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The main purpose of this paper is to explore the main characteristics of corporate social media guidelines (SMG) and determine whether companies communicate these guidelines effectively to employees.

An analysis of corporate SMG is conducted using the Competing Values Framework (CVF) formerly used to assess business and ethical codes. The sample is comprised of 20 multinational companies that publish their SMG online.

The results indicate the majority of the guidelines received average scores across the CVF framework, which implies the guidelines barely manage to stimulate change, direct action, provide facts or emphasize the importance of building trust.

A possible limitation of the research could be the issue of interpretability of the features of the framework. Hence, the quality of the research depends on the quality of the training raters receive prior to the guideline rating process. Additionally, the researchers were limited with the guideline availability and could analyze only the guidelines available online. This analysis can be broadened by identifying factors that may influence the characteristics of the guidelines (e.g. corporate culture or industry).

Managers can use this framework to analyze their companies’ guidelines to reveal the gaps, point to opportunities for improvement or take the findings into account when developing new guidelines.

The first paper that analyzes corporate SMG and their respective characteristics.

This paper aims to study the role of self-concept in consumer relationships with anthropomorphised conversational artificially intelligent (AI) agents. First, the authors investigate how the self-congruence between consumer self-concept and AI and the integration of the conversational AI agent into consumer self-concept might influence such relationships. Second, the authors examine whether these links with self-concept have implications for mental well-being.

This study conducted in-depth interviews with 20 consumers who regularly use popular conversational AI agents for functional or emotional tasks. Based on a thematic analysis and an ideal-type analysis, this study derived a taxonomy of consumer–AI relationships, with self-congruence and self–AI integration as the two axes.

The findings unveil four different relationships that consumers forge with their conversational AI agents, which differ in self-congruence and self–AI integration. Both dimensions are prominent in replacement and committed relationships, where consumers rely on conversational AI agents for companionship and emotional tasks such as personal growth or as a means for overcoming past traumas. These two relationships carry well-being risks in terms of changing expectations that consumers seek to fulfil in human-to-human relationships. Conversely, in the functional relationship, the conversational AI agents are viewed as an important part of one’s professional performance; however, consumers maintain a low sense of self-congruence and distinguish themselves from the agent, also because of the fear of losing their sense of uniqueness and autonomy. Consumers in aspiring relationships rely on their agents for companionship to remedy social exclusion and loneliness, but feel this is prevented because of the agents’ technical limitations.

Although this study provides insights into the dynamics of consumer relationships with conversational AI agents, it comes with limitations. The sample of this study included users of conversational AI agents such as Siri, Google Assistant and Replika. However, future studies should also investigate other agents, such as ChatGPT. Moreover, the self-related processes studied here could be compared across public and private contexts. There is also a need to examine such complex relationships with longitudinal studies. Moreover, future research should explore how consumers’ self-concept could be negatively affected if the support provided by AI is withdrawn. Finally, this study reveals that in some cases, consumers are changing their expectations related to human-to-human relationships based on their interactions with conversational AI agents.

This study enables practitioners to identify specific anthropomorphic cues that can support the development of different types of consumer–AI relationships and to consider their consequences across a range of well-being aspects.

This research equips marketing scholars with a novel understanding of the role of self-concept in the relationships that consumers forge with popular conversational AI agents and the associated well-being implications.