Digital tourism experience for tourist site revisit: an empirical view from Ghana

PurposeDigital tourism has drawn the attention of researchers around the globe. This study aims to assess the digital tourism experience for tourist site revisit from an emerging market perspective.Design/methodology/approachAnchored on the social cognitive theory, the study employed a quantitative method, using the convenience sampling to select 328 participants who responded to tourism and technology sharing items through an online questionnaire. The study's hypotheses were tested utilizing structural equation modelling.FindingsThe results suggest a significant influence of technology-based service innovativeness on service value, tourist site revisits and experience sharing through technology. Further, the findings also revealed the significant influence of service value on tourist site revisit and experience-sharing through technology.Research limitations/implicationsThis study was conducted with only clients or tourists, and this limits generalization of the study's findings.Practical implicationsThe study offers the understanding of how tourist site operators and all stakeholders have to deploy new ways of technology-based service innovation to get maximum return on their investment in the hospitality industry.Originality/valueThe outcome of this research advanced the linkage between technology and tourism in context, which is important to policymakers and practitioners in the sector.

Examining students’ satisfaction with online learning during the Covid-19 pandemic - an extended UTAUT2 approach

Given that the educational sector was particularly hard hit in most countries around the world during the Covid-19 pandemic, the study employs an extended UTAUT2 to investigate student use and satisfaction with e-learning in a developing country setting. We employed partial least squares structural equation modelling (PLS-SEM) on 616 data responses gathered online from university students during the pandemic to evaluate our integrated model. This paper found that not all the predictors of the UTAUT2 model were statistically significant in predicting behavioural intention. Our results revealed that performance expectancy, effort expectancy, and facilitating conditions, as predictors, have no influence on various outcomes relating to the behavioural intention to use e-learning. The study also found that e-learning interaction quality predicts student satisfaction. Other theoretical and practical implications are also detailed in our study. [ FROM AUTHOR] Copyright of Journal of Further & Higher Education is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)