The purpose of this paper is to focus on online sales configurators (SCs), also known as mass-customization toolkits, which enable consumers to self-customize their product solutions online. The paper aims to provide new insights into which characteristics of an online SC increase the consumer-perceived benefits of possessing a mass-customized product.
Previous studies on mass customization (MC), sales configuration, and learning psychology are used to develop the research hypotheses, which are tested by analyzing data from 675 configuration experiences from a convenience sample of potential consumers using 31 real online SCs for laptops/notebooks, economy cars, and sport shoes/sneakers.
The paper finds support for the hypotheses that SCs with higher flexible-navigation, focused-navigation, and easy-comparison capabilities enhance not only the traditionally considered utilitarian benefit (UT), but also the consumer-perceived uniqueness benefit (UN) and self-expressiveness (SE) benefit (SE). Furthermore, consistent with the study’s hypotheses, SCs with higher benefit-cost communication and user-friendly product-space description capabilities are found to improve UT. The hypotheses that these two capabilities enhance UN and SE, however, are not supported. Post-hoc analyses suggest that the examined SCs are generally UT-centered and need improvement of their ability to communicate the UN and the SE a consumer could derive from the purchase of his/her configured product.
While prior research has primarily been concerned with conceptually arguing and empirically showing that uniqueness and self-expressiveness are two additional sources of consumer value in business-to-consumer MC, this is the first empirical study that offers insights into which characteristics online SCs should have in order to draw from these two value sources.
Sandrin, E., Trentin, A., Grosso, C. and Forza, C. (2017), "Enhancing the consumer-perceived benefits of a mass-customized product through its online sales configurator: An empirical examination", Industrial Management & Data Systems, Vol. 117 No. 6, pp. 1295-1315. https://doi.org/10.1108/IMDS-05-2016-0185
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