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Barriers to women entrepreneurship. Different methods, different results?

Building on research by Akehurst et al. (Serv Ind J 32:2489-2505, 2012), this study analysed internal and external factors in women entrepreneurship and linked these factors to the barriers that women face when starting businesses. To do so, two contrasting statistical techniques were used: PLS and QCA. After analysing results from each of these techniques, we observed that family duties and difficulties in obtaining financing (both internal and external) were the main factors related to barriers faced by women entrepreneurs

International entrepreneurship in SMEs: a study of influencing factors in the textile industry

The final publication is available at Springer via http://dx.doi.org/10.1007/s11365-012-0242-3International entrepreneurship is an incipient research area with a rapidly increasing body of knowledge and contributions. An important part of this literature has focused on the analysis of the contributing factors to IE development. From these studies, this work attempts to analyse and validate through an integrative model the effect on this construct in SME of some of the main factors proposed by the literature such as Skills and Competences, Attitude and Proactiveness, Creativity and Innovation, Networking, Employees and Activity. To proceed with this aim, we conducted an empirical research focused on 174 textile SME in Spain. The results obtained confirm a positive relationship between the studied factors and the IE development. In consequence, this work agrees with previous literature that point out the need to use multi-theoretical perspectives, combining multiple factors.Gil Pechuán, I.; Expósito Langa, M.; Tomas Miquel, JV. (2013). International entrepreneurship in SMEs: a study of influencing factors in the textile industry. International Entrepreneurship and Management Journal. 9(1):45-57. doi:10.1007/s11365-012-0242-3S455791Akgün, A., Keskin, H., & Byrne, J. (2012). Organizational emotional memory. Management Decision, 50(1), 95–114.Andersén, J. (2011). Strategic resources and firm performance. Management Decision, 49(1), 87–98.Anderson, A. R., Dodd, S. D., & Jack, S. L. (2012). Entrepreneurship as connecting: some implications for theorising and practice. Management Decision, 50(5), 958–971.Appelbaum, S. H., Roy, M., & Gilliland, T. (2011). Globalization of performance appraisals: theory and applications. Management Decision, 49(4), 570–585.Arribas, I., Hernández, P., Urbano, A., & Vila, J. E. (2012). 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Does instructional video advertising influence behavioral intention? Comparative study between Hong Kong and Malaysia

This study aims to examine the influence of video type (commercial video vs. instructional video) and three cultural/individual difference factors (location: Hong Kong vs. Malaysia; education background: engineering/science vs. non-engineering/science; experience of using touchless technology: yes vs. no) on an individual’s behavioral intention to use the touchless system. This study was conducted with 60 participants (30 males and 30 females) from Hong Kong and 62 participants from Malaysia (31 males and 31 females) to investigate whether the video type and cultural/individual difference factors has an impact on individuals’ behavioral intention. The findings of this study reveal interesting comparative results between Hong Kong and Malaysia as well as provide useful insights for marketing practitioners

Recurrent type-1 fuzzy functions approach for time series forecasting

Tak, Nihat/0000-0001-8796-5101; Egrioglu, Erol/0000-0003-4301-4149WOS: 000419006000005Forecasting the future values of a time series is a common research topic and is studied using probabilistic and non-probabilistic methods. For probabilistic methods, the autoregressive integrated moving average and exponential smoothing methods are commonly used, whereas for non-probabilistic methods, artificial neural networks and fuzzy inference systems (FIS) are commonly used. There are numerous FIS methods. While most of these methods are rule-based, there are a few methods that do not require rules, such as the type-1 fuzzy function (T1FF) approach. While it is possible to encounter a method such as an autoregressive (AR) model integrated with a T1FF, no method that includes T1FF and the moving average (MA) model in one algorithm has yet been proposed. The aim of this study is to improve forecasting by taking the disturbance terms into account. The input dataset is organized using the following variables. First, the lagged values of the time series are used for the AR model. Second, a fuzzy c-means clustering algorithm is used to cluster the inputs. Third, for the MA, the residuals of fuzzy functions are used. Hence, AR, MA, and the degree of memberships of the objects are included in the input dataset. Because the objective function is not derivative, particle swarm optimization is preferable for solving it. The results on several datasets show that the proposed method outperforms most of the methods in literature

Innovation, progress, entrepreneurship and cultural aspects

In the case of economic progress, some of the literature has considered economic growth and economic progress to be the same thing. However, there is a relevant difference between the two concepts. As Holcombe states, economic growth considers the quantity of products and economic progress the quality of products. Innovation has been considered as a key factor to promote economic progress. A culture would have a direct and an indirect effect on innovation through entrepreneurship. The goal of this paper is to analyze the relationship between culture and innovation. To carry out this study, an empirical estimation has been developed for the case of 11 countries. © 2012 Springer Science+Business Media, LLC