Theoretical Models for Acceptance of Human Implantable Technologies: A Narrative Review

• Published in: Informatics (Basel) 2023-08, Vol.10 (3), p.69
• Main Authors: Chaudhry, Beenish Moalla, Shafeie, Shekufeh, Mohamed, Mona
• Format: Article
• Language: English
• Subjects: Access control; Analysis; Communication; Demographic variables; Design and construction; embeddable; Ethics; implantable; Implants, Artificial; Innovations; insideable; Medical equipment; microchip; Motivation; Narratives; New technology; Physiology; Prosthesis; Psychology; Search engines; Social aspects; Symbiosis; Technological innovations; Technology; Technology Acceptance Model; Technology and civilization; Technology utilization; theory; Transplants & implants; User needs
• ISSN: 2227-9709; 2227-9709
• DOI: 10.3390/informatics10030069

Theoretical models play a vital role in understanding the barriers and facilitators for the acceptance or rejection of emerging technologies. We conducted a narrative review of theoretical models predicting acceptance and adoption of human enhancement embeddable technologies to assess how well those models have studied unique attributes and qualities of embeddables and to identify gaps in the literature. Our broad search across multiple databases and Google Scholar identified 16 relevant articles published since 2016. We discovered that three main theoretical models: the technology acceptance model (TAM), unified theory of acceptance and use of technology (UTAUT), and cognitive–affective–normative (CAN) model have been consistently used and refined to explain the acceptance of human enhancement embeddable technology. Psychological constructs such as self-efficacy, motivation, self-determination, and demographic factors were also explored as mediating and moderating variables. Based on our analysis, we collated the verified determinants into a comprehensive model, modifying the CAN model. We also identified gaps in the literature and recommended a further exploration of design elements and psychological constructs. Additionally, we suggest investigating other models such as the matching person and technology model (MPTM), the hedonic-motivation system adoption model (HMSAM), and the value-based adoption model (VAM) to provide a more nuanced understanding of embeddable technologies’ adoption. Our study not only synthesizes the current state of research but also provides a robust framework for future investigations. By offering insights into the complex interplay of factors influencing the adoption of embeddable technologies, we contribute to the development of more effective strategies for design, implementation, and acceptance, thereby paving the way for the successful integration of these technologies into everyday life.