Year: 2024
Type: review
Study designs: Systematic Review, Meta-Analysis
Abstract: Purpose The integration of augmented reality retail technology (ARRT) in retailing industry has emerged as a key driver for transforming the consumer experiences and driving online sales. However, inconsistent findings in the existing research literature highlight the need for subsequent research efforts. The purpose of this study is to examine how augmented reality (AR) features like interactivity, flow experiences, augmentation and consumer engagement, contribute to retailer’s success in the online marketplaces. Design/methodology/approach This meta-analysis study uses the theoretical lens of task-technology fit to synthesize and analyze the empirical results of existing AR studies published until March 2024. Findings All ARRT features examined in this study directly impacted online buyers’ purchase intention. Moderators’ analysis only partially supported the hypotheses proposing a significant difference in the effect size of ARRT features on purchase intention based on the country’s GDP per capita and innovation level. While GDP per-capita moderated three relationships, innovation level moderated four out of six interactions studied. Originality/value This meta-analysis enhances the existing ARRT literature by using the task-technology fit theoretical framework, offering a dual perspective encompassing customers and businesses. Unlike popular technology innovation theories and models, such as Unified Theory of Acceptance and Use of Technology and technology acceptance model, which predominantly focus on consumers, this perspective provides concrete directions for designing AR-enabled retail platforms that meet the task requirements of both consumers and retailers.
Authors: Parul Gupta, Sumedha Chauhan, Sandeep Goel
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4404767223