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EN
Customer citizenship behaviour (CCB) is an important consumer trend observed in the contemporary market. It may be described as an extra-role, voluntary behaviour performed in favour of other customers or companies. One of the CCB dimensions, namely, providing customer feedback to company offering, overlaps with value co-creation as a booming marketing concept. Our knowledge about factors determining this behaviour is relatively week. Trying to fill the gap, this paper discusses inclination for value co-creation among customers on the basis of literature review and explorative research. This explorative study aims to identify some company-related and customerrelated antecedents to customer citizenship behaviour in the form of value co-creation in favour of companies. The theoretical deliberation is based on a critical literature review. The empirical part of the paper is based on explorative research in the form of a survey of 105 non-randomly selected customers. Aiming to identify the key drivers for customer inclination to participate in value co-creation, the exploratory factor analysis (EFA) was conducted; next, the quality of factor structure was assessed with the help of SmartPLS 3.0 using standard measures of validity; and finally, structural links between the inclination to co-create and distinct antecedents were estimated using the partial least square structural equitation modelling technique (PLS_SEM). The factor analysis suggested distinguishing two aspects of customer co-creation, i.e., either initiated by companies (Organised Co-Creation) or by customers (Spontaneous Co-creation). The estimated PLS structural model shows that only some casual paths were found statistically significant, i.e., the appreciation showed by companies towards customers engaging in the organised co-creation process (as extrinsic motivation) and customer innovativeness, as well as the fulfilment of the need for stability (as intrinsic motivation) with regards to spontaneous co-creation. The ex-post moderation analysis with the help of the PLS_MGA algorithm enabled to identify gender as the factor potentially explaining inter-group differences in the structural model.
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