Nathan Line's research while affiliated with Florida State University and other places

Purpose This study aims to identify the causes of the academia-industry divide in hospitality marketing research in the form of the “Research Devaluation Map” and offers ideas for discussion points and suggestions for change. Design/methodology/approach The conceptualization of the Research Devaluation Map was developed at an invitational thought-leadership conference. The authors were asked to produce a forward looking, critical reflection of hospitality marketing scholarship. The authors generated a preliminary idea and developed a methodology for its implementation. They then proposed a framework that explicated the divide between hospitality marketing research and industry practice and a list of discussion points regarding possible solutions. Findings The issues currently challenging the hospitality research field are found to include the choice of research topics (the “what”), the methods used in research (the “how”) and the systemic factors that shape the academic culture (the “systemic”). These three factors lead to a mutual devaluation of the academic–industry relationship in hospitality marketing, causing a schism between research and industry practice. Research limitations/implications The Research Devaluation Map serves as a springboard for future research studies, providing a framework for naming and operationalizing the antecedents and results of the divide between hospitality marketing research and practice. Originality/value This paper takes a holistic look at the gaps in current hospitality marketing research and puts forth a framework to explain the roots of these issues. While certain of these issues are known to both researchers and practitioners, the originality of this paper lies in the creation of the Research Devaluation Map that identifies the causes and results of the disconnect between research and practice.

Generative artificial intelligence (GAI) offers important opportunities for the hospitality and tourism (HT) industry in the context of operations, design, marketing, destination management, human resources, revenue management, accounting and finance, strategic management, and beyond. However, the implementation of GAI in HT contexts comes with ethical, legal, social, and economic considerations that require careful reflection by HT firms. The purpose of this study is to offer a critical examination of the effects of GAI applications across a broad spectrum of stakeholders in the HT industry, in an effort to integrate practical and academic insights and foresights and drive academic research forward. Through the contributions of a purposeful selection of scholars, educators, and industry-practitioners, along the tenets of the stakeholder theory of the firm, this study highlights the potential challenges and opportunities of GAI and considers how academics can navigate the (research) complexities of this rapidly evolving technological phenomenon.

The adverse impact of the recent pandemic on the lodging industry has largely been based on anecdotal evidence. The extent to which different parts of this broad industry were individually affected by the COVID-19 pandemic also remains unclear. The purpose of this study is to investigate the effects of the COVID-19 pandemic in the various sectors of the lodging industry to identify patterns that may not be consistent with the idea that the entire hospitality industry was negatively affected by the pandemic. The results show that while the COVID-19 pandemic did have a generally negative effect on lodging demand, hotel room and peer-to-peer accommodation property bookings were not affected equally. Importantly, it appears that these variations were attributable, at least in part, to state-level variations in policy that made travel and hospitality services relatively more (or less) difficult for consumers to obtain. Theoretical and managerial implications are extensively discussed.

During the COVID-19 pandemic, many restaurants faced a shift from a dine-in based service model to a takeout-based model. As a result of the qualitative differences between dine-in and take-out experiences, there was a corresponding change in customers’ electronic word of mouth (EWOM) behavior. While pre-pandemic EWOM behavior relied on dine-in specific factors such as décor, lighting, and employee interactions, take-out dining relies less on these types of atmospheric elements to drive post-consumption evaluations. Accordingly, the purpose of this research was to explore the drivers of take-out dining EWOM by examining the effects of altruism, self-enhancement, and restaurant affiliation. Using the psychological framework of Underdog Theory, the results showed that both self-enhancement and altruistic motives result in positive EWOM, but that this relationship was moderated in important ways based on whether the restaurant was independently owned or part of a chain.

In sharing accommodation business such as Airbnb, while the provision of personalized amenities and services may seem like good business, hosts should be aware of the potential unintended consequences when they are not able to deliver what they promise. The present research examines how expectation gaps created by guest reviews interact with different types of preferential services to subsequently affect consumer behavior in the peer-to-peer accommodation economy. Grounded in attribution theory, this study offers new insights on customer responses to unfulfilled preferential treatment. The results suggest that in the condition of utilitarian services (e.g., airport transportation), participants in the low dispersion condition exhibited more negative attitudes, a lower level of repurchase intention, and a decreased willingness to write an online review. Conversely, in the condition of hedonic services (e.g., perform a talent show), expectation discrepancy did not result in different consumer evaluations across the dispersion conditions.

This study proposes an integrative model that investigates the role of coping strategies in the relationship between severe service failures and online vindictive word of mouth (OVWOM) behavior. While the mechanism of online negative word of mouth (ONWOM) have been widely studied, the underlying processes by which OVWOM develops remain unclear, specifically when it comes to the use of OVWOM as a coping mechanism. Achieving a better understanding of the mechanism by which OVWOM develops is important given its potential to significantly harm a firm’s image, loyalty and market share. The results of a survey of restaurant customers confirm the role of the psychological and situational factors that result in OVWOM behavior as a strategy to cope with severe service failures. Accordingly, this study provides an initial response to calls for research on the role of situational and personal factors that explain retaliation behavior in response to severe service failures.

Extant research provides ample evidence that Airbnb has an adverse impact on hotel revenues and that a majority of Airbnb hosts offer multiple listings on the platform. Arguably, multi-unit host listings are the primary driving force for the associated decreases in hotel revenues. To test this proposition, this study examines the extent to which single- and multi-unit host Airbnb host listings have differing effects on hotel revenues. The results show that the adverse impact of Airbnb supply on hotel revenues was mainly driven by decreases in hotel prices rather than decreases in hotel demand. Contrary to expectations, single-unit host listings exerted greater downward pricing pressure on hotels than multi-unit host listings. These effects were consistently observed across hotel segments, with economy-scale hotels experiencing substitution effects in addition to pricing pressure. The findings have significant implications for researchers and stakeholders impacted by the sharing economy in the lodging industry.

This study examines and compares the extent to which Airbnb and hotel supply affect key hotel performance measures in the United States. The results show that although both Airbnb and hotel supply adversely affect hotel revenues (i.e., RevPAR), the magnitude of the impact of hotel supply on RevPAR is much larger than that of Airbnb. Airbnb adversely affects hotel room prices (i.e., ADR), however; it does not affect occupancy rates (i.e., OCC). Yet, increasing hotel supply negatively affects OCC but not ADR. The results from the state-level analyses further showed that the negative effects of Airbnb and hotel supply on RevPAR, ADR and OCC persist only in states with high hotel supply. Analyzing the joint effects of Airbnb and hotel supply on hotel performance in a manner that is both geographically-comprehensive and spatially-meaningful, this study provides a more complete and nuanced understanding on the economic dynamics of the accommodation industry.