Using Social Media Mining and PLS-SEM to Examine the Causal Relationship between Public Environmental Concerns and Adaptation Strategies.

With growing scientific evidence showing the harmful impact of air pollution on the environment and individuals' health in modern societies, public concern about air pollution has become a central focus of the development of air pollution prevention policy. Past research has shown that social media is a useful tool for collecting data about public opinion and conducting analysis of air pollution. In contrast to statistical sampling based on survey approaches, data retrieved from social media can provide direct information about behavior and capture long-term data being generated by the public. However, there is a lack of studies on how to mine social media to gain valuable insights into the public's pro-environmental behavior. Therefore, research is needed to integrate information retrieved from social media sites into an established theoretical framework on environmental behaviors. Thus, the aim of this paper is to construct a theoretical model by integrating social media mining into a value-belief-norm model of public concerns about air pollution. We propose a hybrid method that integrates text mining, topic modeling, hierarchical cluster analysis, and partial least squares structural equation modelling (PLS-SEM). We retrieved data regarding public concerns about air pollution from social media sites. We classified the topics using hierarchical cluster analysis and interpreted the results in terms of the value-belief-norm theoretical framework, which encompasses egoistic concerns, altruistic concerns, biospheric concerns, and adaptation strategies regarding air pollution. Then, we used PLS-SEM to confirm the causal relationships and the effects of mediation. An empirical study based on the concerns of Taiwanese social media users about air pollution was used to demonstrate the feasibility of the proposed framework in general and to examine gender differences in particular. Based on the results of the empirical studies, we confirmed the robust effects of egoistic, altruistic, and biospheric concerns of public impact on adaptation strategies. Additionally, we found that gender differences can moderate the causal relationship between egoistic concerns, altruistic concerns, and adaptation strategies. These results demonstrate the effectiveness of enhancing perceptions of air pollution and environmental sustainability by the public. The results of the analysis can serve as a basis for environmental policy and environmental education strategies.

Evaluating the Investment Projects of Spinal Medical Device Firms Using the Real Option and DANP-mV Based MCDM Methods.

In an era of global aging, spinal and other joint degeneration issues have become a major problem for many elders. Bone-related operations have become the largest percentage of surgeries, accounting for 40% of the top 10 operations in the United States. Further, these spine-related operations are now ranked second among all bone-related operations. Due to this enormous and daily increasing market demand, more and more firms have started to pay closer attention to related medical devices and products. The global venture capitalists (VCs) have also started to follow the mega trend and will continue to invest heavily in this industry. Although most VCs recognize that investing in firms that produce innovative spinal products or devices is a must, very few practical managers or research scholars have defined the appropriate evaluation methods for these firms to use. The traditional net present value (NPV) method, which does not consider operation flexibility and changes in strategy, is far from the reality. The real option method can reveal the vagueness and flexibilities of the values being embedded in the investment projects at spinal medical device firms. However, the real option method is strictly quantitative. Usually, the evaluation aspects contain qualitative factors or local criteria which are hard to quantify in monetary terms. Thus, the adoption of multiple criteria decision making (MCDM) methods that can manipulate both quantitative and qualitative factors will be very helpful in evaluating and selecting investment cases like the spinal medical device firms, where both quantitative and qualitative factors should be considered. An analytical framework that consists of hybrid MCDM methods and the real option method will thus be very useful to evaluate the newly established firms producing spinal medical devices. Therefore, the authors propose a real option valuation as well as the Decision-Making Trial and Evaluation Laboratory (DEMATEL) based analytic network process (DANP) and the modified VIsekriterijumsko KOmpromisno Rangiranje (VIKOR) method (DANP-mV) based MCDM framework for evaluating the investment projects offered by these firms of spinal medical devices. An empirical study based on three newly established spinal medical device companies specializing in vertebral compression fracture (VCF) surgical devices was used to demonstrate the feasibility of the proposed analytical framework. Sensitivity analysis is performed to determine the influence of modeling parameters on ranking results of alternatives. This analytical framework can thus serve as a tool for VCs to use to determine the value of a potential candidate for investment. The proposed method can also serve as an effective and efficient tool for investment projects in other fields.

Strategies for Heterogeneous R&D Alliances of In Vitro Diagnostics Firms in Rapidly Catching-Up Economies.

Most developed countries already have high-quality in vitro diagnostic (IVD) techniques for diseases, but developing countries often do not have access to these technologies and cannot afford them. Enabling firms to leverage external resources to optimize their research and development (R&D) performance has become one of the most critical issues for small and medium-sized late-coming IVD firms. R&D alliances, especially heterogeneous alliances, are necessary for releasing the resource limitations of late-coming small and medium-sized enterprises (SMEs) and reaching the metaoptimum of the R&D performances. However, to the authors' knowledge, a few, if any, previous studies have investigated the key success factors and strategies of heterogeneous alliances in the IVD industry. Therefore, the authors aim to define the critical factors for evaluating and selecting strategies for heterogeneous alliances in the IVD industry. A Decision-Making Trial and Evaluation Laboratory (DEMATEL)-based analytic network process (DANP) was proposed to prioritize the weights associated with the evaluation criteria. Then, a heterogeneous R&D alliance strategy was derived from the compromise ranking based on the modified VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. An empirical study of major Taiwanese IVD firms' evaluation and selection of heterogeneous R&D alliance strategies will be used to reveal the practicability of the analytic framework. Based on the analytic results, the joint venture strategy is the most suitable heterogeneous R&D alliance strategy for IVD firms in rapidly catching-up economies. These results can serve as the basis for heterogeneous R&D alliance strategy definitions in the IVD industry in the future.

An Exploration and Confirmation of the Factors Influencing Adoption of IoT-Based Wearable Fitness Trackers.

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.