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Big data analytics often refer to the breakdown of huge amounts of data into a more readable and useful format. This study utilises Google Trends big data as a proxy for an analysis of housing demand. We employ a qualitative method (fuzzy set/Qualitative Comparative Analysis, fsQCA), instead of a quantitative method, for our estimate and forecast. The empirical results show that fsQCA successfully forecasts seasonal time series, even though the dataset is small in size. Our findings fill the gap in the qualitative and time series forecasting literature, and the forecasting procedure herein also offers a good standard for industry.

The progressive advances in the fifth-generation standard for cellular networks have led to innovations in the artificial intelligence of things, which have changed the layout of the medical industry. The applications of wearable medical devices and services are under continual expansion. Key factors influencing intention to use (e.g., economic burden, data privacy, perceived ease of use, and perceived usefulness) require further investigation. In the present study, the participants were surveyed by questionnaire, and path analysis was used to examine causality. The results revealed that intention to adopt was likely to be higher for wearable medical devices that offered high data privacy, had high perceived ease of use, and provided reliable data (with regard to accurate references for physical health). Conversely, the economic burden imposed by wearable medical devices and services was likely to reduce intention to adopt. Therefore, the payment mechanism warrants cautious evaluation.

An accurate electricity load prediction is important to optimizing building electricity load performance. However, building electricity load prediction is complex due to many influencing factors. This study develops a hybrid algorithm that combines clustering approach, empirical mode decomposition, and support vector regression to develop a prediction model for building electricity load. k-shape clustering is used to extract similar building electricity load pattern, and empirical mode decomposition is employed to decompose electricity load data into several Intrinsic Mode Functions (IMF). Finally, a prediction model using support vector regression is built for each IMF individually, and the prediction result of all IMFs is combined to obtain an aggregated output of electricity load. Numerical testing demonstrated that the proposed method can accurately predict the electricity load in the building.

Educators, administrators, and policy makers' interest in the concept of corporate reputation is growing. However, no researcher examines causal recipes for the value relevance of corporate reputation. This study therefore uses fuzzy-set qualitative comparative analysis (fsQCA) to explore the value relevance of corporate reputation for Taiwan listed companies over the period 2010–2013. The results show that corporate reputation adds to market value, even after controlling for earnings performance. These findings inform the affective component of corporate reputation is, at least, as important as the cognitive component. Furthermore, the findings extend previous research by showing that more than one casual combination of corporate reputation measurements is value-relevant. This study provides useful insights into the nature of corporate reputation.

Due to Taiwan's limited energy resources, the development of solar photovoltaic (PV) in Taiwan has become one of the most important solutions for meeting future energy supply needs and ensuring environmental protection. A huge amount of researches about renewable energy sources has emerged recently in response to these issues. However, the amount of researches considering the effects of various influential parameters on the efficiency and performance of PV systems remains small, and is still limited to some specific parts of PV systems. In particular, researches considering thoughtfully the influence of government subsidies on PV financial assessment are still in development. This paper proposes an approach to analyze the benefit of installing a PV system under the impact of government financial subsidies, focusing especially on feed-in-tariff (FIT) and tax abatement policies for industrial users in Taiwan. In addition, a method for selecting the most appropriate policies is proposed for the government through the analysis of both user demand and the government's PV installation capacity target.

This special issue presents studies on innovation and diffusion of high-tech products, services, and systems (high-tech for short). High-tech are core components of marketing, innovation, entrepreneurship, and diffusion. High-tech may affect consumer behaviors. High-tech can boost innovation in tourism, services, branding, and products. Entrepreneurship highly relates to innovation. Strategies and forecasting techniques can facilitate high-tech diffusion. This special issue selects articles from 2014 Global Innovation and Knowledge Academy (GIKA)—Asia/Pacific Conference, December 5–6, Feng Chia University, Taiwan. The GIKA conference selects high-quality conference articles for publication in a special issue of the Journal of Business Research. After double-blind reviews and revisions, 17 conference articles appear in the 2015 special issue, together with this editorial.

Healthcare is turning a big business. A better understanding of the factors affecting healthcare expenditure (HCE) can assist expenditure control. This study uses fuzzy set/Qualitative Comparative Analysis (fsQCA) to explore the sufficient conditions for the outcome, HCE. FsQCA provides causal recipes for each year to show the causal complexities leading to the outcome of that year with high consistencies. The three most recurrent causal recipes are: (1) longevity countries with many doctors and aging population; (2) longevity and rich countries with many doctors; and (3) longevity and rich countries with aging population. The analysis also shows strong predictive validities. The causal recipes of the first few years can successfully forecast the causal recipe(s) of following years.

Considerable prior research argues that time to death, not population aging, explains the growth of healthcare expenditures. The objective of this study is to shed light on this debate by presenting new evidence on the red herring hypothesis. This study adopts quantile regression analysis to reexamine variations of the red herring effect on healthcare expenditures in Taiwan over the period 2005–2009. Findings show that population aging estimates decrease from positive to negative along quantiles for the whole sample and become insignificant across most quantiles for the subsample of people aged 65 and over. For whole sample and subsample of people aged 65 and over, proximity-to-death coefficients are significantly positive in most quantiles. Moreover, time-to-death estimates show a substantial upward trend towards date of death. In particular, quarters one and two prior to death produce a significant positive impact on healthcare expenditures at the highest healthcare expenditure quantiles. The new empirical evidence from this study provides a more complete picture of the red herring effect on healthcare expenditures.

Purpose – Because quantile regression gets more popular and provides more comprehensive interpretations, it is important to advance quantile regression for forecasting. By extending the convention quantile regression, the purpose of this paper is to propose a quantile regression-forecasting model to forecast information and communication technology (ICT) development. Design/methodology/approach – This paper proposes an approach to forecasting based on quantile regression method. Findings – Via quantile information criterion, the proposed approach can identify whether the independent variables are predictable. For those which are predictable, the proposed approach can be used to forecast these variables. Practical implications – The proposed approach is used to forecast ICT development. It can also be used to forecast other problem domains. Originality/value – Based on the empirical results, the proposed approach advances the application of quantile regression model to forecast. The applicability of quantile regression model is greatly enhanced.

Quantile regression is popular because it provides more information as well as comprehensive interpretations. To improve forecasting performance, this study proposes a new quantile information criterion (NQIC), on the basis of the coefficient of variation, and expects the NQIC to reflect whether a variable is predictable. The health care expenditure data determine the thresholds for the NQICs. The thresholds assist in forecasting the development of information and communication technology. From the empirical analyses, the NQICs and thresholds greatly improve the forecasting performance.

Empirical evidence has suggested that R&D investment is positively related to economic growth. This paper extends prior research by further examining the heterogeneous effects of R&D expenditures in the high-tech sector on economic growth. This study adopts a quantile regression approach to explore the marginal effect of R&D expenditures in the high-tech sector across different quantiles of the conditional GDP distribution for 23 OECD countries and Taiwan during 1991–2006. Empirical evidences show that the impacts of R&D expenditures in the high-tech sector are heterogeneous across levels of per capita income. High-tech industrial R&D spending has a strong positive effect on GDP per capita at the highest quantile of the distribution. However, all sectors' R&D spending relative to GDP is subject to significant negative returns only when considering the middle income countries. The study provides a more comprehensive understanding of the correlation between R&D investment and economic growth.

Wealth creation is critical to the performance of entrepreneurial firms. The two major issues that entrepreneurial firms face are as to when to issue the initial public offering (IPO) and how to invest in the stock market. Stock market forecasting can facilitate the provision of financial services for entrepreneurial firms in relation to both issues. Hence, this study proposes a novel neural network multivariate model to forecast stock markets. The proposed model can assist in deciding the timing of an IPO for the entrepreneurial firms and when to invest in the stock market.

The main purpose of this paper is to examine the relation that exists between the skills possessed by women entrepreneurs and their motivations, barriers and performance. Thus, on the theoretical framework we review literature some aspects that are related to the skills required of a business owner: level of education, previous occupational experience, and prior business expertise and management skills. The analysis undertaken shows that the lack of education and managerial skills of women business owners are two of the most important variables when it comes to understanding the motivations and the difficulties they have to face.

Services tend to be intangible and not visible (Cherubini 2001), which make them very different from production-oriented industries. Hence, we need different approaches to solving problems in services. This special issue invites five papers from the 2011 Global Entrepreneurship and Services Conference held in Taichung, Taiwan, from August 29 to 31, 2011. These papers cover various industries including finance, manufacturing, and Internet. Meanwhile, these papers cover many CSFs for services including brand equity for financial services, customer satisfaction on an auction website, the effects of social networking, the servitization for manufacturers via R&D, and the comparisons of the internal service quality for three Chinese subcultures including Taiwan, China, and Singapore. All these papers contribute to innovations in service business from different perspectives.

The purpose of this article is twofold. First, it attempts to explore the factors contributing to the brand values of Taiwan’s financial service companies. Second, it aims to examine the value relevance of their brand equity. Our empirical findings from OLS regression show that advertising expenditure in banking firms have positive significant effects on brand evaluation and that the brand equity of banking firms is positively related to their market value. The general findings remain valid when we apply conditional quantile regressions to re-examine these relationships. Moreover, the quantile estimates indicate that the factor of brand loyalty could bring benefits to the banks with lower levels of brand equity. However, brands do not offer insights into the banks that have extremely low market capitalization.

Numerous studies focus on the analysis of the effects of different factors on global information and communication technology (ICT) adoption. Due to the limitations of the least squares method in showing the impact of the estimated coefficients on the mean, this study applies quantile regression to examine the heterogeneous effects of various factors on global ICT adoption. This study finds that the impacts on the tail quantiles can be very different from the impacts on the conditional means. The empirical results show that the effects at different quantile levels are very different from those at their counterpart conditional means. We also provide relevant explanations for the effects.

The Internet has been extremely popular because of its many unique and powerful characteristics, such as ubiquity and global reach. Hence, it is interesting to study whether Internet companies can be sustained through a bubble and even after a bubble. This study therefore compares the Internet companies in the USA with other high-tech companies listed on the National Association of Securities Dealers Automated Quotations (NASDAQ) over a period covering the years from 1995 to 2006. First, we check whether there has been a bubble for these Internet companies, and we then try to compare the pre- and post-bubble periods to ascertain whether any of their stocks have been ‘overvalued’. Conclusions are drawn regarding the respective futures of these Internet companies.

Neural networks have been popular due to their capabilities in handling nonlinear relationships. Hence, this study intends to apply neural networks to implement a new fuzzy time series model to improve forecasting. Differing from previous studies, this study includes the various degrees of membership in establishing fuzzy relationships, which assist in capturing the relationships more properly. These fuzzy relationships are then used to forecast the stock index in Taiwan. With more information, the forecasting is expected to improve, too. In addition, due to the greater amount of information covered, the proposed model can be used to forecast directly regardless of whether out-of-sample observations appear in the in-sample observations. This study performs out-of-sample forecasting and the results are compared with those of previous studies to demonstrate the performance of the proposed model.

As the application of the Internet has grown rapidly, electronic commerce is expected to become increasingly prosperous. In addition, since the launching of the Internet, more media in advertising have appeared. Online customer experiences and Web 2.0 are believed to be two of the driving forces behind the growth in online sales. A more advanced advertising medium is thus needed to stimulate customers’ purchasing intentions. One creative medium of advertising on the Internet is instructional video advertising. It provides instructions on how to operate the products being sold and/or on how the products function. Hence, the customers may in this way view the advertising, and finally the intention to purchase may be triggered. In order to explore how Internet instructional video advertising may affect customers’ purchasing intentions, we propose a new model that adopts the theory of reasoned action, the technology acceptance model, and others. We perform the survey to analyze how instructional video advertising may impact customers’ purchasing intentions.

Due to the challenge of rising public awareness of environmental issues and governmental regulations, green supply chain management (SCM) has become an important issue for companies to gain environmental sustainability. Supplier selection is one of the key operational tasks necessary to construct a green SCM. To select the most suitable suppliers, many economic and environmental criteria must be considered in the decision process. Although numerous studies have used economic criteria such as cost, quality, and lead time in the supplier selection process, only some studies have taken into account the environmental issues. This study proposes a comprehensive fuzzy multicriteria decision making (MCDM) approach for green supplier selection and evaluation, using both economic and environmental criteria. In the proposed approach, a fuzzy analytic hierarchy process (AHP) is employed to determine the important weights of criteria under vague environment. In addition, a fuzzy technique for order performance by similarity to ideal solution (TOPSIS) is used to evaluate and rank the potential suppliers. Finally, a case study in Luminance Enhancement Film (LEF) industry is presented to illustrate the applicability and efficiency of the proposed method.

Many studies have found strong and positive relationship between per capita income and health care expenditure. These studies usually adopt the assumptions that (1) the relationships among the variables are constant; and/or (2) the supply-side market for health care could be treated by using standard demand functions models. To take into account of the supply side of health care, we use the demand and supply approach with the cointegration model to re-examine this issue. By using Taiwan health care expenditure data, our results show that the real income elasticity is smaller than unity and the health care expenditures are primarily for ‘curing’ rather than ‘caring’.

Fuzzy time-series models have been widely applied due to their ability to handle nonlinear data directly and because no rigid assumptions for the data are needed. In addition, many such models have been shown to provide better forecasting results than their conventional counterparts. However, since most of these models require complicated matrix computations, this paper proposes the adoption of a multivariate heuristic function that can be integrated with univariate fuzzy time-series models into multivariate models. Such a multivariate heuristic function can easily be extended and integrated with various univariate models. Furthermore, the integrated model can handle multiple variables to improve forecasting results and, at the same time, avoid complicated computations due to the inclusion of multiple variables.

To shed light on the issue of the black box of healthcare expenditure growth determinants, this study performs quantile regression analysis on examining the heterogeneous effect of healthcare expenditure determinants in Taiwan and the OECD countries. A total of 25 countries are studied over a period from 1992 to 2007. The results show that income, elderly populations, and number of physicians have significant positive effects on long-term healthcare expenditures. The income elasticity of healthcare expenditure lies between 1.20 and 1.47, which indicates that healthcare is a luxury good. And, income elasticity increases in higher quantiles of healthcare expenditures. However, the effects of elderly populations and number of physicians are decreasing towards the higher quantiles of healthcare expenditures.

The healthcare expenditures in Taiwan and the Organisation for Economic Cooperation and Development (OECD) countries have significantly increased over recent years. The governments of these countries are facing a funding crisis in healthcare. Their healthcare systems have also come under increasing financial pressure as healthcare demand rises. To shed light on the issue of healthcare funding, this paper performs cluster analysis and a convergence test on the sources of healthcare funding for Taiwan and the OECD countries. The empirical results find that some countries with similar healthcare systems in accordance with sources of healthcare funding are converging together as their principal sources of healthcare funding display convergence trends.

The Healthcare Expenditure (HCE) in Taiwan and the OECD countries have significantly increased over the recent years. To shed light on the issue of the upward trend of HCEs, this paper performs convergence tests and cluster analysis on HCEs in Taiwan and the OECD countries. The results support the overall and pairwise conditional convergence hypotheses. The cluster analysis shows that most countries are converging towards three different health system models.