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This study aims to explore families that travel with children, as focuses on vulnerabilities, resource constraints and service exclusion through the lens of transformative service research (TSR). This paper investigates: how the experienced vulnerability of these families is shaped by structural, interpersonal and intrapersonal constraints, and how the constraints influence the family tourist-resource interaction in the air travel service encounter.

In total, 2,855 reviews of the family tourists with children were analyzed with text mining, t-test and multidimensional scaling using the interpretive language R to answer the research questions with analyses on unstructured (e.g. text) and structured (e.g. consumer rating) data.

The findings of the empirical investigation answered how experienced vulnerability is shaped by structural, interpersonal and intrapersonal resource constraints and the types of family tourist-resource interaction in the travel service encounter to understand the resource constraints. The findings of this paper help examine family tourism experiences from a value formation perspective to unfold how stakeholders interact to form value while increasing and decreasing their well-being by the value of co-creation and co-destruction.

This research helps advance the TSR’s service inclusion framework by enabling opportunities, offering choice, relieving suffering and fostering happiness with empirical findings in travel service encounters. These findings are particularly insightful to family tourists with children struggling with unfair access and treatment in aeromobility service encounters, which may help enhance the well-being of individuals and communities.

The aim of this study is to identify the travel constraints of people with special needs (PwSN) and the strategies used to overcome them. The article also intends to analyze the differences in travel constraints and negotiation strategies according to the type of disability and/or special need.

A qualitative study was conducted in which a sample of PwSN (people with disabilities – mobility, sensory and cognitive – seniors and people with food allergies) were interviewed in depth.

PwSN face a wide range of constraints when engaging in tourism activities. These constraints are very diverse and influence people with different intensity and have to be overcome using different negotiation strategies. However, differences in the constraints were observed according to the type of special needs.

One of the most significant practical implications of this research is the need for raising awareness regarding human diversity among tourism stakeholders. It is critical to link sectoral policies that are reflected in the social and business reality, creating public–private partnerships to raise the sharing of knowledge, equipment and services. By addressing the constraints that prevent PwSN from traveling, the tourism sector can create more opportunities for them to participate in social activities, improving their quality of life and contributing to a more sustainable and inclusive industry.

Traveling can present significant challenges for people with special needs, which affect the quality of their tourism experience. Despite growing academic attention to this issue in recent years, research in this field has primarily focused on specific aspects of disability. This study seeks not only to identify the barriers to travel faced by people with different special needs but also to explore the negotiation strategies used to overcome these barriers and the differences according to the type of special need.

This study aims to discuss the current context, scope and impacts of a metaverse in the hospitality and tourism industry. Although existing literature discussed the potentials of the metaverse in this context, the ways the metaverse work is still being defined and accessing a complete metaverse is still not yet possible. This existing knowledge will become increasingly sophisticated and complex as developments in the metaverse continue, eventually contributing to a knowledge gap in knowledge, and its implications in shaping how the future digital environment should take form.

This work is based on a critical reflection of the existing developments and applications of the metaverse. Drawing from authors’ experiences, and synthesis of existing works and narratives, this work discusses the applications of the metaverse, critical factors for considerations and applications of the metaverse and proposes the way forward for potential users.

The metaverse provides new opportunities for the hospitality and tourism industry but the impact of the technology may not be felt immediately. The real challenge lies in developing a responsible digital environment for users and suppliers. Although the aspects to be considered are many, a lack of preparedness is a great obstacle.

This paper provides a comprehensive evaluation of how the metaverse can be applied in the hospitality and tourism sector aiming to provide diverse stakeholders insights into the associated opportunities and pitfalls.

To the best of the authors’ knowledge, this paper is among the first attempts to critically reflect on the possibilities of the metaverse, and contributes to the discussion on the attributes of the metaverse for tourism and hospitality (e.g. SSIs, decentralization) and includes discussion on special needs users, sustainable usage and climate change, and presents several agendas for further actions.

This study uncovers how government interventions, in terms of stringency and support, shape coronavirus disease 2019's (COVID-19) detrimental impact on organizations' performance. Specifically, this paper studies whether stringency and support play complementary or substitutive roles in lowering COVID-19's impact on organizations' performance.

The authors gathered primary data from USA manufacturing companies and combined this with secondary data from the Oxford COVID-19 Government Response Tracker (OxCGRT) to test the proposed model with structural equation modeling (SEM).

The results show that the stringency approach increases the detrimental impact on both operational and financial performance, while economic support (to households) and fiscal spending (to organizations) work differently on lowering the impacts of COVID-19. Further, these combinative effects only influence the firm's operational performance, albeit in opposite directions.

This study advances the knowledge of government interventions by examining stringency and support's direct and interaction effects on firm performance as a result of the COVID-19 pandemic. The findings contribute to the literature by uncovering the unique roles of both supportive policies, thus differentiating economic support (to individuals/households) from fiscal spending (to organizations) and providing important academic, managerial and policy insights into how government should best initiate and blend stringency and support policies during the COVID-19 pandemic.

This study investigated the travel intentions (TIs) of Chinese travelers and their utilization of virtual tourism technology during the Zero COVID-19 policy period by using a stimulus-organism-response (SOR) model. The study specifically examines the interplay between knowledge of policy, perception of risk, TI, usage of virtual tourism technology (UVTT) and the mediating role of personal values.

The data were collected from 333 Chinese travelers through an online questionnaire, and structural equation modeling (SEM) was employed to test the proposed hypotheses.

The study suggests that knowledge of policy and risk perception increase changes in personal values (PVs), which, in turn, affect Chinese travelers' TIs and the UVTT, with PVs playing a mediating role. Risk perception has a positive effect on the UVTT.

This study highlights the positive impact of tourism policy knowledge and risk awareness on individual values as a stimulus. Stakeholders need to implement industry-specific policies that are in line with scientific developments. Tourism managers should prioritize understanding the psychological reactions of tourists in crises and provide support to mitigate negative emotions. Anticipating changes in PVs is crucial, as instability affects tourists' behavior. The findings of the study also provide valuable insights for technology designers and underscore the substitutability of virtual technologies in improving the tourism experience.

This study is the first to examine the mediating role of PVs in the relationship between knowledge of policy, tourism risk perception (TRP), TI and the UVTT based on the SOR model. The insights gained from this analysis can assist policymakers and tourism managers in understanding the psychological changes of tourists, thereby facilitating the development of appropriate tourism planning.

This study aims to predict overnight stays in Italy at tourist accommodation facilities through a nonlinear, single factor, stochastic model called CIR#. The contribution of this study is twofold: in terms of forecast accuracy and in terms of parsimony (both from the perspective of the data and the complexity of the modeling), especially when a regular pattern in the time series is disrupted. This study shows that the CIR# not only performs better than the considered baseline models but also has a much lower error than other additional models or approaches reported in the literature.

Typically, tourism demand tends to follow regular trends, such as low and high seasons on a quarterly/monthly level and weekends and holidays on a daily level. The data set consists of nights spent in Italy at tourist accommodation establishments as collected on a monthly basis by Eurostat before and during the COVID-19 pandemic breaking regular patterns.

Traditional tourism demand forecasting models may face challenges when massive amounts of search intensity indices are adopted as tourism demand indicators. In addition, given the importance of accurate forecasts, many studies have proposed novel hybrid models or used various combinations of methods. Thus, although there are clear benefits in adopting more complex approaches, the risk is that of dealing with unwieldy models. To demonstrate how this approach can be fruitfully extended to tourism, the accuracy of the CIR# is tested by using standard metrics such as root mean squared errors, mean absolute errors, mean absolute percentage error or average relative mean squared error.

The CIR# model is notably simpler than other models found in literature and does not rely on black box techniques such as those used in neural network (NN) or data science-based models. The carried analysis suggests that the CIR# model outperforms other reference predictions in terms of statistical significance of the error.

The proposed model stands out for being a viable option to the Holt–Winters (HW) model, particularly when dealing with irregular data.

The proposed model has demonstrated superiority even when compared to other models in the literature, and it can be especially useful for tourism stakeholders when making decisions in the presence of disruptions in data patterns.

The novelty lies in the fact that the proposed model is a valid alternative to the HW, especially when the data are not regular. In addition, compared to many existing models in the literature, the CIR# model is notably simpler and more transparent, avoiding the “black box” nature of NN and data science-based models.

一般来说, 旅游需求往往遵循规律的趋势, 例如季度/月的淡季和旺季, 以及日常的周末和假期。该数据集包括欧盟统计局在打破常规模式的2019冠状病毒病大流行之前和期间每月收集的在意大利旅游住宿设施度过的夜晚。

本研究旨在通过一个名为cir#的非线性单因素随机模型来预测意大利游客住宿设施的过夜住宿情况。这项研究的贡献是双重的:在预测准确性方面和在简洁方面(从数据和建模复杂性的角度来看), 特别是当时间序列中的规则模式被打乱时。我们表明, cir#不仅比考虑的基线模型表现更好, 而且比文献中报告的其他模型或方法具有更低的误差。

当大量搜索强度指标被作为旅游需求指标时, 传统的旅游需求预测模型将面临挑战。此外, 鉴于准确预测的重要性, 许多研究提出了新的混合模型或使用各种方法的组合。因此, 尽管采用更复杂的方法有明显的好处, 但风险在于处理难使用的模型。为了证明这种方法能有效地扩展到旅游业, 使用RMSE、MAE、MAPE或AvgReIMSE等标准指标来测试cir#的准确性。

cir#模型明显比文献中发现的其他模型简单, 并且不依赖于黑盒技术, 例如在神经网络或基于数据科学的模型中使用的技术。所进行的分析表明, cir#模型在误差的统计显著性方面优于其他参考预测。

这个模型作为Holt-Winters模型的一个拟议模型, 特别是在处理不规则数据时。

即使与文献中的其他模型相比, 所提出的模型也显示出优越性, 并且在数据模式中断时对旅游利益相关者做出决策特别有用。

创新之处在于所提出的模型是Holt-Winters模型的有效替代方案, 特别是当数据不规律时。此外, 与文献中的许多现有模型相比, cir#模型明显更简单、更透明, 避免了神经网络和基于数据科学的模型的“黑箱”性质。

Normalmente, la demanda turística tiende a seguir tendencias regulares, como temporadas altas y bajas a nivel trimestral/mensual y fines de semana y festivos a nivel diario. El conjunto de datos consiste en las pernoctaciones en Italia en establecimientos de alojamiento turístico recogidas mensualmente por Eurostat antes y durante la pandemia de COVID-19, rompiendo los patrones regulares.

El presente estudio pretende predecir las pernoctaciones en Italia en establecimientos de alojamiento turístico mediante un modelo estocástico no lineal de un solo factor denominado CIR#. La contribución de este estudio es doble: en términos de precisión de la predicción y en términos de parsimonia (tanto desde la perspectiva de los datos como de la complejidad de la modelización), especialmente cuando un patrón regular en la serie temporal se ve interrumpido. Demostramos que el CIR# no sólo aplica mejor que los modelos de referencia considerados, sino que también tiene un error mucho menor que otros modelos o enfoques adicionales de los que se informa en la literatura.

Los modelos tradicionales de previsión de la demanda turística pueden enfrentarse a desafíos cuando se adoptan cantidades masivas de índices de intensidad de búsqueda como indicadores de la demanda turística. Además, dada la importancia de unas previsiones precisas, muchos estudios han propuesto modelos híbridos novedosos o han utilizado diversas combinaciones de métodos. Así pues, aunque la adopción de enfoques más complejos presenta ventajas evidentes, el riesgo es el de enfrentarse a modelos poco manejables. Para demostrar cómo este enfoque puede extenderse de forma fructífera al turismo, se comprueba la precisión del CIR# utilizando métricas estándar como RMSE, MAE, MAPE o AvgReIMSE.

El modelo CIR# es notablemente más sencillo que otros modelos encontrados en la literatura y no se basa en técnicas de caja negra como las utilizadas en los modelos basados en redes neuronales o en la ciencia de datos. El análisis realizado sugiere que el modelo CIR# supera a otras predicciones de referencia en términos de significación estadística del error.

El modelo propuesto destaca por ser una opción viable al modelo Holt-Winters, sobre todo cuando se trata de datos irregulares.

El modelo propuesto ha demostrado su superioridad incluso cuando se compara con otros modelos de la bibliografía, y puede ser especialmente útil para los agentes del sector turístico a la hora de tomar decisiones cuando se producen alteraciones en los patrones de datos.

La novedad radica en que el modelo propuesto es una alternativa válida al Holt-Winters especialmente cuando los datos no son regulares. Además, en comparación con muchos modelos existentes en la literatura, el modelo CIR# es notablemente más sencillo y transparente, evitando la naturaleza de “caja negra” de los modelos basados en redes neuronales y en ciencia de datos.

To explain how technology will replace a great deal of human labor in knowledge markets using a theory of reasoned action applied to demand and theories of procedural rationality, cost structure and system dynamics applied to supply.

Two illustrative scenarios are presented. The first is a third-party Best Treatments site, and its effect on the expert advice pharmaceutical representatives provide doctors. The second scenario is an online higher education business course module with embedded AI.

Both scenarios demonstrate the advantages of online expertise and teaching platforms over the in-person alternative in variable and marginal cost, ease and convenience of use, quality conformance, scalability, knowledge reach and depth and most importantly, speed of evolutionary adaptability. Despite such overwhelming advantages, a number of reasons why the substitution might be slowed are presented, and some strategies firms might adopt are discussed. Opportunities for service scholars to confirm, challenge and extend the conclusions are presented throughout the paper.

Increasing cost structure and adaptability advantages of online technology and AI over in-person delivery of expertise and training services are demonstrated. It is also demonstrated that the innovation-imitation cycle is accelerating because of exogenous innovation in knowledge access and online influence networks and an endogenous effect where imitators accelerate their innovation that drives innovators to accelerate their innovation, which drives imitators to further accelerate their imitation.

It has been established that values, beliefs and norms are good predictors of pro-environmental behaviour; however, it is less explored how these constructs can be influenced. The purpose of this paper is to assess whether the COVID-19 pandemic, as a shock event, was a “game changer” or it had limited impacts on deep-rooted travel patterns.

To understand whether the pandemic has influenced people’s value orientations with regards to sustainable travel, the authors have made use of the value–belief–norm (VBN)theory of environmentalism and conducted a large-scale survey in Germany, Italy, France, The Netherlands and UK (N = 1545).

The findings showed that the pandemic caused only temporary change in deep-rooted travel patterns as (self-reported) pre-pandemic behaviour of respondents align with their value orientations and proved to be a good predictor of projected travel behaviour. Based on projected travel behaviour and value orientations, four market segments were identified – Frontrunners, Laggards, Comfortable Crowd and Entitled Stewards. While all segments showed willingness to adapt, the authors found variations in the extent of adaption and in the phases of the customer journey where travellers are likely to make changes.

According to our knowledge, using the VBN theory of environmentalism to understand the impact of shock events on deep-rooted travel patterns represents a new perspective. Moreover, to the best of authors’ knowledge, this study is one of the first, written in the midst of the COVID-19 pandemic, which looks at the impact of the health crisis on deep-rooted travel patterns, instead of the commonly studied risk perception.

The purpose of this paper is to investigate how fairness evaluations are constructed in a B2B context.

This paper conducts a field study of Air Greenland and its internal and external customers based on strong structuration theory (Stones, 2005). The authors employ context and conduct analysis to analyze how fairness evaluations emerge across four levels of structuration.

The paper finds that fairness evaluations emerge as a result of the interaction between external institutional pressures, agents' internal structures, and situated reflection and outcomes. The construction of fairness evaluations was embedded in contradictory institutional structures, where groups of actors constructed different evaluations of fair profits, procedures and prices. Actors furthermore worked on changing position-practice relations which shifted relations, external structures and affected outcomes and fairness evaluations.

This paper offers a conceptualization of embedded agency as emerging across the four levels of structuration. This contributes to debates in strong structuration theory through conceptualizing and analyzing how actors may be both be constrained and oriented by structures while reflexively adapting structures across the four levels of structuration. The paper extends extant pricing fairness research by illustrating how actors' construction of fairness flexibly develop fairness evaluations while responding to legitimacy and societal demands, including the needs of particular customer groups.

The ongoing COVID-19 pandemic highlights the importance of destination marketing and media profiling to re-engage international tourists. However, potential crisis-induced nation brand (NB) deficits must be addressed to re-ignite tourism demand. The study examines the possible intervening effect of the contemporary NB in the international destination marketing and media-travel motives nexus.

A deductive quantitative study was undertaken with an online Amazon Mechanical Turk sample of n = 454 respondents. Hypotheses were tested using PROCESS Macro, Model 4.

The results show that the NB [people and negative events] had a practically significant partial mediating effect in the destination marketing – nature-cultural oriented travel motivation nexus.

New insights are provided via a practical model which facilitates the measurement of potential nuances in the influence of destination marketing and media profiling on leisure tourists' travel motives amid crises. The intervening effect implies that a better understanding of the NB as an indirect antecedent to travel motivation may result in more effective crisis communications and tourism recovery-oriented marketing.

The study is amongst the first to extend marketing and behavioural theory to explore the interplay between the marketing and media profile, a nation's brand and tourists' travel behaviour amid a crisis. The study addresses a discernible dearth of knowledge related to the influence of the NB on tourist behaviour from an emerging market perspective.