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This 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.

The authors employ five-point Likert scale measurements in the questionnaires and fuzzy-set/Qualitative Comparative Analysis (fsQCA) for the investigation.

The 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.

The 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.

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.

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.

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.

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.

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.

The purpose of this paper is to propose an occurrence-based model to improve the forecasting of regime switches so as to assist decision making.

This paper proposes a novel model where occurrences of relationships are taken into account when forecasting. Taiwan Stock Exchange Capitalization Weighted Stock Index is taken as the forecasting target.

Due to the consideration of occurrences of relationships in forecasting, the out of sample forecasting is improved.

The proposed model can be applied to forecast other time series for regime switches. In addition, it can be integrated with other time series models to improve forecasting performance.

The empirical results show that the proposed model can improve the forecasting performance.

This study aims to adapt a neural network based fuzzy time series model to improve Taiwan's tourism demand forecasting.

Fuzzy 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.

This study outperforms previous studies undertaken during the SARS events of 2002‐2003.

The 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.

Non‐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.