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Given the importance of performance consistency of chain hotels in customers’ decision-making and service evaluation, this study aims to explore how consistently chain hotel brands offer quality service and carry out their performance from the eyes of customers through online reviews on TripAdvisor of the top five US hotel chains (i.e. Choice, Hilton, InterContinental, Marriott and Wyndham) and their brands.

The research objectives were achieved through methodological triangulation: business intelligence, data visualization analytics and statistical analyses. First, the data collection and pre-processing of consumer-generated media (CGM) (i.e. TripAdvisor online reviews) were performed using business intelligence for further analyses. Using data visualization analytics (i.e. box-and-whisker plot by region and brand), the geographic patterns of performance attributes (i.e. online review ratings, including location, sleep, cleanliness, room and service) were depicted. Using a series of analyses of variance and regression analyses, the results were further assessed for the impacts of brand performance inconsistency on consumers’ perceived value, sentiment and satisfaction.

The empirical results demonstrate that there are significant performance inconsistencies in performance attributes (location, sleep, cleanliness, room and service) by brands throughout the six regions in the US hotel market. More importantly, the findings confirm that brand performance consistency significantly influences consumers’ perceived service quality (i.e. perceived value, satisfaction and sentiment).

This study is one of the first attempts to empirically explore hotel brand performance consistency in the US hotel market from customer reviews on CGM. To measure hotel brand performance in the US hotel market, this study collected and analyzed user-generated big data for the top 5 US hotel chains through business intelligence, visualization analytics and statistical analysis. These integrated and novel research methods would help tourism and hospitality researchers analyze big data in an innovative data analytics approach. The findings of the study contribute to the tourism and hospitality field by confirming hotel brand performance inconsistency and such inconsistent performance affected customers’ service evaluations.

This study demonstrates the significant impact of hotel brand performance consistency on consumers’ perceived value, emotion and satisfaction. Considering that online reviews are perceived as a credible source of information, the findings suggest that the hotel industry pays special attention to brand performance consistency to improve consumers’ perceived value, emotion and satisfaction.

This study aims to advance the understanding of the relationships between employee engagement (EE), satisfaction and turnover intention (TI) beyond their known linear functions by providing a set of significant empirical evidence on nonlinear functions including quadratic, cubic and interactive effects.

This study used four 2 × 2 between-subjects experiments sampling 640 hospitality sales professionals through online data collection methods. EE and employee satisfaction (ES) were examined in disaggregation into personal and organizational dimensions. Residual regression models controlling for age and gender as covariates were the main approaches for analyzing data for nonlinear effects.

Both EE and ES consistently have significant negative quadratic and positive cubic effects on employees’ TI. EE and ES have a negative interaction effect, that is, complementing each other, on TI such that the effect is more pronounced at higher levels than lower levels of EE and satisfaction.

Organizations need to understand some threshold phenomena that may exist in the widely believed linear effects of EE and satisfaction on TI. Doing so may help allocate resources more effectively for EE and satisfaction.

This study examined the nonlinear as well as interactive nature of the relationships between EE and TI and ES and TI to expand our understanding of these relationships beyond the known linearity and add new empirical evidence to the literature.