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This study aims to explore the factors affecting the adoption of mGov apps using the theoretical foundations of service-dominant (SD) logic. The authors specifically explore the impact of resource distribution (infrastructure and knowledge) and environmental variables (health infrastructure) among the Indian states on the adoption of Aarogya Setu (the COVID-19 tracking app) by their citizens.

For the meso-level study, the states of India are the unit of analysis. The authors have used secondary data published by the government and other reliable organizations. The study is based on 29 states of India and Delhi, a union territory and the capital of India. The authors conducted a regression and moderation analysis using SPSS and PROCESS macros on the collected secondary data.

The findings reveal that operand resources (state domestic productivity per capita and internet penetration rate) positively impact the adoption of the mGov app. The operant resource (literacy) and the environmental variable (health index) are, however, negatively affecting the adoption of the mGov app. On the other hand, another operant resource, digital literacy, was found to have no significant effect on the adoption of the mGov app. Further, the moderating variable, health index, is found to be moderating all the relationships except internet penetration and adoption of the mGov app.

The study is novel in two aspects. First, in using the theoretical foundation of SD logic to examine the factors impacting the adoption of mGov app. Second, it is a meso-level study, which is not a widely explored avenue in mGov research.

The purpose of this paper is to explore how the use of social media by citizens has impacted the traditional conceptualization and operationalization of political participation in the society.

This study is based on Teorell et al.’s (2007) classification of political participation which is modified to suit the current context of social media. The authors classified 15,460 tweets along three parameters suggested in the framework with help of supervised text classification algorithms.

The analysis reveals that Activism is the most prominent form of political participation undertaken by people on Twitter. Other activities that were undertaken include Formal Political participation and Consumer participation. The analysis also reveals that identity of participant does not play a classifying role as expected from the theoretical framework. It was found that the social media as a platform facilitates new forms of participation which are not feasible offline.

The current work considers only the microblogging platform of Twitter as the data source. For a more comprehensive insight, analysis of other social media platforms is also required.

To the best of the authors’ knowledge, this is one of the few analyses where such a large database covering multiple social media events has been created and analysed using supervised text classification algorithms. A large proportion of previous studies on social media have been based on case study and have limited analysis to only a particular event on social media. Although there exist a few works that have studied a vast and varied collection of social media data (Gaby and Caren, 2012; Shirazi, 2013; Rane and Salem, 2012), such efforts are few in number. This study aims to add to that stream of work where a wider and more generalized set of social media data is studied.