Identification of critical brand community variables and constructs using importance-performance analysis and neural networks

Abstract

Purpose

This paper aims to use a unique statistical analysis tool to examine the importance and performance of critical brand community constructs and indicators to make concrete recommendations for brand community managers going forward.

Design/methodology/approach

An online survey was used to gather 501 responses from North American members of the Qualtrics panel. The data was analyzed with partial least squares (PLS) modeling software SmartPLS and neural networks available in statistical software JMP by SAS.

Findings

Using the brand community motives by Madupy and Cooley (2010), the results of this paper indicated that there was significant room for improvement in customer engagement. Based on further analysis, entertainment and identification with the brand community were the most important constructs in driving community engagement so that the identification construct received a “do better” ruling meaning that the improvement of the indentification construct score would enhance significantly the score of the target construct engagement score.

Originality/value

For brand community managers, it is important to know the true importance of the critical brand community constructs and indicators, along with an assessment of current performance. This helps to increase satisfaction and relationship quality among brand community members. The current study uses unique statistical analysis tools to make such concrete recommendations.

Citation

Haverila, M., Haverila, K.C. and McLaughlin, C. (2021), "Identification of critical brand community variables and constructs using importance-performance analysis and neural networks", Journal of Modelling in Management, Vol. 16 No. 1, pp. 124-144. https://doi.org/10.1108/JM2-11-2019-0259