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Literature on the relationship between social performance and economic performance of social enterprises has long been inconclusive. This paper aims to investigate whether and, if so, how social performance contributes to economic performance of social enterprises. Specifically, drawing from the resource-based view and signalling theory, the study examines how the development of reputation, which enables social enterprises to signal the enterprises' stakeholders' commitment towards social causes, mediates the relationship between the two.

Employing a quantitative research design, data were collected from a sample of 164 social enterprises in the UK and analysed using structural equation modelling (SEM).

The results illustrate that whilst the direct relationship between social and economic performance is inconclusive, social performance contributes indirectly to improve economic performance through improving social enterprise reputation.

To the best of the authors' knowledge, this study is the first of this kind in the context of social enterprises which sheds light on the long-standing conflicting literature on the relationship between the dual objectives (i.e. social and economic) by providing reputation as the mediating variable.

There has been substantial research on branding globally. However, there is a dearth of studies empirically investigating branding strategies during the pandemic to mitigate COVID-19 effects on the airline industry. The paper considers three factors which are brand familiarity, brand communication and brand reputation to develop the brand trust of customers and that ultimately influence their brand preferences.

The study is based on self-administrative surveys as 450 questionnaires were spread, received 339 responses and a total of 301 questionnaires were selected for data analysis by structural-based modeling after the deletion of outliers and partially filled questionnaires. The data was collected through purposive sampling from Malaysian airports.

The findings confirm the relationship of brand communication, brand familiarity and brand reputation to brand trust and brand preference through mediation and directly except direct relationship of brand familiarity to brand preference. The study is limited to the provided dataset of surveys. The present study couldn't interview respondents which can be done by future studies and also effects of COVID-19 can be examined on related industries or through comparative studies among countries.

The present study is the first to investigate the effects of COVID-19 on airline brands and explored the strategies to respond to crises. The study is one of the rare studies that consider branding strategies to the uplift airline industry and mitigate post-pandemic effects from the airline sector.

This study aims to conceptualize the relationship of perceived artificial intelligence (AI) credibility with consumer-AI experiences. With the widespread deployment of AI in marketing and services, consumer-AI experiences are common and an emerging research area in marketing. Various factors affecting consumer-AI experiences have been studied, but one crucial factor – perceived AI credibility is relatively underexplored which the authors aim to envision and conceptualize.

This study employs a conceptual development approach to propose relationships among constructs, supported by 34 semi-structured consumer interviews.

This study defines AI credibility using source credibility theory (SCT). The conceptual framework of this study shows how perceived AI credibility positively affects four consumer-AI experiences: (1) data capture, (2) classification, (3) delegation, and (4) social interaction. Perceived justice is proposed to mediate this effect. Improved consumer-AI experiences can elicit favorable consumer outcomes toward AI-enabled offerings, such as the intention to share data, follow recommendations, delegate tasks, and interact more. Individual and contextual moderators limit the positive effect of perceived AI credibility on consumer-AI experiences.

This study contributes to the emerging research on AI credibility and consumer-AI experiences that may improve consumer-AI experiences. This study offers a comprehensive model with consequences, mechanism, and moderators to guide future research.

The authors guide marketers with ways to improve the four consumer-AI experiences by enhancing consumers' perceived AI credibility.

This study uses SCT to define AI credibility and takes a justice theory perspective to develop the conceptual framework.