Determinants of continuance intention to use mobile self-scanning applications in retail

Abstract

PurposeRetailers are increasingly using self-service technologies to improve customer experience and reduce costs. The purpose of this study is to identify factors that could explain the level of continuance intention of mobile self-scanning applications in retail. Based on previous theoretical streams, the present study integrates technology readiness (TR) and service quality into the technology acceptance model.Design/methodology/approachUsing data collected through an online survey of 217 users of a mobile self-scanning application of a large supermarket chain operating in Portugal, the study uses partial least squares structural equation modeling to test the proposed hypotheses.FindingsThe results indicate that the continuance usage of the self-scanning apps is directly driven by users' satisfaction and perceived usefulness. Findings also show that TR has a positive and significant impact on ease of use and perceived usefulness. Ease of use has a positive impact on users' satisfaction and perceived usefulness but has no direct effect on the continuance intention to use the application. Perceived quality has a positive direct effect on satisfaction and a positive indirect effect on continuance intention. Finally, need for interaction has a negative effect on TR.Originality/valueThis work contributes to a better understanding of the emerging market for mobile self-scanning applications in retail applications, particularly relevant in a digital transition context.

Continuity of Use of Food Delivery Apps: An Integrated Approach to the Health Belief Model and the Technology Readiness and Acceptance Model

“…However, some have argued that this model may not be sufficient to explain individuals' technology adoption behavior, as the main variables of TAM measure utilitarian aspects of technology use, i.e., ease of use and usefulness (e.g., [17]). Thus, several authors suggest an integration of additional factors in order to extend TAM to better explain individuals' psychological processes in their behavior regarding technology adoption (e.g., [17,19]).…”

“…The integration of TR and TAM can provide a deeper understanding of the psychological process involved in application adoption behavior [20]. Since Lin et al [17] introduced TRAM, several researchers have conducted studies to examine users' technology adoption behavior in a wide range of settings, such as m-services [65] and mobile self-scanning applications [19].…”

“…It is expected that technology readiness has a direct positive effect on perceived usefulness, since individuals with higher innovativeness and higher optimism towards technological innovations should be more able to see the utility related to their adoption [17]. Previous studies that linked technology readiness to TAM constructs in various technology adoption contexts, for example self-service technologies [83], online stock trading systems [17], mobile self-scanning applications [19], and m-commerce [84], among others, found a positive and significant relationship between it and perceived usefulness and perceived ease of use. Moreover, Jin [85] also confirmed a positive and a negative effect of positive technology readiness and negative technology readiness, respectively, on perceived usefulness and perceived ease of use.…”

“…TAM is a representative model used to explain and predict individuals' adoption of information technology. Several studies have used this model as well its extensions to explain the process of information technology acceptance, such as studies of e-service, service mobile apps, information technology systems, and internet-based services, among others (e.g., [11,15,19]), further indicating that behavioral intentions to use a given technology are determined, in part, by users' perceived ease of use (PEOU) and perceived usefulness (PU). According to TAM, PEOU is a determinant of PU [11,12].…”

“…Despite the recognized importance of TR as an antecedent of TAM, the application of TRAM to explain the adoption and post-adoption of technologies in the mobile applications arena has been scarce. Some exceptions are the study of Aboelmaged et al [18] in the context of mobile apps' use for wellness and fitness applications, Ferreira et al [19] in the context of mobile self-scanning applications, Jin (2020) in the context of brand applications, and Chiu and Cho [20] in the context of health and fitness applications. To the best of our knowledge, no study has applied TRAM in the context of FDAs.…”