Investigating the Drivers of Wearable Technology Adoption for Healthcare in South America

The Purpose and Objectives of the Research

The study investigates factors driving wearable technology acceptance in healthcare in Chile, a less developed country in South America. The primary objective of the research is to investigate antecedents that influence consumer adoption of wearable technology in healthcare within South America. Specifically, the study aims to explore drivers that influence nonusers to adopt wearable healthcare technology in Chile. In addition, the researchers analyze the indirect effects of wearable technology on health motivation that influence consumers’ adoption intentions while providing useful implications of wearable technology to public policymakers, managers, and retail businesses.

Theory Adopted in the Study

The researchers analyzed multiple theoretical frameworks to understand consumer adoption of wearable technology. The models included the Technology Acceptance Model (TAM), the Unified theory of acceptance and use of technology (UTAUT2), and the theory of planned behavior (TPB). TAM suggests that perceptions of the ease of use and significance of new technology can affect consumers’ attitudes toward accepting the technology, eventually influencing their intentions toward adoption. The theory of TAM is based on examining the user’s acceptance of a technology. According to Bianchi et al. (2023), some researchers argued that the TAM model failed to illustrate behaviors that influence user’s technology acceptance, and the framework requires additional variables to fit the context. Therefore, in the study, the UTAUT2 was the framework adopted to explore the adoption of wearable technology.

The theory of UTAUT explores consumers’ intention to adopt a new technology based on social influence, perceived usefulness, and ease of use. UTAUT2 framework can integrate new determining factors that optimize predicting capabilities by focusing on user settings instead of organizational. In addition, UTAUT2 incorporates hedonic motivations, privacy risks, and price value, which determine the cost needed to adopt a wearable technology. Also, the framework includes health motivation and consciousness. Consequently, UTAUT2 was selected based on several reasons. First, UTAUT2 can indicate future technology use since several consumer technologies are still evolving. The framework has also been utilized in several research studies related to wearable technology. According to Bianchi et al. (2023), the study findings from multiple researchers have illustrated that the UTAUT2 framework has the potential power to assess factors that influence acceptance of wearable technology. Also, the model’s prediction capability varies depending on the context, such as different cultures.

The Framework and Hypothesis

The theoretical framework utilized in the study is the UTAUT2 model. In the research, six antecedent variables were considered. The variables included privacy risks, perceived ease of use, hedonic motivation, perceived usefulness, social influence, and price value. The perceived ease of use involves the degree to which consumers consider the technology free of effort. Social influence involves how users’ perception of the technology is based on family and friends. Hedonic motivation is the fun experience obtained when using the technology. The privacy risks evaluate whether The dependent variable was consumers’ intentions in accepting the wearable technology.

The hypothesis proposed based on the UTAUT2 framework and findings from past literature included the following:

• H1. Perceived usefulness has a positive association with users’ intentions to accept wearable technology
• H2. Perceived ease of use positively connects customers’ intentions to adopt wearable technology.
• H3. Greater social influence has a positive association with consumer intention to accept wearable technology for healthcare.
• H4. Hedonic motivation (pleasure) has a positive relationship with consumer intent to accept wearable technology for healthcare.
• H5. Privacy risk has a negative connection with consumer intent to accept wearable technology for healthcare.
• H6. Price value has a positive connection with consumer intent to adopt wearable technology for healthcare.
• H7. Users’ health motivation has a positive relationship with perceived usefulness.
• H8. Users’ health consciousness has a positive relationship with perceived usefulness.

Methodology

Sampling and Data Collection

The data utilized in the study were sampled through an online survey applied to adults (18 years and above) living in Santiago. Conducting the surveys online helped in maintaining consistency in the data sampling process. The questionnaires had three sections. One section described a fitness tracker, images of different healthcare tracking devices, and questions about whether respondents had tracking devices. During sampling, only respondents who did not possess a tracking device were directed to the second section. The respondents were questioned on their attitude and behavior towards wearable technology. The third section sampled the demographic variables of the participants.

The original questions were developed in English but were translated into Spanish by native speakers and back-translated by Chilean colleagues. After two months, the online survey sampled about 470 responses, of which 59.6% were males and 40.4% were females. Despite the higher male response, the study was consistent with past studies on wearable technology. Additionally, from the sample characteristics, most respondents were between the ages of 24 to 44 years. They were mostly college graduates or post-graduates with annual salaries above US$50,000.

Measures

The construct measures used in the study followed the scales established for adopting a technology. The perceived usefulness, perceived ease of use, and social influence were measured in a five-item, four-item, and three-item scale. Privacy risk and hedonic motivations were both measured on a four-item scale. The HOS scale was developed to measure health consciousness and health motivation. The Likert scales collected information from strongly agree to disagree strongly.

The Results

The researchers conducted three analyses: common bias analysis, discriminant validity, and hypothesis testing. The single-factor test addressed common method bias. The results from a single factor indicated that common method bias was not a critical issue. Since the single-factor method has limitations, the marker variable approach was adopted. The approach identified variance in health consciousness indicators. Also, after excluding the two indicators, the internal reliability and variance consistency were unaffected. On discriminant analysis, the HTMT, Fornell, and Larcker technique values revealed a valid discriminant in all the constructs. In hypothesis testing, the Partial least squares structural equation modeling (PLS-SEM) was adopted to evaluate the hypothesized association. The supported hypothesis included health motivation, perceived usefulness, hedonic motivation, and social influence. The hypothesis on price value, perceived ease of use, and privacy risks were not supported. Health consciousness failed to have a significant relationship with perceived usefulness. From the study findings, hedonic motivation was the strongest driver for wearable technology, followed by perceived usefulness. The cultural impact was revealed as contributing to social influence when adopting wearable technology. Insignificant predictors included price value, privacy risk, and perceived ease of use. In addition, the research revealed that health motivation indirectly influences adoption through perceived usefulness.

The Implication of the Research

In South America, more research is needed on adopting wearable technology in healthcare. Besides contributing a piece of literature predominantly conducted in industrialized nations, the findings from the study revealed the effects of cultural differences on technology acceptance. The study provides guidelines for managers, decision-makers, and manufacturers in the healthcare sector in Chile. The study findings are crucial for educating managers that perceived usefulness and hedonic enjoyment are major predictors for adopting fitness tracking systems in healthcare. Therefore, when marketing wearable technology devices, the marketing managers should highlight enjoyment elements besides the performance and functionalities of the product. Based on the results, for consumers in the country, managers should put less emphasis on price value, perceived ease of use, and privacy risks when marketing the devices to nonusers. Social influences from friends and family can affect the adoption of technology devices. Therefore, when marketing the products, managers should consider trustworthy physicians or influencers to provide relevant information to families when promoting the devices to overcome social influences. The marketing managers should focus on educating the families on the health-related benefits of the products rather than enjoyment elements. Additionally, since the government of Chile is concerned with the rise in obesity and chronic diseases, the results from the study are essential to healthcare organizations in Chile. When implementing policies that promote the adoption of wearable technology, they can monitor chronic-related complications and the personal health of the entire population in Chile.

The Limitation of the Research

Despite the research being successful, the study had some limitations. Despite the growing market for technology in South America, wearable technology was only adopted by a specific segment of the Chilean population. The study sample was contributed by a cluster of young individuals who are socioeconomically privileged. Most of the sample were males with higher education, which created a potential bias. Although multiple studies show that most wearable technology users are adults between 24 and 34, future studies must consider older consumers and the middle class that require health monitor devices in Chile and different South American countries. The dependent variable contributed to another limitation of the study. The study analyzed adoption intent instead of actual usage. Despite their close relation, it is important to note that behavioral intentions do not contribute to user intent. Therefore, the information should be analyzed cautiously when interpreting the user samples.

Reference

Bianchi, C., Tuzovic, S., & Kuppelwieser, V. G. (2023). Investigating the drivers of wearable technology adoption for healthcare in South America. Information Technology & People, 36(2), 916-939.