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The purpose of this study is to examine the post-event perceptions of Rio de Janeiro residents regarding the legacies left to them from hosting the 2016 Olympic Games. Additionally, this study examines how perceptions of Olympic legacies differed amongst demographic groups.

Utilizing a mixed-methods approach, participants were asked to complete a survey questionnaire and answer three open-ended questions. An adapted version of Fredline et al. (2003)'s General Scale to Measure Social Impacts (GSMSI) was selected for this study. In total, 156 useable responses were collected. An exploratory factor analysis was utilized for the survey items while the three open-ended questions were coded abductively as positive, negative or mixed feelings.

The quantitative results shows that Rio residents believe that hosting the 2016 Olympic Games had a mostly negative impact on their perception of legacies. Through reading open-ended responses, participants expressed disappointment over the legacies left to them. These findings suggest that hosting the Olympic Games may not have been beneficial to the life of Rio de Janeiro residents, and it may have been particularly worse for members of the middle class.

Due to the recency of the Rio 2016 Olympic Games, a few studies have explored the long-term legacies left to the local population. However, the uniqueness of this study lays on the perceptions of legacies from hosting the Rio 2016 after a full Olympic cycle has passed.

The purpose of this study was to gather insights from sport marketing professionals and identify key opportunities, challenges and knowledge that sport marketing educators and researchers could utilize in developing curriculum and research agendas.

A qualitative approach was used, and data were collected through in-depth interviews with 15 sport marketing professionals. Participants were asked questions related to the knowledge, skills and experiences that they believe are important for students to have to be successful in the industry, as well as the types of research that would be most useful in their day-to-day work.

Industry professionals noted collaboration, transformation in digital marketing, data and analytics and experiential marketing as key trends facing the industry today. The findings suggest that sport marketing curriculum should focus on soft skill development such as communication, relationship building and empathy alongside hard skill development such as data analysis and storytelling. As well, findings show research areas where scholars can aid practitioners with a focus on consumer insights, technology, measuring ROI and experiential marketing.

With these findings, educators and scholars can better prepare students for successful careers in industry and contribute to the ongoing advancement of the scholarly field. This study serves as a starting point for further research in this area, and it is hoped that it will spark continued collaboration between academia and industry.

This study aims to develop and validate an accurate machine learning model to categorize NBA fans into meaningful clusters based on their perceptions of sport sponsorship. Additionally, by predicting the intensity of NBA fans’ attitudes toward sponsors, the authors intend to identify the specific features that influence prediction, discuss these findings and offer implications for academics and practitioners in sport sponsorship.

This study used a sample of 1,142 NBA fans who were recruited through Amazon Mechanical Turk (MTurk). Fans identification, sponsorship fit, behavioral intentions, sponsor altruistic motive, sponsor normative motive, sponsor egoistic motive were surveyed as predictors, whereas fans’ attitudes toward sponsors was collected as the dependent variable. The LASSO regression, SVM, KNN, RF and XGboost were used to develop and validate the prediction model after verifying the measurement model by the Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA).

The RF model had the best accurate in predicting the intensity of fans’ attitudes toward sponsors, achieving an AUC of 0.919 with a sensitivity of 0.872, a specificity of 0.828, a PPV of 0.873, a NPV of 0.828 and an accuracy of 0.848. The most influential feature in the model was “the fit of 0.301”. “Fans’ perceptions of sponsor’s normative motive”, “behavioral intentions supporting sponsors”, “fans’ identification with their favorite team”, “fans’ perceptions of sponsor’s altruistic motive” and “fans’ perceptions of sponsor’s egoistic motive” were exhibited in descending order.

This study is the first in sport sponsorship to accurately classify the intensity of fans’ attitudes toward sponsors as either high or low using machine learning models, and to formulate how fans’ attitudes formed toward sponsors from their perceptions of sponsorship process.

Guided by stimulus-organism-response (SOR) theory, this study analyzed the user-generated content (UGC) produced by attendees from six anime conventions in the USA.

A total of 739 online reviews and 1,932 photos were collected from the social platforms of six large anime conventions in the USA (Yelp and Facebook), and the study employed thematic analysis and image analysis to analyze the collected UGCs.

The findings revealed eight main themes (i.e. ambient and space, customers, service and products, sign and symbol, social density, emotional status, motivation, and behavior intention) and 32 subthemes across the three dimensions of SOR theory. Leveraging the power of cutting-edge image analysis, the image labels obtained from the analysis contributed to the creation of network clusters. The result of the image analysis also continued consistently with the thematic analysis result, which reflected SOR theory.

Theoretically, the study applied SOR theory and blended thematic and image analyses to gain a comprehensive understanding of anime convention attendees’ experience and categorized the attendees’ emotional status as positive or negative to reflect their overall evaluation. Practically, this study highlighted some complaints from attendees and provided suggestions for operators. However, the study focused only on large anime conventions in the USA; future studies should compare attendees’ experiences with small and large conventions or anime conventions worldwide.

The study utilized UGCs to understand the key patterns essential to attendees during anime conventions in the USA and applied SOR theory to its investigation.

The COVID-19 pandemic was the most ‘over governed’ tourism crisis since World War II. Unlike disruptions prior to 2020, it challenged many long-held paradigms about global tourism including fundamental issues like the right to travel. Therefore, it qualified fully as a ‘wicked problem’ defined by Rittel and Webber (1973) and Head (2022). The extent of government-imposed regulation was central to the management of and response to COVID-19. All tiers of government-mandated measures, including border closures, area lockdowns, quarantine regimes, social distancing, pressure to accept vaccinations, masks and testing requirements applied in most countries and regional territorial jurisdictions throughout 2020 and 2021. In some cases, notably in China, restrictions remained until early 2023. Tourism’s recovery from crises involves rebuilding consumer and stakeholder confidence and focussing on marketing positive perceptions of destinations and enterprises. However, during the COVID-19 crisis, marketing and promotion of tourism recovery were hampered by a regulatory environment restricting tourism activity across sectors. As a ‘wicked problem’ for global tourism, COVID-19 incorporates many of Head’s (2022) seven policy strategies to deal with Rittel and Webber’s (1973) ten elements of wicked problems including authoritative imposition, micro-management and science (health) based technocracy. The Australian response to COVID-19 was significantly complicated by the fact nine governments (one Federal, six states and two territories) frequently had differing policy and regulatory approaches to COVID-19. These affected international and domestic travel to, from and within Australia. Australia’s fragmented, federalist governance of a health-based crisis exacerbated the restrictions imposed on both domestic and international travel.

Racism occurs in many ways and varies across countries, evolving and adapting to sociocultural history, as well as contemporary economic, political and technological changes. This chapter discusses the multilevel dimensions of racism and its diverse manifestations across multiracial societies. It examines how different aspects of racism are mediated interpersonally, and embedded in institutions, social structures and processes, that produce and sustain racial inequities in power, resources and lived experiences. Furthermore, this chapter explores the direct and indirect ways racism is expressed in online and offline platforms and details its impacts on various groups based on their intersecting social and cultural identities. Targets of racism are those who primarily bear the adverse effects. However, racism also affects its perpetrators in many ways, including by limiting their social relations and attachments, and by imposing social and economic costs. This chapter thus analyses the many aspects of racism both from targets and perpetrators' perspectives.

The purpose of this study was to uncover representative emergent areas and to examine the research area of marketing, tourism and big data (BD) to assess how these thematic areas have developed over a 27-year time period from 1996 to 2022. This study analyzed 1,152 studies to identify the principal thematic areas and emergent topics, principal theories used, predominant forms of analysis and the most productive authors in terms of research.

The articles for this research were all selected from the Web of Science database. A systematic and quantitative literature review was performed. This study used SciMAT software to extract indicators. Specifically, this study analyzed productivity and produced a science map.

The findings suggest that interest in this area has increased gradually. The outputs also reveal the innovative effort of industry in new technologies for developing models for tourism marketing. Ten research areas were identified: “destination marketing,” “mobility patterns,” “co-creation,” “gastronomy,” “sustainability,” “tourist behavior,” “market segmentation,” “artificial neural networks,” “pricing” and “tourist satisfaction.”

This work is unique in proposing an agenda for future research into tourism marketing research with new technologies such as BD and artificial intelligence techniques. In addition, the results presented here fill the current gap in the research since while there have been literature reviews covering tourism with BD or marketing, these areas have not been studied as a whole.

El objetivo de esta investigación fue descubrir nichos representativos de áreas emergentes y examinar el área de Marketing, Turismo y Big Data, evaluando cómo han evolucionado estas áreas temáticas durante un período de 27 años desde 1996–2022. Analizamos 1.152 investigaciones para identificar las principales áreas temáticas y temas emergentes, las principales teorías utilizadas, las formas de análisis predominantes y los autores más productivos en términos de investigación.

Todos los artículos para esta investigación fueron seleccionados de la base de datos Web of Science. Realizamos una revisión sistemática y cuantitativa de la literatura. Utilizamos el software SciMAT para extraer indicadores. Específicamente, analizamos la productividad y elaboramos un mapeo científico.

Los hallazgos sugieren que el interés en esta área ha aumentado gradualmente. Los resultados también revelan el esfuerzo innovador de la industria en nuevas tecnologías para desarrollar modelos de marketing turístico. Se identificaron diez áreas de investigación (“marketing de destinos”, “patrones de movilidad”, “co-creación”, “gastronomía”, “sostenibilidad”, “comportamiento turístico”, “segmentación de mercado”, “redes neuronales artificiales”, “precios”, y “satisfacción del turista”).

Este trabajo es único al proponer una agenda para futuras investigaciones en investigación de Marketing Turístico con nuevas tecnologías como Big Data y técnicas de Inteligencia Artificial. Además, los resultados presentados aquí llenan el vacío actual en la investigación ya que si bien se han realizado revisiones de literatura que cubren Turismo con Big Data o Marketing, estas áreas no se han estudiado como un conjunto.

这一特定研究领域的目标是发现具有代表性的新兴领域, 并考察市场营销、旅游和大数据研究领域, 以评估这些主题领域在1996年至2022年的27年间是如何发展的。我们分析了1152项研究, 以确定主要专题领域和新兴主题、使用的主要理论、主要的分析形式以及在研究方面最有成效的作者。

本研究的文章都是从Web of Science数据库中选出的。我们进行了系统化的定量文献审查, 并使用SciMAT软件来提取指标。具体来说, 我们分析了生产力并制作了一个科学研究地图。

研究结果表明, 人们对这一领域的兴趣已经逐渐增加。本文也揭示了工业界在开发旅游营销模式的新技术方面的创新努力。研究确定了十个研究领域：“目的地营销”、“流动模式”、“共同创造”、“美食”、“可持续性”、“游客行为”、“市场细分”、“人工神经网络”、“定价 “和游客满意度”。

这项研究的独特之处在于提出了未来利用大数据和人工智能技术等新技术进行旅游营销研究的议程。此外, 本文的结果填补了目前的研究空白, 因为虽然有文献综述涉及旅游与大数据或市场营销, 但这些领域还没有被作为一个整体来研究。

Most college athletics department have not sold corporate naming rights to their athletics facilities. Popp et al. (2016) suggests two primary reasons: (1) difficulty in determining proper valuation and (2) fear of stakeholder backlash. The purpose of the current study is to address both concerns by utilizing a hedonic pricing model predicting collegiate naming rights values and utilizing fixed-effects models to determine if consumer behavior (event attendance and donations) is impacted by a corporate name change.

Data from 110 naming rights agreements among NCAA Division I programs were examined, alongside market-related variables, institution-related variables and venue-related variables. Utilizing hierarchical model building to reduce independent variables and OLS regression modeling, significant relationships with annual value of naming rights agreements were uncovered. Fixed effects models were utilized to determine if naming rights impacted attendance and donations.

A final model explained more than 53% of the variance in average annual value of naming rights agreements, with three significant factors: (1) attendance, (2) all-time winning percentage and (3) venue construction cost. Fixed-effects models revealed no significant differences in attendance or donations after a naming rights deal was signed.

Corporate naming rights agreements for college athletics facilities are a recent phenomenon. While a similar study examining drivers of collegiate sport naming rights was previously conducted, the current study revealed a shifting marketplace. In addition, no prior study has examined the impact of a corporate naming rights agreement on future attendance and donations.

The purpose of this work is (1) to supply a framework of actors in sport sponsorship and articulate the service relationships that support these partnerships and (2) to propose research questions in this space that are unaddressed and forward-looking.

Sponsorship is part of a complex network of actors and service relationships found in sport. The sports team, activity, or event is a sport property, often with long-term and dynamic service relationships. The authors consider how a sponsor's relationship with the sport property intersects with organizing bodies, venues, communities and society. The authors identify clusters of actors that interact with and influence other clusters (e.g. governing bodies, media, host community and venue/teams/fans) within an ecosystem, paying special attention to aspects of co-creation and co-destruction and the feedback loops that cause them.

Through this analysis, the authors identify areas of needed research at the intersection of sport sponsorship and service. The model synthesizes the literature from service-dominant logic, sports, sponsorship, systems thinking and co-creation/co-destruction research areas. Using the model and relevant cases, the authors can better understand the complexities of sport service relationships and advance research at the intersection of sport sponsorship and service.

This is the first sport sponsorship service ecosystem model. It is also the first integration of systems thinking with constructs in sport sponsorship and services.

No Olympics in recent history have come in on or under budget, with past Olympics having overrun their budgets by an average of 172% (Flyvbjerg et al., 2021). Growing concerns among taxpayers about the expense have led many cities to drop out of the bidding process for the Olympics: for the 2024 Olympic Games, bids were withdrawn by Boston, Budapest, Hamburg, and Rome (Okada & Greyser, 2018). Sports infrastructure, including the main stadium, is a major cost category for the Olympic Games. Building or renovating venues to Olympic specifications can leave cities with heavy costs of long-term maintenance as well as the cost of initial construction (Baade & Matheson, 2016). In practice, cities tend to overestimate the size and suitability of existing venues, leading to underestimated costs and budget overruns (Preuß et al., 2019). The Brisbane 2032 Olympic bid says that Brisbane’s Olympics are confident of breaking even, and avoiding building stadiums that end up as white elephants. But does the choice of Olympic stadium make a difference to the final cost overrun? This study finds that cost overruns for past Olympics have been predicted using a few characteristics of the main stadium: its age, its distance from the city centre, and its seating capacity. This has important policy implications for selecting stadiums to be used at the Olympic Games and particularly for the Brisbane 2032 Olympic Games where a stadium has been earmarked for selection without data-driven evidence to support the decision.