MOBILE APP MARKETING
Research Paper
Why users won't download your app*
*Determinants Affecting Mobile Application Adoption Based on Consumers’ Adoption Rate - An Empirical Analysis
INTRODUCTION
Mature Market | High competition
65% of all smartphone users didn't download a single app within the last month
The majority of users gets less motivated in trying new apps resulting from their satisfaction with the applications they are already using
Users with a monthly adoption rate of 6 or more mobile applications represent with 5.2% a minority
Each year, the number of apps increase by nearly 38%
83% of all apps available on the iOS App Store are so called Zombie-Apps, as they are not even visible
UNDERSTANDING WHAT USERS VALUE
Let's get scientific: Proposed Conceptual Framework and Hypotheses
The framework of the Unified Theory of Acceptance and Use of Technology 2, developed by Venkatesh et al. in 2012, which explains up to 74% of the variance in intention of technology acceptance, was adapted into the mobile application adoption context in order to analyze the influence of its core determinants on mobile application adoption based on user’s current adoption rate (Analyzed adoptions rates: 0 Apps, 1 -2 Apps, 3 - 5 Apps, and 6+ Apps/month and complete sample).
Hover over each construct and hypothesis (H1 - H7) to find out more!
The framework of the Unified Theory of Acceptance and Use of Technology 2, developed by Venkatesh et al. in 2012, which explains up to 74% of the variance in intention of technology acceptance, was adapted into the mobile application adoption context in order to analyze the influence of its core determinants on mobile application adoption based on user’s current adoption rate (Analyzed adoptions rates: 0 Apps, 1 -2 Apps, 3 - 5 Apps, and 6+ Apps/month and complete sample).
Check the statistical analysis below to find out more about each construct and hypothesis (H1 - H7)!
Independent Variable - The degree to which using mobile applications will provide benefits to consumers in performing certain activities.
Independent Variable - The degree of ease associated with consumers' use of mobile applications.
Independent Variable - Consumers' perceptions of the resources and support available to use mobile applications.
Independent Variable - The extent to which consumers perceive that important others (e.g. family and friends) believe they should adopt mobile applications.
Independent Variable - The fun or pleasure derived from using mobile applications.
Independent Variable - Consumers' cognitive trade off between the perceived benefits of mobile applications and the monetary effort for using them.
Independent Variable - The extent to which people tend to use mobile applications automatically because of learning.
Dependent Variable - Consumers' intention and actual usage of mobile applications.
Performance Expectancy influences the adoption of mobile applications for each adoption rate.
Effort Expectancy influences the adoption of mobile applications for each adoption rate.
Facilitating Conditions influences the adoption of mobile applications for each adoption rate.
Social Influence influences the adoption of mobile applications for each adoption rate.
Hedonic Motivation influences the adoption of mobile applications for each adoption rate.
Price Value influences the adoption of mobile applications for each adoption rate.
Habit influences the adoption of mobile applications for each adoption rate.
STATISTICAL ANALYSIS*
*I won't bore you with the math. Here are just some key facts.
N = 200
α = 0.05
Survey Strategy (Questionnaire)
Assessment via Multiple Linear Regressions in SPSS
Up to 79% of variance explained
| Hypothesis | Adoption Rate (within last month) | Remark | ||||
|---|---|---|---|---|---|---|
| N = 200 | 0 | 1 - 2 | 3 - 5 | 6+ | ||
| H1: Performance Expectancy influences the adoption of mobile applications for each adoption rate | Not Supported | Not Supported | Not Supported | Not Supported | Not Supported | Not Supported |
| H2: Effort Expectancy influences the adoption of mobile applications for each adoption rate | Supported | Supported (negative influence) | Not Supported | Not Supported | Supported | Partially Supported |
| H3: Facilitating Conditions influences the adoption of mobile applications for each adoption rate | Not Supported | Supported | Not Supported | Not Supported | Not Supported | Partially Supported |
| H4: Social Influence influences the adoption of mobile applications for each adoption rate | Not Supported | Supported (negative influence) | Not Supported | Not Supported | Not Supported | Partially Supported |
| H5: Hedonic Motivation influences the adoption of mobile applications for each adoption rate | Supported | Supported | Not Supported | Not Supported | Not Supported | Partially Supported |
| H6: Price Value influences the adoption of mobile applications for each adoption rate | Not Supported | Not Supported | Not Supported | Not Supported | Not Supported | Not Supported |
| H7: Habit influences the adoption of mobile applications for each adoption rate | Supported | Supported | Supported | Supported | Supported | Supported |
KEY TAKEAWAYS
Conclusion and Managerial Implications
It is not appropriate anymore, to target mobile application users in general*
*But how can you attract more users with lower adoption rates to adopt your mobile applications?
Reinforce habit by providing upgrades, app notifications or in-app advertising
Advertise fun and enjoyable aspects of your mobile application
Help less technologically advanced users via In-App tutorials
Distinguish your product from similar mobile applications by adding other useful features and advertise the additional benefits to perform certain activities with your mobile app
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