The Relevance of Relational Ontologies in Driving New Methodological Approaches in Virtual and Digital Contexts

Introduction

In the age of Big Data, thick data from qualitative research continues to be of interest (Thompson, 2019). Often rooted in the anthropological and ethnographic tradition, the qualitative methods used in social sciences have been inspired by humanist ontology, which focuses on the human as a source of knowledge (Levy & Hollan, 2014). However, in a digitalized and virtual word, social personal interactions as well as formal and informal relationships are often mediated by technology. Moreover, social phenomena are not only anchored in a geographical space as people move and both personal and professional connections can be networked, sometimes offline and sometimes online. This perspective is a challenge for qualitative methods. For example, Law (2004) states that to understand a fluid or networked word, ethnography needs to work differently. For example, computer-mediated communication is incorporated into every aspect of social and daily life, so technological development increases the range and scope of social spaces and the time and forms of participation. And so, in a digitalized and virtual society, spatial relationships cannot be considered as distinct (Dholakia, Reyes, & Bonoff, 2015) and temporality represents a challenge to conducting netnographic research (Lugosi & Quinton, 2018). Recently, several consumer researches have begun to study fluidity, mobilities and networks (Thompson, 2019) with a specific attention to the technological mediator (Epp, Schau, & Price, 2014) that becomes an active actor (Morgan-Thomas, Dessart and Veloutsou, 2020). Researchers who focus on movement networks of people and objects in both geographical and virtual spaces (Arsel, 2016) use different theories (e.g. assemblage, actor-network or mobility turn) that support an object-centered approach (Campbell & McHugh, 2016) linked to relational ontologies. This is a different perspective characterized by the primacy of relationships human-object over entities. That is to say, objects, understood as nonhuman entities (such as technologies) became more and more important, for example to mediate relationships and consumption (Epp, Schau and Price, 2014) or to generate human practices (Morgan-Thomas, Dessart and Veloutsou, 2020). Different sociologists explained what are nonhuman entities (Callon, 1999; Latour, 2005; Law, 1987). This term is used to denote entities as diverse as texts, natural phenomena, animals, material structures, transportation devices, tools and technical artifacts, economic goods (Sayes, 2014, p.136). However, if more and more researchers use theories derived from relational ontologies to understand human and nonhuman relationships, little is done to adapt qualitative methods to relational ontologies.

This chapter invites the reader to re-examine the qualitative methodologic challenges in conducting researches in a networked, virtual and digitalized context to overcome some limits of methods linked to humanist ontology. First, the author presents some qualitative methods human-centered, largely used in consumer research and underline theirs limits in a digitalized and virtual context. Second, it is introduced the perspective of relational ontologies to overcome these limits and to shift from a human-centered perspective to an object-centered perspective to consider nonhuman agency, non-linear temporality and networked social spaces. In relational ontologies the term ‘object’ is intended as all that is not human, such as technology, devices, artifacts, images, information (Lucarelli and Giovanardi, 2019). In this chapter the author use the term ‘object’ or ‘technological object’ to specially refer to devices, technology, platforms, artificial intelligence (AI), robots. Third, some adaptation of existing qualitative methods under the perspective of relational ontologies, such as netnography, interviews and videography are explained. Finally, the author proposes some possible adaptations of others qualitative methods, such as object observation, object-centered autovideography and AI interviews as suggested future opportunities.