A comprehensive overview of smart wearables: The state of the art literature, recent advances, and future challenges. (April 2020)

• Record Type: Journal Article
• Authors: Niknejad, Naghmeh; Ismail, Waidah Binti; Mardani, Abbas; Liao, Huchang; Ghani, Imran
• Is Part Of: Engineering applications of artificial intelligence. Volume 90(2020)
• Publication Date: 2020-04
• Subjects: Smart wearables -- Information systems -- Potential factors -- Weight analysis technique; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2020.103529
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Smart wearables have gained considerable attention from Information Systems (IS) academics, business managers, and health practitioners. In spite of the availability of authentic research studies of smart wearables, there is still a lack of systematic review on the different aspects of smart wearables concept to find the current state of research, particularly from the perspective of IS field. Therefore, the predominant aim of this research is to review smart wearables literature, recent advances, and future challenges. Accordingly, a systematic literature review was conducted to explore smart wearables by reviewing previous studies from 2010 to 2019. For covering all related papers during these years, an integrated review protocol consisting of automatic and manual stages was pursued. 244 papers were identified to address smart wearables issues and challenges. According to the findings, it is observed that smart wearable studies have increased dramatically during the last years. Moreover, the results show that current studies covered different research themes which are related to smart wearables area, particularly user behavior, technology-focused, security and privacy, design, and social acceptability. Furthermore, based on the results of the weight analysis technique, perceived usefulness, attitude toward technology, social influence, and privacy concerns are identified as the best predictors of smart wearables adoption. Additionally, the results show that the Technology Acceptance Model (TAM) is the most commonly adopted theory in the smart wearable studies. The findings of this review would assist academics to realize the existing limitations and gaps as well as the future works for smart wearables research studies.