The Internet of Things (IoT) adds a new dimension to how people and things can communicate and collaborate. Society and the Internet are now being interconnected tightly and purposely. The research aims to analyze how IoT as a persuasive technology can affect human behavior and increase the awareness and effectiveness of IoT products among users. How will the Internet of Things infrastructure facilitate humans to change their attitudes and behaviors towards specific routine work? Our objective is to analyze which factors influence the acceptance and rejection of particular behaviors and the core motivators that persuade people to do something or to avoid something. We aim to determine whether IoT will facilitate humans to change their focused behaviors or not. Because of the rapid convergence of digital and physical worlds and the advent of digital technology, the Internet and social media have opened up a new world of affordances, constraints, and information flows from a design perspective. This article discusses how digital architecture affects behavior and the ramifications for designers who want to influence behavior for social and environmental good. In this paper we aim to give a brief introduction to persuasive technology, especially as it pertains to human adoption of IoT technology. We discuss a number of current research opportunities in IoT gadgets and their adoptions [1]. Our results indicate that persuasive (IoT) infrastructure can be expected to achieve a change of driving behaviour among their adopters. Furthermore, attention should be paid to an appropriate selection and implementation of persuasive strategies.

Crowdsourcing is a famous technique to get innovative ideas and soliciting contribution from a large online community particularly in e-business. This technique is contributing towards changing the current business techniques and practices. It is also equally famous in analysis and design of m-business services. Mobile app stores are providing an opportunity for its users' to participate and contribute in the growth of mobile app market. App reviews given by users usually contain active, heterogeneous and real life user experience of mobile app which can be useful to improve the quality of app. Best to our knowledge, the strength of mobile app reviews as a crowdsource is not fully recognized and understood by the community yet. In this paper, we have analysed a crowdsourcing reference model to find out which features of crowdsource are present and are related to our case of mobile app reviews as a crowdsource. We have analyzed and discussed each construct of the reference model from the perspective of mobile app reviews. Moreover, app reviews as a crowdsourcing technique is discussed by utilizing the four pillars of the reference model: the crowd, the crowdsourcer, the crowdsourcing, and the crowdsourcing platform. We have also identified and partially validated certain constructs of the model and highlighted the significance of app reviews as a crowdsource based on existing literature. In this study, only one crowdsourcing reference model is used which can be a limitation of our study. The study can be further investigated and compared with other crowdsourcing reference models to get better insights of app reviews as a crowdsource. We believe that the understanding of app reviews as a crowdsourcing technique can lead to the further development of the mobile app market and can open further research opportunities.

Mobile app reviews are gaining importance as a crowd source to improve the quality of mobile apps. Mobile app review systems are providing a platform for users to share their experiences and to support in decision making for a certain app. Developers, on the other side, are utilizing the review system to get real-life user experience as a source of improving their apps. This paper has analyzed existing review system and proposed few recommendations for the current review system to improve the quality of app reviews. The proposed review system can help for collection and analysis of user reviews to make it more meaningful with less intensive data mining techniques. The proposed system can help the end users to get an overview of mobile apps. The recommendations in this paper are derived from the existing literature related to app reviews and will help to improve the current review systems for better app reviews from users as well as developers perspective.