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Big data management capabilities in the hospitality sector: Service innovation and customer generated online quality ratings

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dc.title Big data management capabilities in the hospitality sector: Service innovation and customer generated online quality ratings en
dc.contributor.author Shamim, Saqib
dc.contributor.author Yang, Yumei
dc.contributor.author Zia, Najam Ul
dc.contributor.author Shah, Mahmood Hussain
dc.relation.ispartof Computers in Human Behavior
dc.identifier.issn 0747-5632 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2021
utb.relation.volume 121
dc.type article
dc.language.iso en
dc.publisher Elsevier Ltd
dc.identifier.doi 10.1016/j.chb.2021.106777
dc.relation.uri https://www.sciencedirect.com/science/article/pii/S074756322100100X
dc.subject big data management en
dc.subject dynamic capabilities en
dc.subject service innovation en
dc.subject knowledge creation en
dc.subject customer generated online quality rating en
dc.subject hospitality en
dc.description.abstract Despite the wide usage of big data in tourism and the hospitality sector, little research has been done to understand the role of organizations’ capability of managing big data in value creation. This study bridges this gap by investigating how big data management capabilities lead to service innovation and high online quality ratings. Instead of treating big data management as a whole, we access big data management capabilities at the strategic and operational level. Using a sample of 202 hotels in Pakistan, we collected the primary data for big data capabilities, knowledge creation and service innovation; the secondary data about quality rating were collected from Booking.com. Structural equation modelling through SmartPLS was used for data analysis. The results indicated that big data management capabilities lead to high online quality ratings through the mediation of knowledge creation and service innovation. We contribute to the current literature by empirically testing how strategic level big data capabilities enable the firm to add value in innovativeness and positive online quality ratings through acquiring, contextualizing, experimenting and applying big data. © 2021 Elsevier Ltd en
utb.faculty Faculty of Management and Economics
dc.identifier.uri http://hdl.handle.net/10563/1010263
utb.identifier.obdid 43882306
utb.identifier.scopus 2-s2.0-85102810733
utb.identifier.wok 000651382100011
utb.identifier.coden CHBEE
utb.source j-scopus
dc.date.accessioned 2021-04-07T07:50:42Z
dc.date.available 2021-04-07T07:50:42Z
dc.description.sponsorship Internal Grant Agency [IGA/FaME/2021/006]; Faculty of Management and Economics through Tomas Bata University in Zln, Czech RepublicCzech Republic Government
dc.description.sponsorship IGA/FaME/2021/006; Univerzita Tomáše Bati ve Zlíně
utb.contributor.internalauthor Zia, Najam Ul
utb.fulltext.sponsorship The authors also acknowledge the financial support provided by the Internal Grant Agency ( IGA/FaME/2021/006 ) of Faculty of Management and Economics through Tomas Bata University in Zlín , Czech Republic.
utb.wos.affiliation [Shamim, Saqib] Univ Kent, Kent Business Sch, Canterbury, Kent, England; [Yang, Yumei] Bournemouth Univ, Bournemouth Univ Business Sch, Bournemouth, Dorset, England; [Ul Zia, Najam] Tomas Bata Univ Zlin, Zlin, Czech Republic; [Shah, Mahmood Hussain] Northumbria Univ, Newcastle Business Sch, Newcastle Upon Tyne, Tyne & Wear, England
utb.scopus.affiliation Kent Business School, University of Kent, Canterbury, United Kingdom; Bournemouth University Business School, Bournemouth University, Bournemouth, United Kingdom; Tomas Bata University in Zlin, Czech Republic; Newcastle Business School, Northumbria University, Newcastle, United Kingdom
utb.fulltext.projects IGA/FaME/2021/006
utb.identifier.jel -
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