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This study aims to examine whether indirect customer-to-customer interactions (CCI) affect consumers’ behavioral intentions and how that effect is generated. It also explores the effect of dining experience on customer behavioral intentions and how that effect varies by party type.

The research consists of an experimental survey-based study of n = 491 real-world consumers from a marketing research panel. Structural equation models are analyzed to examine hypothesized relationships.

Indirect CCIs significantly affect all five dimensions of experiential value. Food and beverage (F&B) excellence, aesthetics and service excellence positively affect customer revisit intentions and word-of-mouth intentions via restaurant image. Furthermore, party type moderates the effect of aesthetics on behavioral intentions so that the effect is significant for the social diner group only. Customer return on investment and playfulness show non-significant impacts on behavioral intentions.

Managers should be aware that indirect CCIs influence all aspects of the restaurant experience. Since F&B excellence, aesthetics and service excellence affect restaurant image and behavioral intentions, management can operationalize these elements of service. The impact of aesthetics differs by consumers’ party type, enabling management to create unique servicescapes based on their target customer segment.

The study pioneers an investigation of how indirect CCI is associated with behavioral intentions through the mediating effects of experiential value and restaurant image. It contributes to the literature by examining how the impact of diners’ experiences differs by party type.

Built upon customer engagement marketing theory and uses and gratification theory, this study examines the link between individual social media marketing (SMM) performance indicators and restaurant sales performance at the firm level. Moreover, the study investigates the moderating effect of advertising expenditure on this proposed relationship.

Random effect regression models were developed in Stata to examine the associations between SMM performance indicators, advertising expenditure, and restaurant firm revenue. Twelve years of SMM data from brands' Facebook pages were collected with a web scraper built in Python. Natural language processing was used to analyze the sentiment of user-generated content (UGC).

The results suggest that restaurant annual sales revenue increases as the volume of brand posts, “like”s, “share”s and positive comments on restaurants' Facebook pages increase. However, the total number of comments and the number of negative comments show non-significant associations with revenue. Firm advertising expenditure negatively moderates the relationships between sales revenue and the number of “like”s, “share”s, total comments and positive comments.

Restaurants benefit from making frequent posts on SNSs. Promotions that motivate online users to “like”, share, and comment on brand posts should be implemented. Firms with limited advertising budgets are encouraged to actively create buzz on SNSs due to evidenced stronger effects of UGC on sales performance than large advertisers.

This research bridges the gap by studying the effects of individual SMM performance indicators on restaurant financial outcomes. The findings support the effectiveness of SMM; and, for the first time, demonstrate that SMM could generate a more profound impact for firms with low advertising budgets.

This study systematically reviewed pricing research published in leading marketing and hospitality and tourism (H&T) journals between 2010 and 2019. It attempts to concretize the understanding of the evolving patterns of pricing research in both fields and suggests an agenda for future research in H&T.

This study performed keyword co-occurrence analyses and co-citation analyses on the bibliographic data of 575 articles from marketing and H&T journals. Content analysis was applied to investigate the emerged topics in H&T.

The marketing discipline showed a persistent focus on research themes including price promotion, reference price, price fairness and pricing strategy. The H&T domain experienced a significant content enrichment of preexistent research topics. H&T scholars showed a growing interest in studying pricing for sharing economy accommodations and the interplay between pricing and electronic word-of-mouth. While marketing research applied theories from multiple disciplines as the theoretical foundations, H&T studies adopted the hedonic pricing model as an overarching theory.

Future pricing research in H&T may incorporate theories from other disciplines such as psychology, sociology and anthropology to broaden the scope of the study. Besides, innovative study designs and complex data analysis techniques should be encouraged. Topicwise, H&T scholars can dive deeper into price promotion and distribution channel price management.

This is the first study consolidating the pricing literature in H&T and marketing. It also suggests the potential research directions for researchers.

The purpose of this paper is twofold. First, the study examines the prolonged effect of policy-related economic uncertainty on hotel operating performance, particularly the room demand (occupancy). Second, the study attempts to explain why occupancy drops when the perceived economic uncertainty is high by studying the mediating effect of consumer sentiment in the relationship between economic policy uncertainty and hotel demand.

This quantitative study uses secondary data – US economic policy uncertainty (EPU) index, University of Michigan's index of consumer sentiment (ICS), and property-level hotel operating data from three states of the US – California, Florida and New York. Data were analyzed using random effect regression and structural equation modeling. Robustness tests were conducted to enhance the reliability of the research findings.

Random-effects regression analysis reveals that policy-related economic uncertainty has a negative and lead-lag effect on hotel occupancy, average daily rate and revenue per available room (RevPAR). Structural equation modeling results show that the relationship between economic policy uncertainty and hotel occupancy is significantly mediated by consumer sentiment. Robustness test results support the findings from the main analysis.

This study offers valuable implications for the hotel professionals in regard to anticipating the economic impact of policy-related uncertainty on hotel industry and understanding how consumer sentiment affects demand at such crises times. Moreover, the study suggests potential course of actions to deal with declining room demand at times of uncertainty.

This empirical study explores how economic policy uncertainty affects hotel performance at the property level and explains the mediating effect of consumer sentiment on hotel room demand. The study provides a first-hand evidence of how consumer sentiment relates to the perception of economic uncertainty and leads to decline in consumer demand. In that regard, findings of the study have valuable implications for hospitality industry practitioners and relevant policymakers.

The purpose of this paper is to report the investigations on the potential of a new evolutionary algorithm based on probabilistic models – the quantum-inspired evolutionary algorithm (QEA) in solving inverse problems.

An improved QEA.

The proposed algorithm is an efficient and robust global optimizer for solving inverse problems.

To enhance the convergence speed without compromising the diversity performances of the populations, a new definition of global information sharing is introduced and implemented. To guarantee the balance between exploration and exploitation searches, a different migration strategy and formula, as well as a novel formulation for adaptively updating the rotation angle, are developed.