From Chance to Serendipity: Knowledge Workers’ Experiences of Serendipitous Social Encounters

Abstract

Serendipity refers to uncontrolled circumstances that lead to unexpected yet fortunate discoveries. The phenomenon has been studied extensively in relation to information retrieval. However, serendipity in the context of social encounters has been the subject of few empirical studies. In professional life, social serendipity might result in benefits such as fruitful collaboration, successful recruitment, discovery of novel information, and acquisition of crucial new perspectives from peers. Despite the potential significance of serendipity, particularly for knowledge work, there is a lack of empirical understanding of related subjective experiences and the role of technology within the process of encountering unsought findings. This qualitative study investigates knowledge workers’ detailed narratives of serendipitous social encounters and the related factors through an analysis of 37 responses to an international online survey. We provide a detailed account of the experiential characteristics and contextual qualities of the reported instances of social serendipity. Finally, we discuss the seemingly minor role of technology in social serendipity and research avenues to computationally enhance social serendipity.

Networking in the Time of COVID

“…For the creation stage social serendipity, the making of new, potentially beneficial acquaintances not encountered in the course of normal business activity, is valued and is considered to be relatively high at largescale networking events where mingling occurs. Social serendipity and serendipity more generally have been theorized in other fields such as management, communication, and computer studies (Olshannikova et al 2020). Björneborn (2017) has suggested three broad affordances of serendipity: (1) diversifiability, being able to encounter diverse contents;…”

Utilizing Structural Network Positions to Diversify People Recommendations on Twitter

“…Our work highlights this goal toward diversification and heterogeneity, especially in the professional networking context where diversity is seen as a key driver for fruitful collaboration [2]. Diversifying people recommendations can enable unexpected yet valuable social encounters [12], which require alternative recommendation strategies to identify relevant people in the vast and complex Twitter network. Traditional recommender systems research seeks to optimize algorithmic accuracy and effectiveness [13,14], creating algorithms that can reproduce actors' current behavior as accurately as possible [15] rather than aiming at increasing diversity.…”

Serendipity in the city: User evaluations of urban recommender systems

“…Ge et al, 2017;Delva, Smets, Colpaert, Ballon, & Verborgh, 2020;Li & Tuzhilin, 2019;Shepard, 2011) or applications to connect strangers in public places (Paulos & Goodman, 2004). Research on serendipity has, however, demonstrated that serendipity evolves differently in different contexts inducing the need to further investigate serendipity in urban recommender systems in particular (Lutz, Pieter Hoffmann, & Meckel, 2017;Sun, Sharples, & Makri, 2011;Olshannikova, Olsson, Huhtamäki, Paasovaara, & Kärkkäinen, 2020).…”

“…These few examples include applications for urban navigation (Delva et al, 2020; Ge et al, 2017; Li & Tuzhilin, 2019; Shepard, 2011) or applications to connect strangers in public places (Paulos & Goodman, 2004). Research on serendipity has, however, demonstrated that serendipity evolves differently in different contexts inducing the need to further investigate serendipity in urban recommender systems in particular (Lutz et al, 2017; Olshannikova et al, 2020; Sun et al, 2011).…”