Who will use augmented reality? An integrated approach based on text analytics and field survey

Han Li, Ashish Gupta, Jie Zhang and Nick Flor

European Journal of Operational Research, 2020, vol. 281, issue 3, 502-516

Abstract: Next-generation technologies such as Augmented Reality and Virtual Reality are fast permeating many industry and society sectors. Their market is projected to reach $95 billion by 2025, representing a large portion of the economy within the next decade. With these technologies gaining wide popularity, it is critical to understand their usage in the context of the various benefits and perils that they offer. Even though top-rated mobile applications face an increasing challenge to retain users, few studies have attempted to decipher the dilemma in their continuance momentum. In this study, we focus on Pokémon GO, a top-rated Augmented Reality app, using it as a special case to investigate factors influencing user continuance and more use intention. We extend expectation confirmation theory by incorporating the effects of subjective norm, perceived risk, technical features and sense of direction. To increase the relevance and richness of our understanding of risks and benefits, we integrate the text analytics and survey-based theory-validating research methodology to build and test our research model. Our findings suggest that rational risk/benefit calculus and satisfaction are two primary inputs for continuance intention. Besides physical health benefits, users also value the benefits in mental health and relationship building. The risks in performance, time and safety are salient risk dimensions that negatively impact satisfaction. Furthermore, we find technical features play a strong role in influencing perceived benefits and user satisfaction. The findings also provide important practical implications for the designers of next-generation mobile apps enabled by Augmented Reality.

Keywords: Text analytics; Multi-method approach; Technical features; Augmented reality; Post-adoption use intention (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221718308725
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:281:y:2020:i:3:p:502-516

DOI: 10.1016/j.ejor.2018.10.019

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().