The Hive Mind at Work: Crowdsourcing E-Tourism Research

Jing Ge-Stadnyk ()
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Jing Ge-Stadnyk: University of California, Berkeley

Chapter 26 in Handbook of e-Tourism, 2022, pp 617-633 from Springer

Abstract: Abstract Tourism scholars are increasingly turning to web-based platforms to conduct e-tourism research. The availability of crowdsourcing websites (e.g., Amazon Mechanical Turk or “MTurk”) has made a range of research approaches, including survey and experimental investigations, more efficient. When used to analyze social media data, human intelligence – an essential component of crowdsourcing research – can also help researchers tackle issues unsolvable through automation or machine learning, such as text and image annotation. However, compared to other domains (e.g., social science, computer science), within e-tourism, crowdsourcing research has not yet been fully leveraged as a scientific method. It is argued herein that, in order to move the field forward, e-tourism scholars must better grasp the unique and dynamic structure and principles of crowdsourcing research. This chapter reviews and synthesizes the relevant literature, proposing a set of building blocks upon which crowdsourcing research may be structured, that is, a crowd of participants, crowdsourcing platforms, and research types. Further, it offers seven guidelines to inform e-tourism crowdsourcing research practice: determining research types, choosing crowdsourcing platforms, defining crowdsourced populations, recruiting participants, managing crowds, handling ethical issues, and reporting.

Keywords: Crowdsourcing research; E-tourism; Crowdsourced data; Crowdsourced data analysis; Crowdsourcing platforms; Crowdsourcing research guidelines (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-3-030-48652-5_119

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