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This research aims to identify the common causal complexity for the Global Innovation Index (GII), which measures various dimensions of the innovation ecosystem by country. We take all these variables as antecedents and the GII score representing the innovation competence of each country as the outcome and employ GII dataset from 2016 to 2020 for analysis. Because fuzzy set/Qualitative Comparative Analysis (fsQCA) has advantages over conventional statistical analysis and is good at expressing different causal complexities for a problem, this study utilizes it as the research method for analysis. The findings identify a common causal combination with the highest consistency and coverage among all the causal combinations in each year. This causal combination can be used as a representative to interpret GII.

PurposeThis research explores causal combinations (personal traits, external factors and self-fulfillment) that could provide holistic views leading to sustainable start-ups via data collected from Taiwanese entrepreneurs.Design/methodology/approachThe authors employ five-point Likert scale measurements in the questionnaires and fuzzy-set/Qualitative Comparative Analysis (fsQCA) for the investigation.FindingsThe study finds four types of sustainable entrepreneurs. Conservative resilient entrepreneurs have an absence of both openness and neuroticism. Conservative achieving entrepreneurs have an absence of openness and the presence of conscientiousness. Conservative-hired entrepreneurs have an absence of both openness and unemployment. Lastly, conservative opportunistic entrepreneurs exhibit an absence of openness and the presence of business opportunity.Originality/valueThe results add to the authors’ knowledge and understanding of the entrepreneurship literature and also offer implications for people who are interested in entrepreneurship as well as to policymakers wanting to promote new start-ups.

Technology is considered an asset for companies, and continuous technological innovation is one of the most effective ways to help firms achieve a competitive advantage. Innovation creates opportunities for entrepreneurship. Entrepreneurship may involve corporate social responsibility. Technology also affects all aspects of marketing. The integration of technology and marketing strategies can affect companies’ success in a continuously changing environment. This special issue presents some recent studies of how technology can support improvement in various areas of business and management, including innovation, entrepreneurship, and marketing.

This research constructs configural models in order to analyze multiple relationships among entrepreneur characteristics (antecedents) and successful/failed entrepreneurship (outcome). This study uses fuzzy sets/qualitative comparative analysis as the research method to construct the multiple relationships after collecting data on entrepreneurs in Taiwan who are still running their business longer than average (long-lived businesses), those who started businesses below average in duration (short-lived businesses), and those who have failed in their entrepreneurship experiences. From 5-point Likert scale measurements utilized in the questionnaires, this study finds relationships that are combinations of antecedents instead of any single one, with one relationship for long-lived and one for short-lived businesses, which are similar but with two antecedents of opposite values. There are six relationships for failed entrepreneurs, showing that different combinations of antecedents can lead to the same outcome. We are surprised to see negative extraversion in the relationship for the successful entrepreneurs for long-lived businesses and positive neuroticism in that for those for short-lived businesses. The six relationships for failed entrepreneurs provide a reference for people making the entrepreneurial decision, and the configural models for successful/failed entrepreneurs differ from the single equations in past literature. Lastly, the configural models summarize the characteristics for entrepreneurs, which can help people make a proper entrepreneurial decision.

In the new era of the sharing economy, firms are setting up Application (APP) service platforms to reach potential consumers and enhance their assessment of how to increase consumers’ perceived benefits. This study thus emphasizes that, via APP assessments in the context of the sharing economy, consumers’ perceived benefits can drive their purchasing intention and behavior. We examine 464 surveys of users who had previous experiences on various related APPs in the sharing economy and present empirical results supporting that APP assessments play mediator roles among perceived benefits and purchasing intention. The research model shows that relational, attitude, and capability benefits are consumers’ three main perceived benefits that influence their purchasing intention. This study thus offers management and marketing implications in the sharing economy by suggesting that firms increase consumers’ perceived benefits in order to raise APP assessments, thereby spurring their intention to use the APPs to finalize their final purchase behavior.

Corporate social responsibility (CSR) has become an international phenomenon, and while enterprises are engaging in related activities for the benefit of stakeholders, one might question whether CS...

This study explores how satisfied customers are when they face surge pricing as well as how customer satisfaction affects customer retention. This study uses fuzzy set/Qualitative Comparative Analysis to generate relations and then qualitative analysis with structural associations to propagate the values and refine these relations. Both methods together generate proper relations for multi-layered problems. With data gathered from a group of executive MBA students in Taiwan, the empirical results show that loyal riders are more tolerable to surge pricing than non-loyal riders. Lastly, the evidence presents that customer satisfaction does not always positively affect customer retention.

This study proposes a new event study method to forecast abnormal returns for individual companies in order to overcome the limitations of current related approaches. We consider two criteria to determine whether there are abnormal returns: individual stock performance and relative performance between an individual stock and the market. We first use in-sample data of Facebook’s stock prices between 2017/1/1 and 2018/6/30 to build rules and then employ those rules to forecast its out-of-sample data between 2018/7/1 and 2018/12/31. The empirical analysis shows that our new method accurately forecasts 4 out of 5 negative CNN news items for Facebook. The proposed new method contributes to the literature on case study methods by utilizing a new way to define abnormal returns and to forecast events successfully.

Research methods affect empirical results and shape theory construction. For years, social science scholars have applied multiple regression analysis (MRA) to analyze data and develop theories. Studies have pointed out the reasons for bad practice in MRA. Fuzzy-set qualitative comparative analysis (fsQCA) is an extension of qualitative comparative analysis (QCA) based on Boolean algebra and fuzzy-set theory. FsQCA identifies the combinations of causes leading to outcomes of interest and is suitable to reflect the complexities of many research problems. To handle multiple-layer problems (i.e., the outcome of a relationship becomes an antecedent of another outcome in another relationship), qualitative analysis with structural associations has been proposed to extend fsQCA. This special issue presents new research results by using or contrasting quantitative and qualitative research methods in the fields of innovation, entrepreneurship, and knowledge.

Purpose The use of linear regression analysis is common in the social sciences. The purpose of this paper is to show the advantage of a qualitative research method, namely, structured qualitative analysis (SQA), over the linear regression method by using different characteristics of data. Design/methodology/approach Data were gathered from a study of online consumer behavior in Taiwan. The authors changed the content of the data to have different sets of data. These data sets were used to demonstrate how SQA and linear regression works individually, and to contrast the empirical analyses and empirical results from linear regression and SQA. Findings The linear regression method uses one equation to model different characteristics of data. When facing a data set containing a big and a small size of different characteristics, linear regression tends to provide an equation by modeling the characteristics of the big size data and subsuming those of the small size. When facing a data set containing similar sizes of data with different characteristics, linear regression tends to provide an equation by averaging these data. The major concern is that the one equation may not be able to reflect the data of various characteristics (different values of independent variables) that result in the same outcome (the same value of dependent variable). In contrast, SQA can identify various variable combinations (multiple relationships) leading to the same outcome. SQA provided multiple relationships to represent different sizes of data with different characteristics so it created consistent empirical results. Research limitations/implications Two research methods work differently. The popular linear regression tends to use one equation to model different sizes and characteristics of data. The single equation may not be able to cover different behaviors but may lead to the same outcome. Instead, SQA provides multiple relationships for different sizes of data with different characteristics. The analyses are more consistent and the results are more appropriate. The academics may re-think the existing literature using linear regression. It would be interesting to see if there are new findings for similar problems by using SQA. The practitioners have a new method to model real world problems and to understand different possible combinations of variables leading to the same outcome. Even the relationship obtained from a small data set may be very valuable to practitioners. Originality/value This paper compared online consumer behavior by using two research methods to analyze different data sets. The paper offered the manipulation of real data sets to create different data sizes of different characteristics. The variations in empirical results from both methods due to the various data sets facilitate the comparison of both methods. Hence, this paper can serve as a complement to the existing literature, focusing on the justification of research methods and on limitations of linear regression.

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.

Maintenance cost contributes a large portion in total life-cycle cost of offshore wind systems and improves the efficiency of maintenance activities that has emerged as an effective solution in enhancing cost-effectiveness of offshore wind energy. This study proposes a mathematical model to determine the optimal maintenance schedules, by highlighting the deterioration process of offshore wind systems and accounting the optimal vessel routing of maintenance activities, maintenance cost therefore could be shifted to an optimal level. Firstly, reliability of each component is precisely analyzed with respect to the effect of previous maintenance activities. Then, an optimal individual maintenance schedule and a grouping maintenance schedule in daily manner are established based on reliability analysis and opportunity cost consideration. Finally, an optimal maintenance schedule approach accounting vessel routing is apparently presented to optimize the transportation cost of maintenance activities. Numerous influential factors such as system reliability, weather condition, maintenance resource, maintenance duration, production loss cost, and vessel related issues are deliberately examined to enhance the robustness and efficiency of the proposed approach. The experimental results investigating four different scenarios demonstrate that by applying the proposed approach, number of maintenance activity is lowered down, maintenance duration and vessel travelling distance are also significantly reduced in the range of 43.86% - 52.77%, and 52.90% - 58.35%, respectively. Therefore, the economic benefit on maintenance cost could be achieved in an interval of 15.15% - 23.25% by selecting the optimal maintenance schedule.

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.

This research analyzes and forecasts the causal combinations of innovation for economic growth, utilizing yearly data from Organization for Economic Co-operation and Development (OECD) countries. Employing the method of fuzzy sets/Qualitative Comparative Analysis (fsQCA), the two causal combinations from the estimation are 1) Institute, Human Capital and Research, Infrastructure, and Market Sophistication as well as 2) Human Capital and Research, Infrastructure, Market Sophistication, and Business Sophistication. This study proposes a time series method to forecast fsQCA results that offer performances demonstrating strong predictive validities.

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.

This study intends to explore the associations between the energy consumption relevant antecedents and economic development by using fuzzy set/Qualitative Comparative Analysis (fsQCA). FsQCA yields the causal recipes (causal combinations) for the outcome, GDP. The energy relevant data are from the U.S. Energy Information Administration and economic relevant data are from the International Monetary Fund. The analysis results provide two groups of causal recipes: One group explains the conditions for knowledge-intensive industrialized economies and the other explains those conditions for traditional industrialized economies. Both groups lead to high GDP. The results illustrate that only one equation may be insufficient to describe the associations between the energy consumption relevant antecedents and the economic development. This study also applies multivariate regression analysis (MRA) for similar analysis. The results report that not every variable of each individual year is significant, thus reflecting the problem of MRA.

Drastic changes (named regime switches) often exist in economic and financial time series causing the forecasting of time series difficult. Hence, we need robust models to detect and forecast the regime switches. Most previous studies apply quantitative methods to forecast time series and regime switches. Contrast to these studies, this study attempts a novel approach to use a qualitative method to forecast regime switches. Fuzzy set/qualitative comparative analysis (fsQCA), based on fuzzy set and logic theory, yields the relationships between antecedent combinations and outcome. Studies support fsQCA analysis is more proper to reflect the real situations. Hence, this study uses fsQCA to analyze the autoregressive relationships of the upward and downward regime switches in the in-sample data. Then, the relationships are used to forecast the regime switches in the out-of-sample data. Taiwan Capitalization Weighted Stock Index is taken as the data for analysis. The empirical results show that fsQCA provides strong predictive validities.

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.

Over the last decade, educators, administrators, and policy makers increasingly focus on corporate social responsibility. However, no studies examine the relationships among corporate social responsibility, brand equity, and firm performance. This study uses quantile regression and structural equation modeling to explore the causal linkages among these factors in Taiwanese high-tech companies over the period 2010–2013. The results of quantile regression analysis show that the economic dimension of corporate social responsibility and the prestige driver of brand equity are positive and significant for all the quantiles. The brand extension driver provides a significant positive effect at the higher quantiles of firm performance. However, the findings indicate a significant negative effect on firm performance for the brand loyalty driver. The findings of structural equation modeling suggest that corporate social responsibility and brand equity positively affect firm performance. This study provides useful insights on brand equity and corporate social responsibility.

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.

Regression is a common method to calculate relationships between variables. Quantile regression extends the calculation to the coefficients of various quantiles, providing a more complete overview. In addition, quantile forecasting models forecast coefficients. This study proposes a new algorithm to calculate the quantile confidence intervals of the in-sample data to forecast the coefficients of the out-of-sample data. The algorithm analyzes ICT data for 78 countries between 1999 and 2010. Results show that the algorithm provides valid forecasting results and outperforms previous studies. These quantile confidence intervals can also forecast the independent variables' impact trends on the dependent variable. The algorithm is applicable to different domains.

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.

Franchising provides a channel for small businesses to grow successfully into nationwide or even worldwide chains. Franchising is considered to be common among chain stores, of which the convenience store is a typical example. This study aims to examine the expansion strategy of each individual convenience store franchisor and the competition to expand among the franchisors. Taiwan is considered to have the highest convenience store density in the world. This study performs empirical analyses of four major convenience store franchisors in Taiwan, namely, 7-Eleven, Family Mart, Hi-Life, and OK. The evidence shows that the franchisors tend to be slow to expand when facing a highly dense competitive environment. There exists a strong first-mover advantage in convenience store franchising. The first mover (7-Eleven) has played a dominating role throughout the years. As a result, the franchisors' outlet shares have remained relatively stable.

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.