2,737 research outputs found

    How social media brand community development impacts consumer engagement and value formation; perspectives from the cosmetics industry

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    Social media and social media brand communities (SMBCs) are powerful tools for long-term consumer-brand relationship building. As a result, SMBCs are becoming significant marketing channels. Despite the wide use and adoption of SMBCs, further research is called for, as both practitioners and academics lack an understanding of the processes taking place within SMBCs. This study aims to contribute to knowledge of: (1) consumer engagement, (2) value formation in SMBCs, and (3) establishing the relationship between consumer engagement and value formation within the SMBC environment. This thesis adopts netnography, a method commonly employed to explore online communities in the social media environment. Three cosmetics brands were selected for this study. The selection was driven by geographical location, posting frequency and user activity. Data were retrospectively collected from Facebook SMBCs between 1st December 2019 and 31st January 2020. The data analysis employed thematic analysis techniques and was further guided by netnographic procedural steps, encompassing 25 distinct data operations. In total, 87 conversation threads were examined, which included 6,401 consumer comments. The findings present a typology of brand posts consisting of five overarching themes: presentation of offerings, belongingness building, engagement building, value-led, and educational. The research also identified a consumer comment typology consisting of four overarching themes brand-centred communication, cognitive-centred communication, conversation-centred communication, and personal experience-centred communication. Additionally, the thesis explores value formation processes within SMBCs, and the value types formed through consumer-to-consumer value formation interaction, brand-to-consumer value formation interaction, consumer-to-brand value formation interaction, as well as individual value formation processes, i.e., customer independent value formation and brand independent value facilitation. Through the findings, thesis broadens knowledge of the implication of SMBC development on consumer engagement. Additionally, this study extends the scope of value formation beyond service marketing, providing valuable insights into how value is created and perceived in the context of SMBCs. This research is also of significance for practice as it offers guidance and insight into how different brand posts can facilitate SMBC development, and, in turn, consumer engagement and value formation. The research provides a link between SMBC development and consumer engagement, highlighting the importance of SMBCs in the successful facilitation of consumer engagement. In particular, it provides evidence that the development of an SMBC has a significant impact on consumer engagement. The typology of brand posts that this study generates highlights the link between the types of posts published by the brand and SMBC development. In addition, the typology of consumer posts also suggests that there is a link between the types of comments published by consumers and the degree of SMBC development. As a result, the findings indicate significant growth in the variety of topics discussed within more developed SMBCs alongside a shift within the topics discussed. The study also investigates value formation within SMBCs, thereby enhancing the understanding of how SMBCs can facilitate value formation. By doing so, this research successfully extends the value formation lens predominantly applied in service marketing. In particular, the findings highlight the role of different actors in enabling the formation of different value types. Furthermore, the research emphasises the value of SMBCs as knowledge repositories as important virtual spaces for both brands and consumers. The findings facilitate understanding of the importance of SMBCs in value formation processes, contributing to advancing knowledge of the role of SMBCs in the development of consumer engagement and value formation. The thesis presents a contextualised conceptual framework of value formation within SMBCs, that captures different interactions taking place in the SMBC environment but also draws attention to the different value types generated through interaction between different actors. Finally, the thesis offers a conceptual framework of SMBCs, consumer engagement and value formation, which captures the correlation between the three researched concepts

    Self-supervised learning for transferable representations

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    Machine learning has undeniably achieved remarkable advances thanks to large labelled datasets and supervised learning. However, this progress is constrained by the labour-intensive annotation process. It is not feasible to generate extensive labelled datasets for every problem we aim to address. Consequently, there has been a notable shift in recent times toward approaches that solely leverage raw data. Among these, self-supervised learning has emerged as a particularly powerful approach, offering scalability to massive datasets and showcasing considerable potential for effective knowledge transfer. This thesis investigates self-supervised representation learning with a strong focus on computer vision applications. We provide a comprehensive survey of self-supervised methods across various modalities, introducing a taxonomy that categorises them into four distinct families while also highlighting practical considerations for real-world implementation. Our focus thenceforth is on the computer vision modality, where we perform a comprehensive benchmark evaluation of state-of-the-art self supervised models against many diverse downstream transfer tasks. Our findings reveal that self-supervised models often outperform supervised learning across a spectrum of tasks, albeit with correlations weakening as tasks transition beyond classification, particularly for datasets with distribution shifts. Digging deeper, we investigate the influence of data augmentation on the transferability of contrastive learners, uncovering a trade-off between spatial and appearance-based invariances that generalise to real-world transformations. This begins to explain the differing empirical performances achieved by self-supervised learners on different downstream tasks, and it showcases the advantages of specialised representations produced with tailored augmentation. Finally, we introduce a novel self-supervised pre-training algorithm for object detection, aligning pre-training with downstream architecture and objectives, leading to reduced localisation errors and improved label efficiency. In conclusion, this thesis contributes a comprehensive understanding of self-supervised representation learning and its role in enabling effective transfer across computer vision tasks

    Location Reference Recognition from Texts: A Survey and Comparison

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    A vast amount of location information exists in unstructured texts, such as social media posts, news stories, scientific articles, web pages, travel blogs, and historical archives. Geoparsing refers to recognizing location references from texts and identifying their geospatial representations. While geoparsing can benefit many domains, a summary of its specific applications is still missing. Further, there is a lack of a comprehensive review and comparison of existing approaches for location reference recognition, which is the first and core step of geoparsing. To fill these research gaps, this review first summarizes seven typical application domains of geoparsing: geographic information retrieval, disaster management, disease surveillance, traffic management, spatial humanities, tourism management, and crime management. We then review existing approaches for location reference recognition by categorizing these approaches into four groups based on their underlying functional principle: rule-based, gazetteer matching–based, statistical learning-–based, and hybrid approaches. Next, we thoroughly evaluate the correctness and computational efficiency of the 27 most widely used approaches for location reference recognition based on 26 public datasets with different types of texts (e.g., social media posts and news stories) containing 39,736 location references worldwide. Results from this thorough evaluation can help inform future methodological developments and can help guide the selection of proper approaches based on application needs

    TM-vector: A Novel Forecasting Approach for Market stock movement with a Rich Representation of Twitter and Market data

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    Stock market forecasting has been a challenging part for many analysts and researchers. Trend analysis, statistical techniques, and movement indicators have traditionally been used to predict stock price movements, but text extraction has emerged as a promising method in recent years. The use of neural networks, especially recurrent neural networks, is abundant in the literature. In most studies, the impact of different users was considered equal or ignored, whereas users can have other effects. In the current study, we will introduce TM-vector and then use this vector to train an IndRNN and ultimately model the market users' behaviour. In the proposed model, TM-vector is simultaneously trained with both the extracted Twitter features and market information. Various factors have been used for the effectiveness of the proposed forecasting approach, including the characteristics of each individual user, their impact on each other, and their impact on the market, to predict market direction more accurately. Dow Jones 30 index has been used in current work. The accuracy obtained for predicting daily stock changes of Apple is based on various models, closed to over 95\% and for the other stocks is significant. Our results indicate the effectiveness of TM-vector in predicting stock market direction.Comment: 24 pag

    Evolution of artificial intelligence research in Technological Forecasting and Social Change: Research topics, trends, and future directions

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    Artificial intelligence (AI) is a set of rapidly expanding disruptive technologies that are radically transforming various aspects related to people, business, society, and the environment. With the proliferation of digital computing devices and the emergence of big data, AI is increasingly offering significant opportunities for society and business organizations. The growing interest of scholars and practitioners in AI has resulted in the diversity of research topics explored in bulks of scholarly literature published in leading research outlets. This study aims to map the intellectual structure and evolution of the conceptual structure of overall AI research published in Technological Forecasting and Social Change (TF&SC). This study uses machine learning-based structural topic modeling (STM) to extract, report, and visualize the latent topics from the AI research literature. Further, the disciplinary patterns in the intellectual structure of AI research are examined with the additional objective of assessing the disciplinary impact of AI. The results of the topic modeling reveal eight key topics, out of which the topics concerning healthcare, circular economy and sustainable supply chain, adoption of AI by consumers, and AI for decision-making are showing a rising trend over the years. AI research has a significant influence on disciplines such as business, management, and accounting, social science, engineering, computer science, and mathematics. The study provides an insightful agenda for the future based on evidence-based research directions that would benefit future AI scholars to identify contemporary research issues and develop impactful research to solve complex societal problems

    La traduzione specializzata all’opera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.

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    Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The “Language Toolkit – Le lingue straniere al servizio dell’internazionalizzazione dell’impresa” project, promoted by the Department of Interpreting and Translation (ForlĂŹ Campus) in collaboration with the Romagna Chamber of Commerce (ForlĂŹ-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices

    Personality-Driven Social Media Curation: How Personality Traits Affect Following Decisions on Twitter

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    As social media occupies an increasingly important place in people’s lives, new opportunities are presented for people to select and modify their online environments. On many platforms, users have significant control over what kind of information and experiences they are exposed to. For example, on Twitter, virtually everything users see is a function of their decisions about what accounts to follow. What drives those decisions? My dissertation explores the extent to which personality is reflected in our social media environment by examining the relationship between personality traits and the accounts that users follow on Twitter. Particularly, what features of accounts influence following decisions and how personality traits of users align with characteristics of Twitter accounts. Exploring the relationship between who we are and the decisions we make online provides a better understanding of how characteristics, such as personality traits, drive the curation of our social environments. Overall, findings indicate that personality does influence the decisions we make about which Twitter accounts to follow and in turn, how our social media environment is curated. The strength and stability of this relationship shows some heterogeneity across traits, though is generally comparable to the effect of some commonly used demographic variables. Personality traits of users also align with characteristics of Twitter accounts and moderate the effect of different Twitter profile features on our following decisions, highlighting potential psychological processes that drive following decisions. For example, extraverts want to feel connected to popular accounts and seek content on topics that lots of other people care about while Neuroticism is associated with following accounts that conform to gender and age norms. Perhaps most notably, these relationships demonstrate remarkable generalizability when tested on a set of real-world followed accounts. Though this research is a first step in exploring the influence of personality on the vast number behaviors that occur on social media, these findings establish foundational knowledge and inform future research

    “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

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    Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT’s capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT’s use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts

    The Individual And Their World

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    Unveiling the frontiers of deep learning: innovations shaping diverse domains

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    Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table
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