Self-service kiosks: an investigation into human need for interaction and self-efficacy

2022 ◽  
Vol 20 (1) ◽  
pp. 33
Author(s):  
Eun Mi Lee ◽  
Saejoon Oh
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Maria Jose Castillo S. ◽  
Enrique Bigne

PurposeThis paper proposes a model that extends the technology acceptance model (TAM) by identifying factors that influence consumers' acceptance of augmented reality (AR) self-service technologies (AR-based SSTs) in the retail sector, resulting in the Augmented Reality in Retail Model (ARiR Model).Design/methodology/approachThis study is based on an online questionnaire responded to by 284 makeup-using women from Nicaragua and the USA. It is based on an AR mobile app used to shop in retail stores. Partial least squares-structural equation modelling was used to validate the ARiR model and test the hypotheses.FindingsAesthetics and navigation are significant predictors of perceived usefulness and perceived ease of use (PEOU), and self-efficacy also explains perceived ease of use. Technology readiness and the need for personal interaction were not found to be influencing factors. A cross-cultural comparison indicated that both countries have similar overall attitudes towards AR-based SSTs.Research limitations/implicationsThis paper provides insights into the perceived value of, and motives for customer acceptance of, AR-based SSTs, which can serve as guidelines for their future implementation. Furthermore, it validates and confirms the application of the proposed ARiR model for technology acceptance in both developed and developing countries.Practical implicationsThe paper provides new insights for retailers on the implementation of AR at the point of sale.Originality/valueThe model extends the original TAM to AR and introduces five new constructs: need for personal interaction, aesthetics, navigation, self-efficacy and technology readiness. It was tested in both a developing and a developed country.


2008 ◽  
Vol 11 (4) ◽  
pp. 407-428 ◽  
Author(s):  
Jacqueline van Beuningen ◽  
Ko de Ruyter ◽  
Martin Wetzels ◽  
Sandra Streukens
Keyword(s):  

Author(s):  
Virginia Dickenson

As our education system becomes more technology-driven and dependent, technical support is a part of the infrastructure for successful higher education performance. Self-service technology (SST) systems allow users to access solutions without agent involvement are viewed as the most cost-effective way to provide learning support. Support design in SSTs is every bit as critical as instructional design is to content-based education. Designing a technical learning center may be far more effective to address technical support needs, as well as creating other beneficial outcomes. In theory, SST systems are viewed as beneficial because they support self-efficacy, which has a direct relation to self-actualization. Self-efficacy has also been found to promote problem solving as well as higher achievement. Creating a learning center as an SST may contribute to transformative learning in that it changes the learner's perspective of their own capabilities and encourages them to be more flexible when introduced to other technologies. Several examples are presented.


Author(s):  
Shunzhong Liu

The provision of self-service technologies in the service industry has increased rapidly in recent years. Despite the advantages with increased self-service technologies, removing the frontline employee support can influence customer behavioral intentions towards the service providers. According to social support theory and social cognitive theory, this study develops a conceptual model to investigate how and when perceived employee support affects customer behavioral intentions. The model is tested using a factorial between-subjects experimental design in the self-service environment of China's bank. The results show that the relationship between perceived employee support and customer self-efficacy is moderated by forced use and service complexity. Moreover, the results indicate that self-efficacy is a mediator that explains how perceived employee support may come to be associated with customer behavioral intentions towards the service providers.


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