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A design concept of big data analytics model for managers in hospitality industries
The hospitality and tourism sector has long played a significant role in Australia’s economy, especially in regional areas. Due to the onslaught of COVID-19, numerous businesses have experienced lockdowns, restrictions, and closures due to the fact that people’s activity in restaurants, shopping cen...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer London
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9930036/ https://www.ncbi.nlm.nih.gov/pubmed/36818420 http://dx.doi.org/10.1007/s00779-023-01714-3 |
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author | Mousavian, Seyedmohammad Miah, Shah J. Zhong, Yifan |
author_facet | Mousavian, Seyedmohammad Miah, Shah J. Zhong, Yifan |
author_sort | Mousavian, Seyedmohammad |
collection | PubMed |
description | The hospitality and tourism sector has long played a significant role in Australia’s economy, especially in regional areas. Due to the onslaught of COVID-19, numerous businesses have experienced lockdowns, restrictions, and closures due to the fact that people’s activity in restaurants, shopping centers, and recreational destinations was restricted, and many other places went into hibernation. After about 2 years since the outbreak, businesses in this sector are gradually starting to reopen and revitalize themselves, but in order to have better decision support about the future of this sector, thus being able to plan, businesses are suffering from an effective analytics solution due to the lack of broken data trends. Starting from fresh day-to-day real-time big data, the study aims to develop a new data analytics model, adopting the design science research methodology, which can provide invaluable options and techniques to make prediction easier from immediate past datasets. This study introduces an innovative design artifact as a big data solution for hospitality managers to utilize analytics for predictive strategic decision-making in post-COVID situation. The artifact can also be generalized for other sectors with tailoring aspects which are subject to further studies. The proposed artifact is then compared with other design artifacts related to big data solutions where it outperforms them in terms of comprehensiveness. The proposed artifact also shows promises for primarily available UGC in managers’ decision support aids. |
format | Online Article Text |
id | pubmed-9930036 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer London |
record_format | MEDLINE/PubMed |
spelling | pubmed-99300362023-02-15 A design concept of big data analytics model for managers in hospitality industries Mousavian, Seyedmohammad Miah, Shah J. Zhong, Yifan Pers Ubiquitous Comput Original Paper The hospitality and tourism sector has long played a significant role in Australia’s economy, especially in regional areas. Due to the onslaught of COVID-19, numerous businesses have experienced lockdowns, restrictions, and closures due to the fact that people’s activity in restaurants, shopping centers, and recreational destinations was restricted, and many other places went into hibernation. After about 2 years since the outbreak, businesses in this sector are gradually starting to reopen and revitalize themselves, but in order to have better decision support about the future of this sector, thus being able to plan, businesses are suffering from an effective analytics solution due to the lack of broken data trends. Starting from fresh day-to-day real-time big data, the study aims to develop a new data analytics model, adopting the design science research methodology, which can provide invaluable options and techniques to make prediction easier from immediate past datasets. This study introduces an innovative design artifact as a big data solution for hospitality managers to utilize analytics for predictive strategic decision-making in post-COVID situation. The artifact can also be generalized for other sectors with tailoring aspects which are subject to further studies. The proposed artifact is then compared with other design artifacts related to big data solutions where it outperforms them in terms of comprehensiveness. The proposed artifact also shows promises for primarily available UGC in managers’ decision support aids. Springer London 2023-02-15 /pmc/articles/PMC9930036/ /pubmed/36818420 http://dx.doi.org/10.1007/s00779-023-01714-3 Text en © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Paper Mousavian, Seyedmohammad Miah, Shah J. Zhong, Yifan A design concept of big data analytics model for managers in hospitality industries |
title | A design concept of big data analytics model for managers in hospitality industries |
title_full | A design concept of big data analytics model for managers in hospitality industries |
title_fullStr | A design concept of big data analytics model for managers in hospitality industries |
title_full_unstemmed | A design concept of big data analytics model for managers in hospitality industries |
title_short | A design concept of big data analytics model for managers in hospitality industries |
title_sort | design concept of big data analytics model for managers in hospitality industries |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9930036/ https://www.ncbi.nlm.nih.gov/pubmed/36818420 http://dx.doi.org/10.1007/s00779-023-01714-3 |
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