Adoption of internet of things (IOT) based wearables for healthcare of older adults – a behavioural reasoning theory (BRT) approach

Brijesh Sivathanu (Symbiosis Centre for Information Technology (SCIT), Symbiosis International University (SIU), Pune, India)

Journal of Enabling Technologies

ISSN: 2398-6263

Publication date: 17 December 2018

Abstract

Purpose

The purpose of this paper is to utilize the novel approach of applying the behavioral reasoning theory (BRT) to examine the adoption of internet of things (IoT) based wearables for the healthcare of older adults and it aims to understand the relative effect of “reasons for” and “reasons against” adoption of IoT-based wearables for health care among older adults.

Design/methodology/approach

The hypothesized relationships were established using the BRT and empirically tested using a representative sample of 815 respondents. The data were analyzed using the PLS-SEM method.

Findings

The findings of this study demonstrate that adoption intention of IoT-based wearables for the health care of older adults is influenced by “reason for” and “reason against” adoption. The finding shows that “reasons for” adoption are ubiquitous, relative advantage, compatibility and convenience and “reasons against” adoption are usage barrier, traditional barrier and risk barrier. Value of “openness to change” significantly influences the “reasons for” and “reasons against” adoption of IoT-based wearables.

Research limitations/implications

This cross-sectional study is conducted only in the Indian context and future research can be conducted in other countries to generalize the results.

Practical implications

This research highlighted both the adoption factors—“for” and “against,” which should be considered while developing marketing strategies for IoT-based wearables for health care of older adults. Adoption of IoT-based wearables for healthcare of older adults will increase when marketers endeavor to minimize the effects of the anti-adoption factors.

Originality/value

This is a unique study that examines the adoption of IoT-based wearables for healthcare among older people using the BRT, by probing the “reasons for” and “reasons against” adoption in a single framework.

Keywords

Citation

Sivathanu, B. (2018), "Adoption of internet of things (IOT) based wearables for healthcare of older adults – a behavioural reasoning theory (BRT) approach", Journal of Enabling Technologies, Vol. 12 No. 4, pp. 169-185. https://doi.org/10.1108/JET-12-2017-0048

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Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited


Introduction

Internet of things (IoT) was a term coined in 1999 by K. Ashton (2009), a British technology pioneer who helped develop the concept (Gubbi et al., 2013). IoT technology is considered as a disruptive innovation, which is transforming the lives of the consumers with the help of new, connected and smart products (Christensen, 1997). The IoT is a global internet-based architecture which is dynamic and growing rapidly. IoT has a self-configuring capability, with virtual and physical things having identities and integrated within the information network, as it is based on standard communication protocols (Sundmaeker et al., 2010). The IoT technology has extensive applications in different fields like tracking, security, measurement, healthcare, payment, maintenance and remote control (Andersson and Mattsson, 2015). One of the vital applications of IoT is in the area of medical and healthcare (Pang, 2013). There are a number of IoT-based wearables in the market for healthcare. Wearable electronics means “devices that can be worn or mated with the human skin to continuously and closely monitor an individual’s activities, without interrupting or limiting the user’s motions” (Haghi et al., 2017). These electronic devices enabled with IoT technology for the healthcare purpose are called as “IoT-based healthcare wearables” (Haghi et al., 2017). IoT-based healthcare wearables are devices designed to be worn anytime during the daily activities to regularly measure and monitor physiological and biomechanical parameters (Steele et al., 2009). These IoT-based wearable devices monitor the sleep quality, pulse, ECG, blood glucose levels, cardiac fitness, brain activities, blood pressure, blood oxygen levels, body temperature, pulmonary readings and blood alcohol content (Vermesan et al., 2011). These wearable devices are in the market in the form of finger rings, hand belts, clothes, spectacles and lenses (Haghi et al., 2017; Raj Pallapothu, 2015).

The increasing awareness of the IoT-based wearables in the Indian market is evident, as there are many startup players like Cardea, Fitbit, GOQii, LeChal’s, SenseGiz, GetActive and Gecko offering these products. There are corporate giants like Infosys, Intel, IBM and Wipro entering into the bandwagon of such IoT device making markets, seeing a huge future market potential with an increasing number in the aging population (Kashyap, 2016).

IoT-based healthcare devices are lifesavers to the older people, as it can guard and monitor the health of older people (Vermesan et al., 2011). As projected by the United Nations, by 2030 around 16.5 percent of the world population will be 60 years or above and by 2050 it will be 30 percent (United Nations Report, 2015). By the year 2050, India will have 20 percent of its population above 60 years of age, which amounts to 32.4m Indians (Indo-Asian News Service, 2017). Older people require special attention toward their healthcare and IoT-based wearables may help in the monitoring and prevention of health issues (Vermesan et al., 2011). IoT-based wearables are a technology-based innovation and it is imperative to study the adoption of wearables by older people. The success of a technology-based innovation such as IoT-based wearables for the healthcare of older people applications depends on the acceptance and adoption behavior of older people. There is no such study discussing the adoption of IoT-based healthcare wearables by older people in the context of a developing country like India. Hence, for organizations marketing and developing new products in healthcare for older people, it is important to understand why and whether older consumers will adopt the innovation. Marketers frequently draw the consumer’s perception and attitude toward adopting new innovation through market research. The existing adoption studies are mainly developed on the innovation diffusion theory (DOI; Rogers, 1962), technology acceptance model (TAM), the theory of reasoned action (TRA; Fishbein and Ajzen, 1980) and UTAUT (Venkatesh et al., 2003). However, these theories are criticized due to the lack of consideration of resistance to innovation (Ram and Sheth, 1989; Sheth, 1981; Garcia et al., 2007). Behavioral reasoning theory (BRT) provides the common platform to study the relative influence of “reasons for” adoption and “reasons against” adoption. (Westaby, 2005a, b; Claudy et al., 2015). This research extends the current innovation resistance literature and employs the BRT (Westaby, 2005a, b). This empirical study takes a step further to study the adoption of IoT-based wearables by the older people; as there is no such exploratory study on IoT-based wearables’ adoption using the BRT in the context of a developing country like India:

RQ1.

To examine the role of adoption factors and barriers in IoT-based wearables for the health care of older people using the BRT perspective.

Underpinning theories

Adoption models

The adoption studies of healthcare technology innovation using different adoption models are mentioned in Table I. These studies focus on the adoption factors of healthcare technology; however, there is no study that highlights the adoption of IoT-based healthcare devices. The variables indicated in Table I were considered for understanding the factors “for” and “against” adoption of IoT-based wearables.

Resistance to IoT

As per Ram and Sheth (1989), innovation resistance means the resistance by the consumers to the innovation due to the changes in the current status or maybe because the innovation is not matching the belief of the consumer. There are mainly two core resistance constructs to innovation and those are psychological resistance and functional resistance as discussed by Ram and Sheth (1989) in their research on innovation resistance. These two factors are further categorized into value barrier, usage barrier, traditional barrier, risk barrier and image barrier. IoT network security risk is one of the important parameters for resistance to IoT (Mani and Chouk, 2017). Consumer resistance to innovation is universal and needs to be addressed while formulating marketing strategies for innovative products (Garcia et al., 2007).

Behavioural reasoning theory

The determinants of adoption and different factors of resistance have been studied separately and there is no such study which investigates the adoption and resistance factors related to IoT-based healthcare wearables in a single framework. It has been supported in the psychology domain that adoption factors and anti-adoption or resistance factors may not be logically opposite to each other (Westaby et al., 2010) and it can be elaborated by the BRT, which facilitates to examine the relative influence of adoption and resistance in a single framework (Westaby, 2005b). BRT is related to the behavioral intention theory like the theory of planned behavior (TPB) (Ajzen, 1991). TPB mentions that behavioral intentions are strongly influenced by people’s perceived control, subjective norms and attitudes. Behavioral intention theories are widely accepted and applied in social science context (Van Hooft et al., 2004; Greve, 2001). However, BRT is a solitary framework which provides the insights of decision-making behavior of people which includes the individual’s context (Westaby, 2005b)—“reason for” and “reason against” a particular behavior. BRT has been applied earlier to study innovation adoption and found that adoption and barriers can be studied in a single framework (Claudy et al., 2013; Chatzidakis and Lee, 2013; Claudy et al., 2015; Westaby et al., 2010). “Reason” means in this study—“specific subjective factors people use to explain their anticipated behavior and can be conceptualized as anticipated reasons, concurrent reasons and post hoc reasons” (Westaby, 2005b) and are conceptually distinct from beliefs. Individuals look for reasons to make sense of the world as well as justify their own behavior. Westaby (2005b) conceptualized reasons in two categories: “Reason for” and “Reason against” behaviors which are linked with benefits, cost, facilitator and constraints (Westaby, 2005a). A. Gupta et al. (2017) supported the BRT, highlighting the reasons which are particularly influencing the consumer’s cognitive processing of innovation adoption decision.

Research objective

The study focuses on the adoption of IoT-based wearables for the healthcare of older people. It has the following objectives:

  1. To develop a theoretical model using BRT to understand the adoption of IoT-based wearables for the health care of older people.

  2. To empirically examine the theoretical framework.

Theoretical background and hypotheses development

Attitude and adoption intention

Attitude is defined as the individual’s psychological tendency, which is expressed after the evaluation of a particular thing showing disfavor or favor (Eagly and Chaiken, 1993) and attitude influences behavior across various areas (Westaby, 2005a). The TAM (Davis, 1989), the TRA (Fishbein and Ajzen, 1980) and TPB (Ajzen, 1985) confirm that attitude is a predictor of behavioral intention. Hospital professional’s attitude toward mobile healthcare affects the behavioral intention to adoption (Wu et al., 2011). It is confirmed that attitude influences the behavioral intentions, with reference to mobile banking and e-commerce adoption (Gupta et al., 2017; Pavlou and Fygenson, 2006). It was also found that attitude toward the innovation influences the behavioral intention (Claudy et al., 2015) and IoT-based healthcare wearables are new technology-based innovation, so it is postulated that:

H1.

Older people’s attitude toward IoT-based healthcare wearables will influence their adoption intentions.

Adoption reasons and attitude

It is discussed in the BRT that context-specific reasoning directly influences the people’s behavioral intention (Westaby, 2005b). It is also mentioned in BRT that reasons are particular cognitions and represent the subjective probability that a specific factor is part of a person’s behavioral explanation (Westaby, 2005b). There is a difference between belief and reasons in terms of the temporary orientation of memory (Westaby, 2005b). People depend on the reasons to explain behavior and it is connected with the psychological concepts like coherence and sense-making (Westaby, 2005b). There are two dimensions to reasons for performing behavior—“reason for” and “reason against.” It is found that reasons show incremental predictive validity in comparison with traditional belief concept (Westaby, 2005a). The innovation adoption research confirms the association between reasons and attitude (Claudy et al., 2015). In this study, it is important to consider the hypotheses with reference to above discussion:

H2a.

Older people’s reason for the adoption of IoT-based healthcare wearables will positively influence their attitude toward IoT-based healthcare wearables.

H2b.

Older people’s reason against adoption of IoT-based healthcare wearables will negatively influence their attitude toward IoT-based healthcare wearables.

Reasons and intentions

BRT mentions that reasons directly influence the behavioral intention of the individual (Westaby, 2005b). The context-specific aspects explaining the user adoption intention is discussed in TAM (Davis et al., 1989), UTAUT (Venkatesh et al., 2003) and many other technology adoption models. Reasons to adopt an innovation are strong predictors of behavioral intention (Claudy et al., 2015). So the following hypotheses were formed:

H3a.

Older people’s “reasons for” IoT-based healthcare wearables will (positively/negatively) influence their adoption intentions for IoT-based healthcare wearables.

H3b.

Older people’s “reasons against” IoT-based healthcare wearables will (positively/negatively) influence their adoption intention for IoT-based healthcare wearables

Value of openness to change and reasons

As per Westaby (2005b), consumer’s deep-rooted values are expected to influence the reasons for adoption. When consumers find the new innovation compatible with their personal values, then they tend to adopt it (Claudy et al., 2013). Values are considered to be a motivation which shows the desirable goals for the individual to attain. So, value provides the underlying direction to the individual’s choice or/and evaluation of the behavioral alternatives (Schwartz, 2006). The study on innovation adoption confirms that value influences the reasons (Claudy et al., 2015; Gupta et al., 2017). So the following hypotheses were postulated:

H4a.

Older people’s value will (positively/negatively) influence their “reason for” IoT-based healthcare wearables.

H4b.

Older people’s value will (positively/negatively) influence their “reason against” IoT-based healthcare wearables.

Value of openness to change and attitude

It is confirmed that belief and value can have a direct effect on attitude, as in some cases the consumer may depend on the heuristics motive (Westaby, 2005b; Kahneman et al., 1982). Many consumer behavior studies also found that value has a crucial role in the decision of consumption (Rokeach, 1973; Kahle et al., 1986). So this study considers the hypothesis:

H5.

Older people’s value of openness to change will (positively/negatively) influence their attitude toward IoT-based healthcare wearables (Figure 1).

Research methodology

Measures

To prepare the survey instrument, a detailed study of extant literature, interviews and a pilot study was conducted. The measures of this study used the scales from the earlier research of BRT (Westaby, 2005b; Westaby et al., 2010; Claudy et al., 2015) demonstrated in Table II. All the constructs were measured on a five-point Likert scale (1=“strongly disagree,” and 5=“ strongly agree”). In line with the previous research (Fishbein and Ajzen, 1980; Gupta and Arora, 2017), adoption intention and attitude were measured. Openness to change relates to a value which motivates individuals to pursue their own emotional and intellectual interest in uncertain directions (Schwartz, 1992). As per Raajpoot and Sharma (2006), people who are ready for openness to change accept the changes and adoption of new products (Wang et al., 2008). Value of openness to change can be applied to this research as IoT-based healthcare wearables are new technology-based innovative products in the market. The construct for openness to change is adopted from earlier research (Claudy et al., 2015; Gupta et al., 2017; Wang et al., 2008).

Reason extraction

To extract the context-specific “reason for” and “reason against” to use IoT-based healthcare wearables, qualitative research was carried out and face-to-face semi-structured interviews were conducted with 45 older consumers, which include an equal number of females and males. The age of the participants was 60–65 years, 65 years and above and who were visiting doctors due to different health issues. The questions were asked to understand the “reason for” and “reason against” use of IoT-based healthcare wearables. This method was taken up from earlier research related to BRT (Claudy and Peterson, 2014; Claudy et al., 2015; Gupta et al., 2017). Also, the healthcare technology adoption studies (Chong et al., 2015; Wu et al., 2007, 2011) were considered to conduct the interviews. The reasons were extracted after extensive interviews of the participants who were using any type of technology-based healthcare systems. They are: convenience (Gupta et al., 2017), compatibility (Claudy et al., 2013; Gupta et al., 2017), ubiquitous (Westaby, 2005b; Westaby et al., 2010) and relative advantage (Claudy et al., 2015; Westaby, 2005b; Westaby et al., 2010; Gupta and Arora, 2017). The participants were provided the list of reasons with statements: reasons why I will use IoT-based healthcare wearables as mentioned in Table II. The important categories were listed as: convenience—IoT-based healthcare wearables will save time and effort for health checkup and visiting doctors; ubiquitous—IoT-based healthcare wearables can allow me to access my health information anytime; relative advantage—IoT-based healthcare wearables have more advantages as compared to other healthcare devices; and compatibility—using IoT-based healthcare wearables suit my lifestyle and it is important to me. Participants were requested to point out on a four-point scale; to the extent to which each of the statement could be the “reason for” adoption of IoT-based healthcare wearables. The calibration of the scale was performed following Westaby (2005b) and Oh and Teo (2010), with 0= “not a reason,” 1=“a somewhat influential reason,” 2=“influential reason” and 3=“very influential reason.” Based on this, the top 3 “reasons for” adoption of IoT-based healthcare wearables were identified as convenience (mean=2.87, SD =0.88), ubiquitous (mean=2.68, SD =0.86), relative advantage (mean=2.58, SD =0.83) and compatibility (mean=2.35, SD =0.81).

The same procedure was used to find out the “reason against” using IoT-based healthcare wearables. From the extant literature and semi-structured interviews conducted with the respondents, the “reasons against” were extracted. They were: usage barrier (Claudy et al., 2015; Gupta et al., 2017), traditional barrier (Claudy et al., 2015; Westaby et al., 2010; Gupta et al., 2017) and risk barrier (Claudy et al., 2015; Gupta et al., 2017; Mani and Chouk, 2017). The participants were provided with a list of reasons prefaced with the statement—reason why I will not use IoT-based elderly [sic] wearables. The statement was categorized as—usage barrier (in my opinion, IoT-based healthcare wearables are not easy to operate), risk barrier (I feel that IoT-based healthcare wearables are not safe and secure) and traditional barrier (I am satisfied with traditional health checkups by meeting a doctor at the hospital and getting health information than newer ways of monitoring health like wearables). Participants were asked to indicate, on a four-point scale (0-3), the extent to which each of the statement could be the “reason against” adoption of IoT-based healthcare wearables. The calibration of the scale was performed following Westaby (2005b) and Oh and Teo (2010), with 0 for “not a reason,” 1 for “a somewhat influential reason,” 2 for “influential reason” and 3 for “very influential reason.” With this, three “reasons against” adoption of IoT-based healthcare wearables were identified as risk barrier (mean=2.67, SD =0.83), usage barrier (mean=2.46, SD =0.77) and a traditional barrier (mean=2.19, SD =0.72) which are same as perceived risk, self-efficacy and relative advantage, respectively. The other resistance factors, namely value barrier and image barrier, were not found relevant in the context of this study. The following measures were used to test the hypotheses.

Value of openness to change

According to Schwartz (1992), openness to change pertains to a value that “motivates people to follow their own intellectual and emotional interest in unpredictable and uncertain directions.” It includes two dimensions, i.e. self-direction (reflects the need for independence and autonomy) and stimulation (reflects the need for variety, novelty and excitement). Consumers who are high on an openness to change value system appreciate new experiences (Raajpoot and Sharma, 2006) and exhibit new product adoption behaviors (Wang et al., 2008). IoT-based healthcare wearables are an alternative way to consume healthcare services using technology, hence the openness to change value is applied in this study.

Research instrument design

This study used the research instrument from earlier studies conducted with reference to BRT (Westaby, 2005b) to understand the older consumer’s intention toward IoT-based healthcare wearables. The study mainly used the scales from previous studies (Claudy et al., 2015; Westaby, 2005b; Westaby et al., 2010; Wang et al., 2008; Gupta and Arora, 2017) to measure the constructs in the BRT model. As per Fornell and Larcker (1981), it is imperative to establish the validity of the constructs and reliability of the scales, so this study verified the scale for each of above constructs.

Before the data collection process, to establish face validity, six subject matter experts were identified and exposed to the overall scope and objectives of the study. After incorporating the suggestion and feedback regarding the suitability of all constructs, the questionnaire for the pre-test was finalized. To measure the constructs operationalized in this study, a five-point Likert scale was used. To assess the research instrument’s validity and reliability, a pre-test and pilot test were conducted. 45 questionnaires and face-to-face interviews were conducted for the pre-test among the target respondents. The target respondents for this study were consumers using any type of technology-based healthcare system and the age of the respondents was 60–65 years, and 65 years and above. For this study, only those people were considered who were visiting doctors due to various health issues. The researcher ensured that the respondents were aware of IoT-based healthcare wearables as well as no respondents were using IoT-based health wearables. As per their feedback, the questionnaires were modified to ensure that all the questions were well understood and easy to follow. To evaluate the reliability and internal consistency of the data, Cronbach’s α was used. The researcher carried out a pilot test (N=167) and the data were analyzed using PLS-SEM. The main data collection was carried out after satisfactory results were obtained in the pilot test. The operationalized constructs used in this study are as shown in Table II.

Sampling and data collection

The PLS path modeling technique has the benefit that it is not affected by small sample size. To ascertain the adequate sample size required for this study, the thumb rule (Gefen et al., 2000) was considered. For assessing the required sample size, ten times the number of items of the biggest construct in the research model was used. Hence, the required sample size for this study is 110 respondents. The primary data collection was done using a structured questionnaire (Table II), which was administered to the respondents. The target respondents selected for this study were consumers using any type technology-based healthcare system. The convenience sampling method was used to survey in Pune city and its suburbs. The respondents chosen for this study were appropriate as these respondents were people in the age group of 60–65 years, 65 years and above, who were visiting doctors for various health issues.

Non-response bias

In survey methods, non-response bias is considered a serious concern, as it limits the generalization of the findings (Michie and Marteau, 1999). Hence, it needs to be addressed by the researchers (Lewis et al., 2013). When the response of the individuals to a survey differs systematically from those who were invited to participate but did not respond, it results in response bias (Menachemi, 2011). Hence, appropriate steps were taken by the researcher to ensure that non-response is not an issue for this study. This was done by dividing the data into two data sets (early respondents vs late respondents) by performing the wave analysis. The comparison of the early and late wave responses was done to test for non-response bias (Armstrong and Overton, 1977). The t-test analysis showed no significant differences (p=0.49) between the early wave (423) and late wave (392) groups, indicating that non-response bias does not affect this study. Finally, 815 responses were found fit for analysis in all respects.

Common method bias

Survey-based method for data collection was used to test the research hypothesis in this work. When the response data are collected from multiple sources, it may suffer from the risk of the presence of common method bias (MacKenzie and Podsakoff, 2012; Podsakoff et al., 2003; Podsakoff and Organ, 1986). The researcher conducted the single factor Harman test (Podsakoff and Organ, 1986) to examine the occurrence of common method bias in this work. On performing the analysis of the research framework, the results of this test show that 19.48 percent of the variance is explained by a single factor which is less than 50 percent. This proves that common method bias is not a concern in this research.

With reference to the above criteria, there is sufficient evidence of validity and reliability in the measures of this study.

PLS-SEM

As the PLS-SEM path modeling technique has the capability to represent the latent variable constructs in this work, it is used to test the conceptual research framework and assess the relationship between the indicators and latent constructs (Bollen and Pearl, 2013; Gudergan et al., 2008). When the objective of the research is to extend the existing theory (Hair et al., 2011), PLS-SEM is preferred over maximum likelihood as it is a flexible technique to model the research constructs (Henseler, 2010). So, the smart PLS software (Ringle et al., 2005) was used for data analysis.

Results

The demographic profile of the respondents is shown in Table III

Measurement model

The multi-item reflective constructs in the hypothesized framework were used to calculate the measurement properties in the final measurement model. The indicator’s factor loadings were more than 0.5 (Hair et al., 2006). The reliability of the measure was established as the Cronbach’s α for all the constructs were more than 0.7. This shows the high internal consistency of all the research constructs used (Nunnally, 1978).

The outer item loadings and composite reliability (CR) was checked to evaluate the reflective measurement items. To check the convergent validity, the average variance extracted (AVE) was calculated. The outer loadings for all the items were more than the minimum threshold value of 0.6 and all the research constructs show high levels of internal consistency/reliability shown by the CR values as displayed in Table IV. The convergent validity for all the constructs is confirmed as the AVE values are more than the minimum threshold value of 0.5 (Hair et al., 2006).

The assessment of the discriminant validity of the research constructs was done using the Fornell and Larcker (1981) criterion. The discriminant validity of the constructs is verified by the off-diagonal values in Table V shows the correlation between the latent constructs. The comparison of the construct inter-correlations with AVE was done to prove the discriminant validity as shown in Table V. Discriminant validity was ascertained as the shared variance values were less than the corresponding AVE (Fornell and Larcker, 1981). Hence, discriminant validity between the research constructs is verified.

Structural model

After the evaluation of the measurement model, it was found to be reliable and valid. Hence, the path analysis was carried out to assess the structural model relationships among the constructs using PLS-SEM by calculating the path coefficients and its significance as shown in Table VI. It implies the hypothesized relationships between the various research constructs, the assessment of the level of R2 values and predictive relevance Q2 (Table VII and Figure 2).

The R2 values of and the predictive relevance Q2 of the endogenous latent constructs attitude toward adoption, “reasons for” adoption, “reasons against” adoption and adoption intention of IoT healthcare wearables are shown in Table VII.

The results show that reasons are context specific and are salient predictors of older people’s adoption of IoT-based healthcare wearables. The attitude significantly influences the adoption intention toward the IoT-based healthcare wearables (H1: β=0.58, p<0.01). It shows that older people’s attitude toward the IoT-based healthcare wearables affects the adoption intention. It is found that “reason for” adoption has a positive effect (H3a: β=0.33, p<0.01) toward adoption intention and attitude (H2a: β=0.64, p<0.01) toward the adoption of IoT-based healthcare wearables. It confirms that “reason for” adoption positively affects attitude and adoption intention of IoT-based healthcare wearables. The “reason against” the adoption of IoT-based healthcare wearables negatively affects (H3b: β=−0.43, p<0.01) the adoption intention and attitude (H2b: β=−0.54, p<0.01) toward the adoption of IoT-based healthcare wearables. It is found that “reason against” has a more negative effect on adoption intention than the “reason for” adoption intention of IoT-based wearables. It was also found that value of openness to change significantly affects the “reasons for” and indirectly affects the attitude via reasoning (H4b, H2b) toward the IoT-based healthcare wearables. As per Westaby (2005a, b), it reflects a deeper level of cognitive processing, which facilitates consumers to better validate their decisions. Openness to change value positively influences the “reasons for” adoption (H4a: β=0.311, p<0.01) and negatively influences the “reasons against” adoption (H4b: β=−0.42, p<0.01). It shows that value has a positive influence on “reasons for” adoption and negative influence on “reason against” adoption of IoT-based healthcare wearables. Value of openness to change does not directly influence the attitude (H5: β=0.33, not significant) toward the IoT-based healthcare wearables.

All second-order constructs of reasoning are significant. It is found that ubiquitous (β=0.890, p<0.01), relative advantage (β=0.893, p<0.01), compatibility (β=0.881, p<0.01) and convenience (β=0.791, p<0.01) are the “reasons for” adoption of IoT-based healthcare wearables. This shows that IoT-based healthcare wearables can be used anytime and anywhere, it takes less time and effort, offers greater value to manage the health and provides more advantages for older people. Hence, the “reasons for” adoption are ubiquitous and relative advantage. The IoT-based wearables are very convenient to wear; provide an easy way to manage the health and compatible, as they suit the lifestyle of older citizens. Hence, compatibility and convenience are “reasons for” adoption.

Traditional barrier (β=0.754, p<0.01), usage barrier (β=0.675, p<0.01) and risk barrier (β=0.768, p<0.01) are “reasons against” adoption for IoT-based healthcare wearables. Older people generally prefer to visit a doctor for health checkup and feel that doctors can provide personalized healthcare services, so traditional barrier is one of the “reasons against” adoption of IoT-based healthcare wearables. Older people feel that healthcare wearables are not easy to operate, so usage barrier is one of the “reasons against” adoption of IoT-based healthcare wearables. Since the IoT wearables store the health information on the cloud through the internet, older people feel that it is not safe and secure as their personal health data are vulnerable and can be misused. Hence, risk barrier is one of the important “reasons against” adoption.

Discussion

In this study, the BRT theory is applied to study the adoption of IoT-based wearables which extends the BRT theory and diffusion of innovation literature. A lot of prior research was carried out for healthcare technology adoption (Wu et al., 2011; Chau and Hu, 2002; Kijsanayotin et al., 2009; Chong et al., 2015). However, they do not provide the “reasons for” and “reasons against” adoption in a single framework. This unique study extends the application of BRT in the domain of IoT-based wearables highlighting the context-specific reasons that influence older people’s cognitive processing of innovation adoption in a developing country like India.

Older people have a traditional habit of visiting doctors for medical health checkups, they find it difficult to use IoT-based wearables and they also perceive the risk that their healthcare data can be accessed by anyone on the cloud through the internet. The usage barrier, traditional barrier and risk barrier are “reasons against” adoption of IoT-based healthcare wearables. The primary “reasons for” adoption of IoT wearables as per the older citizens’ perspective are convenience, relative advantage, ubiquitous and compatibility. It was found that IoT-based wearables offer convenience to older people to measure their health status, as it saves time and effort of visiting doctors for small checkups. This finding contributes to the innovation adoption literature.

Implications

This study contributes to the extant literature of the adoption of IoT-based wearable products and also the diffusion of innovation by studying the beliefs and reasons of adoption of IoT-based healthcare wearables. This study is unique, as it employs the BRT and studies the context-specific reasons that influence the older consumer’s cognitive processing of innovation adoption decision for the innovation such as IoT-based healthcare wearables.

This study has vital implications for marketers, retailers, researchers and doctors. It has a major contribution to the domain of healthcare studies of older citizens. This study confirms that “reasons against” IoT wearables have a more significant influence than the “reasons for” adoption intentions of older people. The BRT framework provides the difference between the pro- and anti-adoption factors and they are not merely logical opposite to each other. Marketers should concentrate on the “reasons against” adoption and develop suitable strategies to overcome the barriers. Hence, while communicating with older consumers’ retailers would be myopic, if they highlight only the benefits. Marketers can understand the “reasons against” and the “reasons for” adoption and include both these factors to estimate their relative influence, which will help them to comprehend the factors of adoption of IoT wearables in detail. These insights of consumer behavior shall help the marketers to formulate suitable strategies in the domain of healthcare for older people. As the factors for the adoption IoT wearables are “ubiquitous” and “compatibility,” it shows that the doctors can check the health status of older people from any location at any time.

As older citizens experience usage barriers, so training can be imparted to improve self-efficacy and use of technology. To avoid risk barriers, older citizens can be educated regarding the techniques of password protection, data handling and management of their health data on the cloud through the internet. The marketers should also focus on the factors of adoption, so that healthcare wearables can be widely used by older citizens and leverage their benefits. This study reveals the “reasons for” adoption such as the convenience and relative advantage of IoT-based healthcare wearables, which is vital for the marketing strategies devised by the managers while communicating with older people. Older people prefer to visit a doctor to sustain their long-term relationships that act as a traditional barrier. Older consumers’ believe that their personal health data stored in IoT wearables are not safe, so marketers and retailers should focus on the safety aspects of data management in IoT wearables while communicating with older people.

Limitations and future research directions

This study is conducted only in the Indian context and further studies can be extended to other countries to generalize the results. This study does not consider the actual usage behavior which can be pursued in future studies. Further, this study can be extended to understand the demographic factors except for age, as this study is for IoT-based wearables for healthcare of older people. Future studies can also examine the individual characteristics such as risk-taking behavior and innovation which may be included in a theoretical framework to study the moderating role. Examination of the influence of cross-cultural differences in the adoption of IoT wearables is an area which needs future consideration as the context-specific “reasons for” and “reasons against” could differ across diverse cultures. This work also can be extended to the rural areas to study the perspective in the rural context for IoT-based healthcare wearables.

Figures

Conceptual model

Figure 1

Conceptual model

Results of PLS-SEM model

Figure 2

Results of PLS-SEM model

Adoption studies in healthcare technology innovation

Health care technology innovation Reference Adoption model Variables examined
Mobile computing healthcare Wu et al. (2007) TAM Perceived usefulness,
Perceived ease of use
Compatibility
Self-efficacy
Technical support
Mobile healthcare Wu et al. (2011) TAM and TPB Perceived usefulness
Perceived ease of use
Personal innovativeness in IT, perceived service availability attitude
Behavioral control
Subjective norms
Healthcare information technology Kijsanayotin et al. (2009) UTAUT Perceived expectancy
Effort expectancy
Social influence
Facilitating condition
Voluntariness
RFID adoption Chong et al. (2015) UTAUT Perceived expectancy
Effort expectancy
Social influence
Facilitating condition

Operationalization of constructs

Main construct Type Indicator/item Reference
Reasons for
Convenience Reflective CN1 IoT-based healthcare wearables are convenient for healthcare Claudy et al. (2015), Westaby (2005b), Westaby et al. (2010), Anil Gupta and Neelika Arora (2017)
CN2 IoT-based healthcare wearables will save time and effort for a health checkup and visiting doctors
CN3 IoT-based Healthcare wearables are an easy way of managing my health
Ubiquitous Reflective UQ1 IoT-based healthcare wearables can assist me to be well informed about my health Claudy et al. (2015), Westaby (2005b), Westaby et al. (2010), Anil Gupta and Neelika Arora (2017)
UQ2 IoT-based Healthcare wearables can allow me to access my health information anytime
UQ3 IoT-based healthcare wearables can help me to get information and monitor my health regardless of where I am
Relative advantage Reflective RA1 IoT-based healthcare wearables has more advantages as compared to other healthcare devices Claudy et al. (2015), Westaby (2005b), Westaby et al. (2010), Anil Gupta and Neelika Arora (2017)
RA2 IoT-based Healthcare wearables takes less time and effort for health monitoring
RA3 IoT-based healthcare wearables offer greater value to manage my health effectively
Compatibility Reflective CM1 Using IoT-based healthcare wearables suits my lifestyle and it is important to me Shih and Fang (2004)
CM2 Using IoT-based healthcare wearables will suit my needs
Reasons against
Usage barrier Reflective UB1 In my opinion, IoT-based healthcare wearables are not easy to operate Claudy et al. (2015); Westaby (2005b), Westaby et al. (2010), Anil Gupta and Neelika Arora (2017)
UB2 In my opinion, IoT-based healthcare wearables are difficult and cumbersome to use
UB3 In my opinion, IoT-based healthcare wearables are only for technology savvy consumers
Risk barrier Reflective RB1 I feel that IoT-based healthcare wearables are not safe and secure Claudy et al. (2015), Westaby (2005b), Westaby et al. (2010), Anil Gupta and Neelika Arora (2017)
RB2 I fear that while using IoT-based healthcare wearables information will be misused
RB3 I fear that while using IoT-based healthcare wearables, the data of my health will be lost
Traditional barrier Reflective TB1 Visiting a hospital for health checkup is a nice occasion to meet the doctor Claudy et al. (2015), Westaby (2005b), Westaby et al. (2010), Anil Gupta and Neelika Arora (2017)
TB2 Only doctors can offer personalized services for healthcare and checkups
TB3 I am satisfied with traditional health checkups and getting health information than newer ways of monitoring health
Value of openness to change Reflective VO1 I every time look for new things and surprises to do Claudy et al. (2015), Wang et al. (2008)
VO2 I look for adventure and like to take risks
VO3 I am open to new experiences
Attitude Reflective AT1 Using IoT-based healthcare wearables in the near future would be good Claudy et al. (2015), Fishbein and Ajzen (1980), Wang et al. (2008), Gupta and Arora (2017)
AT2 IoT-based healthcare wearables offer a lot of benefits
AT3 Using IoT-based healthcare wearables in the near future will add a lot of value
Adoption intention Reflective AD1 I will use IoT-based healthcare wearables in future Fishbein and Ajzen (1980), Gupta and Arora (2017)
AD2 I can see myself using IoT-based healthcare wearables in future
AD 3 I intend to use IoT-based healthcare wearables in near future

Respondent demographic profile

Demographic Characteristics Frequency Percentage
Gender Male 483 59
Female 332 41
Age 60–65 220 27
65 and above 595 73
Education Diploma 144 18
Graduate degree 433 53
Post graduate/masters 238 29
Using any type of technology-based healthcare system Less than 3 months 87 11
3–6 months 225 28
6–12 months 380 47
More than year 123 15

Note: n=815

Construct validity

Second-order construct First-order construct Item Outer loading AVEa Composite reliabilityb Cronbach’s α
Reason for
Convenience CN1 0.806 0.758 0.878 0.814
CN2 0.873
CN3 0.870
Ubiquitous UQ1 0.880 0.764 0.886 0.826
UQ2 0.881
UQ3 0.811
Relative advantage RA1 0.876 0.742 0.922 0.846
RA2 0.869
RA3 0.826
Compatibility CM1 0.858 0.768 0.914 0.862
CM2 0.848
Reason against
Usage barrier UB1 0.897 0.746 0.868 0.822
UB2 0.844
UB3 0.853
Risk barrier RB1 0.863 0.788 0.912 0.834
RB2 0.870
RB3 0.863
Traditional barrier TB1 0.804 0.748 0.828 0.816
TB2 0.823
TB3 0.846
Value of openness to change VO1 0.894 0.762 0.894 0.854
VO2 0.875
VO3 0.871
Attitude AT1 0.871 0.784 0.922 0.856
AT2 0.890
AT3 0.872
Adoption intention AD1 0.881 0.778 0.904 0.848
AD2 0.891
AD3 0.877

Notes: aAverage variance extracted (AVE) = (summation of the square of the factor loadings)/{(summation of the square of the factor loadings) + (summation of the error variance)}; bcomposite reliability (CR) = (square of the summation of the factor loadings)/{(square of the summation of the factor loadings) + (square of the summation of the error variance)}

Discriminant validity (Fornell-Larcker criteria)

Research construct AD AT RF:CM RF:RA RF:UQ RF:CN RA:TB RA:RB RA:UB VO
AD 0.882
AT 0.591 0.885
RF:CM 0.528 0.472 0.876
RF:RA 0.454 0.468 0.454 0.861
RF:UQ 0.362 0.312 0.336 0.494 0.874
RF:CN 0.356 0.304 0.124 0.108 0.388 0.870
RA:TB −0.184 −0.192 0.136 0.096 0.128 0.282 0.864
RA:RB −0.112 −0.186 0.108 0.086 0.106 0.108 0.522 0.887
RA:UB −0.144 −0.164 0.096 0.072 0.098 0.086 0.262 0.486 0.863
VO 0.282 0.244 0.148 0.142 0.242 0.262 −0.096 −0.078 −0.086 0.872

Structural relationships and results of hypothesis testing

Hypothesis Path Path coefficient SE t-statistics Decision
First order
H1 Attitude → adoption intention 0.581 0.076 5.873*** Supported
H2a Reasons for adoption → attitude 0.645 0.056 4.951*** Supported
H2b Reasons against adoption → attitude −0.542 0.054 2.666*** Supported
H3a Reasons for adoption →adoption intention 0.337 0.068 2.938*** Supported
H3b Reasons against adoption → adoption intention −0.439 0.071 2.623*** Supported
H4a Value → reasons for adoption 0.311 0.062 8.011*** Supported
H4b Value → reasons against adoption −0.428 0.059 8.209*** Supported
H5 Value → attitude 0.331 0.056 1.283 Not Supported
Second order
Reasons for adoption → convenience 0.791 0.058 22.14*** Supported
Reasons for adoption → ubiquitous 0.890 0.066 21.87*** Supported
Reasons for adoption → relative advantage 0.893 0.053 17.98*** Supported
Reasons for adoption → compatibility 0.881 0.064 16.90*** Supported
Reasons against adoption → usage barrier 0.673 0.056 6.918*** Supported
Reasons against adoption → risk barrier 0.768 0.052 3.221*** Supported
Reasons against adoption → tradition barrier 0.754 0.054 4.803*** Supported

Notes: t-Values for two-tailed test: *t-value 1.65 (sig. level 10 percent); **t-value 1.96 (sig. level 5 percent); ***t-value 2.58. *p<0.10; **p<0.05; ***p<0.01

Results of R2 and predictive relevance Q2

Endogenous latent constructs R2 Q2
Attitude toward adoption of IoT healthcare wearables 0.774 0.508
Adoption intention toward IoT healthcare wearables 0.688 0.346
Reasons for adoption of IoT healthcare wearables 0.757 0.462
Reasons against adoption of IoT healthcare wearables 0.661 0.314
Assessment of predictive relevance (Q2)
Value Effect size
0.02 Small
0.15 Medium
0.35 Large

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Corresponding author

Brijesh Sivathanu can be contacted at: brij.jesh2002@gmail.com

About the author

Brijesh Sivathanu is based at the Symbiosis Centre for Information Technology (SCIT), Symbiosis International University (SIU), Pune, India.