An online community-based dynamic customisation model: the trade-off between customer satisfaction and enterprise profit

Abstract

In recent years, the challenges of mass customisation (MC) have been increasing sales conversion rate and effectively matching supply with demand. Additionally, online customer communities (OCCs) have become increasingly popular and have proven to provide substantial value to both customers and enterprises. Therefore, the focus of this paper is to (1) propose a mathematical online community-based dynamic customisation model, (2) explain its practical mechanism and (3) solve its dynamic trade-off challenge. Accordingly, first, the trade-off challenge was formulated according to a multi-objective optimisation model to optimise the trade-off between customer satisfaction and enterprise profit. Second, based on the mechanism of the model, three different matching modes of production between customised products and manufacturers were delineated and analysed. Finally, genetic algorithm (GA) was developed to solve the proposed mathematical model. To validate the proposed model, a case study of an enterprise that provides customised menswear was selected. The degree of customisation and the weights given to the functions of enterprise profit and customer satisfaction were further analysed. The proposed model assists researchers and practitioners to decide the cooperation mode with manufacturers, pricing strategy and the degree of customisation for an optimal trade-off in the context of online community-based dynamic customisation.

Keywords:

• online customer community (OCC)
• mass customisation (MC)
• dynamic trade-off
• multi-objective optimisation
• genetic algorithm (GA)

Acknowledgements

This research was partially supported by Jinan University, the National Natural Science Foundation of China under grant numbers 71772075, 71302153 and 71672074, the Guangzhou Science and Technology Program projects under grant number 201607010012, the Social Science Foundation of Guangzhou Province under grant number 2018GZYB31 and the Foundation of Chinese Government Scholarship No. 201806785010. Any opinions, findings, conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the above funding agencies.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 Additive customisation means providing more designable or selectable attributes for customers to customise.

2 Subtractive customisation means providing fewer designable or selectable attributes for customers to customise.

3 Sensitivity analysis is conducted to measure the uncertainty or changes regarding various parameters and to increase the accuracy of the cost–benefit analysis. Sensitivity analysis calculates the certainty that can be apportioned to various sources of uncertainty in its output (Saltelli 2002).

Additional information

Funding

This research was partially supported by Jinan University, the National Natural Science Foundation of China under grant numbers 71772075, 71302153 and 71672074, the Guangzhou Science and Technology Program projects under grant number 201607010012, the Social Science Foundation of Guangzhou Province under grant number 2018GZYB31 and the Foundation of Chinese Government Scholarship no. 201806785010.