Adoption of IoT-based healthcare devices: An empirical study of end consumers in an emerging economy
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Abhisek Dutta
,
Raj K. Kovid
Abstract
The Internet of things (IoT) is a technology with varied applications in numerous fields. One such field is healthcare, which has a dire need of using this technology to help millions benefit from the attention and availability of healthcare professionals. This study identifies important factors that influence the adoption of IoT-based healthcare devices among end users and suggest a predictive model of adoption. The model is based on the UTAUT2 with newer variables identified from the literature. The sample (n = 253) was collected from four major cities in India, and partial least squares-structural equational model was used to assess the measurement and the structural model. The factors such as ubiquitous, social influence, perceived health risk, and relative advantage had a significant influence on attitude (ATT), which influences behavioral intention (BI) toward IoT-based healthcare devices. Facilitating condition (FC) and price value did not show any significant influence on ATT toward the technology, but FC had a direct influence on BI. The study helps in advancing IS research by adding new variables to the existing knowledge and proposing a model based on UTAUT2. Furthermore, it also brings important practical implications for practitioners.
1 Introduction
In general, end-user adoption of IoT applications in healthcare is very low, which makes it difficult to introduce and facilitate any other service based on IoT such as healthcare services. The world today is no more communicating separately, rather devices are automatically communicating with each other and providing various services. This ubiquitous (UQ) and interconnected mesh of sensors and devices is today known as the Internet of things (IoT) and is one such technology, which will bring a paradigmatic shift in communication and services. Healthcare services in many countries across the globe are in critical need of innovation, so that they may help reach the right patients at the right time. With the enablement of IoT-based healthcare services, this does not seem like a distant future. With the increase in a greater number of patients who need doctors, nurses, and other health professionals, these professionals are under increasing pressure to handle the growing needs. Moreover, the cost of good healthcare has increased manifold, which also affects the quality of life of individuals. Healthcare can be supplemented and aided with the inclusion of IoT-based devices to help both patients and healthcare professionals equally. Such IoT-based systems can help in the timely detection of diseases so that patients can be provided treatment at the right time, and the healthcare process can become more streamlined to accommodate more patients at the right moment and allocate resources smartly. Figure 1 shows how very basic IoT-based healthcare devices aid medical professionals to detect and respond to critical situations of patients without time lag. The developments in the area of healthcare in the past decade have shown that technological inclusiveness in development of the healthcare services can improve efficiency and optimize the usage of healthcare resources [1,2,3]. IoT is a technology, which can bring in a paradigmatic shift in the way services are offered and would bring a new wave of technological innovation [4]. IoT lets objects such as gadgets, vehicles, and houses interact with one another over the Internet, allowing them to become a part of the Internet [5,6]. Adding IoT applications to these types of objects can be done using cloud computing technology as well [7]. Even other technologies such as sensors, radio frequency identification, nanotechnology, and embedded system technology are all important components of the IoT. These technologies are all utilized to enhance IoT applications for a variety of objectives [3,8]. The health industry is always under a pressure to provide quality healthcare at a reasonable cost, and IoT-based services have the potential to make this a reality. For example, using sensor-based systems aids in quick and better patient monitoring, resulting in fewer tests, and thus eliminating unnecessary doctor and patient interactions, and, as a result, lesser expenses. IoT technology can also play a vital role in early illness detection and intervention. In an emergency, IoT apps can help to swiftly notify and inform caretakers of an elderly person and save critical time [9]. IoT-based healthcare devices/services can offer myriad of solutions such as real-time monitoring of electrocardiogram, blood pressure, temperature, blood glucose, oxygen saturation in blood, and asthma [10].
A basic structure of IoT-based healthcare device/services.
But all these benefits only will scale out positively if the adoption happens for IoT medical devices and services by both institutions and consumers. Currently, the adoption of IoT-based healthcare wearables is low, thus making it difficult for institutions to implement these benefits since end consumers are not willing to adopt them. Companies and governments are investing heavily in such breakthrough technologies such as IoT, which can cause a paradigmatic change in the lifestyle of its users [11]. Although the IoT can provide a better approach to healthcare management, its success will finally depend on the acceptance of the technology among consumers.
Thus, this study aims to bring together various factors affecting the acceptance of IoT-based healthcare devices in an emerging economy such as India where the need for medical services is ever-growing and has huge potential not only as a social cause but also as a business. Also, the objective is to integrate the critical factors affecting the adoption of IoT-based healthcare devices by end consumers to provide a technology acceptance model (TAM) based on the popular UTAUT model [12] and the TAM [13].
2 Literature review
The capabilities of wireless technologies have substantially increased in the past few years, and its benefits can be seen in the development of healthcare technologies. IoT-based healthcare devices are growing very fast, and a large number of companies whether they are existing companies or startups have joined the bandwagon of providing end consumers with such devices. Most of these devices are wearable and must be worn on the body of the individual so that they can track and monitor the changes in body functions and inform or trigger necessary actions at the right time. Various studies have been carried out to understand the acceptance of these devices using various TAMs such as TAM, theory of resoned action (TRA), theory of planned behaviour (TPB), and UTAUT2 [14,15,16,17]. In recent years, the awareness of self-health monitoring devices has increased since these devices have become more affordable and easier to carry along, also providing monitoring of patients remotely [18]. All these devices can be connected to a mobile device or a smartphone, which most individuals nowadays carry along; thus, these mobile app-based platforms bring together the patients, doctors, and healthcare-providing institutions ubiquitously. The past research has highlighted various reasons for the acceptance of healthcare technologies such as ubiquity, health risk, relative advantage (REL), price value (PV), social influence (SI), and facilitating conditions (FCs) [15,19]. These wearable healthcare technologies may include health bands, diabetic patches, cardio monitoring devices, body temperature sensors, and oxygen saturation meters. Even after the introduction of various devices, industry practitioners are concerned about psychological factors linked to trust toward these devices and not only by the end consumers but also by the healthcare professionals.
Studies carried out earlier suggest that widespread adoption does depend not only on a few factors but also on a wide range of multi-issues [20]. Hence, an integrated model, which is based on a sociological personal perspective, was the need of the hour. Many theories and models were used in the past to study and understand the acceptance of technology. The first of this kind was the TAM [13], which introduced the concept of technology acceptance by individuals [21]. Based on TAM, the UTAUT also combines constructs from eight different TAMs [12]. The UTAUT model was applied to various technologies after its introduction in the last 20 years [22]. The basic UTAUT had some limitations since it was designed for organizational settings; thus, to overcome such limitations, three more constructs were introduced, namely, PV, hedonic motivation, and habit, which extended the UTAUT model in the context of end consumers [23]. Thus, UTAUT2 can help to determine consumers’ acceptance and usage of new and complex technologies [23]. The model also helps researchers study the factors that affect the adoption of information technology while considering the social and emotional aspects of the end user [24,25]. The study uses a few factors from the UTAUT2 such as SI, FCs, performance expectancy, and PV along with newer variables such as UQ and perceived health risk (PHR) added from various other studies. The variable habit was excluded from the study since IoT-based healthcare devices are not very common and most people have just started buying or may own their first device; thus, habit formation has not yet happened.
3 Hypothesis development
3.1 Attitude (ATT) and behavioral intention (BI)
ATT is described as an individual’s psychological propensity to show like or dislike toward an object after evaluating it [26], and ATT impacts behavior in a variety of ways [27]. The TAM, the TRA, and the TPB all show that ATT predicts BI. It has been evaluated how the ATT of healthcare professionals toward mobile healthcare influences BI to adopt the technology [28]. Also, the influence of ATT on BIs was found to be significant in the context of the adoption of mobile banking and e-commerce [29,30]. Thus, an individual’s ATT toward new technologies affects BI [31]. Since IoT-based healthcare devices are examples of new technology, we propose the following hypothesis:
H1: ATT toward IoT-based healthcare devices will influence the BI of end users.
3.2 UQ and ATT
UQ means that the technology is accessible anywhere easily. The advantage of any IoT-based technology is that it can be accessed anywhere if a mobile and Internet connection are available. This ubiquity of IoT also applies to healthcare devices based on IoT. The past study has shown that iniquitousness of IoT-based healthcare devices can influence acceptance of the technology [32]. Also, it has been named as information pervasiveness, which means that information is transparently available to its stakeholders to take action, like a physician suggesting the right treatment to the patient in time [33]. Thus, we hypothesize that
H2: UQ has an influence on the ATT toward IoT-based healthcare devices.
3.3 SI and ATT
SI is defined as the relevance of peers’ opinions on utilizing new technology or system [12]. The literature on predicting the BI of patients in the acceptance of information and communication technology in healthcare has demonstrated SI as a key factor. It has been observed that peer and colleague opinions have a considerable influence on user behavior [34]. Also, some research studies have highlighted the complex role of SI in new technology acceptability [14,35]. Thus, we propose the following hypothesis:
H3: SI will influence the ATT toward IoT-based healthcare devices.
3.4 PHR and ATT
PHR is an individual’s impression of the probable outcomes of an activity because of the level of uncertainty about a certain outcome related to healthcare. People seek to prevent losses as much as possible while making decisions in dangerous situations, especially if it is about someone’s health. Various research studies have shown how perceived risk about the technology can impact the users’ ability to decide and finally affect BIs [36,37]. An increase in perceived risk toward technology will ultimately reduce the effect on its ability to adapt. As a result, users of healthcare services might not be willing to adopt IoT-based healthcare devices; thus, we hypothesize that
H4: PHR has a negative influence on the ATT toward IoT-based healthcare devices.
3.5 REL and ATT
REL is described as the extent to which an innovation is considered to be superior to the rest in consideration [31]. Literature has studies related to innovation diffusion theory confirming the effect of REL on technological adoption [38]. This study uses the more generic term “relative advantage” over perceived expectancy as in UTAUT since perceived expectancy describes what a user expects from a technology, whereas users will always compare with whatever technology they have presented before showing any BI. Most importantly, the biggest comparison will be personally visiting a healthcare professional. As suggested in a study that financial profitability to social benefit can all be a part of REL [39], thus hypothesizing,
H5: REL has an influence on the ATT toward IoT-based healthcare devices.
3.6 PV and ATT
PV is the financial cost that represents customers’ trade-off between the perceived advantages of using technology and the monetary cost of using it [23]. It covers the expenses of buying the device, associated additional services, and even Internet bills data. Previous research has found that the perceived PV of IoT-based devices is a barrier to their acceptance of the new technology since IoT is a novel idea for customers, and as with any novel technology, they face challenges in adopting IoT-based products/services [40,41]. Thus, we hypothesize the following:
H6: PV has an influence on the ATT toward IoT-based healthcare devices.
3.7 FC on ATT and BI
The availability of resources and infrastructure necessary to use technology is referred to as FCs. Several studies have examined and found the effect of FCs as a significant factor enabling perceived ease of use of a technology [14,35]. Thus, it can be concluded that FCs may also impact customers’ perceptions of the ease of use of IoT-enabled devices since they require not just a mobile phone but also a device, proper internet connection, a back-end service to connect the health professionals, and so on. Even, the past research has found a link between the utility perceived by the user about technology and FCs available. The FC was also considered to be an important factor influencing both ATT and BI toward technology [22,23]. Thus, it can be hypothesized that,
H7: FC has an influence on the ATT toward IoT-based healthcare devices.
The FC has been found to have significantly influenced BI to adopt new technologies [23]. Studies in the past have found the FC to influence BI toward mobile banking [42], mobile payment system [29], and food delivery apps [43]. Thus, we hypothesize the following:
H8: FCs have an influence on the BI toward IoT-based healthcare devices.
4 Method
A quantitative survey-based research method was utilized as an approach to assess the stated hypotheses and the research model. Because many of the respondents had very less prior awareness of the technology, a summary of the IoT healthcare product was presented as a short 2-min video at the start of the survey. All the statements used to measure the items were obtained from previous studies, with slight variations to fit the study setting of IoT healthcare devices. To examine the research model, the partial least squares-structural equational model (PLS-SEM) approach was applied. PLS-SEM was chosen as the method for the study since it can study both reflective and formative models together, i.e., it helps in analyzing complex relationships [44]. It can also handle small sample sizes, and hence, the ten times rule [45] was employed to make sure the number of respondents was enough.
4.1 Data collection and sampling method
We used purposive sampling to gather data using an online questionnaire from people who own some kind of smart device. This strategy employs sampling from a pre-decided target population. Employing purposive selection improves the study’s validity [46]. This strategy is centered on looking for people who match a specific requirement, i.e., owning a smart device since anyone with a smart device can become a prospective consumer of IoT-based healthcare devices in near future. Similar sampling techniques have been employed by previous studies [47,48]. The data were collected from working professionals and retired individuals from different cities, in India, namely, Delhi, Kolkata, Bengaluru, and Mumbai. The number of respondents was proportionately divided among the four cities based on the urban population of the city. The total number of individuals to whom the questionnaire was administered was 300. Of which 265 fully completed the questionnaire and removed missed/incomplete questions, and finally, the total number of respondents was 253. The sample size was calculated using the ten times rule, which states that the sample size is ten times the number of relationships in the model [45]. According to this technique, a sample size of 70 people is required for the study’s minimal number of samples.
4.2 Data analysis
PLS-SEM was used to test the proposed model since it offers researchers an advantage to test complex statistical analysis at once. The analysis was carried out in a two-step approach on the data collected using an online survey. In the first step, the measurement model was examined, and in the second step, the structural model was assessed with the help of path coefficients between the constructs. SmartPLS version 4 was used for data analysis. Table 1 shows the demographic profile of the respondents.
Demographic information
| Demographic information (n = 253) | ||
|---|---|---|
| Gender | Male | 65.20% |
| Female | 34.80% | |
| Age (in years) | Below 21 | 5.36% |
| Between 21–29 | 34.65% | |
| Between 30–39 | 12.20% | |
| Between 40–49 | 25.01% | |
| Above 50 | 22.51% | |
| Household income (Rupees/year) | 0–250,000 | 15.60% |
| 250,001–500,000 | 21.40% | |
| 500,001–1,000,000 | 23.38% | |
| 1,000,001–1,500,000 | 17.88% | |
| Above 1,500,000 | 21.74% | |
5 Results
This section discusses the reliability, validity, and convergence of the model, and the discriminant validity of the constructs is also discussed. This section also presents the goodness-of-fit index of the proposed model and evaluates the significance of path coefficients between various constructs.
5.1 Measurement model
The measurement model of this study is reflective, and hence, the reliability and validity (convergent and discriminant validity) is checked to assess the model [45]. To assess the construct reliability, we used the outer loadings and composite reliability. Table 2 presents the factor loadings and composite reliability of the constructs, all of which are more than 0.7. Convergent validity is used to detect if the items within a construct are unrelated to each other [49]. The average variance extracted (AVE) is calculated to examine convergent validity and values of more than 0.70 or higher are preferred [50]. To assess the composite reliability, all values of more than 0.70 are preferred, and Table 2 presents the values for each construct [51]. Discriminant validity also needs to be checked to make sure that the observed variables are unique and are not related to other observed variables. Three approaches were used to test the discriminant validity of this study, which are the Fornell–Larcker criteria, Heterotrait–Monotrait (HTMT) ratio criteria, and cross-loadings.
Results of the measurement model
| Constructs | Items | Factor loadings | Cronbach’s alpha | Composite reliability | AVE |
|---|---|---|---|---|---|
| UQ | UQ1 | 0.823 | 0.855 | 0.881 | 0.699 |
| UQ2 | 0.862 | ||||
| UQ3 | 0.922 | ||||
| UQ4 | 0.726 | ||||
| SI | SI1 | 0.916 | 0.826 | 0.849 | 0.743 |
| SI2 | 0.898 | ||||
| SI3 | 0.764 | ||||
| REL | REL1 | 0.895 | 0.889 | 0.890 | 0.819 |
| REL2 | 0.928 | ||||
| REL3 | 0.891 | ||||
| FC | FC1 | 0.943 | 0.934 | 0.935 | 0.883 |
| FC2 | 0.948 | ||||
| FC3 | 0.929 | ||||
| PHR | PHR1 | 0.837 | 0.872 | 0.893 | 0.721 |
| PHR2 | 0.838 | ||||
| PHR3 | 0.890 | ||||
| PHR4 | 0.829 | ||||
| PV | PV1 | 0.960 | 0.940 | 0.985 | 0.891 |
| PV2 | 0.962 | ||||
| PV3 | 0.909 | ||||
| ATT | ATT1 | 0.795 | 0.892 | 0.893 | 0.757 |
| ATT2 | 0.889 | ||||
| ATT3 | 0.895 | ||||
| ATT4 | 0.897 | ||||
| BI | BI1 | 0.706 | 0.882 | 0.882 | 0.675 |
| BI2 | 0.866 | ||||
| BI3 | 0.845 | ||||
| BI4 | 0.845 | ||||
| BI5 | 0.835 |
AVE: average variance extracted, AW: awareness, SI: social influence, EC: environmental concern, FC: facilitating condition, PT: perceived trust, PV: price value, ATT: attitude, BI: behavioral intention.
5.2 Discriminant validity
The first technique used to test the discriminant validity of this study is the Fornell–Larcker criterion. Table 3 shows the values of the criterion, which is the AVE, and to establish the discriminant validity, the correlation values of the latent variables present should be lesser than the AVE value [52].
Fornell–Larcker criterion
| ATT | BI | FC | PHR | PV | REL | SI | UQ | |
|---|---|---|---|---|---|---|---|---|
| ATT | 0.870 | |||||||
| BI | 0.767 | 0.821 | ||||||
| FC | 0.525 | 0.579 | 0.940 | |||||
| PHR | 0.469 | 0.464 | 0.473 | 0.849 | ||||
| PV | 0.139 | 0.203 | 0.126 | 0.108 | 0.944 | |||
| REL | 0.626 | 0.632 | 0.628 | 0.450 | 0.159 | 0.905 | ||
| SI | 0.635 | 0.653 | 0.547 | 0.494 | 0.106 | 0.614 | 0.862 | |
| UQ | 0.578 | 0.570 | 0.617 | 0.377 | 0.122 | 0.554 | 0.499 | 0.836 |
Note: All the italicized values are the square roots of AVE.
UQ: ubiquitous, SI: social influence, PHR: perceived health risk, REL: relative advantage, PV: price value, FC: facilitating condition, ATT: attitude, BI: behavioral intention.
Table 4 presents the results of the HTMT ratio, which also shows that the discriminant validity is well established since all the values are less than 0.85.
HTMT ratio
| ATT | BI | FC | PHR | PV | REL | SI | UQ | |
|---|---|---|---|---|---|---|---|---|
| ATT | ||||||||
| BI | 0.865 | |||||||
| FC | 0.570 | 0.642 | ||||||
| PHR | 0.518 | 0.517 | 0.512 | |||||
| PV | 0.145 | 0.214 | 0.125 | 0.120 | ||||
| REL | 0.696 | 0.718 | 0.688 | 0.502 | 0.164 | |||
| SI | 0.735 | 0.763 | 0.625 | 0.565 | 0.116 | 0.716 | ||
| UQ | 0.647 | 0.647 | 0.667 | 0.424 | 0.125 | 0.611 | 0.592 |
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UQ: ubiquitous, SI: social influence, PHR: perceived health risk, REL: relative advantage, PV: price value, FC: facilitating condition, ATT: attitude, BI: behavioral intention.
5.3 Structural model
The second step of the analysis included the evaluation of the structural model and testing of the hypothesis. The entire model shown in Figure 2 was evaluated together in SmartPLS using PLS-SEM bootstrapping. The p-value for individual relationships was examined to determine the significance of the relationships between the two constructs. A p-value lesser than 0.50 is accepted as significant [53] (Figure 3). The R 2 values and the significance of the path coefficients are used to evaluate the structural model. The R 2 value can be evaluated differently for different kinds of research disciplines. In social sciences, an R 2 value of 0.20 is accepted high, whereas for numerical studies, an R 2 value of 0.75 would be considered high. An R 2 value of more than 0.67 is considered to be substantial, a value more than 0.33 is considered moderate, and a value less than 0.19 is weak [54]. The model accounted for 62.8% (R 2 = 0.628) variance in BI toward IoT-based healthcare devices, which is strong predictive relevance. Also, the R 2 value of ATT is moderately strong at 53.5% (R 2 = 0.535).
Hypothesized research model.
Another measure that was used to evaluate the structural model is the Q 2 value. The Q 2 value evaluates the model’s predictive relevance. A value of more than zero suggests that the model has predictive relevance. Table 5 shows the model fit indices. Furthermore, standardised root mean square residual (SRMR) was used to assess the model fit, which is 0.073. Thus, an SRMR value of less than 0.10 states that the model fit is acceptable [55].
Model fit indices
| Dependent constructs | R 2 | R 2 adjusted | Q 2 |
|---|---|---|---|
| ATT | 0.546 | 0.535 | 0.520 |
| BI | 0.631 | 0.628 | 0.532 |
| SRMR | 0.073 |
Output of the research model from SmartPLS.
The evaluation of the significance of the path coefficients between the constructs was carried out using PLS-SEM using bootstrapping at 5,000 resamples. The hypothesis testing was performed at 95 and 90% significance levels, and the hypothesis evaluation details are presented in Table 6. All the path coefficients were significant at p = 0.05 except PHR and ATT, which is significant at p = 0.10.
Evaluation of hypothesis
| Hypothesis | Path | β | St. Dev. | T statistics | p values | Significance |
|---|---|---|---|---|---|---|
| H1 | ATT → BI | 0.640 | 0.047 | 13.656 | 0.000 | Significant** |
| H2 | UQ → ATT | 0.245 | 0.067 | 3.664 | 0.000 | Significant** |
| H3 | SI → ATT | 0.307 | 0.067 | 4.612 | 0.000 | Significant** |
| H4 | PHR → ATT | 0.115 | 0.063 | 1.810 | 0.070 | Significant* |
| H5 | REL → ATT | 0.249 | 0.067 | 3.736 | 0.000 | Significant** |
| H6 | PV → ATT | 0.025 | 0.044 | 0.559 | 0.576 | Not significant |
| H7 | FC → ATT | −0.012 | 0.063 | 0.187 | 0.852 | Not significant |
| H8 | FC → BI | 0.245 | 0.053 | 4.586 | 0.000 | Significant** |
Notes: **p < 0.05 = Significant; *p < 0.10 = Significant.
6 Discussion
The present study examines the factors affecting the adoption of IoT-based healthcare devices by end users. Many studies in the past have examined the adoption of IoT-based healthcare systems by hospitals, doctors, or other medical professionals, but these professionals do not pay for it [40], rather these facilities are provided by the institutions they work for. Thus, examining the acceptance of end consumers would play a significant role in the spread of this technology. The research identifies factors considering the popular UTAUT2 model and a few other factors from literature such as UQ, PHR, REL, SI, and PV affecting behavioral intention via ATT. The proposed model was evaluated using PLS-SEM, where data from 253 respondents were collected through an online survey. The researched model has 8 latent variables and 28 manifest variables that help in explaining the adoption of IoT-based healthcare devices.
Most of the hypotheses developed by the study were supported by the data. Two relationships, namely, PV on ATT and FC on ATT, were not supported. However, FCs have a direct significant effect on behavioral intention toward the technology. UQ, SI, PHR, and REL are seen to affect BI significantly, but ATT has the strongest effect on the intention to adopt. The FC was found not to be significantly influencing BI, which is different from the past study [56] where performance expectancy, effort expectancy, FC, and SI were found to significantly influence BI. PV in this study has a significant influence on BI, which means that the consumers weigh the price paid for the actual benefit it receives by using the technology. This is similar to studies done in the context of the healthcare system by integrating mobile phones, which suggested that implementation of such technology can lower prices, save time, and improve doctor–patient interaction [57]. Especially for an emerging economy like India, such technologies should be cost-effective to be able to scale and reach the masses.
UQ and REL of using IoT-based healthcare devices over the conventional way of reaching a doctor were considered more beneficial. It is in line with the previous research, which also found UQ and REL to be a significant influencer of BI [19,58]. These studies also found that perceived advantage, perceived risk, and perceived vulnerability can influence the acceptance of IoT-based healthcare systems.
The hypothesized model has a strong model fit since the independent variables identified by the study can explain 62.8% of the variance in the dependent variable, i.e., BI. Even the predictive relevance Q 2 of the model is also high at 53.2%. Various previous studies have reported lower R 2 values of 27% [59], 51% [60,61], 53% (males) and 58% (females) [62], and 56% [63] to predicted acceptance of BI toward healthcare technology. Thus, the model suggested in this study has predictability much higher than many previous studies.
6.1 Theoretical and practical implications
The outcomes of this research can contribute to healthcare technology systems in many ways. Some of the potential benefits of IoT-based healthcare devices are reducing the cost of healthcare and increasing the effectiveness and efficiency of the healthcare system. Hence, the study is crucial for IS research to understand the effect of several factors on the adoption of this technology among end users since the acceptance on the end of end users is purely individualistic, whereas the adoption, on the other hand, by healthcare professionals may be driven by organizational policies.
The study found that REL, SI, and FC (directly on BI) to be predictors of BI; thus, the companies in the market must recognize that it is very important to keep a track of users’ perception of the usefulness of the technology, which should provide better control and monitoring of users, thus leading to the better health condition. Previous studies [56,64] have shown that people who are considering adopting these technologies are particularly influenced by factors such as their perception of the technology’s usefulness, the perceived ease of use, SI, and the presence of supportive conditions. These factors can all contribute to the adoption of mobile health technologies.
The study uses the UTAUT2 model, which is one of the most comprehensive models of IS acceptance research to date, which assimilated eight different past TAMs into one [23], and extends it further in the context of IoT-based healthcare devices. The suggested model also has a higher predictive ability than many of the past research. Thus, providing a robust predictive model of IoT-based healthcare technology acceptance by end users.
Moreover, this research also provides practical implications for several stakeholders. Most of the proposed hypotheses are supported; thus, these factors will be beneficial for firms producing IoT healthcare devices and social organizations to better frame policies. Both companies and social planners can take SIs, PHRs, and RELs to better propagate and popularize the technology. REL is a crucial factor affecting the adoption of IoT-based healthcare devices, and it means that individuals consider using this technology over the other only when they find an advantage over the rest. The PHR is also having a significant impact on adoption; thus, firms should keep in consideration various communication methods to make consumers feel at ease with this technology and reduce the anxiety that they might relate to it. One of the strong variables to affect the adoption is the ubiquity of this technology, and marketers should use this benefit to, its greatest advantage, attract not only for end consumers but also attract healthcare professionals to adopt the technology at a faster pace. Furthermore, this study discovered a significant relationship between SI and BI. To increase the adoption of IoT-based healthcare devices, manufacturers and application developers could investigate ways that can take benefit of users’ sphere of SI. Concerned policymakers may arrange forums for exchanging best practices, conducting seminars with people who benefited from the technology and talk about the advantages of the system [25]. At the same time, firms can use SI to manage negative feedback, which may deter the spread of this technology.
7 Conclusion
Although this study contributes to the field of research in IoT-based healthcare technologies, the study has certain drawbacks. First, the sample was limited to a single nation, India, which was the frame of this study. But, suggests that further research should undertake follow-up study using data from multiple nations. Second, the respondents were screened based on their ownership of smart devices assuming they would quickly adopt IoT-based healthcare devices. Thus, it cannot be said that the outcome of the study is generalizable across all segments of society. Third, since the technology is very new, the research conducted only considered cross-sectional data, whereas a longitudinal study could help us understand the habit formation and its effect on adoption. Continuous usage of any product by consumers is considered important since acquiring new customers is five times costlier than delivering to existing customers [65]. Also as stated in the past research, any information system-based technology can only be successful when continued usage happens [66]. We collected data on income, gender, and age, but did not delve into the group analysis of these cohorts. However, the explanatory power of the model is 62.8%, which is high as far as IS research is concerned, and further study can try to identify factors that may increase the predictability of this model. The factors identified by this study also can be further studied to understand the psychological lens to view the reasons for the nonadoption of IoT-based healthcare devices.
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Funding information: None declared.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Conflict of interest: No conflict of interest stated by the authors.
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Informed consent: Informed consent was obtained from all individuals included in this study.
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Ethical approval: The conducted research is not related to either human or animals use.
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Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Articles in the same Issue
- Regular Article
- The role of prior exposure in the likelihood of adopting the Intentional Stance toward a humanoid robot
- Review Articles
- Robot-assisted therapy for upper limb impairments in cerebral palsy: A scoping review and suggestions for future research
- Is integrating video into tech-based patient education effective for improving medication adherence? – A review
- Special Issue: Recent Advancements in the Role of Robotics in Smart Industries and Manufacturing Units - Part II
- Adoption of IoT-based healthcare devices: An empirical study of end consumers in an emerging economy
- Early prediction of cardiovascular disease using artificial neural network
- IoT-Fog-enabled robotics-based robust classification of hazy and normal season agricultural images for weed detection
- Application of vibration compensation based on image processing in track displacement monitoring
- Control optimization of taper interference coupling system for large piston compressor in the smart industries
- Vibration and control optimization of pressure reducer based on genetic algorithm
- Real-time image defect detection system of cloth digital printing machine
- Ultra-low latency communication technology for Augmented Reality application in mobile periphery computing
- Improved GA-PSO algorithm for feature extraction of rolling bearing vibration signal
- COVID bell – A smart doorbell solution for prevention of COVID-19
- Mechanical equipment fault diagnosis based on wireless sensor network data fusion technology
- Deep auto-encoder network for mechanical fault diagnosis of high-voltage circuit breaker operating mechanism
- Control strategy for plug-in electric vehicles with a combination of battery and supercapacitors
- Reconfigurable intelligent surface with 6G for industrial revolution: Potential applications and research challenges
- Hybrid controller-based solar-fuel cell-integrated UPQC for enrichment of power quality
- Power quality enhancement of solar–wind grid connected system employing genetic-based ANFIS controller
- Hybrid optimization to enhance power system reliability using GA, GWO, and PSO
- Digital healthcare: A topical and futuristic review of technological and robotic revolution
- Artificial neural network-based prediction assessment of wire electric discharge machining parameters for smart manufacturing
- Path reader and intelligent lane navigator by autonomous vehicle
- Roboethics - Part III
- Discrimination against robots: Discussing the ethics of social interactions and who is harmed
- Special Issue: Humanoid Robots and Human-Robot Interaction in the Age of 5G and Beyond - Part I
- Visual element recognition based on profile coefficient and image processing technology
- Application of big data technology in electromechanical operation and maintenance intelligent platform
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