Purpose The purpose of this paper is to investigate the motivations, concerns, benefits and consequences associated with non-use of social media. In doing so, it extends Wyatt’s commonly used taxonomy of non-use by identifying new dimensions in which to understand non-use of social media. This framework encompasses a previously unidentified category of non-use that is critical to understand in today’s social media environment. Design/methodology/approach This is an exploratory interview study with 17 self-identified social media non-users distributed across age groups and socioeconomic backgrounds. A thematic analysis is conducted based on a novel extension of Wyatt’s framework and the risk-benefits framework. This is supplemented by open coding to allow for emerging themes. Findings This paper provides empirical insights into a formerly uninvestigated population of non-users who are prevented from using social media because of social engagement (rather than functional) barriers. It identifies how these individuals face social consequences both on and off social media, resulting in social disenfranchisement. Research limitations/implications This is an initial exploration of the phenomenon using an interview study. For generalizability, future research should investigate non-use with a broader and random sample. Practical implications This paper includes design recommendations and implications for social media platform designers to mitigate the consequences experienced by socially disenfranchised non-users. Social implications Addressing concerns of this newly identified class of non-users is of utmost importance. As others are increasingly connected, these non-users are left behind and even ostracized – showing the dark sides of social media use and non-use. Originality/value This work identifies types of non-use of social media previously unrecognized in the literature.

In algorithmic work, algorithms execute operational and management tasks such as work allocation, task tracking and performance evaluation. Humans and algorithms interact with one another to accomplish work so that the algorithm takes on the role of a co‐worker. Human–algorithm interactions are characterised by problematic issues such as absence of mutually co‐constructed dialogue, lack of transparency regarding how algorithmic outputs are generated, and difficulty of over‐riding algorithmic directive – conditions that create lack of clarity for the human worker. This article examines human–algorithm role interactions in algorithmic work. Drawing on the theoretical framing of organisational roles, we theorise on the algorithm as role sender and the human as the role taker. We explain how the algorithm is a multi‐role sender with entangled roles, while the human as role taker experiences algorithm‐driven role conflict and role ambiguity. Further, while the algorithm records all of the human's task actions, it is ignorant of the human's cognitive reactions – it undergoes what we conceptualise as ‘broken loop learning’. The empirical context of our study is algorithm‐driven taxi driving (in the United States) exemplified by companies such as Uber. We draw from data that include interviews with 15 Uber drivers, a netnographic study of 1700 discussion threads among Uber drivers from two popular online forums, and analysis of Uber's web pages. Implications for IS scholarship, practice and policy are discussed.

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This chapter introduces relevant privacy frameworks from academic literature that can be useful to practitioners and researchers who want to better understand privacy and how to apply it in their own contexts. We retrace the history of how networked privacy research first began by focusing on privacy as information disclosure. Privacy frameworks have since evolved into conceptualizing privacy as a process of interpersonal boundary regulation, appropriate information flows, design-based frameworks, and, finally, user-centered privacy that accounts for individual differences. These frameworks can be used to identify privacy needs and violations, as well as inform design. This chapter provides actionable guidelines for how these different frameworks can be applied in research, design, and product development.

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