (TPB), the technology acceptance model (TAM), the unified theory of acceptance and use of technology (UTAUT) 1 and 2, the innovation diffusion theory (IDT), and the social cognitive theory (SCT) ( Song et al, 2018 ). The TRA has been developed to study human behavior from a social psychology perspective by considering one's feeling toward a specific behavior (attitude) and their perception regarding other people's perspective about that specific behavior (subjective norms; Kang, Ha, & Hambrick, 2015 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Finally, the UTAUT-2 suggests that the intention to use a technology is influenced by performance expectancy, effort expectancy, social influence, hedonic motivation, price value, and habit; at the same time, the actual usage is mainly influenced by the intentions to use, facilitating conditions, and habit ( Palau-Saumell et al, 2019 ). An additional theory that has been often used in ICTs literature is the IDT ( Song et al, 2018 ). This theory includes variables such as relative advantage (of economic gains or perceived convenience), complexity (of the necessary effort), compatibility (with users' values, needs, and experience), observability (the degree to which innovation products are visible to others), and trialability (the degree to which products are experimented with before adoption) ( Song et al, 2018 ; Min, So, & Jeong, 2019 , based on Rogers, 1995 , pp.…”

Section: Literature Reviewmentioning

confidence: 99%

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Promoting built-for-disaster-purpose mobile applications: An interdisciplinary literature review to increase their penetration rate among tourists

Aliperti

Cruz

2020

Journal of Hospitality and Tourism Management

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“…Android, iOS or Windows Phone, or simply WP, in this study). From a technical dimension, a large amount of data is often readily available in those Q&A repositories, and the data is stable and not influenced by researchers (Shull et al, 2008). In this scenario, we can use methods from mining software repositories to conduct empirical studies in the ecosystem field (Farias et al, 2016).…”

Section: Mobile Software Ecosystemsmentioning

confidence: 99%

Supporting governance of mobile application developers from mining and analyzing technical questions in stack overflow

Fontão

Ábia

Wiese

et al. 2018

J Softw Eng Res Dev

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There is a need to improve the direct communication between large organizations that maintain mobile platforms (e.g. Apple, Google, and Microsoft) and third-party developers to solve technical questions that emerge during the project and development of developers' contributions in a Mobile Software Ecosystem (MSECO). In this context, those organizations may not know how to define and evolve strategies to govern their developers towards achieving their organizational goals. Such organizations use an infrastructure to support developers, for example, questions and answers (Q&A) portals such as Stack Overflow. Interactions among developers in these portals feed a Q&A repository that can serve as a mechanism to understand and define strategies to support developers. In this paper, we mined 1,568,377 technical questions from Stack Overflow related to Android, iOS, and Windows Phone platforms. Next, we performed comparisons among those MSECO regarding: (i) developers' activity intensity, (ii) hot-topics (using Latent Dirichlet allocation algorithm) from all and more commented/viewed questions, (iii) "What" and "How to" questions, (iv) hot-topics from more viewed unanswered questions, and (v) relationship among questions and official developer events. From the results, we identified four key insights: recruiting, educating, and monitoring strategies; barrier reduction; management of technology insertion; and fostering of relationships. The relevance of the four key insights to support developer governance was evaluated by practitioners through a survey. Finally, for each key insight we associated a total of 10 strategies to support developer governance activities. Such strategies were extracted from 65 studies identified through a systematic mapping of the literature. IntroductionIn Software Engineering, the relationship among mobile application developers and an organization responsible for a technological platform (keystone) which involves cooperation and competition has been investigated as a Software Ecosystem (SECO) (Bosch, 2009). In the mobile application (app) scenario, this context refers to a specific type of SECO, known as Mobile Software Ecosystem (MSECO) (Lin & Ye, 2009;Fontão et al., 2015). The developer is an essential actor to sustain MSECO contributions, such as apps and technical documentation (Fontão et al., 2016;Koch & Kerschbaum, 2014). Such contributions are usually stored in an official, internal MSECO repository, such as Android 1 and Apple 2 Developers repositories. Moreover, there are links between MSECO and external repositories, such as code (e.g. GitHub) (Casalnuovo et al., 2015) and questions and answers (Q&A) repositories. As an example of an external Q&A repository, Stack Overflow has a set of technical questions/answers that arise from the use of APIs, SDKs, and development tools (Ahmad et al., 2018). Such external repositories help to maintain the interaction among developers over a common platform, resulting in a set of contributions and influencing directly or indirectly the...

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Platform adoption by mobile application developers: A multimethodological approach

Abstract: Abstract

Cited by 27 publications

References 101 publications

Online Recommendation Systems: Factors Influencing Use in E-Commerce

Online Recommendation Systems: Factors Influencing Use in E-Commerce

Promoting built-for-disaster-purpose mobile applications: An interdisciplinary literature review to increase their penetration rate among tourists

Supporting governance of mobile application developers from mining and analyzing technical questions in stack overflow

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