Evidence map›Paper›PMID 36818420›Full record

ArticlePersonal and ubiquitous computing2023

A design concept of big data analytics model for managers in hospitality industries.

Seyedmohammad Mousavian, Shah J Miah, Yifan Zhong

Abstract read
In one paragraph

Article in Personal and ubiquitous computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Seyedmohammad MousavianNewcastle Business School, University of Newcastle, Callaghan, NSW Australia.
Shah J MiahNewcastle Business School, University of Newcastle, Callaghan, NSW Australia.ORCID 0000-0002-3783-8769
Yifan ZhongSchool of Management and Marketing, Curtin University, Perth, WA Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Big dataData analyticsDecision supportHospitalityMachine learningTourism

Identifiers

PMID36818420
PMCPMC9930036

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.