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2023 | nr 3 (49) | 65--100
Tytuł artykułu

Mapping Research on User-Generated Content in the Service Sector - a Bibliometric Analysis

Warianty tytułu
Diagnoza badań nad treściami generowanymi przez użytkownika sieci w sektorze usług - analiza bibliometryczna
Języki publikacji
EN
Abstrakty
EN
The Web 2.0 era and the following phases of web development bring new challenges to businesses, but also new opportunities to establish and maintain relationships with market participants, indulge in direct contact with customers and learn about their needs, emotions and opinions. The advancement of content creation and sharing technologies creates an opportunity to collect information from anyone with access to the Internet. User-generated content (UGC) information is increasingly supporting decision-making and analysis for various types of business, management or marketing activities. Such information is also increasingly used as a source of data in scientific research. The present study seeks to evaluate the relevance of UGC in scientific research and the scope and ways in which content created by Internet users can be used by researchers of phenomena existing in the service sector. To achieve this goal, a bibliometric literature review (quantitative analysis of publications, identification of research collaborators, co-author analysis, co-citation analysis and co-word analysis) was conducted covering articles between 2012 and 2022 published in journals indexed in the Scopus database. The analysis used descriptive statistics and text and content analysis. A significant increase was observed in publications between 2020 and 2022. Among the various service branches, the researchers most often chose data sets in the form of comments posted online by customers of tourism industries, mainly those using accommodation services, but also restaurants. TripAdvisor was observed to be the most frequently used data source. In their analysis, the authors used both qualitative and quantitative methods, as well as a combination of them. It is observed that more sophisticated machine learning algorithms have been implemented for text analysis. Finally, the paper also presents future research recommendations. (original abstract)
Era Web 2.0 i kolejne fazy rozwoju sieci oferują przedsiębiorstwom nowe wyzwania, ale także nowe możliwości w zakresie nawiązywania i utrzymywania kontaktów z uczestnikami rynku, bezpośredniego kontaktu z klientami, ale także poznawania ich potrzeb i opinii. Rozwój technologii tworzenia i udostępniania treści stwarza okazję do zbierania informacji od każdego, kto ma dostęp do Internetu. Informacje generowane przez użytkowników (user-generated content [UGC]) w coraz większym stopniu wspierają podejmowanie decyzji i analizę w odniesieniu do różnych rodzajów działalności biznesowej, zarządczej czy marketingowej. Coraz częściej wykorzystywane są także jako źródło danych w badaniach naukowych. Celem opracowania jest ocena znaczenia UGC w badaniach naukowych oraz skali i sposobów wykorzystania treści tworzonych przez internautów przez współczesnych badaczy zjawisk występujących w sektorze usług. Dla realizacji celu przeprowadzona została analiza bibliometryczna (ilościowa analiza publikacji, analiza współautorów, analiza cytowań i analiza słów kluczowych) obejmująca publikacje z lat 2012-2022 w czasopismach indeksowanych w bazie Scopus. W analizie wykorzystano opis statystyczny oraz analizę treści i zawartości. Wykazano znaczny przyrost publikacji w latach 2020-2022. Spośród różnych branż usługowych, badacze najczęściej wybierali zbiory danych w postaci komentarzy zamieszczanych w sieci przez klientów branż turystycznych, głównie korzystających z usług noclegowych, ale także restauracji. Najczęściej wykorzystywanym źródłem danych był serwis TripAdvisor. Autorzy w analizach posługiwali się zarówno metodami jakościowymi, jak i ilościowymi oraz metodami mieszanymi. Zaobserwowano wzrost znaczenia metod wykorzystujących rozbudowane algorytmy uczenia maszynowego do analizy tekstu. W podsumowaniu do opracowania wskazano kierunki przyszłych badań. (abstrakt oryginalny)
Rocznik
Numer
Strony
65--100
Opis fizyczny
Twórcy
  • SGH Warsaw School of Economics
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Typ dokumentu
Bibliografia
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bwmeta1.element.ekon-element-000171672076

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