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Digital platforms (DP) are transforming service delivery and affecting associated actors. The position of DPs is impacted by the regulations. However, emerging economies often lack the regulatory environment to support DPs. This paper aims to explore the regulatory developments for DPs using the multi-level perspective (MLP).

The paper explores regulatory developments of ride-hailing platforms (RHPs) in India and their impacts. This study uses qualitative interview data from platform representatives, bureaucrats, drivers, experts and policy documents.

Regulatory developments in the ride-hailing space cannot be explained as a linear progression. The static institutional assumptions, especially without considering the multi-actors and multi-levels in policy formulation, do not serve associated actors adequately in different times and spaces. The RHPs regulations must consider the perspective of new RHPs and the support available to them. Non-consideration of short- and long-term perspectives of RHPs may have unequal outcomes for established and new RHPs.

This research has implications for the digital economy regulatory ecosystem, DPs and implications for policymakers. Though the data from legal documents and qualitative interviews is adequate, transactional data from the RHPs and interviews with judiciary actors would have been insightful.

The study provides insights into critical aspects of regulatory evolution, governance and regulatory impact on the DPs’ ecosystem. The right balance of regulations according to the business models of DPs allows DPs to have space for growth and development of the platform ecosystem.

This research shows the interactions in the digital space and how regulations can impact various actors. A balanced policy can guide the paths of DPs to have equal opportunities.

DP regulations have a complex structure. The paper studies regulatory developments of DPs and the impacts of governance and controls on associated players and platform ecosystems.

Influencer transgressions can disappoint their followers. However, there is a lack of clarity about the effects of a false allegation on an influencer–follower relationship. Drawing from cognitive dissonance and moral reasoning theory, the current study aims to examine how this relationship is shaped across three time periods (before the allegation is leveled, after the allegation is leveled, and when the allegation is found to be baseless).

We study comments posted by followers of two falsely alleged social media influencers (SMI) on their YouTube and Instagram channels. Latent Dirichlet allocation (LDA) followed by netnography is used for thematic analysis. LDA is a social media topic modeling method that processes a statistically representative set of words to explain the tone and tenor of qualitative conversations. A sentiment analysis of the comments is done using SentiStrength.

When an allegation is leveled initially, the response from followers is overwhelmingly negative toward the influencer owing to moral coupling. However, when the allegations are proven to be false, the followers return to a positive opinion of the influencer, owing to feelings of dissonance and guilt.

The study contributes to the fields of influencer marketing, cognitive dissonance and moral reasoning. It highlights how endorsers can take advantage of the positive sentiment that arises once an accused SMI’s transgression is proven false.

This study introduces the concept of “Sentiment Reversal,” which is exhibited in the social media space. In this phenomenon, sentiments move from negative to positive toward the falsely accused SMI as they are vindicated of the previous charge.

This study aims to examine the factors that influence the adoption of public services through the latest smart apps designed to provide maximum services within urban local government.

The research is based on a thorough literature review, expert advice on public service delivery and four complementary theories. An empirical survey used a structured questionnaire to collect primary data from citizens in Indian local governments. The data was analysed using structural equation modelling (SEM) and SmartPLS version 4 to assess the measurement and structural models.

The study confirmed that service quality, trust, effort expectancy and social influence are key factors for adopting smart app services. Trust and intention are mediators, while performance expectations do not impact the decision to use smart app services.

The study includes a moderate sample size and a focus on a single developing country, which restricts its cross-cultural applicability. This research investigated the factors influencing individual adoption and use of local government services delivered through smart apps.

The study has significant implications for policymakers, researchers and implementers. It provides insights for developing strategies to increase the use of smart services, thereby enhancing local governance's efficiency, transparency and accountability.

This study enhances existing knowledge by adding service quality, trust and adoption to the model, which helps examine the factors affecting adoption. With limited research on local smart service app adoption, this study provides valuable insights.