The smart mobile devices have emerged during the past decade and have become one of the most dominant consumer electronic products. Therefore, exploring and understanding the factors which can influence the acceptance of novel mobile technology have become the essential task for the vendors and distributors of mobile devices. The Phablets, integrated smart devices combining the functionality and characteristics of both tablet PCs and smart phones, have gradually become possible alternatives for smart phones. Therefore, predicting factors which can influence the acceptance of Phablets have become indispensable for designing, manufacturing, and marketing of such mobile devices. However, such predictions are not easy. Meanwhile, very few researches tried to study related issues. Consequently, the authors aim to explore and predict the intentions to use and use behaviors of Phablets. The second generation of the Unified Theory of Acceptance and Use of Technology (UTAUT2) is introduced as a theoretic basis. The Decision Making Trial and Evaluation Laboratory (DEMATEL) based Network Process (DNP) will be used to construct the analytic framework. In light of the analytic results, the causal relationships being derived by the DEMATEL demonstrate the direct influence of the habit on other dimensions. Also, based on the influence weights being derived, the use intention, hedonic motivation, and performance expectancy are the most important dimensions. The analytic results can serve as a basis for concept developments, marketing strategy definitions, and new product designs of the future Phablets. The proposed analytic framework can also be used for predicting and analyzing consumers’ preferences toward future mobile devices.

In recent years, IoT (Internet of Things)-based smart devices have penetrated a wide range of markets, including connected health, smart home, and wearable devices. Among the IoT-based smart devices, wearable fitness trackers are the most widely diffused and adopted IoT based devices. Such devices can monitor or track the physical activity of the person wearing them. Although society has benefitted from the conveniences provided by IoT-based wearable fitness trackers, few studies have explored the factors influencing the adoption of such technology. Furthermore, one of the most prevalent issues nowadays is the large attrition rate of consumers no longer wearing their device. Consequently, this article aims to define an analytic framework that can be used to explore the factors that influence the adoption of IoT-based wearable fitness trackers. In this article, the constructs for evaluating these factors will be explored by reviewing extant studies and theories. Then, these constructs are further evaluated based on experts’ consensus using the modified Delphi method. Based on the opinions of experts, the analytic framework for deriving an influence relationship map (IRM) is derived using the decision-making trial and evaluation laboratory (DEMATEL). Finally, based on the IRM, the behaviors adopted by mass customers toward IoT-based wearable fitness trackers are confirmed using the partial least squares (PLS) structural equation model (SEM) approach. The proposed analytic framework that integrates the DEMATEL and PLS-SEM was verified as being a feasible research area by empirical validation that was based on opinions provided by both Taiwanese experts and mass customers. The proposed analytic method can be used in future studies of technology marketing and consumer behaviors.

Technological innovations are regarded as the tools that can stimulate economic growth and the sustainable development of technology. In recent years, as technologies based on the internet of things (IoT) have rapidly developed, a number of applications based on IoT innovations have emerged and have been widely adopted by various public and private sectors. Applications of IoT in the manufacturing industry, such as manufacturing intelligence, not only play a significant role in the enhancement of industrial competitiveness and sustainability, but also influence the diffusion of innovative applications that are based on IoT innovations. It is crucial for policy makers to understand these potential reasons for stimulating IoT industrial sustainability, as they can facilitate industrial competitiveness and technological innovations using supportive means, such as government procurement and financial incentives. Therefore, there is a need to ascertain different factors that may affect IoT industrial sustainability and further explore the relationship between these factors. However, finding a set of factors that affects IoT industrial sustainability is not easy. Recently, the robustness of a theoretical framework, termed the technological innovation system (TIS), has been verified and has been used to explore and analyze technological and industrial development. Thus, it is suitable for this research to use this theoretical model. In order to find out appropriate factors and accurately analyze the causality among factors that influence IoT industrial sustainability, this research presents a Bayesian rough Multiple Criteria Decision Making (MCDM) model based on TIS functions by integrating random forest (RF), decision making trial and evaluation (DEMATEL), Bayesian theory, and rough interval numbers. The proposed analytical framework is validated by an empirical case of defining the causality between TIS functions to enable the industrial sustainability of IoT in the Taiwanese smart manufacturing industry. Based on the empirical study results, the cause group consists of entrepreneurial activities, knowledge development, market formation, and resource mobilization. The effect group is composed of knowledge diffusion through networks’ guidance of the search, and creation of legitimacy. Moreover, the analytical results also provide several policy suggestions promoting IoT industrial sustainability that can serve as the basis for defining innovation policy tools for Taiwan and late coming economies.

During the past years, the three-dimensional printing (3DP) has become a dominant rapid prototyping (RP) technology due to its very viable process in terms of cost, speed, and sales of related equipment. Nowadays, numerous 3DP based RP services are available. Because of the capability, service quality, and pricing of the services varies, how to select a suitable 3DP based RP service provider is very critical to the companies being engaged in new product developments. However, the issue was seldom studied. To resolve this problem, a hybrid multiple-criteria decision making (HMCDM) framework for evaluating and enhancing an appropriate 3DP based RP service provider based on the Decision Making Trial and Evaluation Laboratory (DEMATEL) based Network Process (DNP) as well as VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) was proposed. The analytic framework was verified as feasible by an empirical study based on the opinions being provided by 3DP and RP experts. The well-verified framework can serve as the basis of future evaluation, selection, and enhancement of 3DP based RP service providers.

In this study, we propose a modified gold nanoparticle-graphene oxide sheet (AuNP-GO) nanocomposite to detect two different interactions between proteins and hybrid nanocomposites for use in biomedical applications. GO sheets have high bioaffinity, which facilitates the attachment of biomolecules to carboxyl groups and has led to its use in the development of sensing mechanisms. When GO sheets are decorated with AuNPs, they introduce localized surface plasmon resonance (LSPR) in the resonance energy transfer of spectral changes. Our results suggest a promising future for AuNP-GO-based label-free immunoassays to detect disease biomarkers and rapidly diagnose infectious diseases. The results showed the detection of antiBSA in 10 ng/ml of hCG non-specific interfering protein with dynamic responses ranging from 1.45 nM to 145 fM, and a LOD of 145 fM. Considering the wide range of potential applications of GO sheets as a host material for a variety of nanoparticles, the approach developed here may be beneficial for the future integration of nanoparticles with GO nanosheets for blood sensing. The excellent anti-interference characteristics allow for the use of the biosensor in clinical analysis and point-of-care testing (POCT) diagnostics of rapid immunoassay products, and it may also be a potential tool for the measurement of biomarkers in human serum.Electronic supplementary materialThe online version of this article (10.1186/s11671-018-2565-7) contains supplementary material, which is available to authorized users.