How smart is e-tourism? A systematic review of smart tourism recommendation system applying data management

Hamid, Rula A.; Albahri, A. S.; Alwan, Jwan K.; Al-qaysi, Z.T.; Albahri, O. S.; Zaidan, A. A.; Alnoor, Alhamzah; Alamoodi, A. H.; Zaidan, B. B.

doi:10.1016/j.cosrev.2020.100337

Cited by 173 publications

(98 citation statements)

References 76 publications

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“…The present research was conducted to comply with the criteria established in the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) [ 25 – 29 ]. PRISMA recommends not relying on a single database search for literature when doing a systematic review because no single database is likely to include all relevant references.…”

Section: Methodsmentioning

confidence: 99%

Hybrid artificial neural network and structural equation modelling techniques: a survey

Albahri

Alnoor

Zaidan

et al. 2021

Complex Intell. Syst.

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Topical treatments with structural equation modelling (SEM) and an artificial neural network (ANN), including a wide range of concepts, benefits, challenges and anxieties, have emerged in various fields and are becoming increasingly important. Although SEM can determine relationships amongst unobserved constructs (i.e. independent, mediator, moderator, control and dependent variables), it is insufficient for providing non-compensatory relationships amongst constructs. In contrast with previous studies, a newly proposed methodology that involves a dual-stage analysis of SEM and ANN was performed to provide linear and non-compensatory relationships amongst constructs. Consequently, numerous distinct types of studies in diverse sectors have conducted hybrid SEM–ANN analysis. Accordingly, the current work supplements the academic literature with a systematic review that includes all major SEM–ANN techniques used in 11 industries published in the past 6 years. This study presents a state-of-the-art SEM–ANN classification taxonomy based on industries and compares the effort in various domains to that classification. To achieve this objective, we examined the Web of Science, ScienceDirect, Scopus and IEEE Xplore® databases to retrieve 239 articles from 2016 to 2021. The obtained articles were filtered on the basis of inclusion criteria, and 60 studies were selected and classified under 11 categories. This multi-field systematic study uncovered new research possibilities, motivations, challenges, limitations and recommendations that must be addressed for the synergistic integration of multidisciplinary studies. It contributed two points of potential future work resulting from the developed taxonomy. First, the importance of the determinants of play, musical and art therapy adoption amongst autistic children within the healthcare sector is the most important consideration for future investigations. In this context, the second potential future work can use SEM–ANN to determine the barriers to adopting sensing-enhanced therapy amongst autistic children to satisfy the recommendations provided by the healthcare sector. The analysis indicates that the manufacturing and technology sectors have conducted the most number of investigations, whereas the construction and small- and medium-sized enterprise sectors have conducted the least. This study will provide a helpful reference to academics and practitioners by providing guidance and insightful knowledge for future studies.

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Section: Methodsmentioning

confidence: 99%

Hybrid artificial neural network and structural equation modelling techniques: a survey

Albahri

Alnoor

Zaidan

et al. 2021

Complex Intell. Syst.

Self Cite

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“…Smart e‐tourism data management applications per category and the 12 key concepts should be defined as the alternatives and evaluation criteria in each DM, respectively. The 12 key concepts for smart e‐tourism are defined according to Reference [1]: Context‐awareness (C1): The sensors in smartphones provide a means of interaction between the mobile phone and the environment, improving smart e‐tourism services 93 …”

Section: Methodsmentioning

confidence: 99%

“…The data within the decision matrices (Tables 1–5) in both smart e‐tourism categories are adopted from a prior study 1 . These data represent the values of each evaluated smart e‐tourism data management application based on the given criteria.…”

Section: Methodsmentioning

confidence: 99%

“…This discrepancy raises a major challenge in evaluating and benchmarking data management applications of each category. These concepts will result in different performances of the data management applications 1 . The above discussion has pointed out three pressing issues: multiple evaluation criteria, data variation, and importance criteria.…”

Section: Introductionmentioning

confidence: 99%

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Interval type 2 trapezoidal‐fuzzy weighted with zero inconsistency combined with VIKOR for evaluating smart e‐tourism applications

Krishnan

Mohammed

Alnoor

et al. 2021

Int J of Intelligent Sys

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The benchmarking of smart e‐tourism data management applications falls under the problem of multicriteria decision‐making (MCDM). This claim is supported by three issues: 12 smart key concepts need to be considered in the evaluation, criteria importance, and data variation among these criteria. Thus, an MCDM solution is essential to overcome problem complexity. To end this, this study presents a decision‐making framework on the basis of the extension of interval type 2 trapezoidal‐fuzzy weighted with zero inconsistency (IT2TR‐FWZIC) integrated with the Vlsekriterijumska Optimizcija I Kaompromisno Resenje (VIKOR) method for evaluating and benchmarking the smart e‐tourism data management applications. Our methodology comprises two consecutive phases. In the first phase, a decision matrix is constructed using the intersection between the 12 key concepts and smart e‐tourism data management applications of each category and subcategory in smart e‐tourism. In the second phase, the integration of the IT2TR‐FWZIC formulation and VIKOR is presented to compute the weights for the 12 key concepts and benchmark the smart e‐tourism data management applications for each category. The results are as follows: (1) A clear difference is found among the criteria weights (12 smart key concepts). Specifically, the real‐time criterion achieves the highest importance weight (0.098), whereas augmented reality obtains the lowest weight (0.068). The context‐awareness and recommender systems have the same weight value (0.087), and the other eight criteria are distributed in between. (2) The smart e‐tourism data management applications are evaluated and benchmarked effectively per category and subcategories. (3) Benchmarked applications in each category are subjected to a systematic ranking in the evaluation process. The sensitivity analysis has shown high correlation outcomes to the systematic ranking results over the 31 scenarios of criteria weight changing. Moreover, a comparative analysis of the proposed work with other existing studies is also discussed.

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“…This is important to ensure visitors have adequate knowledge when visiting a tourist destination. Content must be built using available information technology in a friendly manner so that smart technology-based content is produced (Hamid et al, 2021) which makes visitors have a satisfying experience when visiting tourist destinations (Stankov & Gretzel, 2020). Smart tourism must be built on strong knowledge by paying attention to the development of science from the results of previous research (Shafiee et al, 2019).…”

Section: Developmentmentioning

confidence: 99%

Competitive intelligence approach for developing an e-tourism strategy post COVID-19

Tulungen¹,

Batmetan²,

Komansilan³

et al. 2021

JISIB

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The COVID-19 pandemic has brought many fundamental changes in running atourism business. Many countries need to reformulate their post-19 strategy so that the tourismsector will revive. This study aims to formulate a strategy for developing e-tourism by utilizinginformation technology. The method used is a competitive intelligence approach. This researchtakes samples from tourist destinations in Indonesia. The results of this study indicate that theright strategy can encourage the tourism industry to grow back in the post-COVID-19 period.The resulting strategy is based on campaign, content, community, cooperation, andcompetitiveness. These five basic strategies are implemented with an e-tourism model and asimple management pattern utilizing information technology. The results of this research canhave implications for the formulation of e-tourism policies and produce recommendations forpolicymakers.

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How smart is e-tourism? A systematic review of smart tourism recommendation system applying data management

Cited by 173 publications

References 76 publications

Hybrid artificial neural network and structural equation modelling techniques: a survey

Hybrid artificial neural network and structural equation modelling techniques: a survey

Interval type 2 trapezoidal‐fuzzy weighted with zero inconsistency combined with VIKOR for evaluating smart e‐tourism applications

Competitive intelligence approach for developing an e-tourism strategy post COVID-19

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