The development of Information Systems (IS) and Information and Communication Technologies (ICT) is offering new opportunities for businesses to implement promotion strategies focused on customer attraction and retention. In this sense, mobile coupon usage has increased as a promotion tool, especially in the fast-food sector. However, the use by consumers of these coupons is not homogeneous and it is conditioned by prior experience. Thus, this study aimed to examine variations between Fast Food Restaurant (FFR) customers based on their prior experiences with the use of mobile coupon (expert vs novice users).
A sample of 400 fast-food customers was collected using a structured questionnaire. In order to compare the proposed relationships between expert and novice users, a multigroup approach was applied through new, recently proposed evaluation procedures designed for PLS–SEM.
The results show that the two groups of consumers (expert vs novice users) have notable differences regarding the relationship between perceived ease of use and perceived usefulness. This relationship was the strongest in both groups. However, there are no differences found in other aspects considered as antecedents to mobile coupons usage, for instance, usage intention and attitude.
This work emphasises the importance of considering differences based on experience between mobile coupon users. Ease of use, perceived consumer utility and increased mobile coupons in apps can be the key to driving effective business strategies based on promotional tactics by FFRs. Likewise, this study can help other researchers in their empirical applications of PLS–SEM analysis.
This study is the first to provide an in-depth analysis of differences based on users' experience with mobile coupons at FFRs. It is innovative in its introduction of the consumer's coupon proneness variable.
Carranza, R., Díaz, E., Martín-Consuegra, D. and Fernández-Ferrín, P. (2020), "PLS–SEM in business promotion strategies. A multigroup analysis of mobile coupon users using MICOM", Industrial Management & Data Systems, Vol. 120 No. 12, pp. 2349-2374. https://doi.org/10.1108/IMDS-12-2019-0726
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