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This study measured influencer characteristics, consumer emotions, self-construal, and behavioural intentions of Gen Z consumers for selecting fast-food restaurants in India. A consumer behaviour model was conceptualized based on established theories.

A cross-sectional design was employed for hypothesis testing. Influencer characteristic perceptions, consumer emotions, self-construal, and behavioural intentions were measured for Gen Z consumers in Tier 1 cities in India using structural equation modelling.

Influencer characteristics significantly influenced behavioural intentions, consumer emotions, and self-construal in Gen Z consumers. Self-construal was also a significant predictor of behavioural intentions. Consumer emotions had a negative effect on behavioural intentions. Self-construal was a mediator between influencer characteristics and behavioural intentions and between consumer emotions and behavioural intentions. However, consumer emotions did not mediate the relationship between influencer characteristics and behavioural intentions.

Marketers can leverage these insights to design influencer campaigns that resonate with the emotions and self-construal of Gen Z consumers. Microinfluencers with characteristics that align with the target demographic’s emotions and self-perception can be strategically chosen.

Only a limited number of studies have investigated the influence of social media marketing on consumer behaviour within the fast-food industry, specifically with Gen Z consumers. This study sheds new light on the behavioural intention of Gen Z consumers predicted through influencer characteristics, consumer emotions, and self-construal through a conceptual model. The results support choosing microinfluencers and investing in them judiciously to promote fast-food businesses.

Gig workers form the backbone of any crowdsourcing platform where they showcase their talent and choose a job of their choice and freedom. The study explores the role of information quality (IQ) and social-mediated dialogue (SMD) in evaluating gig worker engagement and productivity on crowdsourcing platforms. The authors also propose to understand how gig worker productivity could be improved under the moderating effect of game elements.

A conceptual model was developed and empirically tested by integrating media richness theory and dialogic public relation theory. Data were collected from gig workers that are involved in crowdsourcing activities for the past three years. An overall sample of 346 gig workers contributing to at least one of the crowdsourcing platforms was collected. The authors tested the hypotheses using Warp PLS 7.0. Warp PLS 7.0 uses partial least square (PLS) structured equation modeling (SEM) and has been used widely to test path analytical models.

Results reveal that the information quality plays an essential role in the SMD, thereby fostering gig workers' productivity and engagement, which could be improved in the presence of game elements due to their nature of supporting rewards. However, engagement in the platform leading to improved productivity was not supported.

The study lays practical foundations for crowdsourcing platforms as it sets the importance of both IQ and dialogic communication channels. The two-way communication between gig workers and the platforms via accurate, timely, valuable and reliable information forms the key to the task's success. The introduction of the right game element will help to achieve better engagement and productivity.

This study also offers a new dimension to media richness theory and dialogic public relation theory in crowdsourcing platforms. The results would help platform designers and gig employers understand gig workers' quality and performance in a platform economy. The study uniquely positions itself in the area of crowdsourcing platforms by using game elements.