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This study aims to analyze how restaurants' collaboration with mobile food delivery applications (MFDAs) affects product development efficiency and argues that technological capabilities moderate relational ties impact the joint decision-making and development efficiency of restaurant products.

A product development efficiency model was formulated using a resource-based view and real options theory. In all, 472 samples were collected from restaurants collaborating with MFDAs, and partial least squares structural equation modeling was applied to the proposed model.

The findings of this study indicate three factors are critical to the product development efficiency between restaurants and MFDAs; restaurants must develop a strong connection with the latter to ensure meals are consistently served promptly. Developers of MFDAs should use artificial intelligence analysis, such as order records of different genders and ages or various consumption attributes, to collaborate with restaurants.

To the best of the authors’ knowledge, this study is one of the few that considers the role of MFDAs as a product strategy for restaurant operations, and the factors the authors found can enhance restaurants’ product development efficiency. Second, as strategic artificial intelligence adaptation changes, collaborating firms and restaurants use such applications for product development to help consumers identify products.

Big data analytics allows researchers and industry practitioners to extract hidden patterns or discover new information and knowledge from big data. Although artificial intelligence (AI) is one of the emerging big data analytics techniques, hospitality and tourism literature has shown minimal efforts to process and analyze big hospitality data through AI. Thus, this study aims to develop and compare prediction models for review helpfulness using machine learning (ML) algorithms to analyze big restaurant data.

The study analyzed 1,483,858 restaurant reviews collected from Yelp.com. After a thorough literature review, the study identified and added to the prediction model 4 attributes containing 11 key determinants of review helpfulness. Four ML algorithms, namely, multivariate linear regression, random forest, support vector machine regression and extreme gradient boosting (XGBoost), were used to find a better prediction model for customer decision-making.

By comparing the performance metrics, the current study found that XGBoost was the best model to predict review helpfulness among selected popular ML algorithms. Results revealed that attributes regarding a reviewer’s credibility were fundamental factors determining a review’s helpfulness. Review helpfulness even valued credibility over ratings or linguistic contents such as sentiment and subjectivity.

The current study helps restaurant operators to attract customers by predicting review helpfulness through ML-based predictive modeling and presenting potential helpful reviews based on critical attributes including review, reviewer, restaurant and linguistic content. Using AI, online review platforms and restaurant websites can enhance customers’ attitude and purchase decision-making by reducing information overload and search cost and highlighting the most crucial review helpfulness features and user-friendly automated search results.

To the best of the authors’ knowledge, the current study is the first to develop a prediction model of review helpfulness and reveal essential factors for helpful reviews. Furthermore, the study presents a state-of-the-art ML model that surpasses the conventional models’ prediction accuracy. The findings will improve practitioners’ marketing strategies by focusing on factors that influence customers’ decision-making.

This study aims to investigate the decision-making process consumers engage in when choosing to return to a restaurant that has experienced a foodborne illness outbreak.

A scenario-based survey was conducted to collect data from 1,025 respondents on their propensity to return to restaurants that have been cited for serving foods that caused a foodborne illness outbreak. Partial least squares-based structural equation modeling was used to analyze the data.

The findings of this study show perceived vulnerability, perceived severity and attitude are statistically significant, whereas subjective norms and perceived behavioral control are not statistically significant predictors of consumers’ intention to patronize a restaurant that has experienced a foodborne illness outbreak.

This study examined consumers’ intentions to return to a restaurant that has experienced a foodborne illness outbreak. Findings are based on a scenario-based survey, and thus, the results cannot be generalized.

By implementing protection motivation theory (PMT) and the theory of planned behavior (TPB), findings from this study extend the understanding of patrons’ revisit intentions regarding restaurants with foodborne illness outbreak.

University teaching involves delivering resource intensive subjects that have practical components, such as a science laboratory, hospitality practical, computer laboratory, or simulated clinical setting. Teaching practical subjects in the non-traditional, virtual classroom requires careful decisions about the methods of teaching that kind of knowledge. The outbreak of the COVID-19 virus and the subsequent hurried closure of the traditional campus that disrupted in-person teaching, led many higher education lecturers and professors who teach practical subjects to reflect deeply on their practice by thinking how to replicate the teaching of virtual culinary classes when students are not on campus. In an outcome-based learning dispensation, students’ learning outcomes precede consideration of the mode of delivery or the structure of teaching content. This chapter reflects on a case study involving the teaching of subjects in hospitality and culinary arts through gamification, both of which having learning outcomes grounded in practice. The chapter explores the seemingly impossible world of taking practical based subjects and making them work in an online space. It describes and offers a measure by which to justify a pedagogy for teaching the practical in a virtual context. The chapter offers important initial conceptualisations that challenge assumptions of virtual meaningful learning design for practical module delivery.

The purpose of this research is to gain a better understanding of how a crowd-shipping platform can achieve a critical mass of senders and carrier crowd members to yield network effects that are necessary for the platform to grow and thrive. Specifically, this research studies the participation decisions of both senders and carriers over time and the impacts of the resulting feedback loop on platform growth and performance.

An agent-based model is developed and used to study dynamic behavior and network effects within a simulated crowd-shipping platform. The model allows both carriers and senders to be represented as autonomous, heterogeneous and adaptive agents, whose decisions to participate in the platform impact the participation of other agents over time. Survey data inform the logic governing agent decisions and behaviors.

The feedback loop created by individual sender and carrier agents' participation decisions generates complex and dynamic network effects that are observable at the platform level. Experimental results demonstrate the importance of having sufficient crowd carriers available when the platform is initially launched, as well as ensuring that sender and carrier participation remains balanced as the platform grows over time.

The model successfully demonstrates the power of agent-based modeling (ABM) in analyzing network effects in crowd-shipping systems. However, the model has not yet been fully validated with data from a real-world crowd-shipping platform. Furthermore, the model's geographic scope is limited to a single census tract. Platform behavior will likely differ across geographic regions, with varying demographics and sender/carrier density.

The modeling approach can be used to provide the manager of a volunteer-based crowd-shipping program for food rescue with insights on how to achieve a critical mass of participants, with an appropriate balance between the number of restaurant food donation delivery requests and the number of crowd-shippers available and willing to make those deliveries.

This research can help a crowd-shipping platform for urban food rescue to grow and become self-sustainable, thereby serving more food-insecure people.

The model represents both senders and the carrier crowd as autonomous, heterogeneous and adaptive agents, such that network effects resulting from their interactions can emerge and be observed over time. The model was designed to study a volunteer crowd-shipping platform for food rescue, with participant motivations driven by personal values and social factors, rather than monetary incentives.

The purpose of this study is to segment consumers according to their decision-making styles in the context of social commerce. Additionally, the differences among consumer segments in consumer innovativeness, perceived risk, satisfaction and demographic characteristics are evaluated.

A total of 384 respondents who shopped for restaurant deals through social commerce participated in this study through an online survey. Two-step cluster analyses were used to segment social commerce consumers into groups, using their decision-making styles.

The results showed three types of social commerce consumers of restaurant deals: innovative brand-preferring consumers; realistic consumers; and passive consumers. Innovative brand-preferring consumers chose specific brands and showed the most innovativeness, while realistic consumers and passive consumers were price-conscious and far more cautious in purchasing restaurant deals using social commerce. Passive consumers were, in addition, confused by overchoice. All three consumer groups perceived higher risks to privacy in purchases using social commerce. Passive consumers were especially aware of the risk, while the innovative brand-preferring consumers and the realistic consumers were less concerned about risk. Consumers were especially likely to perceive economic risk, performance risk, social risk, psychological risk, privacy risk and time risk. Innovative brand-preferring consumers were more likely to be innovative and showed a higher level of satisfaction, while passive consumers showed the lowest satisfaction and the least innovativeness.

This study provides additional insights on consumer decision-making styles in the context of social commerce in Korea.

Consumer decision-making styles can help restaurant managers to develop deals tailored to specific types of consumers, as well as create customized products and services.

This study is one of the very few attempts to investigate consumer decision-making styles in social commerce for restaurant deals, so it contributes to the literature on social commerce in the hospitality industry. This study shows that consumer decision-making styles are important in understanding the behavior of social commerce consumers.

Many restaurants offer high-quality service to their customers, hoping to provide memorable experiences that influence their loyalty and electronic word of mouth (eWOM). However, consumers' memorable experiences do not always imply positive eWOM. This study aims to (1) verify the direct impacts of the perceived quality by consumers of casual dining restaurants on positive emotions, negative emotions and memorable experiences; (2) investigate the impacts of memorable experiences on the propensity to loyalty and eWOM; (3) test the moderating effect of consumer behavioural engagement on social networking sites (CBE-SNS) on the relationship between memorable experiences and eWOM.

This survey included 475 university students in Brazil. Participants answered an electronic form about their experiences in casual dining restaurants. Structural equation modelling tested the hypothetical model based on the stimulus-organism-response (S-O-R) theory (Mehrabian and Russell, 1974).

The quality perceived by restaurant consumers (stimulus) positively impacts their memorable experiences and positive emotions and negatively affects their negative emotions (organism). Memorable experiences positively impact the propensity to loyalty (response). The CBE-SNS moderates the intensity of the relationship between memorable experiences (organism) and eWOM (response).

This study is the first that demonstrates the relationships between perceived quality, positive and negative emotions, memorable experiences, the propensity to loyalty and CBE-SNS and e-WOM in restaurants. Casual dining restaurants must offer their customers services with high perceived quality, positively impacting their emotions and their memorable experiences. Finally, restaurants must create strategies and actions to increase the CBE-SNS to encourage them to share their memorable experiences through eWOM.

This paper explores the potential impact of mobile marketing tools on consumer buying behaviour within the context of dining. The aim is to examine the influence of mobile marketing tools through their different functions on the stages of the consumer buying process. The study addresses a lack of relevant research with evidence from both customer and supplier perspectives.

The mobile tools that are found useful for dining were considered in this study. Qualitative interviews with marketers and consumer opinion leaders were conducted, given the limited extant research.

The results reveal that mobile marketing tools influence consumers' decision-making differently and their effect varies according to the customer type. Additionally, it shows that loyalty has a direct influence on mobile marketing effectiveness, as the decision-making process of loyal customers is more affected by mobile marketing tools than the non-loyal customers.

The limitations are mainly based on the qualitative nature of this study and are relevant to the research context. Further research could examine these findings in different service and geographical contexts.

Marketing activity through the smartphone should focus on loyal customers and opinion leaders with the use of appropriate mobile tools.

The study provides empirical evidence on the variable influence of mobile marketing tools on consumer decision-making and develops a conceptual framework. It is also found that loyalty is an important factor that positively affects smartphone tools adoption.

This study aims to investigate how upper echelon theory accounts for franchising by selecting the top management team to proxy for the upper echelon and using age, tenure, education, equity ownership and stock options as its main attributes.

The sample was drawn from the Execucomp and Compustat databases and from other publicly accessible resources (e.g. LinkedIn and Business Week, in addition to Annual 10-K reports). A total of 29 restaurant companies were used for data collection, which covered the period of 2000-2013. A panel feasible generalized least squares (FGLS) regression was used to analyze the data.

The study found a significant moderating effect of the degree of internationalization on the relation between the attributes of the upper echelon (e.g. tenure, education and share ownership) and franchising decisions.

The results verified that top managers in the restaurant industry with more tenure and share ownership become more risk averse when they operate under riskier conditions, whereas highly educated restaurant top management teams tend to take more risks in strategic decision-making.

This study expanded internationalization research to upper echelon theory and into the arena of franchising.

This paper aims to situate restaurant experiences and in particular the wines available on wine lists, within the wider context of wine tourism. This is done by examining the wine lists of restaurants in two New Zealand destinations, focusing in particular on the showcasing of “local wines” and the factors behind these offerings, and outlines the potential implications for hospitality managers and a wider academic audience.

The population of restaurants in each destination was identified using online directories, from which a sample of wine lists, comprising 84 in Christchurch and 43 in Queenstown, was systematically analysed to identify number of wines, regional origin, price and other information. Following this, key informant interviews in restaurants in each destination explored decision-making factors in stocking local wines, including consumer base, existing networks and reputation and additional challenges and opportunities.

Restaurants in each destination offered more New Zealand than foreign wines on their lists, though significant regional differences are apparent. Queenstown restaurants offered slightly fewer imported wines and significantly more local (Central Otago) wines than Christchurch restaurants. The global awareness of Central Otago pinot noir is a factor in this wine list representation, but there are also other influences, including the greater concentration of overseas visitors (pre-pandemic) and more significant visibility and greater opportunities for wine tourism experiences within the destination.

This paper represents an important addition to academic research on wine marketing in the on-premise sector of emerging wine regions. This paper also highlights the potential significance of restaurant meals – including wine choices – in overall wine destination experiences and demonstrates differences in approach between restaurants in wine regions of similar size but with different reputations, international visitation and wine tourism infrastructure.