Extracting Value from Social Big Data: Empirical Studies on Online Customer Reviews and Managerial Responses

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorFan, Wenjie
dc.contributor.departmentTieto ja palvelujohtamisen laitosfi
dc.contributor.departmentDepartment of Information and Service Managementen
dc.contributor.schoolKauppakorkeakoulufi
dc.contributor.schoolSchool of Businessen
dc.contributor.supervisorYong Liu, Assoc. Prof., Aalto University, Department of Information and Service Management, Finland; Tuunainen, Virpi Kristiina, Prof., Aalto University, Department of Information and Service Management, Finland
dc.date.accessioned2022-05-02T09:00:09Z
dc.date.available2022-05-02T09:00:09Z
dc.date.issued2022
dc.descriptionDefence is held on 19.5.2022 12:00 – 16:00 Zoom: https://aalto.zoom.us/j/5702251554
dc.description.abstractThe advance of information technology has significantly digitalized our economy and activities and brought people into a big data era. The flourishing of social media has enabled massive-scale user-generated information sharing, which makes social big data available. In the context of e-commerce, consumers face a higher level of uncertainty and greater risk when purchasing online. As a result, many users utilize online customer review (OCR), as a novel Information Systems (IS) artifact, to alleviate their perceived uncertainty and the risks that hamper their online purchasing decisions. OCRs can influence consumer attitude and business performance, which drives companies to proactively intervene in the OCRs through the use of the managerial response (MR) function. An enormous amount of social big data in the form of OCRs and MRs has been accumulated online so far and presents both opportunities and challenges to researchers and stakeholders to use them as a rich source of business insights. The objective of this dissertation is to offer new knowledge on the value of social big data in the form of OCRs and MRs for different stakeholders in the context of the tourism industry from three perspectives: the consumer perspective, the company perspective, and the industry perspective. Such new knowledge is derived from reflections on four previous papers that I had authored and co-authored. From the angle of consumers, Paper 1 synthesizes literature on the helpfulness of OCRs and provides an integrated understanding of the determinants of OCR helpfulness through a systematic literature review. Paper 2 analyzes big data of OCRs in light of the attribution theory to investigate the impact of review content structures on OCR helpfulness and to demonstrate the important moderating effects of the reviewer reputation and review sentiments. Paper 3 focuses on the impacts of the MR function on company performance, specifically utilizing Kano's theory of attractive quality to investigate the effect of dissipating benefits of IS service availability. This paper shows that whereas companies offering MRs gain constant advantages over those not employing the MR function, the ability of the MR function to improve business performance dissipates over time among the companies adopting it. Paper 4 quantifies the detrimental effect of air pollution on the revisit behaviors of foreign tourists by analyzing a large volume of OCRs, which introduced a novel approach to examining collective consumer behavior using social big data from the industry perspective. This dissertation contributes to the scholarly discussion of social big data in the form of OCRs and MRs and concretely demonstrates the value of social big data to various stakeholders. In addition, this work can benefit IS researchers and stakeholders striving to exploit phenomena connected to IS artifacts and consumer behavior in the big data era.en
dc.format.extent66 + app. 122
dc.format.mimetypeapplication/pdfen
dc.identifier.isbn978-952-64-0777-7 (electronic)
dc.identifier.isbn978-952-64-0776-0 (printed)
dc.identifier.issn1799-4942 (electronic)
dc.identifier.issn1799-4934 (printed)
dc.identifier.issn1799-4934 (ISSN-L)
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/114097
dc.identifier.urnURN:ISBN:978-952-64-0777-7
dc.language.isoenen
dc.opnSuomi, Reima, Prof., University of Turku, Finland
dc.opnPappas, Ilias, Prof., University of Agder, Norway
dc.publisherAalto Universityen
dc.publisherAalto-yliopistofi
dc.relation.haspart[Publication 1]: Fan, Wenjie. 2021. What Makes Consumer Perception of Online Review Helpfulness: Synthesizing the Past to Guide Future Research. In: Proceedings of the 54th Hawaii International Conference on System Sciences (HICSS 2021), Kauai, HI, January 2021. Pages 2738–2747. DOI: 10.24251/HICSS.2021.334
dc.relation.haspart[Publication 2]: Fan, Wenjie; Liu, Yong; Li, Hongxiu; Tuunainen, Virpi K.; Lin, Yanqing. 2021. Quantifying the Effects of Online Review Content Structures on Hotel Review Helpfulness. Internet Research. DOI: 10.1108/INTR-11-2019-0452
dc.relation.haspart[Publication 3]: Fan, Wenjie; Liu, Yong; Tuunainen, Virpi K.; Li, Hongxiu; Tan, Chee-Wee; Saarinen, Timo. The Timing Effect of IS Service Availability: The Case of Managerial Response Service Usage in the Hospitality Industry. Submitted to Journal of Management Information Systems in 2022
dc.relation.haspart[Publication 4]: Fan, Wenjie; Li, Yijing; Upreti, Bikesh Raj; Liu, Yong; Li, Hongxiu; Fan, Wei; Lim, Eric T.K. 2021. Big Data for Big Insights: Quantifying the Adverse Effect of Air Pollution on the Tourism Industry in China. Journal of Travel Research. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202109299364. DOI: 10.1177/00472875211047272
dc.relation.ispartofseriesAalto University publication series DOCTORAL THESESen
dc.relation.ispartofseries57/2022
dc.revSuomi, Reima, Prof., University of Turku, Finland
dc.revPappas, Ilias, Prof., University of Agder, Norway
dc.subject.keywordBig Dataen
dc.subject.keywordsocial mediaen
dc.subject.keywordonline customer reviewen
dc.subject.keywordonline review helpfulnessen
dc.subject.keywordreview content structuresen
dc.subject.keywordmanagerial responseen
dc.subject.keywordconsumer behavioren
dc.subject.keywordbusiness performanceen
dc.subject.otherConsumption, Servicesen
dc.subject.otherInformation systemsen
dc.titleExtracting Value from Social Big Data: Empirical Studies on Online Customer Reviews and Managerial Responsesen
dc.typeG5 Artikkeliväitöskirjafi
dc.type.dcmitypetexten
dc.type.ontasotDoctoral dissertation (article-based)en
dc.type.ontasotVäitöskirja (artikkeli)fi
local.aalto.acrisexportstatuschecked 2022-05-20_0819
local.aalto.archiveyes
local.aalto.formfolder2022_05_02_klo_10_00
local.aalto.infraScience-IT
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