Startups and Consumer Purchase Behavior: Application of Support Vector Machine Algorithm

Abstract

This study evaluated the impact of startup technology innovations and customer relationship management (CRM) performance on customer participation, value co-creation, and consumer purchase behavior (CPB). This analytical study empirically tested the proposed hypotheses using structural equation modeling (SEM) and SmartPLS 3 techniques. Moreover, we used a support vector machine (SVM) algorithm to verify the model’s accuracy. SVM algorithm uses four different kernels to check the accuracy criterion, and we checked all of them. This research used the convenience sampling approach in gathering the data. We used the conventional bias test method. A total of 466 respondents were completed. Technological innovations of startups and CRM have a positive and significant effect on customer participation. Customer participation significantly affects the value of pleasure, economic value, and relationship value. Based on the importance-performance map analysis (IPMA) matrix results, “customer participation” with a score of 0.782 had the highest importance. If customers increase their participation performance by one unit during the COVID-19 epidemic, its overall CPB increases by 0.782. In addition, our results showed that the lowest performance is related to the technological innovations of startups, which indicates an excellent opportunity for development in this area. SVM results showed that polynomial kernel, to a high degree, is the best kernel that confirms the model’s accuracy.

Keywords

• startup technological innovations
• CRM performance
• customer participation
• consumer purchasing behavior
• value co-creation
• support vector machine