Conclusion

Major Findings
The research leads us to the following findings:

• As suggested by TAM, perceived ease of use, perceived usefulness and credibility are important predictors for acceptance of Smartphone applications. In addition, Social norms and Flow experience bear a direct impact on the adoption of Smartphone apps.
• The relative importance of purchase determinants of Smartphone apps vary according to the category of the app, i.e.,  Productivity, Entertainment, Information and Networking .
• The users of Smartphone apps can be broadly classified into 2 clusters –1.   Those who use their Smartphone mostly for Productivity or Information apps. These users give high importance to Usefulness and Trial Performance of an app while purchasing it.2.   Those who use their Smartphone mostly for Entertainment or Networking apps. These users give high importance to Word of Mouth and Pleasure while purchasing an app.

Implications

User acceptance of Smartphone applications is of paramount importance for both researchers and industry practitioners. A deeper insight into theory-based research is required to better understand the underlying motivators and barriers that will lead users to or inhibit them from adopting these applications. In this paper we have explored and critically reviewed existing technology acceptance theories.

Businesses must adapt to the technological changes in the business world. App developers must be able to meet users’ needs. Our model and results can help smartphone application developers better understand how to meet the desires of their target users. This study provides them with a framework for which areas they need to focus upon when launching new apps, such as making their app easier to use, and enhancing the perceived usefulness and perceived credibility of the applications.

Limitations

1. This study was restricted to Social media applications such as Facebook, LinkedIn and Skype. Whether the results hold true for other categories of apps as well needs to be tested.

2. This study directly measured flow using a three-item on a Likert-scale, and required users to fill out questionnaires regarding their previous flow experience in usage of apps. Thus, a bias exists because the sample was self-selected and only those users with experience answered the questionnaires.

3. This research investigated users whose age range was between 20 to 30 years. Majority of the respondents were students. So the data may be biased to some extent.

Future Research

Future researchers may want to examine the behavioral intention characteristics of other age groups and look at Smartphone applications’ acceptance in other countries. Expanding the number of constructs measured may provide researchers with new insight on users’ acceptance of apps. Adding other variables could increase the predictive power of the model. Researchers could also look at the correlation between the Smartphone OS and acceptance of apps. Researchers may further look to validate if the model holds true for other category of apps as well, such as Productivity apps.