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This study aims to explore the heterogeneity of the tourist market for people with a physical disability (PwPD) based on travel barriers, to serve them better, from a tourism marketing perspective.

A market segmentation analysis was conducted on a sample of 480 PwPD in Sichuan Province, China, based on their perceived travel barriers. Data were obtained through three on-site and four online surveys. A four-step factor-item mixed segmentation, including factor analysis, cluster analysis, discriminant analysis and chi-square tests, was applied to examine the differences among PwPD tourist market segments in terms of various demographic characteristics, disability conditions (e.g. duration of disabilities and causes of impairment) and travel features (e.g. travel frequency and tourist destinations).

This study revealed that the PwPD tourist market is heterogeneous due to individual perceived travel barriers. Three market segments were identified, namely, the Explorer Moderates group, the Explorer Minimals group and the Explorer Intensives group. Additionally, the three market segments were found to have significant differences in terms of travel barriers, demographic characteristics, travel features and disability conditions.

This research provides suggestions for authorities and private entities to optimize the layout of accessible facilities in public areas for the benefit of all. It also offers crucial implications for tourism marketers to determine the key facets of marketing, for travel organizers to evolve the organization of travel groups for PwPD, and for practitioners to provide personalized tourism services.

To the best of the authors’ knowledge, this study is the first to apply perceived travel barriers as a market segmentation criterion in understanding PwPD as a heterogeneous travel market. The findings of this study initially expand the scope of application of the travel barrier model and deepen understanding of the Chinese PwPD tourist market from a marketing perspective. The study results elucidated the heterogeneity and characteristics of this market through a four-step factor-item mixed segmentation approach, offering new insights into the behaviors and experiences of travelers with disabilities.

本研究旨在探索肢体残障人士旅游市场的异质性, 以便从旅游营销的角度更好地为他们服务。

基于对中国四川480名肢残人士出游障碍感知的问卷调查, 探索了肢残人士的旅游市场细分。数据是通过七次现场和在线调查获得; 采用四步因子-项目混合细分法, 根据残障状况、人口统计特征和旅游特征, 识别出肢残群体旅游细分市场之间的差异。

研究发现, 基于个体感知的出游障碍, 肢残群体旅游市场是异质的, 研究确定了三个细分市场, 即低度、中度和高度受限群体。三个细分市场在出行障碍、人口特征、出游特征和残障状况方面存在显著差异。

这项研究有助于政府管理部门优化公共区域无障碍设施布局; 旅游营销者确定营销的重点, 并为旅游组织者设计肢残旅游团体成员构成, 以及从业者提供个性化旅游服务提供重要的启示。

论文首次将感知出游障碍作为市场细分标准, 用以理解肢残群体作为异质游客市场。本研究的发现拓展了出游障碍模型的应用范围, 并从市场营销的角度加深了对中国肢残游客市场的理解。研究结果通过四步因子-项目混合细分方法阐明了该市场的异质性和特点, 为肢残游客的行为和体验研究提供了新见解。

Este estudio explora la heterogeneidad del mercado turístico de las personas con discapacidad física (PcDF) en función de las barreras percibidas para viajar, con el fin de prestarles un mejor servicio desde una perspectiva de marketing turístico.

Se realizó un análisis de segmentación de mercado en una muestra de 480 PcDF en Sichuan, China, en función de las barreras que percibían para viajar. Los datos se obtuvieron a través de tres encuestas in situ y cuatro encuestas en línea. Se aplicó una segmentación mixta factor-ítem de cuatro pasos que incluye análisis factorial, análisis de conglomerados, análisis discriminante y pruebas de chi-cuadrado para examinar las diferencias entre los segmentos del mercado turístico de PcDF, en términos de diversas características demográficas, condiciones de discapacidad (por ejemplo, duración de la discapacidad, causas de la discapacidad) y características de los viajes (por ejemplo, frecuencia de viaje, destinos turísticos).

Este estudio reveló que el mercado turístico de las PcDF es heterogéneo debido a las barreras de viaje percibidas por cada individuo. Se identificaron tres segmentos de mercado, a saber, el grupo de Exploradores Moderados, el grupo de Exploradores Mínimos y el grupo de Exploradores Intensivos. Los tres segmentos de mercado presentaban diferencias significativas en cuanto a las barreras para viajar, las características demográficas, las características del viaje y las condiciones de discapacidad.

Este estudio es el primero en aplicar las barreras percibidas para viajar como criterio de segmentación de mercado para comprender a las PcDF como un mercado turístico heterogéneo. Los hallazgos de este estudio amplían inicialmente el ámbito de aplicación del modelo de barreras para viajar y profundizan en la comprensión del mercado turístico chino de PcDF desde una perspectiva de marketing. Los resultados de nuestro estudio explicaron la heterogeneidad y las características de este mercado a través de un enfoque de segmentación mixta factor-ítem de cuatro pasos, contribuyendo a la literatura sobre el comportamiento y las experiencias de los viajeros con discapacidad.

Esta investigación proporciona sugerencias para que las autoridades y las entidades privadas puedan optimizar la disposición de instalaciones accesibles en zonas públicas en beneficio de todos. También ofrece implicaciones importantes a los comercializadores turísticos para que determinen aspectos clave del marketing, a los organizadores de viajes para que evolucionen en la organización de grupos de viaje para PcDF y a los profesionales para que presten servicios turísticos personalizados.

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.

This study, rooted in affordance-actualization theory and communication theory, aims to critically examine how ChatGPT influences users’ transition from new adopters to loyal advocates within the context of travel decision-making. It incorporates constructs including communication quality, personalization, anthropomorphism, cognitive and emotional trust (ET), loyalty and intention to adopt into a comprehensive model.

This study used quantitative methods to analyze data from 477 respondents, collected online through a self-administered questionnaire by Embrain, a leading market research company in South Korea. Lavaan package within R studio was used for evaluating the measurement model through confirmatory factor analysis and using structural equation modeling to examine the proposed hypotheses.

The findings reveal a pivotal need for enhancing ChatGPT’s communication quality, particularly in terms of accuracy, currency and understandability. Personalization emerges as a key driver for cognitive trust, while anthropomorphism significantly impacts ET. Interestingly, the study unveils that in the context of travel recommendations, users’ trust in ChatGPT predominantly operates at the cognitive level, significantly impacting loyalty and subsequent adoption intentions.

The findings of this research provide valuable insights for improving Generative AI (GenAI) technology and management practices in travel recommendations.

As one of the few empirical research papers in the burgeoning field of GenAI, this study proposes a highly explanatory model for the process from affordance to actualization in the context of using ChatGPT for travel recommendations.

This study aims to progress scholarly understanding of the staycation phenomenon by examining customer segments and documenting local customers’ attribute preferences.

A stated choice experiment is used to examine customer preferences for staycation package attributes. Latent class discrete choice modeling is deployed to classify customers into market segments based on their preferences. The profile of each segment is enhanced by documenting customer characteristics and consumption styles.

Six prominent market segments are identified using a combination of sociodemographics, consumption styles and staycation attribute preferences. The findings draw on consumer experiences during the COVID-19 pandemic to generate theoretical insights into preferred staycation packages. Empirically, the estimation results from the research framework and choice experimental method demonstrate that staycation market segments exhibit distinct preference structures.

Practitioners and policymakers can incorporate the findings of this study in designing and/or assessing staycation packages. This can ensure differentiated products for defined segments that resonate within local communities through positive word of mouth, thus offering prospective spillovers to visiting friends and relatives.

This is a pioneering study on preference heterogeneity from the customer perspective, with a focus on staycation markets. The findings can encourage and assist hotel sector leaders to capitalize on local market developments to achieve a more resilient hospitality business model.

This paper aims to deepen the current knowledge of seasonality by investigating visitors’ intentional and behavioural patterns during peak and off-peak seasons. It compares the variation in several key behavioural factors, namely, duration of stay, party size, revisit intention, spending and breakdown of spending in different sectors in hospitality and tourism including entertainment, restaurant, accommodation and transportation. Moreover, this research expands the understanding by examining the effectiveness of two innovative strategies of offering a digital app and organising a unique event to tackle seasonal imbalances through stimulating visitors’ intention to change their timing of visit from peak to off-peak periods.

The author initially used a Delphi approach to gather experts’ opinion on the two scenario settings: event organisation and a trip planner app. The scenarios aimed to potentially encourage visitors to change their visit time to off-peak seasons. Then, using a quantitative survey, the travel habits and spending behaviours of 310 participants were captured. Furthermore, the survey assessed their intention to travel during off-peak seasons in response to the implementation of the two innovative strategies.

The results revealed that although the number of visitors who travel in off-peak seasons may be lower, their daily spending is higher than peak season visitors. In addition to total spending per day, the duration of stay, part size, quality of accommodation and re-visit intention of visitors indicated significant variation between peak and off-peak seasons. According to the statistical analysis’ results, organising events (including festivals) proves more effective in encouraging visitors to travel during off-peak seasons compared to digital innovation (i.e. a trip planner app). This finding is in line with the tenets of the Jobs-to-be-Done Theory of innovation.

This study contributes by conceptualising the mechanism of seasonality and its impacts on subsectors of tourism and hospitality. To the best of the author’s knowledge, this is one of the few empirical research that compares the behavioural patterns of visitors including their average spending per day between peak and off-peak seasons. Previous studies focused on specific regions or sectors, whereas this research investigates visitors’ behaviour on a broader scale to provide more comprehensive view. Furthermore, this study is novel due to practising an outside-in approach through investigating the effectiveness of the two innovative strategies aimed at addressing seasonality in the hospitality and tourism industry from visitors’ point of view.

As the world is becoming more volatile and uncertain, organizations face much complexity in their daily operations. Further, there is a much ambiguity in business operations to achieve the effective utilization of resources. The work optimizes a novel constraint programming model approach of the utilization of shuttle services vehicle while considering cost savings, employee wellbeing and other real an Information Technology enabled service (ITES) industry constraints.

The present work considers a novel extension of the vehicle routing problem related to the shuttle service operation in an ITES industry in VUCA context. Additionally, the model considers the women safety aspects, which engages the company to provide a security guard for women employees in the night shift.

Numerical experiments were conducted on real instances data of ITES industrial partner. The results show that the vehicle utilization increased from 75% up to 96% while ensuring in parallel the wellbeing of employees and women safety during the night shift. Finally, the proposed model is converted to a decision support application allowing ITES partner to plan employees shuttle service operations efficiently.

Study has evaluated the shuttle services optimization for ITES industry using data from industrial which makes it a unique contribution to literature in shuttle operations. Further, the study used constraint programming to evaluate the vehicle utilization and security allocation, thereby introducing new parameter on security allocation in open VRP problem.

Untimely responses to emergency situations in urban areas contribute to a rising mortality rate and impact society's primary capital. The efficient dispatch and relocation of ambulances pose operational and momentary challenges, necessitating an optimal policy based on the system's real-time status. While previous studies have addressed these concerns, limited attention has been given to the optimal allocation of technicians to respond to emergency situation and minimize overall system costs.

In this paper, a bi-objective mathematical model is proposed to maximize system coverage and enable flexible movement across bases for location, dispatch and relocation of ambulances. Ambulances relocation involves two key decisions: (1) allocating ambulances to bases after completing services and (2) deciding to change the current ambulance location among existing bases to potentially improve response times to future emergencies. The model also considers the varying capabilities of technicians for proper allocation in emergency situations.

The Augmented Epsilon-Constrained (AEC) method is employed to solve the proposed model for small-sized problem. Due to the NP-Hardness of the model, the NSGA-II and MOPSO metaheuristic algorithms are utilized to obtain efficient solutions for large-sized problems. The findings demonstrate the superiority of the MOPSO algorithm.

This study can be useful for emergency medical centers and healthcare companies in providing more effective responses to emergency situations by sending technicians and ambulances.

In this study, a two-objective mathematical model is developed for ambulance location and dispatch and solved by using the AEC method as well as the NSGA-II and MOPSO metaheuristic algorithms. The mathematical model encompasses three primary types of decision-making: (1) Allocating ambulances to bases after completing their service, (2) deciding to relocate the current ambulance among existing bases to potentially enhance response times to future emergencies and (3) considering the diverse abilities of technicians for accurate allocation to emergency situations.

This paper aims to focus on a medical goods distribution problem and pharmacological waste collection by plug-in hybrid vehicles with some real-world restrictions. In this research, considering alternative energy sources and simultaneous pickup and delivery led to a decrease in greenhouse gas emissions and distribution costs, respectively.

Here, this problem has been modeled as mixed-integer linear programming with the traveling and energy consumption costs objective function. The GAMS was used for model-solving in small-size instances. Because the problem in this research is an NP-hard problem and solving real-size problems in a reasonable time is impossible, in this study, the artificial bee colony algorithm is used.

Then, the algorithm results are compared with a simulated annealing algorithm that recently was proposed in the literature. Finally, the results obtained from the exact solution and metaheuristic algorithms are compared, analyzed and reported. The results showed that the artificial bee colony algorithm has a good performance.

In this paper, medical goods distribution with pharmacological waste collection is studied. The paper was focused on plug-in hybrid vehicles with simultaneous pickup and delivery. The problem was modeled with environmental criteria. The traveling and energy consumption costs are considered as an objective function.

This study aims to minimize the transportation-related cost in distribution while utilizing a heterogeneous fixed fleet to deliver distinct demand at different geographical locations with a proper workload balancing approach. An increased cost in distribution is a major problem for many companies due to the absence of efficient planning methods to overcome operational challenges in distinct distribution networks. The problem addressed in this study is to minimize the transportation-related cost in distribution while using a heterogeneous fixed fleet to deliver distinct demand at different geographical locations with a proper workload balancing approach which has not gained the adequate attention in the literature.

This study formulated the transportation problem as a vehicle routing problem with a heterogeneous fixed fleet and workload balancing, which is a combinatorial optimization problem of the NP-hard category. The model was solved using both the simulated annealing and a genetic algorithm (GA) adopting distinct local search operators. A greedy approach has been used in generating an initial solution for both algorithms. The paired t-test has been used in selecting the best algorithm. Through a number of scenarios, the baseline conditions of the problem were further tested investigating the alternative fleet compositions of the heterogeneous fleet. Results were analyzed using analysis of variance (ANOVA) and Hsu’s MCB methods to identify the best scenario.

The solutions generated by both algorithms were subjected to the t-test, and the results revealed that the GA outperformed in solution quality in planning a heterogeneous fleet for distribution with load balancing. Through a number of scenarios, the baseline conditions of the problem were further tested investigating the alternative fleet utilization with different compositions of the heterogeneous fleet. Results were analyzed using ANOVA and Hsu’s MCB method and found that removing the lowest capacities trucks enhances the average vehicle utilization with reduced travel distance.

The developed model has considered both planning of heterogeneous fleet and the requirement of work load balancing which are very common industry needs, however, have not been addressed adequately either individually or collectively in the literature. The adopted solution methodologies to solve the NP-hard distribution problem consist of metaheuristics, statistical analysis and scenario analysis are another significant contribution. The planning of distribution operations not only addresses operational-level decision, through a scenario analysis, but also strategic-level decision has also been considered.

The planning of distribution operations not only addresses operational-level decisions, but also strategic-level decisions conducting a scenario analysis.

This study aims to examine the co-creation of customer experiences at different levels in service ecosystems, analyzing the case of a tourist destination.

A questionnaire was designed based on previously validated scales. The questionnaire was distributed through the social media platforms Facebook and Instagram. The survey yielded 1,476 valid responses for three types of destinations. Structural equation modeling and multigroup analysis were performed to test the hypotheses.

Aggregate service experience and memorable customer experience (MCE) in service ecosystems are determined by customer experiences at a dyadic level. Service experience at the ecosystem level is formed from ordinary experiences at the actor level, while MCE is formed from extraordinary experiences at the dyadic level. The type of ecosystem moderates the relationships between the variables but does not alter the importance of each of them.

The relationship between the co-creation of customer experiences at different levels of service ecosystems (dyadic vs aggregate) is addressed. A relationship is established between the ordinary and extraordinary character of experiences and their memorability at the ecosystem level.