An interdisciplinary Co-authorship networking perspective on AR and human behavior: Taking stock and moving ahead. (June 2023)

• Record Type: Journal Article
• Authors: Heller, Jonas; Mahr, Dominik; de Ruyter, Ko; Schaap, Eric; Hilken, Tim; Keeling, Debbie I.; Chylinski, Mathew; Flavián, Carlos; Jung, Timothy; Rauschnabel, Philipp A.
• Is Part Of: Computers in human behavior. Volume 143(2023)
• Publication Date: 2023-06
• Subjects: Augmented reality -- Co-authorship networks -- Topic modeling; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2023.107697
• Languages: English
• ISSNs: 0747-5632

Abstract

Abstract: The field of augmented reality (AR) and human behavior emerged when Azuma et al. (2001) refined the term augmented reality in 2001. Research on the topic has grown steadily in the past decade, yet there is a notable lack of consensus on humans' motivations and outcomes in interacting with AR. The present research takes a bibliographic approach to shed light on current research on AR in human-computer interaction and, using topic modeling, to identify and classify the topics that have drawn researchers' interest. The results reveal three major topics of interest to researchers, namely "Education, Learning & Training Research", "Marketing, Consumer Behavior & Business Research", and "Digital Tourism & Cultural Heritage Research". Drawing upon co-authorship theory, we identify prominent AR expert co-authorship networks that work on similar topics, yet also highlight that AR research is concentrated in a few research groups that publish articles with similar groups of authors and little outside their own networks. Together with AR experts from the four largest co-authorship networks, we highlight the common challenges that emerge in AR research, suggest solutions, and jointly propose a research agenda for AR and human behavior research. Highlights: Using a bibliographic approach we summarize and classify 20 years of AR and human-computer interaction research Using topic modeling we identify and classify the topics that have drawn researchers' interest We reveal three major topics of interest to AR researchers given the corpus we analyzed Drawing upon co-authorship theory, we identify prominent AR expert co-authorship networks We highlight the common challenges that emerge in AR research, suggest solutions, and propose a research agenda