Search

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.

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.

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.

PurposeThis study aims to adapt a neural network based fuzzy time series model to improve Taiwan's tourism demand forecasting.Design/methodology/approachFuzzy sets are for modeling imprecise data and neural networks are for establishing non‐linear relationships among fuzzy sets. A neural network based fuzzy time series model is adapted as the forecasting model. Both in‐sample estimation and out‐of‐sample forecasting are performed.FindingsThis study outperforms previous studies undertaken during the SARS events of 2002‐2003.Research limitations/implicationsThe forecasting model only takes the observation of one previous time period into consideration. Subsequent studies can extend the model to consider previous time periods by establishing fuzzy relationships.Originality/valueNon‐linear data is complicated to forecast, and it is even more difficult to forecast nonlinear data with shocks. The forecasting model in this study outperforms other studies in forecasting the nonlinear tourism demands during the SARS event of November 2002 to June 2003.

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.

This study investigates the economic content of the two firm‐specific characteristics, size and book‐to‐market equity. Size is found to be significantly related to a combination of betas on all of the macro variables proposed in this research. Its significance persists through out the entire sample period. This provides further evidence that size is a proxy for pervasive risk factors in the stock market. The support for book‐to‐market equity’s role as a risk proxy is also evidenced, however to a lesser extent. Securities are then sorted into size and book‐to‐market equity portfolios and their effects on investment decisions are examined in the context of macro variables. Important investment implications are drawn based on the findings.