Purpose This study aims to predict hotel demand through text analysis by investigating keyword series to increase demand predictions’ precision. To do so, this paper presents a framework for modeling hotel demand that incorporates machine learning techniques. Design/methodology/approach The empirical forecasting is conducted by introducing a segmented machine learning approach of leveraging hierarchical clustering tied to machine learning and deep learning techniques. These features allow the model to yield more precise estimates. This study evaluates an extensive range of social media–derived words with the most significant probability of gradually establishing an understanding of an optimal outcome. Analyzes were performed on a major hotel chain in an urban market setting within the USA. Findings The findings indicate that while traditional methods, being the naïve approach and ARIMA models, struggled with forecasting accuracy, segmented boosting methods (XGBoost) leveraging social media predict hotel occupancy with greater precision for all examined time horizons. Additionally, the segmented learning approach improved the forecasts’ stability and robustness while mitigating common overfitting issues within a highly dimensional data set. Research limitations/implications Incorporating social media into a segmented learning framework can augment the current generation of forecasting methods’ accuracy. Moreover, the segmented learning approach mitigates the negative effects of market shifts (e.g. COVID-19) that can reduce in-production forecasts’ life-cycles. The ability to be more robust to market deviations will allow hospitality firms to minimize development time. Originality/value The results are expected to generate insights by providing revenue managers with an instrument for predicting demand.

Casinos rely extensively on free slot play (FSP) offers for incentivizing patron visitation. However, there has been a lack of understanding its influence on driving patron visitation and patrons’ valuation of FSP compared to other casino promotional offers. This study conducted a conjoint analysis on patrons’ valuations of FSP compared to other promotional offerings at a casino resort. Moreover, this study investigated the roles inter-casino competition and visitation frequency have on patrons’ perceived valuation of FSP through a hierarchical Bayes model. The results show that competition plays a significant negative role on patrons’ valuation of FSP, while competition held insignificant influence on patrons’ valuation of food and beverage (F&B) comp offers. Additionally, patrons who visited the casino more frequently valued FSP greater, while less active patrons valued F&B comp offers more. Using the study’s results, casinos can increase their margins through increased efficiencies with their promotional offering mix.

Little is known on how turnover and senior leadership attributes affect the long-term performance of a casino resort. The ability to longitudinally measure the turnover effect on market share has been problematic due to most gaming markets exhibiting dynamic conditions with exogenous factors that provide competitive advantages. This study analyzed the effect the turnover rate and successor attributes of the CEO and Chairman of Tribal Council positions have on their casinos’ market share within a balanced oligopolistic market of Connecticut. Additionally, this study investigated which attributes amplify the sensitivity of the CEO tenure status to market share growth. The results suggest increased CEO turnover and CEO hires who already had prior CEO casino experience hinder long-term market share. Moreover, the tenures of more experienced CEOs were less susceptible to market share performance. The results can be leveraged for improved hiring practices at the senior levels of Native American casinos.

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PurposeThis study investigates restaurant patrons' comfort level with the sudden shift in the dining-in climate within the state of Massachusetts during the onset of the COVID-19 pandemic.Design/methodology/approachAn exploratory study utilized learning algorithms via gradient boosting techniques on surveyed restaurant patrons to identify which restaurant operational attributes and patron demographics predict in-dining comfort levels.FindingsPast consumers' eating habits determine how much their behavior will change during a pandemic. However, their dining-in frequency is not a predictor of their post-pandemic dining-in outlook. The individuals who were more comfortable dining in prior to the pandemic dined in more often during the COVID pandemic. However, they had a poorer outlook on when dining in would return to normal. Although there are no clear indicators of when and how customers will embrace the new norm (a combination of pre-, peri-, and post-pandemic), the results show that some innovative approaches, such as limiting service offerings, are not well accepted by customers.Practical implicationsThe study offers several managerial implications for foodservice providers (i.e. restaurants, delivery services, pick-up) and investors. In particular, the study provides insights into the cognitive factors that determine diners' behavioral change in response to a pandemic and their comfort level. Operators must pay attention to these factors and consider different offering strategies when preparing to operate their business amid a pandemic.Originality/valueThis is a study of a specific location and period. It was conducted in Massachusetts before a vaccine was available. The restaurant industry was beset with uncertainty. It fills a gap in the current literature focused on the COVID-19 pandemic in customers' transition from pre-COVID-19 dining-in behaviors to customers' refreshed COVID-19 outlook and industry compliance with newly established hygiene and safety standards.