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The paper aims to compare two general team identification processes of consumers’ in-group-favor and out-group-animosity responses to sports sponsorship.

The paper draws on two studies and four samples of professional baseball fans in Taiwan (N = 1,294). In Study 1, data from the fans of three teams were analyzed by using multi-group structural equation modeling to account for team effects and to consider parallel in-group-favor and out-group-animosity processes. In Study 2, the fans of one team were sampled and randomly assigned to assess the sponsors of one of three specific competitor teams to account for differences in team competition and rivalry. In both studies, these two processes were compared using patterns of significant relationships and differences in the indirect identification-attitude-outcome relationships.

Positive outcomes of in-group-favor processes were broader in scope and were more pronounced in absolute magnitude than the negative outcomes of out-group-animosity processes across all outcomes and studies.

The research was conducted in one country and considered the sponsorship of one sport. It is possible that the results could differ for leagues within different countries, more global leagues and different fan bases.

The results suggest that managers should carefully consider whether the negative out-group-animosity outcomes are actually present, broad enough or strong enough to warrant costly or compromising intervention, because they might not always be present or meaningful.

The paper demonstrates the comparatively greater breadth and strength of in-group-favor processes when compared directly to out-group-animosity processes.

The purpose of this paper is to use Twitter data to mine personality traits of basketball players to predict their performance in the National Basketball Association (NBA).

Automated personality mining and robotic process automation were used to gather data (player statistics and big five personality traits) of n = 185 professional basketball players. Correlation analysis and multiple linear regressions were computed to predict the performance of their NBA careers based on previous college performance and personality traits.

Automated personality mining of Tweets can be used to gather additional information about basketball players. Extraversion, agreeableness and conscientiousness correlate with basketball performance and can be used, in combination with previous game statistics, to predict future performance.

The study presents a novel approach to use automated personality mining of Twitter data as a predictor for future basketball performance. The contribution advances the understanding of the importance of personality for sports performance and the use of cognitive systems (automated personality mining) and the social media data for predictions. Scouts can use our findings to enhance their recruiting criteria in a multi-million dollar business, such as the NBA.

This paper studies the interactive valuation discourses of an online user community (transfermarkt.de) that seeks to determine market values for soccer players. Despite their seemingly casual nature, these values have featured in newspapers, transfer negotiations, academic research, and capital market communication – and have thus become reified.

The paper employs netnographic research methodology to collect and thematically analyze a wide range of user entries on the platform. These entries are studied using theoretical insights from the sociology of quantification and valuation.

The analysis reveals how values are constructed in constant interaction between value-proposing users and value-justifying “experts.” This dynamic form of relational valuation positions players relative to one another as well as to actual transactions on the transfer market. In the absence of authoritative guidelines, it is this possibility and affordance for interaction that enacts a coherent valuation regime. The paper further reveals the platform's response to a disruptive event, which risked bringing the user-expert dynamics to a halt, requiring intervention from the platform to repair its valuation frame.

The paper responds to increased scholarly interests in the valuation of professional athletes. It contributes to the extant literature on valuation, first, by analyzing the dynamic valuation work that feeds into the social construction of values and, second, by studying platform participation and user interaction in a socially engineered online space.

As the superiority of sports celebrities' endorsements has been questioned, the purpose of this study is to identify various types of endorsers' direct and indirect effects on consumers' purchase intentions.

Empirical data were collected from 240 useful responses to an online experiment, and research hypotheses were tested using (moderated) serial mediation analyses.

The study's findings indicate that an endorser has a positive influence on consumers' purchase intentions through their perceptions of the advertisement and the endorsed brand. A moderated serial mediation analysis finds differences in the four types of endorsers analyzed. A sports celebrity is the most effective type of endorser in increasing consumers' purchase intentions, whereas endorsements by company managers and peer consumers, while also positive, are less effective in influencing advertising outcomes. An expert's endorsement is comparable to that of a manager but not significant.

The generalizability of the study's findings is limited because of a restricted data sample, the use of fictitious endorsers and the limited number of product categories and brands analyzed.

The study systematically analyzes the behavioral influence of four types of endorsers on consumers' purchase intentions, mediated by their perceptions of the advertisements and the endorsed brand. The results of this analysis extend the current state of endorsement research, indicating that endorsements should be integrated into companies' marketing strategies and provide marketing professionals practical guidance on which type of endorser is most effective in influencing advertising outcomes.

Sports marketing and sponsorship research is located at the intersection of behavioral and design research, which means that it analyzes the current world and shapes a future world. This dual focus poses challenges for formulating and testing theories of sports marketing.

This article develops criteria for categorizing theoretical concepts as either behavioral or formed as different ways of expressing ideas of sports marketing research. It emphasizes the need for clear concept categorization for proper operationalization and applies these criteria to selected theoretical concepts of sports marketing and sponsorship research.

The study defines three criteria to categorize theoretical concepts, namely (1) the guiding idea of research, (2) the role of observed variables, and (3) the relationship among observed variables. Applying these criteria to concepts of sports marketing research manifests the relevance of categorizing theoretical concepts as either behavioral or formed to operationalize concepts correctly.

This study is the first in sports marketing to clearly categorize theoretical concepts as either behavioral or formed, and to formulate guidelines on how to differentiate behavioral concepts from formed concepts.

Social media allow for observing different aspects of human behaviour, in particular, those that can be evaluated from explicit user expressions. Based on a data set of posts with user opinions collected from social media, this paper aims to show an insight into how the readers of different news portals react to online content. The focus is on users’ emotions about the content, so the findings of the analysis provide a further understanding of how marketers should structure and deliver communication content such that it promotes positive engagement behaviour.

More than 5.5 million user comments to posted messages from 15 worldwide popular news portals were collected and analysed, where each post was evaluated based on a set of variables that represent either structural (e.g. embedded in intra- or inter-message structure) or behavioural (e.g. exhibiting a certain behavioural pattern that appeared in response to a posted message) component of expressions. The conclusions are based on a set of regression models and exploratory factor analysis.

The findings show and theorise the influence of social media content on emotional user engagement. This provides a more comprehensive understanding of the engagement attributed to social media content and, consequently, could be a better predictor of future behaviour.

This paper provides original data analysis of user comments and emotional reactions that appeared on social media news websites in 2018.

Social commerce websites entail a completely new scenario for retaining e-customers due to the richness of their social interactions. Nowadays, users can interact with companies and with other users; hence, it is considered important to study how social stimuli affect users. Drawing on the Stimulus Organism Response framework and Flow Theory, this paper aims to propose that the social stimulus (sPassion) has a positive effect on the organism (state of flow) causing positive responses from users (flow consciousness, trust and eLoyalty).

The data were collected through an online survey. The sample consists of 771 users of social commerce websites, of which 51 per cent are male and 49 per cent female, aged between 16 and 80 years. The structural equation model statistical software EQS 6 was used to test the model.

The empirical results confirm that passionate users are prone to experience state of flow, and, as a consequence, they are conscious of this optimal experience, resulting in an increase in trust.

The originality of this research stems from analysing how users’ passion on social commerce creates an optimal experience that boost customers’ retention.

Las páginas web de social commerce ofrecen un escenario completamente diferente al estudiado hasta la fecha, favoreciendo la retención de clientes en Internet gracias a la riqueza de las interacciones sociales del medio. En la actualidad los usuarios pueden interactuar tanto con la compañía como con otros usuarios, de ahí que se considere importante estudiar cómo los estímulos sociales afectan a los usuarios. Enmarcado en el modelo SOR (del inglés stimulus, organism, response) y la Teoría del Flujo, este estudio propone que el estímulo social (la pasión en el social commerce) tiene un efecto positivo sobre el organismo (estado de flujo), causando respuestas positivas en los usuarios (consciencia de flujo, confianza y lealtad online).

Los datos fueron recogidos a través de una encuesta online. La muestra está compuesta por 771 respuestas de usuarios de páginas de social commerce, de los cuales el 51 per cent son hombres y el 49 per cent mujeres, con edades comprendidas entre los 16 y los 80 años. Para testar el modelo se utilizó el software estadístico EQS 6 para modelos de ecuaciones estructurales.

Los resultados empíricos confirman que los usuarios más apasionados son más propensos a experimentar el estado de flujo y, como consecuencia, son conscientes más de alcanzar ese estado de experiencia óptima, lo que tiene como resultado un incremento de su confianza en la página web de social commerce.

La originalidad de esta investigación radica en analizar cómo la pasión de los usuarios en entornos de social commerce crea una experiencia óptima que ayuda a retener clientes.

As the tourism industry becomes more vital for the success of many economies around the world, the importance of technology in tourism grows daily. Alongside increasing tourism importance and popularity, the amount of significant data grows, too. On daily basis, millions of people write their opinions, suggestions and views about accommodation, services, and much more on various websites. Well-processed and filtered data can provide a lot of useful information that can be used for making tourists' experiences much better and help us decide when selecting a hotel or a restaurant. Thus, the purpose of this study is to explore machine and deep learning models for predicting sentiment and rating from tourist reviews.

This paper used machine learning models such as Naïve Bayes, support vector machines (SVM), convolutional neural network (CNN), long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM) for extracting sentiment and ratings from tourist reviews. These models were trained to classify reviews into positive, negative, or neutral sentiment, and into one to five grades or stars. Data used for training the models were gathered from TripAdvisor, the world's largest travel platform. The models based on multinomial Naïve Bayes (MNB) and SVM were trained using the term frequency-inverse document frequency (TF-IDF) for word representations while deep learning models were trained using global vectors (GloVe) for word representation. The results from testing these models are presented, compared and discussed.

The performance of machine and learning models achieved high accuracy in predicting positive, negative, or neutral sentiments and ratings from tourist reviews. The optimal model architecture for both classification tasks was a deep learning model based on BiLSTM. The study’s results confirmed that deep learning models are more efficient and accurate than machine learning algorithms.

The proposed models allow for forecasting the number of tourist arrivals and expenditure, gaining insights into the tourists' profiles, improving overall customer experience, and upgrading marketing strategies. Different service sectors can use the implemented models to get insights into customer satisfaction with the products and services as well as to predict the opinions given a particular context.

This study developed and compared different machine learning models for classifying customer reviews as positive, negative, or neutral, as well as predicting ratings with one to five stars based on a TripAdvisor hotel reviews dataset that contains 20,491 unique hotel reviews.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.