Adapting corporate entrepreneurship assessment instrument for Romania

Abstract

Background: The study adapts the corporate entrepreneurship assessment instrument (CEAI), a notable North American psychometric instrument used to evaluate entrepreneurial culture, and investigates its construct validity scale, taking into account that psychometric instruments have limited cross-cultural portability. Objectives: We aim at identifying the perceived internal management key factors in the Romanian entrepreneurial culture (private sector) and applying CEAI to emergent economies. Method: The corporate entrepreneurship assessment instrument uses a 48-item Likert scale questionnaire to collect information from a large sample of employees working in different companies. The questions, seen as random variables, are then factor analysed in order to get a reduced more manageable structure. Factors are finally interpreted with respect to the entrepreneurial propensity of the business sector in study. The software used for statistical analysis was SPSS. Results: The survey conducted on 175 professionals from Romanian technology-based companies yielded a 10-factor structure for this particular business environment: reinforcement and work discretion, dynamic environment and decreased formalisation, delegation, time availability, strategic awareness, management support, stress, vertical communication, horizontal communication and knowledge sharing. Conclusion: The study provides a thorough understanding of the Romanian post-communist corporate culture, and, together with a similar analysis recently performed in South Africa, aims to create a clearer picture of cross-cultural portability of entrepreneurship psychometric instruments.

Analysis of Corporate Entrepreneurship in Public R&D Institutions

“…Factor Interpretation 1 support for opportunity investigations and reinforcement organization's attitude to endorse employees' formal and informal networking, as well their participations to all kind of awareness events etc., but also referring to the extent to which one perceives how superior managers strengthen the individuals' beliefs 2 dynamic environment and recognition the extent to which one perceives the organizational ability to stay up to date from the quality and information management point of view, and also the acknowledgement for achievements 3 decreased formalization the extent to which one perceives the tolerance for failure, decision making centralization, authority and responsibility delegation, supervision, and as well the extent to which aspects such as organizational procedures, policies, rules, job and even furthermore employees' behavior are unclearly established, standardized in an organization The factors presented in Table 3, contrast with previous analyses [6,8,9]: besides the common factors as work discretion, organizational boundaries, as being equivalent to a certain degree with knowledge sharing, time availability, reinforcement; the actual distinction lies in the fact, the Romanian intrapreneurial public culture demands also for aspects such as support for opportunity investigations, dynamic environment and recognition, decreased formalization, and strategic awareness.…”

Paradigm Shift in Business Education: A Competence-Based Approach

“…The refined data was input for analysis by using the dedicated software SPSS 20. By means of Analytic Hierarchy Process [67,68] we managed to build the conceptual framework for developing the six logistic regression models [69,70], one for each educational construct identified. Additionally, the third-layer questions were grouped as indicators for the second-layer questions, namely the constructs.…”

The geographic distribution of Knowledge Economy (KE) within the European Union (EU)

“…Factor Analysis (FA) is a well-known clustering technique used in statistics and econometrics, relying heavily on probabilities, and sometimes acknowledged as Principal Component Analysis (PCA). FA applies to partitioning large sets of random variables into smaller, meaningful clusters, the centroid of each cluster being the socalled factor (Tabachnick and Fidell, 2007;Vizitiu et al, 2018. Since the resulting number of factors is usually of an order of magnitude smaller than the initial number of variables, one expects from such analysis a better comprehension of the dependency among the variables, mathematically expressed by the correlation matrix (Habing, 2003).…”