Summary Table:
What was already known on this topic?
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Development of the application of Internet of Things (IoT) technology makes medical intelligentialization become a trend all over the word.
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The existing researches exploring influential
This study examined and evaluated the key influential factors (and their interrelationships) of consumer adoption behavior in improving and promoting intelligent medical terminals to achieve a set aspiration level [1] in each dimension and criterion of a hybrid modified Multiple Attribute Decision-Making (MADM) model [2], [3], [4], [5]. The model provides reasonable suggestions for business decision-makers in firms or medical institutions that can improve their products’ performance. Intelligent medical treatment can elucidate the interaction among patients, medical workers, medical institutions, and medical devices; it also helps facilitate gradual medical informatisation by creating a regional health records information platform and using the advanced technology of the Internet of Things (IoT) for continuous improvement and sustainable development. Based on digitalisation and visualisation models, the technology encourages more people share medical information and resources accurately and conveniently [6].
New technologies often require a new management model to ensure smooth operation and execution. For example, Dijkman et al. [7] found that the application of the IoT contributes to the development of new applications and the improvement of existing applications, whether in manufacturing use, medical healthcare, or energy domain improvements. Correspondingly, they identified the building blocks of a business framework specifically for IoT applications through a literature review, interviews, and the survey responses of recruited practitioners. After the eighth period of the Fifth Plenary Session in October 2015, the Report on the Development of the Intelligent Medical Industry in China: A Five-Year Plan (2016–2021) was proposed, which provides novel strategies and highlights opportunities for sustainable development of the intelligent medical industry.
Intelligent medical terminals are electronic medical devices that combine various medical functions; terminals can exist in both the digital community medical service system and in the home healthcare scene. Moreover, intelligent medical terminals provide a new medical testing system for resolving various problems, such as addressing the existing uneven regional distribution of medical resources and the scarcity of specialist resources. Notably, intelligent medical terminal technology based on cloud computing has improved in recent years. Compared with the limited storage capacity of existing terminal devices, the cloud-based technology has a greater information storage capacity, can remotely provide medical diagnoses, and can share medical information, while also ensuring that patients have a clear understanding of their health statuses.
Despite its rapid growth, the promotion of intelligent medical terminals has also faced several problems during implementation. Three main factors influence the development of the intelligent medical terminal market: (1) lagging construction of the medical information system, (2) lack of unified planning and standards, and (3) unfinished operating mechanisms. Notably, many other factors also interact with and influence consumer decisions, such as price, battery life, and operation complexity. However, due to the range of types and the substantial amount of intelligent medical terminals, consumers are faced with even more factors that must be perused and considered. Therefore, the use of intelligent medical terminals is an important scientific concern that must be solved. Here, a systematic evaluation model and a complete index system was created to analyse the factors that influence consumers’ adoption of intelligent medical terminals. These factors must satisfy specific relationships to reflect the logic of the real world, the source factors that influence others in the index system are also examined.
Most studies on intelligent medical terminals in China and abroad have concentrated on the technical, rather than management, aspects of the devices. Indeed, several researchers have attempted to improve intelligent medical technology overall, from constructing a whole information system to develop and maintain the relevant medical terminal devices [8]. For example, Zhou et al. [9] comprehensively reviewed intelligent medical terminals, application software, data processing software, and system management software to propose a scheme for dealing with data integration problems accurately and efficiently. This technical progress facilitates research on management. For instance, the development of an intelligent medical chart capture system [10] is closely linked to the development of an intelligent medical image management system [11]; the progress of chart capture technology and the maturity of an image management system both add convenience and efficiency to the operation of the whole intelligent medical management system. However, while technology maturity is easy to achieve, management maturity is relatively difficult. The lack of unified management standards and protocols in different regions has made compatibility the main barrier to medical intelligentisation and informatisation, and it has become industrial consensus that a unified code identification of the IoT must be established.
The intelligent medical services that are currently offered by different agencies strongly influence the efficiency of information transmission. Notably, delays or errors in decision-making can occur without effective communication and coordination among the various agencies [12]. Smith et al. [13] utilised neural networks as an example to address the challenge of a thorough and acceptable inherent performance evaluation of intelligent medical systems. They also argued that the legality of intelligent medical terminal data being used as treatment basis must be reviewed, and pointed out that there should be corresponding rules with which to cope with online therapy disputes.
Some researchers have also focused on information security and privacy issues problems to prevent information loss, tampering, and unauthorised access. Appropriate security features not only protect users’ privacy, but also reduce probable patient disputes. Medical data is sensitive and precise, and different medical businesses ask for various time delays, reliability assurances, transmission rates, and priorities [8]. Weerasingh et al. [14] emphasized that trust is exchanged between the provider and receiver of a medical service, and thus the confidentiality of medical records must be ensured. Yang et al. [6] proposed a prototype system for the accessing and sharing of large-scale medical data that satisfies the diverse requirements of privacy concerns and data utilisation. Other Chinese scholars have conducted research on security, focusing specifically on data encryption, digital signatures, authorized entity, access control, and security audits. Notably, all of these systems require a solid technical foundation about the development of intelligent medical terminals.
Many studies have been conducted on the innovative diffusion and promotion of new products and services. Such research explores product promotion during the whole life cycle from a marketing perspective [15]. After conducting a significance analysis, Burt [16] determined that there are two distinguishable social networks operating in the diffusion process: one concerns the transfer of information, and the other concerns the transfer of social influence. Lee et al. [17] used a sample survey to verify the Stimulus–Organism–Response (SOR) framework, and proposed four factors (innovative technology, visual appeal, prototypicality, and self-expression) as the major influences on approach behavior through attitude (cognitive state) and pleasure (affective state). Elsewhere, Herbig and Day [18] contended that technology base, entrepreneurial skills, and customer needs/wants are the three components that promote innovation and value prosperity, particularly stressing the central status of customer needs. As an innovation technology application, the diffusion of intelligent medical terminals has considerable similarity with the general innovative diffusion process. Notably, research on this topic has also provided the lens through which studies on consumers’ adoption of intelligent medical terminals can be conducted.
With development of the intelligent medical industry, an increasing number of studies have analysed the adoption of intelligent medical terminals. Aubert and Hamel [19] recruited 299 professionals and 7248 intelligent medical card users for a questionnaire survey and interviews, in order to systematically assess key factors influencing peoples’ adoption of intelligent medical cards. Specifically, they analysed the various identified factors in two respects: (1) the attribute factors of the cards, including their compatibility, ease of use, image, quality support, and applicability; and (2) the users’ inner psychological factors, including consumer satisfaction and voluntary adoption of the cards. Each factor was scored accordingly, and the results showed that ease of use and quality support were the most critical factors influencing intelligent card adoption. In another study, Hebert and Benbasat [20] conducted a literature review and established a model to analyse the relationship between expectation/attitude and actual behavior when hospitals implemented new information technology; indeed, behavior related to consumers’ adoption of intelligent medical terminals can be divided into intended behavior and actual behavior.
Using questionnaire surveys, Liu et al. [21] explored the promotion and popularisation problems of medical smart cards across Taiwan and identified the main obstacles to adopting such cards. They concluded that public awareness of smart cards must be improved and deepened through programmes or campaigns, and that the quality of the smart cards needs to be improved. Vishwanath et al. [22] focused on the adoption of personal digital assistant prescription-assistive technology to improve drug safety. Following a rigorous statistical analysis, they argued that early adopters tended to be the younger and less experienced clinical people (e.g. residents), as well as those who frequently use the technology. Moreover, the early adopters attached importance to its ease of use, whereas the use of personal digital assistants among later adopters was based on its clinical usefulness. Moreover, after collecting and reviewing the extant literature, Yang [23] suggested that users’ apply the unified theory of acceptance and use of technology (UTAUT) when they consider adopting wearable smart devices. The UTAUT-based model demonstrated that factors like performance expectation and community influence impacted users’ adoption the most, whereas the technical characteristics had limited influence. Similar to the studies on security discussed earlier, such pioneering research has promoted examination of consumers’ adoption of intelligent medical terminals.
Research on consumers’ and customers’ adoption of intelligent medical terminals is still in its early stages; there are also more qualitative analyses than quantitative analyses. To address the limitations of previous studies, the present study proposed a measurable evaluation index system and used a hybrid modified MADM method to conduct an in-depth exploration of the cause–effect relationships among different factors that influence consumer adoption behavior. By understanding the gaps in each criterion, where an aspiration level is set for specific terminals, business decision-makers can improve the short boards of products, gain consumer trust, and enjoy strategic advantages in a fiercely competitive market.
The remainder of this paper is organised as follows. Section 2 discusses and summarises the extant literature to build the evaluation index system of influential factors synthetically; such a discussion outlined the existing theoretical knowledge about intelligent medical terminals that was critical for conducting the present study. Section 3 details the hybrid modified MADM model. Section 4 verifies the model, and describes the two medicine bottles that were adopted as terminals in the empirical case studies to compare their performance for improvement using the modified VIKOR method, then discuss the results. Finally, Section 5 offers some conclusions and remarks.
Intelligent medical terminals have numerous functions, including user identification, automatic detection and analysis of general physiological parameters, real-time monitoring of the physiological parameters of a patient, storage analysis, remote transmission, and healthy life guidance. They are connected to users’ lives and can ensure rapid implementation of treatment by detecting users’ vital data.
Intelligent medical terminals can be applied to diverse business scenes. First, they are
A MADM model was adopted to conduct this study because it is able to consider several dimensions/criteria, and the interactions between and among them, simultaneously. This in-depth review helps determine the optimal solution (out of several programmes) for business decision-makers [40]. The hybrid modified MADM model in this study included three analysing tools: the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique, the DEMATEL-based analytic network process (DANP) method,
In this section, two empirical cases are reviewed. Specifically, the AdhereTech (A1 alternative) and the Audio/Visual Alerting Pillbox (A2 alternative) medicine bottles are illustrated to test and verify the aforementioned model. Next, the interrelationships among the interactional dimensions and criteria are elucidated through the DEMATEL technique to construct an INRM. After a set of precise mathematical calculations, the influential weights (local weights and global weights) of each
This paper explored the systematic identification and analysis of key factors that influence consumers’ adoption of intelligent medical terminals, and described a method for calculating performance gaps of the factors. Decision-makers in various enterprises can utilize the results, which provide valuable advice for improving consumer adoption, to identify gaps when promoting intelligent medical terminals. The conclusions and contributions of this study are summarised as follows:
Our study
The authors declare no conflict of interest.
Gwo-Hshiung Tzeng designed the research; Liu Yupeng provided the research idea and collected the data; Chen Yifei analyzed the data and drafted the manuscript; Finally, Gwo-Hshiung Tzeng revised the paper. All authors have read and approved the final manuscript. Summary Table: What was already known on this topic? Development of the application of Internet of Things (IoT) technology makes medical intelligentialization become a trend all over the word. The existing researches exploring influential
Special thanks is extended to all the tutors for checking this paper and giving direct or indirect help. This work was sponsored by the National Natural Science Foundation of China [grant number 71402040], the Chinese Postdoctoral Science Foundation [grant number 2015M571310].
However, in the real-world problems there are interdependent relationship or even feedback effects among dimension/criteria. Compared to structural equation modeling (SEM) or traditional regression, the hybrid modified MADM model combined with the DEMATEL may be more ideal in exploring real-world issues [49], avoiding “the unrealistic assumptions in statistics and economics” [82]. The DEMATEL was originally derived from the Science and Human Aviation Program of the Battelle Memorial Institute (United Nations) in Geneva for 1972 to 1976 [83,84] as a structural model for clarifying and solving complex problems, initially focusing on addressing a complex and intertwined global problem [85].
Therefore, for the sake of simplicity, we will also label MADM as MCDM in the following. Many researchers worked on different MCDM methods, e.g. elimination and choice expressing reality (ELECTRE) [8,9], data envelopment analysis (DEA) [10,11], analytic hierarchy process (AHP) [12,13], preference ranking organization method for enrichment evaluations (PROMETHE) [14,15], VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) [16,17], technique for order of preference by similarity to ideal solution (TOPSIS) [12,14], and analytic network process (ANP) [18,19]. In all MCDM methods, decision information is the values and weights of criteria that should be firstly obtained, and then alternatives ranking computed by a certain approach.
E-healthcare investors or entrepreneurs face both the efficiency of each sector on one hand and the optimization of its fitness to business model on the other hands. In literature, researcher yields Hardware platforms and Networking technologies are two prior fundamental sectors to enable an e-healthcare business which based on a survey of 9 different hardware platforms, 7 network technologies, 15 cloud platforms, and 31 Internet of Things (IoT) supported wearables (Liu et al., 2017). Ray has argued that IoT in the e-healthcare market also has to face some challenges that are standardization, quality of service, ecological aspects (Ray, 2016; Ray, 2017).