The purpose of this article is to analyse the specific role of two types of subjective norms in forming the intention to purchase green food. Based on the outcomes of a questionnaire completed by a sample of 411 household primary shoppers from a transitional country in the Southeast Europe region, we developed three models that depict the predictive power of attitudes, perceived behavioural control and subjective norms, and confirmed a significant positive relationship between green food purchasing intention and all three antecedents. Furthermore, regression analysis revealed two important theoretical insights: (1) descriptive norms represent statistically significant predictors of green food purchase behaviour; and (2) incorporating both social and descriptive norms increases the variance explained in intention. The latter also empirically proves that the meaning behind the two variables (social and descriptive norms) is different. These results contribute to the strengthening of the theory of planned behaviour in the part which has so far been referred to as the weakest link.

A study was conducted to assess the relationship between country-level entrepreneurial activity and individuals' perceived abilities, subjective norm and intentions to pursue entrepreneurship. The theory of planned behaviour and the Global Entrepreneurship Monitor (GEM) conceptual model are used to formulate hypotheses concerning factors that influence the level of societies' entrepreneurial intentions and activity in 43 countries included in the GEM 2010 study, as well as factors that influence the level of entrepreneurial intentions in Croatia from 2003 to 2011. In the analyzed GEM countries, the results confirm that antecedents to entrepreneurial intentions, as defined by the theory of planned behaviour, have a significant impact on entrepreneurial intentions which, in turn, significantly influence entrepreneurial activity. The results for Croatia were mixed. Subjective norm had a limited relationship with intentions while perceived behavioural control did.

Abstract. The goal of this study is to identify the contribution of effectuation dimensions to the predictive power of the entrepreneurial intention model over and above that which can be accounted for by other predictors selected and confirmed in previous studies. As is often the case in social and behavioral studies, some variables are likely to be highly correlated with each other. Therefore, the relative amount of variance in the criterion variable explained by each of the predictors depends on several factors such as the order of variable entry and sample specifics. The results show the modest predictive power of two dimensions of effectuation prior to the introduction of the theory of planned behavior elements. The article highlights the main advantages of applying hierarchical regression in social sciences as well as in the specific context of entrepreneurial intention formation, and addresses some of the potential pitfalls that this type of analysis entails.

Background: Alongside the theoretical progress made in understanding the factors that influence firm growth, many methodological challenges are yet to be overcome. Authors point to the notion of interpretability of growth prediction models as an important prerequisite for further advancement of the field as well as enhancement of models’ practical values. Objectives: The objective of this study is to demonstrate the application of factor analysis for the purpose of increasing overall interpretability of the logistic regression model. The comprehensive nature of the growth phenomenon implies propensity of input data to be mutually correlated. In such situations, growth prediction models can demonstrate adequate predictability and accuracy, but still lack the clarity and theoretical soundness in their structure. Methods/Approach: The paper juxtaposes two prediction models: the first one is built using solely the logistic regression procedure, while the second one includes factor analysis prior to development of a logistic regression model. Results: Factor analysis enables researchers to mitigate inconsistencies and misalignments with a theoretical background in growth prediction models. Conclusions: Incorporating factor analysis as a step preceding the building of a regression model allows researchers to lessen model interpretability issues and create a model that is easier to understand, explain and apply in real-life business situations.