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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.

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.

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.

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.

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.

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.

PurposeThis paper aims to propose a novel model to forecast regime switches in a time series to assist decision making.Design/methodology/approachThe authors apply the clustering technique to group the data into five states. Then, a model is proposed to formulate the relationships from in‐sample observations, including regime switch relationships. Afterwards, the model uses the relationships to forecast the regime switches in out‐sample observations.FindingsThe study uses daily Taiwan Stock Exchange Capitalization Weighted Stock Index as the forecasting target. Regime switches in in‐sample observations are identified. And a regime switch is successfully forecasted by the proposed model.Research limitations/implicationsThe proposed model identifies a regime switch which matches the real event. It implies that the proposed model can be applied to other time series, such as Dow Jones or NASDAQ.Originality/valuePrevious studies contribute to the forecasting of regime switches. The forecasting results are validated with the real event. One of the forecasted regime switches matches the event of Lehman Brothers' declaring of bankruptcy.

This paper examines the asymmetric response of the underground economy (UE) in Taiwan to an effective tax rate change. The UE size in Taiwan from 1962 to 2003 is estimated using a cash deposit ratio (CDR) approach and a currency demand approach. The impact of an increase in the effective tax rate on UE is greater than that of a decrease. In addition, the impact on the UE is stronger for direct than for indirect taxes. The difference between upward and downward movements is significant for both indirect and direct taxes in the CDR approach. However, the difference is only significant for indirect taxes in the currency demand approach.

PurposeBy using three key factors – namely, funding, stakeholders, and legitimacy – this study seeks to analyse the successful entrepreneurial experiences of a non‐profit small to medium‐sized enterprise: the Taiwan EBook Supply Cooperative Limited (TEBSCo).Design/methodology/approachThe paper takes the form of a case study.FindingsFrom a legitimacy perspective, TEBSCo is the only registered organisation facilitating e‐book consortia in Taiwan. From a stakeholder perspective, TEBSCo is managed by a board of directors, who are elected from the member representatives. In addition to creating value for its members, TEBSCo also creates value for non‐members and vendors. Its major funding is from annual membership fees. TEBSCo's innovation process, as a collective entrepreneurial activity in a non‐profit SME, creates intangible as well as tangible value. The successful experiences of TEBSCo can be used as examples for new entrants.Originality/valueTEBSCo is the only registered organisation facilitating e‐book consortia in Taiwan. The successful experiences of TEBSCo can be used as examples for new entrants, and shows a new form of entrepreneurial activity.

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.

Fuzzy time series models have been applied to forecast various domain problems and have been shown to forecast better than other models. Neural networks have been very popular in modeling nonlinear data. In addition, the bivariate models are believed to outperform the univariate models. Hence, this study intends to apply neural networks to fuzzy time series forecasting and to propose bivariate models in order to improve forecasting. The stock index and its corresponding index futures are taken as the inputs to forecast the stock index for the next day. Both in-sample estimation and out-of-sample forecasting are conducted. The proposed models are then compared with univariate models as well as other bivariate models. The empirical results show that one of the proposed models outperforms the many other models.

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.

The objective of this study is to explore ways of determining the useful lengths of intervals in fuzzy time series. It is suggested that ratios, instead of equal lengths of intervals, can more properly represent the intervals among observations. Ratio-based lengths of intervals are, therefore, proposed to improve fuzzy time series forecasting. Algebraic growth data, such as enrollments and the stock index, and exponential growth data, such as inventory demand, are chosen as the forecasting targets, before forecasting based on the various lengths of intervals is performed. Furthermore, sensitivity analyses are also carried out for various percentiles. The ratio-based lengths of intervals are found to outperform the effective lengths of intervals, as well as the arbitrary ones in regard to the different statistical measures. The empirical analysis suggests that the ratio-based lengths of intervals can also be used to improve fuzzy time series forecasting.

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.