4,767 research outputs found

    The Impact of Information and Communication Technology on the Tourism Sector

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    Information and Communication Technology (ICT) has changed the global businesses environment by a wide range of tools, methodologies and functions, facilitating the strategic management and supporting firms to achieve a long term competitive advantage. The aim of this paper is to provide an overview of the new applications of Information Communication Technology in tourism industry, the contribution of ICT to the promotion of the tourist product, as well as the potential to the tourism management and the process of decision-making. One important tool, which helps in making decisions in the field of tourism economy, is the Geographic Information System (GIS), which provides a comprehensible representation of the statistical figures of the tourism economy by facilitating decision-making on tourism policy. In this paper is presented some tourist financial figures and their visualization through graphs by Geographic Information System

    DIGITAL WINE: HOW PLATFORMS AND ALGORITHMS WILL RESHAPE THE WINE INDUSTRY

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    La tesi si propone di analizzare come la digitalizzazione e gli approcci basati sui dati, in particolare quelli che sfruttano l'intelligenza artificiale, stiano impattando il settore vitivinicolo e facendo emergere modelli nuovi di business. Quest'ultimo aspetto sarĂ  approfondito tramite due casi studio di piattaforme digitali che, attraverso approcci diversi, stanno contribuendo a generare un ecosistema digitale virtuoso, con potenziali benefici per tutta la catena del valore a livello di settore.The thesis aims to analyze how digitalization and data-driven approaches, in particular those that leverage artificial intelligence, are impacting the wine industry and generating new business models. The latter aspect will be explored through two case studies of digital platforms which, through different approaches, are helping to generate a virtuous digital ecosystem, with potential benefits for the entire value chain at the industry level

    A Normative Classification of Consumer Big Data

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    The big data phenomenon has transformed every area of life and business. Businesses today rely on the volume, velocity, and variety (3Vs) of data available today in product design, advertisement, sales, and post-sale follow up activities. Communication between the firm and the consumer is personalized using data collected on the consumer to match the consumer’s location, time, and needs. Some marketers argue that this has birth a new era of marketing; transformative marketing, in which the firm’s ability to deliver value and to acquire and maintain long-run competitive advantage determined by the firm’s data resources. In other words, data are the currency of the transformative marketing era. This sentiment is pervasive and has led to massive investments in data in recent years. This dissertation puts forward a classification of consumer big data to aid the firm extract value out of big data despite the 3Vs. The classification also demonstrates how value in a transformative marketing era does not have to be created at the expense of the consumer, but with the consumer. Five conceptual dichotomies are put forward in essay two that are more comprehensive than any other classification of data available in the research. Finally, the third essay investigates how the big data phenomenon affects consumer freedom and emotions. Most people agree that freedom is a fundamental human right, and that business practices should respect consumer freedom. However, research on consumer freedom is scant. Two experiments investigate how the characteristics of data collected on consumers affects consumer perception of decision freedom and satisfaction with value propositions. With the big data phenomenon has come a push toward algorithmic decision making. Consumer’s anxiety toward algorithmic decision making is investigated along with the satisfaction derived from decisions made by third parties that collect data on consumers

    Chapter 20 Data Quality and Privacy concerns in Digital Trace Data

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    "The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors.
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