scholarly journals Role of Technology Readiness in Airline Passengers’ Perceptions of Self-service Technology Quality

Author(s):  
Ekkarat Suwannakul

Self-service technology (SST) is more crucial now than before for the service delivery of airlines. Many airlines employ various types of SSTs so that passengers can perform certain services on their own. Although many passengers enjoy using the self-service option, some are not ready to adopt such a technology. This study aims to investigate how passengers possessing different characteristics perceive technology readiness (TR) and examine the influence of TR on perceived SST quality. Questionnaires were used to collect data from airports in Bangkok, Thailand. A total of 382 usable data were analysed using one-way ANOVA and multiple regression analysis. Results indicate significant differences in TR across age, education, occupation, income, types of SSTs and air travel frequency. This study also reveals that TR dimensions, namely, optimism, innovativeness, anxiety and insecurity, are important factors influencing airline passengers’ perceptions of SST quality.

2018 ◽  
Vol 7 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Hory Sankar Mukerjee ◽  
G. K. Deshmukh ◽  
U. Devi Prasad

Present study is an attempt to measure technology readiness of Indian customers towards self checkout service (SCS) through mobile app at retail stores at Hyderabad, India. Self-checkout services (SCS), a key offering of self service technology (SST), ‘is the technological enablement of customers to make payments and complete a checkout, after shopping, with little or no interaction with a service employee.’ Researchers also studied correlations between technology readiness, perceived usefulness, perceived ease of use, and likelihood to use SCS. For the purpose of the study TRI 2.0 developed by Parasuraman and Colby (2015) was used along with items of perceived usefulness and perceived ease of use, adapted from Davis (1989) , and items of ‘likelihood to use’ adapted from Bitner, Ostrom and Meuter (2002) . The findings of the study reveal that respondents’ technology readiness was moderate with respect to mobile based SCS. Significant positive correlations were found between: technology readiness and perceived ease of use, perceived ease of use and perceived usefulness, perceived ease of use and likelihood to use, perceived usefulness and likelihood to use. Further the respondents were categorised in to five technology segments as sceptics, explorers, pioneers, avoiders and hesitators.


2016 ◽  
Vol 11 (4) ◽  
pp. 695-715 ◽  
Author(s):  
Youjae Yi ◽  
Seo Young Kim

Author(s):  
Kevin M. Elliott ◽  
Juan (Gloria) Meng

The proliferation of new self-service technology in retailing suggests a need to assess the extent to which consumers are ready and willing to actually use the technology. This study examines the influence that the dimensions of the Technology Readiness Index (TRI) has on the propensity of consumers in China, one of the fastest growing economies in the world, to use self-service technology to complete retail transactions. Cluster analysis was also used to classify Chinese consumers into consumer types based on their TRI scores. The findings indicate that the dimensions of TRI impacts consumer types differently with respect to influencing the likelihood of using self-service technology. Implications of the consumer classifications are also discussed.


2016 ◽  
Vol 6 (8) ◽  
pp. 1618
Author(s):  
He Bai

Incidental learning in reading is an effective way to expand vocabulary acquisition and there are many factors influencing the effect of it. This proposed paper will make an attempt to explore how and to what extent different kinds of tasks based on reading and different contextual richness affect the recognition and retention of vocabulary encountered in reading. 50 sophomore English majors at the same English proficiency level from a university in Xi’an will be selected and be asked to complete different reading processes, which aim to investigate the role of context richness and task demands respectively in incidental learning. The data will be collected from the scores from every task and be analyzed with one-way ANOVA and multi-way ANOVA through SPSS.


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