Why Do Users Switch Mobile Applications? Trialing Behavior as a Predecessor of Switching Behavior

Markus Salo · Markus Makkonen

16 citations0/0 files storedCommunications of the Association for Information Systems

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Technology Adoption and User Behaviour Customer Service Quality and Loyalty Customer churn and segmentation

Abstract

Mobile application providers face high user losses because users can easily and often switch to an alternative application. Researchers have recently started to study information technology (IT) switching. However, no studies have concentrated on the unique context of mobile applications. Mobile application switching differs from the switching behavior related to many other IT products and services because of the highlighted role of alternatives, beta versions, updates, reviews, and users’ spontaneous behavior. To address this gap, we develop a mobile-specific model by using a qualitative research approach. As a theoretical contribution, we introduce trialing behavior as a predecessor of switching behavior and present six new behavioral antecedents for them. As practical implications, we suggest ways for mobile application providers and developers to reduce user churn. Mobile application providers face high user losses because users can easily and often switch to an alternative application. Researchers have recently started to study information technology (IT) switching. However, no studies have concentrated on the unique context of mobile applications. Mobile application switching differs from the switching behavior related to many other IT products and services because of the highlighted role of alternatives, beta versions, updates, reviews, and users’ spontaneous behavior. To address this gap, we develop a mobile-specific model by using a qualitative research approach. As a theoretical contribution, we introduce trialing behavior as a predecessor of switching behavior and present six new behavioral antecedents for them. As practical implications, we suggest ways for mobile application providers and developers to reduce user churn.

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References

1989Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology 2003User Acceptance of Information Technology: Toward A Unified View1 2019Diffusion of Innovations 1 2012Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology1 2006Qualitative research: Methods in the social sciences 2003The DeLone and McLean Model of Information Systems Success: A Ten-Year Update 2001Understanding Information Systems Continuance: An Expectation-Confirmation Model1 2002Analyzing the Past to Prepare for the Future: Writing a Literature Review

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