4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 4th International Conference on Advanced Research Methods and Analytics (CARMA) is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges. Due to the covid pandemic, CARMA 2022 is planned as a virtual and face-to-face conference, simultaneouslyDoménech I De Soria, J.; Vicente Cuervo, MR. (2022). 4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2022.2022.1595

The power of immersive technologies: a sociopsychological analysis of the relationship between immersive environments, storytelling, sentiment, and the impact on user experience

This dissertation initially focused on exploring the potential of immersive technologies for the distant future. However, the emergence of the COVID-19 virus in late 2019 disrupted the world, causing a pause in many areas. Nevertheless, the butterfly effect of the pandemic spurred the development of immersive technologies, resulting in the rise of the metaverse, web3, non-fungible tokens (NFT), and avatars, which are gaining increasing popularity. The excitement for the metaverse is growing in both academia and industry, leading to new avenues of research, digital marketing, video games, tourism, and social media. This dissertation explores this rapidly emerging technological revolution and its effects on user experience (UX)

Negative online word-of-mouth: Behavioral indicator or emotional release?

The influence of negative online word-of-mouth on the behavior of those receiving it has been addressed extensively in the academic literature. Remarkably, the question whether negative online word-of-mouth should also be seen as a behavioral indicator of its sender remains unaddressed. Answering this question is relevant as it provides companies with insight into the need to engage in interaction with those who negatively express themselves online or whether these expressions should be seen as temporary emotional releases without any intended conduct. To fill the existing research gap, this research paper proposes and empirically tests a sender-oriented model, investigating the influence of emotions, negative online word-of-mouth on repatronage and switching intentions. As disclosing negative feedback online may also reflect the sender's motivation to inform the consumer community or to provide constructive feedback to the company responsible for the dissatisfying consumption, community usefulness and company usefulness are included as behavioral moderators. The results of an empirical survey conducted amongst real senders of negative information confirm that negative online word-of-mouth is directly driven by positive and negative emotions and is strongly predictive for the sender's intended conduct. The motivation to help other consumers was demonstrated to function as behavioral moderator. The paper concludes with theoretical and managerial implications, and suggests avenues for further research. © 2013 Elsevier Ltd. All rights reserved

Graph-Based Conversation Analysis in Social Media

Social media platforms offer their audience the possibility to reply to posts through comments and reactions. This allows social media users to express their ideas and opinions on shared content, thus opening virtual discussions. Most studies on social networks have focused only on user relationships or on the shared content, while ignoring the valuable information hidden in the digital conversations, in terms of structure of the discussion and relation between contents, which is essential for understanding online communication behavior. This work proposes a graph-based framework to assess the shape and structure of online conversations. The analysis was composed of two main stages: intent analysis and network generation. Users' intention was detected using keyword-based classification, followed by the implementation of machine learning-based classification algorithms for uncategorized comments. Afterwards, human-in-the-loop was involved in improving the keyword-based classification. To extract essential information on social media communication patterns among the users, we built conversation graphs using a directed multigraph network and we show our model at work in two real-life experiments. The first experiment used data from a real social media challenge and it was able to categorize 90% of comments with 98% accuracy. The second experiment focused on COVID vaccine-related discussions in online forums and investigated the stance and sentiment to understand how the comments are affected by their parent discussion. Finally, the most popular online discussion patterns were mined and interpreted. We see that the dynamics obtained from conversation graphs are similar to traditional communication activities

Essays on the interaction between users and information systems

The role of information systems has evolved from providing decision support into enabling the majority of our daily operations, and the way users interact with information systems has changed dramatically as a result. The goal of this dissertation is to study phenomena that stem from the close interaction between users and information systems using empirical methodologies. The first essay of this dissertation focuses on the issue of sentiment manipulation. We show that strategic players might be incentivized to manufacture content on social media platforms and opinion forums, in the context of the movie industry. We then identify unusual patterns on Twitter that are consistent with sentiment manipulation. We study the effectiveness of social media advertising in the second essay. Advertisers on popular social media platforms such as Facebook are able to publish ads with popularity and social information. We design and conduct a randomized field experiment to study the extent to which these types of information have an effect on ad performance. In the third essay we study how individuals might be biased toward contents that appear to be written more politely. We use data from an online question answering platform, StackExchange, to show that an individual who posts a question on the platform tends to prefer polite answers to clear answers.Information, Risk, and Operations Management (IROM

Social media and sensemaking patterns in new product development: demystifying the customer sentiment

Artificial intelligence by principle is developed to assist but also support decision making processes. In our study, we explore how information retrieved from social media can assist decision-making processes for new product development (NPD). We focus on consumers’ emotions that are expressed through social media and analyse the variations of their sentiments in all the stages of NPD. We collect data from Twitter that reveal consumers’ appreciation of aspects of the design of a newly launched model of an innovative automotive company. We adopt the sensemaking approach coupled with the use of fuzzy logic for text mining. This combinatory methodological approach enables us to retrieve consensus from the data and to explore the variations of sentiments of the customers about the product and define the polarity of these emotions for each of the NPD stages. The analysis identifies sensemaking patterns in Twitter data and explains the NPD process and the associated steps where the social interactions from customers can have an iterative role. We conclude the paper by outlining an agenda for future research in the NPD process and the role of the customer opinion through sensemaking mechanisms

Can we sense shift in consumer behaviour in Portuguese retail companies due to the pandemic?

The 2019 coronavirus pandemic (COVID-19) has effects in the most diverse fields of our society, from mental health and lifestyle to commerce and education. A huge adaptation by the population and restructuring of habits was necessary to make progress in this new reality, leading several companies to reinvent the way they conducted their businesses and a complete metamorphosis of their business plans. As such, there was an interest in conducting this research to understand how consumer behaviour in Portuguese retail companies was affected by the lockdown in the country, aiming to identify the change in the purchase intention of consumers living in Portugal and what motivated this same change, allowing extracting information to help organizations in the decision making. Thus, 15,000 comments were collected from the social network Facebook referring to the pre-lockdown, lockdown, and post-lockdown period in Portugal. Then, data mining techniques and processes were used to clean the set of collected data and extract knowledge. Furthermore, an Intention Mining analysis was carried out to assess the collected comments and draw conclusions. Finally, the results of this study indicate a negative evolution in the purchase intention of consumers, verifying that the relationship with the company deteriorated and problems in the supply chain increased, indicating that it is necessary to redirect strategies to improve the service of customer support and distribution channels to meet customer satisfaction and may apply to other countries in similar contexts.A pandemia do coronavírus 2019 (COVID-19) tem efeitos nos mais diversos campos da sociedade, desde a saúde mental e estilo de vida, ao comércio e educação. Foi necessária uma enorme adaptação da população e reestruturação de hábitos para conseguir avançar nesta nova realidade, levando várias empresas a reinventar a forma como conduziam os seus negócios e a uma completa metamorfose dos respetivos planos de negócio. Como tal, surgiu o interesse em realizar esta investigação para compreender como o comportamento do consumidor nas empresas de retalho portuguesas foi afetado pelo confinamento no país, tendo como objetivo identificar a mudança na intenção de compra dos consumidores a viver em Portugal e o que motivou essa mesma mudança, permitindo extrair informações que permitam auxiliar na tomada de decisão das organizações. Assim, recolheram-se 15,000 comentários da rede social Facebook referentes ao período pré-confinamento, confinamento e pós-confinamento em Portugal. Em seguida, foram utilizados técnicas e processos de mineração de dados para limpeza do conjunto de dados recolhidos e extração de conhecimento. Ainda, realizou-se uma análise de mineração de intenções para avaliar os comentários recolhidos e extrair conclusões. Por fim, os resultados deste estudo indicam uma evolução negativa na intenção de compra dos consumidores, verificando-se que a relação com a empresa deteriorou-se e problemas ao nível da supply chain aumentaram, indicando ser necessário redirecionar as estratégias para melhorar o serviço de apoio ao cliente e os canais de distribuição para ir ao encontro da satisfação dos clientes, podendo ser aplicável a outros países em contextos semelhantes