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

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  • Li, Han
  • Gupta, Ashish
  • Zhang, Jie
  • Flor, Nick

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.

Suggested Citation

  • Li, Han & Gupta, Ashish & Zhang, Jie & Flor, Nick, 2020. "Who will use augmented reality? An integrated approach based on text analytics and field survey," European Journal of Operational Research, Elsevier, vol. 281(3), pages 502-516.
    • Handle: RePEc:eee:ejores:v:281:y:2020:i:3:p:502-516
    DOI: 10.1016/j.ejor.2018.10.019
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    2. Saridakis, Charalampos & Katsikeas, Constantine S. & Angelidou, Sofia & Oikonomidou, Maria & Pratikakis, Polyvios, 2023. "Mining Twitter lists to extract brand-related associative information for celebrity endorsement," European Journal of Operational Research, Elsevier, vol. 311(1), pages 316-332.
    3. Yi Sun & Shihui Li & Lingling Yu, 2022. "The dark sides of AI personal assistant: effects of service failure on user continuance intention," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 17-39, March.
    4. Tsan-Ming Choi & Alexandre Dolgui & Dmitry Ivanov & Erwin Pesch, 2022. "OR and analytics for digital, resilient, and sustainable manufacturing 4.0," Annals of Operations Research, Springer, vol. 310(1), pages 1-6, March.
    5. Lin Li & Kyung Young Lee & Emmanuel Emokpae & Sung-Byung Yang, 2021. "What makes you continuously use chatbot services? Evidence from chinese online travel agencies," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 575-599, September.
    6. Nikhashemi, S.R. & Knight, Helena H. & Nusair, Khaldoon & Liat, Cheng Boon, 2021. "Augmented reality in smart retailing: A (n) (A) Symmetric Approach to continuous intention to use retail brands’ mobile AR apps," Journal of Retailing and Consumer Services, Elsevier, vol. 60(C).
    7. Lu, Lin & Xu, Pei & Wang, Yen-Yao & Wang, Yu, 2023. "Measuring service quality with text analytics: Considering both importance and performance of consumer opinions on social and non-social online platforms," Journal of Business Research, Elsevier, vol. 169(C).
    8. Tsan‐Ming Choi & Subodha Kumar & Xiaohang Yue & Hau‐Ling Chan, 2022. "Disruptive Technologies and Operations Management in the Industry 4.0 Era and Beyond," Production and Operations Management, Production and Operations Management Society, vol. 31(1), pages 9-31, January.

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