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This paper aims to examine the joint effect of the focal customer’s gender and fellow customer’s gender in influencing voice complaint intentions and intention to convey negative word of mouth (NWOM).

Participants were randomly assigned to one of the two dining conditions (same-gender fellow customer vs opposite-gender fellow customer). Their intention to voice a complaint and to spread NWOM were measured after reading a scenario describing a service failure. A 2 (focal customer gender: male vs female) × 2 (fellow customer: same gender vs opposite gender) between-subjects quasi-experimental design was conducted to test the hypotheses.

The results demonstrate that female customers’ voice complaint intentions were significantly higher when a fellow customer’s gender was female rather than male. In contrast, regardless of the fellow customer’s gender, no significant differences in voice complaint intentions were found among male customers. The results further indicate that voice complaint intentions mediate the impact of a fellow customer’s gender on intention to spread NWOM among female customers. However, both female and male participants show equally high levels of voice complaint intentions in the context of fine-dining restaurant.

This study broadens the understanding of customer complaining behavior and also provides insights to practitioners on how to manage customers who are in same- and mixed-gender situations.

This research extends the literature on agency–communal theory and complaining behaviors by examining the role of a fellow customer’s gender influencing the focal customer’s intentions to voice complaints and to spread NWOM.

Given the unique nature of business events tourism, this paper evaluates the forecasting performance of various models using search query data (SQD) to forecast convention attendance.

This research uses monthly and quarterly business event attendance data from both the U.S. (Las Vegas) and China (Macau) markets. Using SQD as the input, we evaluated and compared the cutting-edge forecasting models including Prophet and Long Short-Term Memory (LSTM).

The study reveals that Prophet outperforms complex neural network models in forecasting business event tourism demand. Keywords related to convention facilities, conventions or exhibitions, and transportation are proven to be useful in forecasting business travel demand.

Prophet is an accessible forecasting model for event-tourism practitioners, especially useful in the volatile business event tourism sector. Using verified search keywords in models helps understand traveler motivations and aids event planning.

Our study is among the first to empirically evaluate the performance of forecasting models for business travel demand. In comparison with other mainstream forecasting models, our study extends the scope to examine both the U.S. and Chinese markets.

This study examined the antecedents of technostressors as well as how customers' perceived convenience and technostressors of using a check-in/out kiosk influence their behavioral intention in a full-service hotel setting.

Using survey data collected from 630 hotel customers, hypotheses were tested via structural equation modeling and multi-group analysis.

The results showed that perceived usefulness of a check-in/out kiosk had direct effects on both technostressors (i.e. work overload and role ambiguity), and that perceived ease-of-use had indirect effects on the technostressors, via perceived usefulness. The findings showed that both role ambiguity and perceived convenience significantly influenced intention to use a check-in/out kiosk. Intention to use was positively associated with intention to revisit a hotel providing the kiosk. These findings were equivalent across the younger and older groups.

Based on the findings, hotels can implement effective strategies to reduce technostressors associated with a check-in/out kiosk and focus on enhancing the factors that influence customer acceptance of the system. This is especially important given the increased emphasis on self-service technology since the onset of the COVID-19 pandemic.

This research contributed to the relevant literature by developing a check-in/out kiosk acceptance model using a multi-theoretical approach, and empirically testing it within the full-service hotel domain. It fills the knowledge gap regarding the antecedents and outcomes of technostressors in the hospitality research literature by providing empirical evidence.