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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