Gianluca Solazzo, Ylenia Maruccia, Gianluca Lorenzo, Valentina Ndou, Pasquale Del Vecchio and Gianluca Elia
This paper aims to highlight how big social data (BSD) and analytics exploitation may help destination management organisations (DMOs) to understand tourist behaviours and…
Abstract
Purpose
This paper aims to highlight how big social data (BSD) and analytics exploitation may help destination management organisations (DMOs) to understand tourist behaviours and destination experiences and images. Gathering data from two different sources, Flickr and Twitter, textual and visual contents are used to perform different analytics tasks to generate insights on tourist behaviour and the affective aspects of the destination image.
Design/methodology/approach
This work adopts a method based on a multimodal approach on BSD and analytics, considering multiple BSD sources, different analytics techniques on heterogeneous data types, to obtain complementary results on the Salento region (Italy) case study.
Findings
Results show that the generated insights allow DMOs to acquire new knowledge about discovery of unknown clusters of points of interest, identify trends and seasonal patterns of tourist demand, monitor topic and sentiment and identify attractive places. DMOs can exploit insights to address its needs in terms of decision support for the management and development of the destination, the enhancement of destination attractiveness, the shaping of new marketing and communication strategies and the planning of tourist demand within the destination.
Originality/value
The originality of this work is in the use of BSD and analytics techniques for giving DMOs specific insights on a destination in a deep and wide fashion. Collected data are used with a multimodal analytic approach to build tourist characteristics, images, attitudes and preferred destination attributes, which represent for DMOs a unique mean for problem-solving, decision-making, innovation and prediction.
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Keywords
Gianluca Solazzo, Gianluca Elia and Giuseppina Passiante
This study aims to investigate the Big Social Data (BSD) paradigm, which still lacks a clear and shared definition, and causes a lack of clarity and understanding about its…
Abstract
Purpose
This study aims to investigate the Big Social Data (BSD) paradigm, which still lacks a clear and shared definition, and causes a lack of clarity and understanding about its beneficial opportunities for practitioners. In the knowledge management (KM) domain, a clear characterization of the BSD paradigm can lead to more effective and efficient KM strategies, processes and systems that leverage a huge amount of structured and unstructured data sources.
Design/methodology/approach
The study adopts a systematic literature review (SLR) methodology based on a mixed analysis approach (unsupervised machine learning and human-based) applied to 199 research articles on BSD topics extracted from Scopus and Web of Science. In particular, machine learning processing has been implemented by using topic extraction and hierarchical clustering techniques.
Findings
The paper provides a threefold contribution: a conceptualization and a consensual definition of the BSD paradigm through the identification of four key conceptual pillars (i.e. sources, properties, technology and value exploitation); a characterization of the taxonomy of BSD data type that extends previous works on this topic; a research agenda for future research studies on BSD and its applications along with a KM perspective.
Research limitations/implications
The main limits of the research rely on the list of articles considered for the literature review that could be enlarged by considering further sources (in addition to Scopus and Web of Science) and/or further languages (in addition to English) and/or further years (the review considers papers published until 2018). Research implications concern the development of a research agenda organized along with five thematic issues, which can feed future research to deepen the paradigm of BSD and explore linkages with the KM field.
Practical implications
Practical implications concern the usage of the proposed definition of BSD to purposefully design applications and services based on BSD in knowledge-intensive domains to generate value for citizens, individuals, companies and territories.
Originality/value
The original contribution concerns the definition of the big data social paradigm built through an SLR the combines machine learning processing and human-based processing. Moreover, the research agenda deriving from the study contributes to investigate the BSD paradigm in the wider domain of KM.
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Salvatore Ammirato, Roberto Linzalone and Alberto Michele Felicetti