User Acceptance of Internet of Vehicles Services: Empirical Findings of Partial Least Square Structural Equation Modeling (PLS-SEM) and Fuzzy Sets Qualitative Comparative Analysis (fsQCA)

Yikai Liang,Weijie Wang,Guijie Zhang,Feng Xu,Peter Brida

doi:10.1155/2020/6630906

Abstract

Recently, IoV-based services and vehicles have come to the forefront as part of the growing market for the automobile industry. Since IoV-based services and vehicles were introduced, they have been expected to grow rapidly. However, contrary to optimistic expectations for future market growth, the IoV-based services and vehicles market has appeared to hit a roadblock and remains at an early market stage. Therefore, research of the determinants leading to consumers’ intention to accept and purchase IoV-based services and vehicles is significant for either academics or practitioners. Drawing upon the extended unified theory of acceptance and use of technology acceptance model (UTAUT2), the perceived risk theory, and the initial trust model, we developed an integrated conceptual model and explored what and how various determinant antecedent conditions fit together on consumer intention to accept IoV-based services and vehicles. The proposed model and hypotheses were assessed by both symmetric (partial least square structural equation modeling, PLS-SEM) and asymmetric (fsQCA) approaches using online survey datasets with 362 Chinese consumers. The findings suggest that PLS-SEM and fsQCA are complementary analytical techniques providing comparable results. PLS-SEM results indicate that performance expectancy, price value, habit, and initial trust have significant effects on behavioral intention to accept IoV services. Despite other determinants, e.g., effort expectancy, social influence, facilitating conditions, hedonic motivation, and perceived risk, have no significant effect. FsQCA results reveal twelve different configurations of determinants resulting in a high level of behavioral intention to accept IoV services, and eight causal paths equifinally leading to the negation of behavioral intention to accept IoV services. These findings suggest that several conditions that were not significant in PLS-SEM are sufficient conditions when combined with other conditions. This study enriches relevant research studies on IoV-based services acceptance and provides relevant insights and marketing suggestions for incentivizing consumers to accept the IoV-based services.

Highlights

In recent years, connected autonomous vehicle (CAVs) or called intelligent connected vehicles, as the core component of the intelligent transportation system (ITS) and a node of Internet of Vehicles (IoV) system [1], have become one of the most popular research fields in network and intelligent transportation system, as well as attracted huge investments from the automotive manufacturers
In the study, we answer the calls for the application of the holistic approach to understand the determinants’ configurations on consumers’ behavioral intention to accept IoV-based services. erefore, based on the review of studies related to the acceptance of IoV-based services and vehicles, we develop an integrated theoretical model including the extended unified theory of acceptance and use of technology (UTAUT2) [41] with the constructs of perceived risk from perceived risk theory [42] and initial trust from the initial trust model [43]. ese factors employed have been extensively used in innovation adoption studies and are appropriate for explaining IoV-based services’ acceptance. is integrated model makes up for the inadequate explanation of the individual behavior adopted by a single model
FsQCA allows for multifinality in which identical conditions can lead to different outcomes [39]. us, fsQCA is considered as an appropriate complementary analysis to partial least square (PLS)-structural equation modeling (SEM) when detecting effects caused by unobserved heterogeneity [40, 92,93,94]

Summary

Introduction

In recent years, connected autonomous vehicle (CAVs) or called intelligent connected vehicles, as the core component of the intelligent transportation system (ITS) and a node of Internet of Vehicles (IoV) system [1], have become one of the most popular research fields in network and intelligent transportation system, as well as attracted huge investments from the automotive manufacturers. Erefore, it is significant to explore the determinants of consumers’ intention to accept and purchase IoV-based services [16]. With ongoing technological advances in automation and connectivity, several studies have been discussed concerning IoV-based services in recent years that have concentrated on different issues [1, 17], such as the concept of IoV [18, 19], architecture (four layers including vehicle network environment sensing and control layer, network access and transport layer, coordinative computing control layer, and application layer) [20, 21], and key technologies for IoV [22, 23], as well as barriers and determinants of IoV-based services and vehicles adoption [24, 25]. In real-life scenarios, a viable outcome often depends on combinations of several antecedents that collectively form what is referred to as an algorithm in the asymmetric method [37]. erefore, qualitative comparative analysis (QCA), as a holistic approach, has been recommended for facilitating the analysis of complex causality and logical relations among combinations of conditions and an outcome, allowing researchers to examine multiple causal paths that lead to the same outcome [38,39,40]

Methods

Results

Discussion

Conclusion