A Causal Configuration Analysis of Payment Decision Drivers in Paid Q&A

Abstract

PurposeThis paper examines factors of payment decision as well as the role each factor plays in casual configurations leading to high payment intention under systematic and heuristic information processing routes.Design/methodology/approachBased on heuristic-systematic model (HSM), we propose a configurational analytic framework to investigate complex casual relationships between influencing factors and payment decision. In line with this approach, we use fuzzy-set qualitative comparative analysis (fsQCA) to analyze data crawled from Zhihu.com.FindingsThe number of previous consultations is a necessary element in all five equivalent configurations which lead to high intention in payment decision. The heuristic processing route plays a core role while the systematic processing route plays a peripheral role in payment decision-making process.Research limitationsResearch is limited in that moderating effect of professional fields has not been considered in the framework.Practical implicationsConfigurations in results can assist managers of knowledge communities and paid Q&A service providers in the management of information elements to motivate more payment decision.Originality/valueThis paper is one of the few studies to apply HSM theory and fsQCA method with respect to the payment decision in paid Q&A.

Construction of intelligent decision model for multi-channel collaborative fee urging

“…At the same time, the hardware environment for algorithm operation is Lenovo server with Core i7 CPU, 64 GB ARM memory, and Windows 10 64 bit operating system. The methods of reference [5] and the methods of reference [6] are selected for comparison to test the effectiveness of the proposed methods.…”

“…Three methods are used for comparison, and the comparison results of intelligent decision-making accuracy of the three methods are shown in Figure 1. It can be seen from Figure 1 that when the number of iterations is 500, the average intelligent decision-making accuracy of the proposed method is 98.8%, while the average intelligent decision-making accuracy of the methods of reference [5] and the methods of reference [6] is 91.9% and 89.4% respectively. It can be seen from this that the intelligent decision-making accuracy of the proposed method is relatively high, and the method has strong multi-channel collaborative fee urging capability.…”

“…The shorter the response time of the intelligent decision model, the higher the efficiency of multi-channel collaborative fee urging. The method of reference [5] and the method of reference [6] are compared. The response time comparison results of the intelligent decision models of the three methods are shown in Table 1.…”

“…It can be seen from Table 1 that the response time of the intelligent decision models of the three methods increases with the number of iterations. When the number of iterations is 500, the response time of the intelligent decision model of the proposed method is 3.8 ms, while the response time of the intelligent decision model of the method of reference [5] and The method of reference [6] is 6.9 ms and 9.4 ms respectively. It can be seen that the response time of the intelligent decision model of the proposed method is relatively short, which can effectively improve the efficiency of multi-channel collaborative fee urging.…”

“…At present, scholars in related fields have conducted research on the decision of payment fee urging. Reference [5] proposed the causal allocation analysis of payment decision drivers in pay question answering. This paper studies the factors of payment decision, and the role of each factor in the random configuration leading to high willingness to pay under the system and heuristic information processing path.…”