Applying the UTAUT2 Model to a Non-Technological Service: The Case of Spa Tourism

Well-being tourism is a $639 billion market. Spa tourism is considered the most important segment within this market. The present study uses the Unified Theory of Acceptance and Use of Technology (UTAUT2) model to analyse tourists’ purchase intentions regarding a thermal suite. The model was originally designed to analyse users’ acceptance of new technologies. The original contribution of this paper is to apply it to a non-technological service. Specifically, data were collected through a questionnaire administered to a sample of 810 potential Spanish spa-goers. A consistent partial least squares (PLSc) SEM technique was used. The proposed model explains 53.3% of the variance in Purchase Intentions regarding the thermal suite. Performance expectancy is the variable that plays the greatest role in tourists’ purchase intentions, followed by hedonic motivation.

Comparison of Spa Choice between Wellness Tourists and Healthcare/Medical Tourists

Spa tourism is considered one of the most important segments of the $639 billion wellness market. The literature refers to two types of spa tourists: wellness tourists and healthcare/medical tourists. However, virtually no studies have compared spa choice models between these two segments. The present study uses the Cognitive-Affective-Normative (CAN) model to compare the variables that explain purchase intention in relation to spas between the two segments. Data were collected through a questionnaire administered to a sample of 810 potential Spanish spa-goers, and consistent partial least squares (PLSc) structural equation modeling (SEM) was used. Contrary to what might be expected, no major differences were found between the spa choice models for wellness tourists and for healthcare/medical tourists. The results show that R2 and Q2 were similar for both models. The most influential variable was performance expectancy, and differences were only found in the influence of the pleasure variable.

Telemedicine Acceptance during the COVID-19 Pandemic: An Empirical Example of Robust Consistent Partial Least Squares Path Modeling

The explanation of behaviors concerning telemedicine acceptance is an evolving area of study. This topic is currently more critical than ever, given that the COVID-19 pandemic is making resources scarcer within the health industry. The objective of this study is to determine which model, the Theory of Planned Behavior or the Technology Acceptance Model, provides greater explanatory power for the adoption of telemedicine addressing outlier-associated bias. We carried out an online survey of patients. The data obtained through the survey were analyzed using both consistent partial least squares path modeling (PLSc) and robust PLSc. The latter used a robust estimator designed for elliptically symmetric unimodal distribution. Both estimation techniques led to similar results, without inconsistencies in interpretation. In short, the results indicate that the Theory of Planned Behavior Model provides a significant explanatory power. Furthermore, the findings show that attitude has the most substantial direct effect on behavioral intention to use telemedicine systems.

Measurement error correlation within blocks of indicators in consistent partial least squares

Purpose The purpose of this paper is to enhance consistent partial least squares (PLSc) to yield consistent parameter estimates for population models whose indicator blocks contain a subset of correlated measurement errors. Design/methodology/approach Correction for attenuation as originally applied by PLSc is modified to include a priori assumptions on the structure of the measurement error correlations within blocks of indicators. To assess the efficacy of the modification, a Monte Carlo simulation is conducted. Findings In the presence of population measurement error correlation, estimated parameter bias is generally small for original and modified PLSc, with the latter outperforming the former for large sample sizes. In terms of the root mean squared error, the results are virtually identical for both original and modified PLSc. Only for relatively large sample sizes, high population measurement error correlation, and low population composite reliability are the increased standard errors associated with the modification outweighed by a smaller bias. These findings are regarded as initial evidence that original PLSc is comparatively robust with respect to misspecification of the structure of measurement error correlations within blocks of indicators. Originality/value Introducing and investigating a new approach to address measurement error correlation within blocks of indicators in PLSc, this paper contributes to the ongoing development and assessment of recent advancements in partial least squares path modeling.