An Investigation of Effectiveness of Information Technology Training Approaches Incorporating Observational, Enactive, Collaborative Learning on Older Adults' Technology Acceptance

科技培訓對長者科技接受的促進效用研究

Student thesis: Doctoral Thesis

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Award date11 Jul 2017

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Abstract

This thesis presents an empirical study of older adults’ technology acceptance after different technology training interventions in Hong Kong.
Currently, many older adults are missing out the benefits that they could get from the use of information technology. Bridging this digital divide is essential to help older adults to live more independently. Training is expected to be an underlying technique that will help older adults to adopt information technology. The importance of training interventions for the enhancement of technology acceptance among older adults has not received much attention.
This study sets out specifically to investigate the effectiveness of different training interventions (i.e., observational learning, enactive learning, and collaborative learning) in enhancing older adults’ technology acceptance, and examine the effects of various training outcomes on technology acceptance at post-training stage.
Through the lens of social cognitive theory, three empirical training experiments were conducted based on the methodology of observational learning, enactive learning, and collaborative learning. Before developing training experiments, a literature meta-analysis and a pilot qualitative study through focus groups was developed, which benefited technology training experiments design in this research. Effectiveness of the three training interventions were evaluated through measuring training outcomes including older adults’ cognitive knowledge acquisition, affective responses, and meta-cognitions. In addition, moderating effects of human elements including models, instructors, and partners on training effectiveness were investigated. After training, a technology acceptance model of older adults at the post-training stage was constructed and the moderating effects of different training conditions including human presence and co-action effect were investigated. The whole research was carried out in Hong Kong with the support of local elderly centres. In total, 163 eligible older adults participated and completed this research (134 novice older trainees completed the entire research); training outcomes of their responses and performance were used in data analysis (content analysis, multivariate analysis of variances, multiple regression, and structural equation modeling).
The qualitative results indicated older adults’ preferences of training program design. The experiments results affirmed the effectiveness of observational learning, enactive learning, and collaborative learning in improving older adults’ technology acceptance, however, to varying degrees. When learning through watching video, the identity-consistency of behavior model from generational perspective could enhance greater training effectiveness. When learning with live instructors, peer instructor contributed to better training outcomes of older adults. And the results revealed the superiority of live human presence in enactive learning experiment compared with the video modeling in the observational learning experiment. When learning in pairs, the expertise of novice older trainees’ learning partner affected their training performance. In addition, the superiority of co-action in collaborative experiment was identified comparing with other two training approaches. The final structural model shed light upon the mechanism of older adults’ technology acceptance at post training stage., and provided initial insights on the dynamics of technology acceptance of older adults.
Findings of the present research contribute to the design of training interventions for older adults to enhance their positive cognitions towards technology acceptance. The identified variables influencing older adults’ technology acceptance prior to and after training provide technology developers with recommendations on product and interface design and iterations, marketing communication, user experience engineering, and end-user training.