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In this chapter, we propose and empirically test a theoretical model on the relationships among gender-role orientation, anticipated emotions and entrepreneurs’ subjective entrepreneurial success (SES). Results using Bayesian path analysis in a sample of Greek entrepreneurs indicated that the effect of femininity on SES was stronger than that of masculinity. Positive anticipated affect mediated the effects of masculinity and femininity on subjective entrepreneurial success. We interpreted this as evidence in support of the idea that the social construction of sex and future emotional thinking are influential factors within the entrepreneurial ecosystem that have previously been researched separately.

Practice demonstrates and research validates that entrepreneurship is moving from the individual to the organization and from the private sector to the social and not‐for‐profit sectors. The present study endeavors to complement the emerging public entrepreneurship literature by aiming to identify which of those factors that stimulate corporate entrepreneurship in the public sector are preferred by entrepreneurial civil servants.

A questionnaire was completed by a random sample (n=247) of public servants across 15 Greek prefectures. Results are based on Bayesian factor analysis, conjoint analysis and cluster analysis.

Results provide preliminary evidence about entrepreneurial civil servants' preferences and make available a well‐documented framework for addressing corporate entrepreneurship in the public sector.

The reported research relied on self‐reporting. In addition, because the sample consisted entirely of public servants across Greek prefectures, findings may not be applicable to other public sector contexts (such as hospitals) and other countries. Data are cross‐sectional and alternative relationships may exist. Future research should be multinational and longitudinal to test the assumptions of the present study.

The results of the study are useful both to academics and policy makers interested in formulating a strategy that fosters corporate entrepreneurship in the public sector.

During the last 15 years, considerable effort has been devoted to developing more effective, more efficient, and more flexible public organizations. Using qualitative methods, the results of the present exploratory research identify which factors that foster corporate entrepreneurship in the public sector are preferred by entrepreneurial public servants.

This paper aims to propose and empirically test a theoretical model positing relationships among emotional intelligence (EI), creativity, proactivity, and attitudes towards entrepreneurship and entrepreneurial intent.

A questionnaire survey was completed by a random sample (n=280) of business, engineering and science students across three Greek universities. Results were based on structural equation modelling analysis.

Results provide strong support for the proposition that students' creativity and proactivity fully mediate the positive effect of trait EI on attitudes towards entrepreneurship. Attitudes towards entrepreneurship fully mediated the effects of creativity and proactivity on entrepreneurial intent.

The paper demonstrates that EI is positively related to three important antecedents of entrepreneurial intentions and provides the literature with another important piece of the puzzle concerning entrepreneurial motivation. This evidence adds to the academic literatures on entrepreneurship and trait EI, and offers several practical implications for entrepreneurship education.

The purpose of this paper is to present the development of a business excellence model applicable in the hospitality industry.

Two surveys using questionnaires were conducted: the first one for the development of the model's criteria and sub‐criteria and the second one for the assessment of the criteria and sub‐criteria weights. The model was tested on a number of Greek high‐class hotels.

Compared with other business excellence models the proposed model includes criteria and sub‐criteria that are more applicable to hotels. The model studies the factors that drive excellence in the hotel sector as well as the importance of these factors as they have been defined by the managers of the hotels. The implementation of the model in a number of high‐class Greek hotels shows its applicability and suitability to be used as a benchmarking system.

Time limitations, as the project was co‐funded by the European Union, have limited the implementation of the proposed business excellence model to a small number of Greek hotels in the area of Crete.

The critical success factors for high‐class hotels have been identified and a business excellence model applicable in the hospitality sector has been developed.

The purpose of this paper is to present an inductive methodology, which supports ranking of entities. Methodology is based on Bayesian latent variable measurement modeling and makes use of assessment across composite indicators to assess internal and external model validity (uncertainty is used in lieu of validity). Proposed methodology is generic and it is demonstrated on a well‐known data set, related to the relative position of a country in a “doing business.”

The methodology is demonstrated using data from the World Banks' “Doing Business 2008” project. A Bayesian latent variable measurement model is developed and both internal and external model uncertainties are considered.

The methodology enables the quantification of model structure uncertainty through comparisons among competing models, nested or non‐nested using both an information theoretic approach and a Bayesian approach. Furthermore, it estimates the degree of uncertainty in the rankings of alternatives.

Analyses are restricted to first‐order Bayesian measurement models.

Overall, the presented methodology contributes to a better understanding of ranking efforts providing a useful tool for those who publish rankings to gain greater insights into the nature of the distinctions they disseminate.

The European Union (EU) clothing and textile industries are characterized by very intense international competition. EU producers face fierce competition from exports of new industrialized countries whose low wages and social charges give them a considerable competitive advantage. This paper seeks to present the results of an analysis of the European textile sector competitiveness.

The analysis is based on an industrial excellence (IE) model developed by INSEAD. This model has been used for the last ten years in an annual award (IEA), given out in France and Germany. This time the model was used not for giving an award, but for assessing and analyzing the current status of industrial excellence in the textile sector. For this reason a sample of textile companies from three European countries was used and results of the analysis are presented. The textile companies that participated in the analysis were benchmarked against the technologically advanced IEA sample consisting of companies from various industries, which participated in the competition during the last three years.

Key performance indicators of the textile sector are analyzed, including quality, flexibility, supply chain management, strategy formulation and strategy implementation. Significant improvement potential, especially in the areas of human resource management and knowledge management, is indicated.

Provides a methodology for employing the IE approach in their operation. Also provides a methodology for analyzing sector performance and new areas of differentiation in the European textile sector.

The results of the analysis were used to define customized IE training in order to promote expertise in IE in textiles and improve competitiveness of the sector.

The IEA model is used for the first time, not for giving an award, but as an IE assessment tool which can assist managers both of textile companies and intermediary bodies.

The purpose of this paper is to exploit student preference and propose, discuss and experimentally validate a strategy that aims to reduce time necessary to introduce tutoring of mind mapping to engineering students.

A survey instrument was designed and used to collect student preferences about mind mapping. Preferences were linked to alternative scenarios of mind mapping deployment. Survey responses from a 100 second‐year students from the Department of Production Engineering and Management were analyzed using conjoint analysis.

Results indicate that an effective strategy to present mind mapping to engineering students is to explain in detail all the possible applications of mind mapping; present mind maps with different colours using both words and drawings and encourage students to use mind maps in team assignments.

The findings of this paper provide a well documented framework in addressing mind mapping technique to engineering students. The proposed framework tries to make the best use of the available time for creativity development programs, in engineering departments. This framework may also be applied to other creativity enhancing techniques, providing solutions for incorporating creativity in engineering curricula.

To demonstrate the applicability of machine‐learning tools in quality management.

Two popular machine‐learning approaches, decision tree induction and association rules mining, were applied on a set of 960 production case records. The accuracy of results was investigated using randomized experimentation and comprehensibility of rules was assessed by experts in the field.

Both machine‐learning approaches exhibited very good accuracy of results (average error was about 9 percent); however, association rules mining outperformed decision tree induction in comprehensibility and correctness of learned rules.

The proposed methodology is limited with respect to case representation. Production cases are described via attribute‐value sets and the relation between attribute values cannot be determined by the selected machine‐learning methods.

Results demonstrate that machine‐learning techniques may be effectively used to enhance quality management procedures and modeling of cause‐effect relationships, associated with faulty products.

The article proposes a general methodology on how to use machine‐learning techniques to support quality management. The application of the technique in ISDN modem manufacturing demonstrates the effectiveness of the proposed general methodology.

The purpose of this paper is to evaluate and improve two popular business process modelling languages (BPMLs) the Integration definition for function modelling (IDEF0) and event‐driven process chain (eEPC).

The paper aims to select, compare and evaluate against a proposed criteria framework two popular BPMLs. In order to meet end‐user requirements, it suggests concrete improvements for either language. Evaluation findings and improvement attempts are documented over a case study within the context of a large European aerospace industry. The languages are applied through the use of appropriate software such as BPWin and Arena Simulation Software for IDEF0 and Architecture of Integrated Information Systems (ARIS) Toolset for eEPC.

Improved languages seem to overcome several deficiencies and increase their modelling performance. Improvements in IDEF0 include: classification of component information and insertion of logical operators. In this approach the language describes not only the information flow within the process, but also the time sequences via component discrimination. Improvements in eEPC include event chain diagram in ARIS house of business engineering methodology, which represents the interrelations between functions of different diagrams and the linkages among components and resources to include resources planning.

Results showed that language deficiencies were clarified. Thus, the improvements made, constitute better modelling performance and the development of more reliable models.

The paper outlines a systematic evaluation framework with concrete criteria. Furthermore, the improvements made, are a further step towards increased modelling performance, lean and comprehend diagrams.