international journal of tourism research impact factor

International Journal of Tourism Research - Impact Score, Ranking, SJR, h-index, Citescore, Rating, Publisher, ISSN, and Other Important Details

Published By: John Wiley and Sons Ltd

Abbreviation: Int. J. Tour. Res.

Impact Score The impact Score or journal impact score (JIS) is equivalent to Impact Factor. The impact factor (IF) or journal impact factor (JIF) of an academic journal is a scientometric index calculated by Clarivate that reflects the yearly mean number of citations of articles published in the last two years in a given journal, as indexed by Clarivate's Web of Science. On the other hand, Impact Score is based on Scopus data.

Important details, about international journal of tourism research.

International Journal of Tourism Research is a journal published by John Wiley and Sons Ltd . This journal covers the area[s] related to Geography, Planning and Development, Nature and Landscape Conservation, Tourism, Leisure and Hospitality Management, Transportation, etc . The coverage history of this journal is as follows: 2008-2022. The rank of this journal is 2725 . This journal's impact score, h-index, and SJR are 5.94, 75, and 1.298, respectively. The ISSN of this journal is/are as follows: 10992340, 15221970 . The best quartile of International Journal of Tourism Research is Q1 . This journal has received a total of 1397 citations during the last three years (Preceding 2022).

International Journal of Tourism Research Impact Score 2022-2023

The impact score (IS), also denoted as the Journal impact score (JIS), of an academic journal is a measure of the yearly average number of citations to recent articles published in that journal. It is based on Scopus data.

Prediction of International Journal of Tourism Research Impact Score 2023

Impact Score 2022 of International Journal of Tourism Research is 5.94 . If a similar upward trend continues, IS may increase in 2023 as well.

Impact Score Graph

Check below the impact score trends of international journal of tourism research. this is based on scopus data., international journal of tourism research h-index.

The h-index of International Journal of Tourism Research is 75 . By definition of the h-index, this journal has at least 75 published articles with more than 75 citations.

What is h-index?

The h-index (also known as the Hirsch index or Hirsh index) is a scientometric parameter used to evaluate the scientific impact of the publications and journals. It is defined as the maximum value of h such that the given Journal has published at least h papers and each has at least h citations.

International Journal of Tourism Research ISSN

The International Standard Serial Number (ISSN) of International Journal of Tourism Research is/are as follows: 10992340, 15221970 .

The ISSN is a unique 8-digit identifier for a specific publication like Magazine or Journal. The ISSN is used in the postal system and in the publishing world to identify the articles that are published in journals, magazines, newsletters, etc. This is the number assigned to your article by the publisher, and it is the one you will use to reference your article within the library catalogues.

ISSN code (also called as "ISSN structure" or "ISSN syntax") can be expressed as follows: NNNN-NNNC Here, N is in the set {0,1,2,3...,9}, a digit character, and C is in {0,1,2,3,...,9,X}

International Journal of Tourism Research Ranking and SCImago Journal Rank (SJR)

SCImago Journal Rank is an indicator, which measures the scientific influence of journals. It considers the number of citations received by a journal and the importance of the journals from where these citations come.

International Journal of Tourism Research Publisher

The publisher of International Journal of Tourism Research is John Wiley and Sons Ltd . The publishing house of this journal is located in the United Kingdom . Its coverage history is as follows: 2008-2022 .

Call For Papers (CFPs)

Please check the official website of this journal to find out the complete details and Call For Papers (CFPs).

Abbreviation

The International Organization for Standardization 4 (ISO 4) abbreviation of International Journal of Tourism Research is Int. J. Tour. Res. . ISO 4 is an international standard which defines a uniform and consistent system for the abbreviation of serial publication titles, which are published regularly. The primary use of ISO 4 is to abbreviate or shorten the names of scientific journals using the technique of List of Title Word Abbreviations (LTWA).

As ISO 4 is an international standard, the abbreviation ('Int. J. Tour. Res.') can be used for citing, indexing, abstraction, and referencing purposes.

How to publish in International Journal of Tourism Research

If your area of research or discipline is related to Geography, Planning and Development, Nature and Landscape Conservation, Tourism, Leisure and Hospitality Management, Transportation, etc. , please check the journal's official website to understand the complete publication process.

Acceptance Rate

• Interest/demand of researchers/scientists for publishing in a specific journal/conference.
• The complexity of the peer review process and timeline.
• Time taken from draft submission to final publication.
• Number of submissions received and acceptance slots
• And Many More.

The simplest way to find out the acceptance rate or rejection rate of a Journal/Conference is to check with the journal's/conference's editorial team through emails or through the official website.

Frequently Asked Questions (FAQ)

What is the impact score of international journal of tourism research.

The latest impact score of International Journal of Tourism Research is 5.94. It is computed in the year 2023.

What is the h-index of International Journal of Tourism Research?

The latest h-index of International Journal of Tourism Research is 75. It is evaluated in the year 2023.

What is the SCImago Journal Rank (SJR) of International Journal of Tourism Research?

The latest SCImago Journal Rank (SJR) of International Journal of Tourism Research is 1.298. It is calculated in the year 2023.

What is the ranking of International Journal of Tourism Research?

The latest ranking of International Journal of Tourism Research is 2725. This ranking is among 27955 Journals, Conferences, and Book Series. It is computed in the year 2023.

Who is the publisher of International Journal of Tourism Research?

International Journal of Tourism Research is published by John Wiley and Sons Ltd. The publication country of this journal is United Kingdom.

What is the abbreviation of International Journal of Tourism Research?

This standard abbreviation of International Journal of Tourism Research is Int. J. Tour. Res..

Is "International Journal of Tourism Research" a Journal, Conference or Book Series?

International Journal of Tourism Research is a journal published by John Wiley and Sons Ltd.

What is the scope of International Journal of Tourism Research?

• Geography, Planning and Development
• Nature and Landscape Conservation
• Tourism, Leisure and Hospitality Management
• Transportation

For detailed scope of International Journal of Tourism Research, check the official website of this journal.

What is the ISSN of International Journal of Tourism Research?

The International Standard Serial Number (ISSN) of International Journal of Tourism Research is/are as follows: 10992340, 15221970.

What is the best quartile for International Journal of Tourism Research?

The best quartile for International Journal of Tourism Research is Q1.

What is the coverage history of International Journal of Tourism Research?

The coverage history of International Journal of Tourism Research is as follows 2008-2022.

Credits and Sources

• Scimago Journal & Country Rank (SJR), https://www.scimagojr.com/
• Journal Impact Factor, https://clarivate.com/
• Issn.org, https://www.issn.org/
• Scopus, https://www.scopus.com/

Note: The impact score shown here is equivalent to the average number of times documents published in a journal/conference in the past two years have been cited in the current year (i.e., Cites / Doc. (2 years)). It is based on Scopus data and can be a little higher or different compared to the impact factor (IF) produced by Journal Citation Report. Please refer to the Web of Science data source to check the exact journal impact factor ™ (Thomson Reuters) metric.

Impact Score, SJR, h-Index, and Other Important metrics of These Journals, Conferences, and Book Series

Check complete list

International Journal of Tourism Research Impact Score (IS) Trend

Top journals/conferences in geography, planning and development, top journals/conferences in nature and landscape conservation, top journals/conferences in tourism, leisure and hospitality management, top journals/conferences in transportation.

International Journal Of Tourism Research impact factor, indexing, ranking (2024)

Aim and Scope

The International Journal Of Tourism Research is a research journal that publishes research related to Business, Management and Accounting; Environmental Science; Social Sciences . This journal is published by the John Wiley and Sons Ltd. The ISSN of this journal is 10992340, 15221970 . Based on the Scopus data, the SCImago Journal Rank (SJR) of international journal of tourism research is 1.298 .

International Journal Of Tourism Research Ranking

The Impact Factor of International Journal Of Tourism Research is 4.737.

The impact factor (IF) is a measure of the frequency with which the average article in a journal has been cited in a particular year. It is used to measure the importance or rank of a journal by calculating the times its articles are cited.

The impact factor was devised by Eugene Garfield, the founder of the Institute for Scientific Information (ISI) in Philadelphia. Impact factors began to be calculated yearly starting from 1975 for journals listed in the Journal Citation Reports (JCR). ISI was acquired by Thomson Scientific & Healthcare in 1992, and became known as Thomson ISI. In 2018, Thomson-Reuters spun off and sold ISI to Onex Corporation and Baring Private Equity Asia. They founded a new corporation, Clarivate , which is now the publisher of the JCR.

Important Metrics

International journal of tourism research indexing.

The international journal of tourism research is indexed in:

• Web of Science (SSCI)

An indexed journal means that the journal has gone through and passed a review process of certain requirements done by a journal indexer.

The Web of Science Core Collection includes the Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), Arts & Humanities Citation Index (AHCI), and Emerging Sources Citation Index (ESCI).

International Journal Of Tourism Research Impact Factor 2024

The latest impact factor of international journal of tourism research is 4.737 .

The impact factor (IF) is a measure of the frequency with which the average article in a journal has been cited in a particular year. It is used to measure the importance or rank of a journal by calculating the times it's articles are cited.

Note: Every year, The Clarivate releases the Journal Citation Report (JCR). The JCR provides information about academic journals including impact factor. The latest JCR was released in June, 2023. The JCR 2024 will be released in the June 2024.

International Journal Of Tourism Research Quartile

The latest Quartile of international journal of tourism research is Q1 .

Each subject category of journals is divided into four quartiles: Q1, Q2, Q3, Q4. Q1 is occupied by the top 25% of journals in the list; Q2 is occupied by journals in the 25 to 50% group; Q3 is occupied by journals in the 50 to 75% group and Q4 is occupied by journals in the 75 to 100% group.

Call for Papers

Visit to the official website of the journal/ conference to check the details about call for papers.

How to publish in International Journal Of Tourism Research?

If your research is related to Business, Management and Accounting; Environmental Science; Social Sciences, then visit the official website of international journal of tourism research and send your manuscript.

Tips for publishing in International Journal Of Tourism Research:

• Selection of research problem.
• Presenting a solution.
• Designing the paper.
• Make your manuscript publication worthy.
• Write an effective results section.
• Mind your references.

Acceptance Rate

Journal publication time.

The publication time may vary depending on factors such as the complexity of the research and the current workload of the editorial team. Journals typically request reviewers to submit their reviews within 3-4 weeks. However, some journals lack mechanisms to enforce this deadline, making it difficult to predict the duration of the peer review process.

The review time also depends upon the quality of the research paper.

Final Summary

• The impact factor of international journal of tourism research is 4.737.
• The international journal of tourism research is a reputed research journal.
• It is published by John Wiley and Sons Ltd .
• The journal is indexed in UGC CARE, Scopus, SSCI .
• The (SJR) SCImago Journal Rank is 1.298 .

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International Journal of Tourism Research

Journal Abbreviation: INT J TOUR RES Journal ISSN: 1099-2340

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Journal Impact

International Journal of Tourism Research Impact Factor & Key Scientometrics

International journal of tourism research overview, impact factor.

I. Basic Journal Info

Journal ISSN: 10992340, 15221970

Publisher: john wiley & sons ltd., history: 2008-ongoing, journal hompage: link, how to get published:, research categories, scope/description:.

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II. Science Citation Report (SCR)

International journal of tourism research scr impact factor, international journal of tourism research scr journal ranking, international journal of tourism research scimago sjr rank.

SCImago Journal Rank (SJR indicator) is a measure of scientific influence of scholarly journals that accounts for both the number of citations received by a journal and the importance or prestige of the journals where such citations come from.

International Journal of Tourism Research Scopus 2-Year Impact Factor Trend

International journal of tourism research scopus 3-year impact factor trend, international journal of tourism research scopus 4-year impact factor trend, international journal of tourism research impact factor history.

• 2022 Impact Factor 5.94 6.35 6.196
• 2021 Impact Factor 5.096 5.047 5.554
• 2020 Impact Factor 3.211 3.893 4.288
• 2019 Impact Factor 3.063 3.433 3.927
• 2018 Impact Factor 2.598 3.232 3.602
• 2017 Impact Factor 2.729 3.163 3.469
• 2016 Impact Factor 2.532 2.851 2.953
• 2015 Impact Factor 1.918 2.203 2.546
• 2014 Impact Factor 1.989 NA NA
• 2013 Impact Factor 1.607 NA NA
• 2012 Impact Factor 1.728 NA NA
• 2011 Impact Factor 1.377 NA NA
• 2010 Impact Factor 1.644 NA NA
• 2009 Impact Factor 0.739 NA NA
• 2008 Impact Factor 0 NA NA
• 2007 Impact Factor NA NA NA
• 2006 Impact Factor NA NA NA
• 2005 Impact Factor NA NA NA
• 2004 Impact Factor NA NA NA
• 2003 Impact Factor NA NA NA
• 2002 Impact Factor NA NA NA
• 2001 Impact Factor NA NA NA
• 2000 Impact Factor NA NA NA

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Impact factor (IF) is a scientometric factor based on the yearly average number of citations on articles published by a particular journal in the last two years. A journal impact factor is frequently used as a proxy for the relative importance of a journal within its field. Find out more: What is a good impact factor?

III. Other Science Influence Indicators

Any impact factor or scientometric indicator alone will not give you the full picture of a science journal. There are also other factors such as H-Index, Self-Citation Ratio, SJR, SNIP, etc. Researchers may also consider the practical aspect of a journal such as publication fees, acceptance rate, review speed. ( Learn More )

International Journal of Tourism Research H-Index

The h-index is an author-level metric that attempts to measure both the productivity and citation impact of the publications of a scientist or scholar. The index is based on the set of the scientist's most cited papers and the number of citations that they have received in other publications

International Journal of Tourism Research H-Index History

scijournal.org is a platform dedicated to making the search and use of impact factors of science journals easier.

Scientometrics

International journal of tourism research, human geography , economics , business, how influential is international journal of tourism research.

International Journal of Culture, Tourism and Hospitality Research

Subject Area and Category

• Tourism, Leisure and Hospitality Management
• Geography, Planning and Development

Emerald Group Publishing Ltd.

Publication type

Information.

How to publish in this journal

[email protected]

The set of journals have been ranked according to their SJR and divided into four equal groups, four quartiles. Q1 (green) comprises the quarter of the journals with the highest values, Q2 (yellow) the second highest values, Q3 (orange) the third highest values and Q4 (red) the lowest values.

The SJR is a size-independent prestige indicator that ranks journals by their 'average prestige per article'. It is based on the idea that 'all citations are not created equal'. SJR is a measure of scientific influence of journals that accounts for both the number of citations received by a journal and the importance or prestige of the journals where such citations come from It measures the scientific influence of the average article in a journal, it expresses how central to the global scientific discussion an average article of the journal is.

Evolution of the number of published documents. All types of documents are considered, including citable and non citable documents.

This indicator counts the number of citations received by documents from a journal and divides them by the total number of documents published in that journal. The chart shows the evolution of the average number of times documents published in a journal in the past two, three and four years have been cited in the current year. The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric.

Evolution of the total number of citations and journal's self-citations received by a journal's published documents during the three previous years. Journal Self-citation is defined as the number of citation from a journal citing article to articles published by the same journal.

Evolution of the number of total citation per document and external citation per document (i.e. journal self-citations removed) received by a journal's published documents during the three previous years. External citations are calculated by subtracting the number of self-citations from the total number of citations received by the journal’s documents.

International Collaboration accounts for the articles that have been produced by researchers from several countries. The chart shows the ratio of a journal's documents signed by researchers from more than one country; that is including more than one country address.

Not every article in a journal is considered primary research and therefore "citable", this chart shows the ratio of a journal's articles including substantial research (research articles, conference papers and reviews) in three year windows vs. those documents other than research articles, reviews and conference papers.

Ratio of a journal's items, grouped in three years windows, that have been cited at least once vs. those not cited during the following year.

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Business intelligence and business analytics in tourism: insights through Gioia methodology

• Open access
• Published: 12 April 2024

Cite this article

You have full access to this open access article

• Montserrat Jiménez-Partearroyo   ORCID: orcid.org/0000-0003-2070-258X 1 ,
• Ana Medina-López   ORCID: orcid.org/0000-0003-1956-1839 2 &
• Sudhir Rana   ORCID: orcid.org/0000-0002-9910-1930 3  

Although Business Intelligence (BI) and Business Analytics (BA) have been widely adopted in the tourism sector, comparative research using BI and BA remains scarce. To fill this gap in the literature, the present study explores how BI and BA contribute to strategic innovation, address operational challenges, and enhance customer engagement. To this end, using a dual-method approach that incorporates both quantitative and qualitative methodologies, we first conduct a bibliometric analysis using SciMAT. This sets the stage for the subsequent application of the Gioia methodology. Specifically, we perform an in-depth qualitative examination of a total of 12 scholarly articles on the tourism sector, evenly split between BI and BA. Upon synthesizing the findings on the roles of BI and BA, we outline distinct pathways through which they influence tourism sector management solutions. Based on the obtained evidence, we argue that, while BI focuses on technological advancement and operational integration, BA is more aligned with predictive analytics and data-driven customer engagement. These insights provide managers with a better understanding of the roles of BI and BA, serving as a guide for their strategic applications, from improving service quality to innovating in customer engagement. The novelty of this approach lies in its use of the Gioia methodology, in a comparative analysis to evaluate the separate yet complementarily roles of BI and BA, and in enhancing tourism industry practices.

Avoid common mistakes on your manuscript.

Introduction

Technological innovation improves efficiency, provides society with new and enhanced goods and services through economic reforms, and improves their living conditions (Wen & Okolo, 2023 ). In recent decades, the most extensively advancing technological branch is related to digital technologies.

As argued by Rawal et al. ( 2023 ), technological advances, in particular digitalization, improve efficiency and quality of service in various sectors. In this context, a particularly important role is played by data analysis and visualization. Rawal et al. ( 2023 ) noted the importance of proper data analysis and visualization to enhance customer experiences and decision-making processes, particularly in the service sector. By extrapolating from their conclusions, the transformative effect of Business Intelligence (BI) and Business Analytics (BA) in the tourism sector becomes apparent.

In the tourism sector, digital technology has given rise to the production of large amounts of data. When processed using advanced BA and BI tools, these data transform into key strategic assets for decision making in tourism as a highly dynamic sector. Although complex, this process facilitates the identification of new opportunities and generation of knowledge for tourism companies seeking superior performance and differentiation in the market. In this respect, Saura et al. ( 2023a ) argued that the decision of shifting to these data applications can bring the same results as open innovation models, which are essential for the development of successful innovation strategies.

It can reasonably be expected that BI and BA may become key channels for making this happen, leading the way in turning data into a valuable resource. Tourism industry moves towards a future that relies on data and BI and BA frequently come together in academic discussions. However, how they are used in tourism requires further investigation.

In this connection, available literature posits that, despite BI and BA being subjects examined in academic research, there remains certain ambiguity regarding their application in scholarly research. Indeed, these two terms are frequently used interchangeably, which overlooks the clear differences between BI’s traditional focus on descriptive analytics and BA’s emphasis on prescriptive and predictive analytics. This is obvious in articles that, despite their titles suggesting a focus on BA, predominantly focus on BI (or vice versa), or discuss both concepts without detailing their differentiations (Chen et al., 2023 ; Yiu et al., 2021 ). This impedes theoretical understanding and hinders an accurate use of these data-focused methods in the practical domain of tourism management.

Seeking to connect the theoretical and real-world gap, in this study, we initiate a process of clarification. To this end, we first conduct a deep bibliometric study, which is then followed by applying the Gioia qualitative methodology (Gioia et al., 2013 ) to distill the essence of BI and BA from secondary data comprising a handpicked selection of 12 research articles. Based on the findings, we identify the core themes and then define the conceptual distinctions between BI and BA within the tourism industry. This innovative approach, which uses real-life data, allows for a thorough synthesis that extends beyond the limited scope of single case studies, and thus provides a broad understanding of the conceptual domain.

Our principal aim in this study is to reveal patterns, follow thematic paths the conceptual mix-up, and obtain a detailed understanding of the roles BI and BA in creating a more knowledgeable, experience-led, and well-managed tourism sector. By addressing widespread conceptual misunderstandings, we aim to develop a structured framework for future research, strengthening the practical impact of BI and BA in building a robust and competitive tourism industry. Accordingly, our main research question can be formulated as follows: RQ1– In what ways do Business Intelligence and Business Analytics manifest as distinct yet interrelated elements in the tourism sector?

Our results bridge the gap in extant literature by illuminating BI’s role in technological and operational integration and BA’s focus on predictive analytics and the enhancement of customer interaction in the tourism sector. Our main findings associate BI’s tools with technological and operational integration and BA’s with predictive analytics and enhance customer interaction. Our comparative study provides fundamental differences in BI/BA approaches to strategic innovation and operational issues.

The remainder of this paper is organized as follows. In the next section, we thoroughly describe the core concepts of BI and BA, which is followed by sizing the database available for our study. This involves conducting a bibliometric analysis, using the SciMAT software. We then perform a purely qualitative analysis using the Gioia methodology. In our bibliometric research, we identify pertinent research clusters and, following previous studies, detect emerging trends within those clusters (Tanwar & Khindri, 2024 ). The strength of Gioia methodology lies in its successive phases of exploration that helps to conceptualize the central and intersecting themes emerging from the contextually rich and profound aspects of the phenomena under examination. This process enables us to properly conceptualize the fundamentals related to the individual and synergistic functions of BI and BA.

Next, upon presenting the theoretical framework that encompasses both quantitative and qualitative approaches, the empirical section focuses on describing the experiment conducted using real data and significant findings are outlined. The results are then presented, analyzed and discussed, followed by a review of the study’s limitations and an outline of future research directions.

Theoretical framework

In order to establish a robust context for understanding the impact of BI and BA within the tourism sector, which is particularly sensitive to information and data analysis dynamics, the initial step is to explore the convergence and divergence of these two concepts. BI and BA represent a paradigm shift towards data-driven decision-making that marks the evolution of the tourism industry. In this section, we review the theoretical framework that contains the definitions and applications of BI and BA and thus lays a solid foundation for understanding these two constructs in the context of tourism.

A review of the literature suggests that BI has diverse definitions, and a consensual definition has not been established yet (Chee et al., 2009 ). Yet, a common thread across these definitions is the perception of BI as a system that assists decision makers in making informed choices about the business’ direction. In addition, BI is viewed as a system encompassing technologies, tools, and software designed to gather data, automate processes, and generate information transformed into knowledge for making quality decisions (Nyanga et al., 2020 ).

According to the definition proposed by Shende and Panneerselvam ( 2018 ), BI refers to the application of technologies and practices for collecting, integrating, analyzing and presenting business information. Such transformation of data into knowledge within BI supports enhanced decision making. BI systems, or data-driven Decision Support Systems (DSS), favor gaining business advantages with robust BI tools over decisions based on mere intuition. What is essential here is establishing a data- or fact-based decision-making framework via a strong computer system that instills confidence in any decisions that is being made. According to Shende and Panneerselvam ( 2018 ), BI has six major components (see Table  1 ).

Furthermore, Shende and Panneerselvam ( 2018 ) define BA techniques as the exploration of historical data from numerous sources using statistical and quantitative analysis, data mining, predictive modelling, among other approaches, in order to identify trends and understand information that can drive business change and support sustainable practices. In this sense, BA involves the use of statistical tools and technologies for pattern identification, variability analysis, relationship identification, and future insights prediction. Similarly to BO, BA also has six major components that are listed in Table  2 .

Therefore, BI and BA are described as the tools supporting process optimization in contemporary organizations. Since both BI and BA enhance data-driven decision making and strategic planning, the two diverge in the sharing of data mining, a technique used to extract patterns and insights from data. In addition, both fields emphasize predictive analytics, which aims to forecast future trends based on historical data. Statistical analysis, which involves employing statistical methods to understand the data, is another common thread.

However, the two aspects where BI and BA diverge are their focus and application. The first divergence between BI and BA is observed when examining their specific components. On the one hand, BI primarily analyzes historical data to inform decision making and understand past performance (Chen et al., 2012 ; Vercellis, 2011 ); accordingly, BI uses OLAP to analyze data using advanced tools, exploring dimensions such as time or hierarchies. It also focuses on data mining and predictive analytics related to corporate performance, enables real-time distribution of metrics, relies on data warehouses for integration, and collects various types of data.

On the other hand, BA involves forward-looking use of data to forecast future trends and outcomes, thereby supporting strategic decisions (Shende & Panneerselvam, 2018 ). Furthermore, BA involves exploring historical data from various sources using statistical and quantitative analysis, data mining, and predictive modeling, with a particular emphasis on pattern identification, variability analysis, and a future-oriented approach to sustainable knowledge and practices. The shift from BI to BA is propelled by the growing volume and complexity of data and the need for real-time insights in an ever-evolving business landscape (Iovan & Ivanus, 2014 ).

The dual methodology approach

After establishing a clear distinction between the two key concepts under examination—those of BI and BA—in this section, we outline the methodology we use to achieve the goals set forth in the present study. As noted in the introduction, we use a dual methodological approach: first, we conduct a bibliometric study that allows for quantification/qualification of scientific publications related to our subject and, second, we perform purely qualitative analysis. Using such mixed methodological approach, as argued by Matenda and Sibanda ( 2023 ) in a study on BRICS economies, makes it possible to gain a more comprehensive understanding of the studied phenomena by combining the numerical depth of quantitative methods and the contextual richness of qualitative analysis, which jointly provide a more holistic understanding of the research topic. In this way, both approaches complement each other by providing a dual perspective, and their fundamentals are described below.

The quantitative-qualitative approach: a bibliometric assessment with SciMAT

In this review, we systematically examine the corpus of literature on BI and BA in tourism, integrating a bibliometric analysis using the SciMAT software to map the intellectual structure and thematic evolution of the field. Using statistical methods to evaluate publication patterns and trends in this field of study provides an understanding of the evolution and impact of research and topics over time. As argued by Ribeiro-Navarrete et al. ( 2023 ), a bibliometric study employs bibliographic analysis, co-citation, and co-occurrence of keywords. This approach enables identifying relevant authors, leading journals and key topics in the academic literature related to the phenomenon you want to study.

The SciMAT software tool, as described by Cobo et al. ( 2012 ), enables performing science mapping analysis within a longitudinal framework. This approach catalogues existing knowledge and it goes deeper by combining performance analysis with science mapping. This method is also instrumental in detecting and visualizing conceptual subdomains within the research field, be them specific themes or broader thematic areas. The resulting analysis hinges on co-word analysis within a longitudinal framework—a technique that uncovers various themes addressed by the field over a specified period. The richness of this analysis is augmented by incorporating performance measurement. These measures fall into the following two distinct categories: quantitative and qualitative. While the former focus on assessing the productivity of the identified themes and thematic areas, the latter aim to gauge the (perceived) quality based on the bibliometric impact of these themes and thematic areas, as expounded by Cobo et al., 2011 ).

The tourism industry has a complex structure that consists of the accommodation sector, attractions sector, transport sector, travel organizer’s sector. as well as destination organization sector (Middleton & Hawkins, 1998 ); along with the above, there are also restaurant services, food and beverage activities, and various auxiliary services involved. All these sectors and interconnected, supporting and complementing each other such that without others, the tourism industry will not be complete (Ramayah et al., 2011 ; Xu, 2010 ). Accordingly, the main features of BI and BA applied to tourism industry should include data analysis, reports, dashboards, data visualization, performance metrics, key performance indicators, predictive analytics, trend indicators, strategic planning tools, profitability analysis, benchmarking, budgeting, and forecasting (Ibrahim & Handayani, 2022 ). These tools are essential for decision making in the tourism sector, where the adoption of AI and Big Data analytics can provide a competitive advantage (Stroumpoulis et al., 2022 ). BI and BA also enable cross-process knowledge extraction and decision support in tourism destinations (Höpken et al., 2015 ) and data-driven management for new business in tourism (Ferreira & Pedrosa, 2022 ; Höpken et al., 2015 ).

Taking all this into account and for our specific research purposes, we selected the WoS, specifically the Web of Science Core Collection, which includes a collection of high-quality scientific journals within several large-scale databases. Another advantage of this database is that furnishes the essential data required for conducting analysis in SciMAT, including key words and abstract. As highlighted by Shu et al. ( 2020 ), the WoS is renowned as one of the most pivotal databases globally, with its significance defined by its inclusion of excellent research spanning together many countries. Similar arguments on using the WoS for its high performance and the breadth of results it yields across multiple disciplines were made byHarzing and Alakangas ( 2016 ) and Vera-Baceta et al. ( 2019 ).

Our search within WoS was guided by specific criteria, with the terms “tourism,” “Business Intelligence,” and “Business Analytics” forming the core of the search and selection process. This initial search yielded a total of 77 documents. However, following a meticulous refinement and filtering of the database, a total of 50 articles were retained as the primary subject of analysis. The included results came from Social Sciences Citation Index (SSCI), Conference Proceedings Citation Index – Social Science & Humanities (CPCI-SSH), Book Citation Index – Science (BKCI-S), Book Citation Index – Social Sciences & Humanities (BKCI-SSH), and Emerging Sources Citation Index (ESCI). The included articles spanned the years 2000 to 2023, thereby offering a comprehensive view of the evolution of the field. In order to conduct a more nuanced evolutionary analysis, we divided this timeline into two distinct periods: (1) pre-pandemic (2000–2020), with 38 papers found and (2) post-pandemic (2021–2023), with a total of 12 articles. This splitting of the data was essential, as the application of data-driven analysis in tourism underwent a significant transformation in response to the pandemic.

More specifically, Zamyatina ( 2023 ) provide critical insights into this transformative shift, highlighting a notable change in consumer preferences. The pandemic period, as Zamyatina ( 2023 ) observed, witnessed a dramatic alteration in consumer priorities, with a newfound emphasis on intangible luxury. This reflected a broader understanding where travel was increasingly considered an essential component of a healthy lifestyle. Chen ( 2021 ) corroborated this observation, noting that user-generated content during the pandemic predominantly focused on pandemic-related keywords. This shift embodies a profound change in public perceptions, notably marked by increased dissatisfaction among tourism service consumers due to quarantine restrictions and health test mandates.

In another relevant study, Ionescu et al. ( 2022 ) addressed the significant compression that the tourism sector experienced since the onset of the COVID-19 pandemic, which destabilized both tourist flows and the economic agents within the industry. As a solution to this situation, Ionescu et al. ( 2022 ) proposed a decision support model designed to aid in the recovery of tourism in Europe, with an emphasis on the growing need for data-driven tools within the industry. Rahim et al. ( 2021 ) discussed the profound impact of COVID-19 on tourism, noting that the pandemic adversely affected both demand and supply in various aspects. On exploring the increasing use of virtual tourism platforms, such as virtual reality (VR) and augmented reality (AR), as a response to the pandemic, Rahim et al. ( 2021 ) suggested that these technologies would come into wider use for data collection and analysis in the future, indicating a potential shift in how the tourism sector was likely to adapt to and overcome challenges.

The pure qualitative approach: insights from the Gioia methodology

Along with the bibliometric research, we complemented our methodology with a pure qualitative analysis of relevant publications. To this end, we used the Gioia methodology, a renowned qualitative approach in organizational research devised by Gioia et al. ( 2013 ). The core of this systematic qualitative approach is theory-building from empirical data. This method is particularly suited for exploring specific contexts, such as the intersection of BI and BA in tourism (Gioia et al., 2013 ). The Gioia methodology involves a dual-layered coding process, beginning with first-order coding to capture participants’ perspectives and progressing to second-order coding for broader, theoretical categorization (Gehman et al., 2018 ; Gioia et al., 2013 , 2022 ; Magnani & Gioia, 2023 ). To date, the Gioia methodology has been extensively used in the literature (Lacoste et al., 2022 ; Niittymies, 2020 ; Riviere et al., 2018 ; Visvizi et al., 2022 ), and many of these were reviewed by Magnani and Gioia ( 2023 ).

In the present study, we adapted the Gioia methodology for secondary data analysis, with a particular focus on previously collected and published scientific academic articles. This departure from conventional primary data collection methods, such as interviews or surveys, allowed us to include a wider range of studies for an extensive comparative analysis in the fields of BI and BA. This adaptation also enhanced robustness and validity of our theoretical framework and helped us to better align our results with the findings previous reported by Taquette and Minayo ( 2017 ) and Mwita ( 2022 ), who highlighted the scientific validity and flexibility of using qualitative approaches with secondary data.

Overall, the Gioia methodology enables transitioning from concrete studies to abstract theoretical insights, enriching qualitative research. Key stages of this methodology as used in the present study are outlined below.

Data Collection : A distinctive aspect of how we adapted the Gioia methodology was our analysis of secondary data, which involved an exhaustive review and synthesis of available literature. Instead of using conventional primary data collection methods, we thoroughly reviewed a wide range of published studies on the topic. This approach allowed for a broader exploration of information sources, aligning with the flexibility inherent in the Gioia methodology (Taquette & Minayo, 2017 ; Mwita, 2022 ),

Data Analysis : At this stage, which is the crux of the Gioia methodology, we systematically identified, coded, and analyzed qualitative data. Data analysis involved a detailed examination of texts to discover underlying patterns, themes, and dimensions (Gehman et al., 2018 ).

Conceptual Framework Development : Based on the insights derived from our data analysis, we created a detailed and data-driven conceptual framework. This framework was meant to aid in comprehending how BI and BA phenomena are constructed and perceived in the tourism sector (Mugizi, 2019 ).

Theory Generation : At this stage, our goal was to generate a rich, well-grounded theory. By interpreting the data within its conceptual framework, we aimed to produce a theory that would provide a deep understanding of the subject matter (Olbrich & Mueller, 2013 ; Tucker, 2016 ).

Our use of the Gioia methodology aligned with major principles of qualitative research including purposive sampling and autonomy in sample size determination (Subedi, 2021 ). Our approach was also consistent with Tipton et al.’s ( 2014 ) principles on sample selection, reinforcing the importance of thoughtful sample selection for representativeness.

Our approach facilitated an in-depth exploration of BI and BA in tourism, ensuring that the results are comprehensive, credible, and reflective of current academic research. Our approach also incorporated the insights from Dufour and Richard’s ( 2019 ) work that underlined the importance of selecting appropriate analytical approaches for specific research contexts. More specifically, in a comparative study that focused on the same dataset, Dufour and Richard ( 2019 ) used Grounded Theory Approach (GT) and General Inductive Approach (GIA) to highlight the strengths and limitations of each method and give the researcher the responsibility of selecting the most appropriate methodological framework. A review of the results of this comparison enhanced our methodology by providing alternative data analysis perspectives in tourism research.

Article selection and analysis process

The Gioia methodology integrates principles of sample selection in randomized experiments, emphasizing strategic and thoughtful sample selection in qualitative research. This approach guided our choice of 12 scholarly articles, providing an overview of BI and BA in the tourism industry and allowing for a detailed analysis of each article.

The selected articles were systematically identified from the WoS Core Collection database. Our decision to use the WoS was underpinned by the consideration that I business scholars prefer using the WoS and Scopus as their primary databases, while authors and academic institutions consider the journals, books, and conferences indexed of these two databases as “quality publications” (Rana et al., 2023 ).

Another important characteristic of the WoS is its multidisciplinary and international coverage (Vieira & Gomes, 2009 ). After a careful search of different keywords, a total of 25 papers were appraised and carefully read by the authors. We found misinterpretations in literature between Artificial Intelligence (AI) and BI. After several rounds of discussions and readings, a meticulous selection of 12 scientific articles on BI and BA in tourism was found the most relevant for further analysis. The articles were chosen based on their relevance, currency, and presence in high-impact journals, while ensuring a broad spectrum of perspectives.

Criteria for article inclusion

The inclusion criteria that we used in selecting the articles centered on whether the articles addressed six specific research questions; we also examined whether the paper provided definitions of BI/BA and discussed influences, advantages, challenges, contributions of BI/BA to tourism. The articles that did not meet these criteria were randomly replaced, thus ensuring methodological robustness and thematic consistency.

Article selection for this study aligned with the qualitative research criteria set by Laumann ( 2020 ) using keywords relevant to BI, BA, and tourism. Our purposive approach was aimed at finding the articles that made meaningful contributions to understanding the intersection of BI and BA in tourism, ensuring comprehensive topic coverage. The process was designed to minimize selection bias, providing a diverse temporal coverage and ensuring that each selected article substantially contributed to our understanding of BI and BA in the tourism sector. This approach underlined the integrity and validity of our research. The very process included identifying, categorizing, and relating themes to our study’s research questions, using a systematic approach to ensure comprehensive theme extraction. The findings from the selected articles were then synthesized to provide a holistic understanding of the research topic. This involved integrating insights from different studies, identifying commonalities and differences and linking them to the overarching research question.

Once six articles from each thematic area were randomly chosen, their content was examined to determine if they could address our main research question: In what ways do Business Intelligence and Business Analytics manifest as distinct yet interrelated elements in the tourism sector? To this end, the following questionnaire was designed, and each candidate publication was screened on how it addressed the questions. The questions included in the questionnaire are listed below.

How is Business Intelligence (BI/BA) defined and used in this specific study within tourism?

What types of tourism business decisions are influenced by Business Intelligence (BI/BA) in this case?

What are the identified advantages and challenges of using Business Intelligence (BI/BA) in this context?

How does this study contribute to the general understanding of Business Intelligence (BI/BA) in tourism?

What practical and theoretical implications can be drawn from this study for the implementation of Business Intelligence (BI/BA) in tourism?

What are the specific Business Intelligence (BI/BA) tools used in this study, and how do they contribute to the presented results and conclusions?

In instances where any of the selected articles did not address one or more of these questions, it was excluded from the dataset and was randomly replaced by another publication that was further screened as described above. The final sample comprised 12 articles spanning the period 2008–2023, sourced from different scientific journals. Table  3 and 4 lists the selected articles related to BA.

The systematic approach outlined in this section provides ensures empirical grounding and theoretical framework that aptly reflect the complexity of BI and BA in the tourism sector. Using this methodology, we conduct an in-depth exploration of BI and BA, thus laying the groundwork for a more comprehensive understanding of the strategic, operational, and customer-centric implications of these two constructs.

In this section, we first report the results of the bibliometric study, which is followed by the presentation of the findings obtained using and the Gioia methodology application. To enhance the data analysis and presentation of the results, an exhaustive examination of the findings derived from the Gioia methodology was conducted. Each identified theme was comprehensively detailed, using insights from the selected articles to highlight their significance in understanding the roles of BI and BA within the tourism industry.

The output included visual representations such as thematic maps and comparative tables for clarity. These visual aids were thoroughly interpreted and linked to our research objectives. A thematic map was created to demonstrate the interconnected aspects of BI and BA, while comparative tables were made to delineate their unique and shared applications in the tourism sector. This refined approach to data presentation aimed to elucidate the study’s primary findings aligning them with the research objectives, thus providing a holistic and accessible understanding of how BI and BA influence the tourism industry.

In-depth bibliometric analysis findings

Despite the relatively modest size of the analyzed sample, we identified a considerable array of themes and concepts, thereby facilitating a thorough analysis using the SciMAT tool. Table  5 and 6 presents the journals where two or more articles on the studied topic were identified.

The results of applying the SciMAT tool yielded a set of clusters that can be understood as conglomerates of different scientific aspects. In the case of co-word analysis, the clusters represented groups of textual information, or semantic or conceptual groups of different topics treated by the research field. Therefore, the detected clusters can be used to quantify the research field by means of a performance analysis. Co-word analysis, a powerful technique for discovering and describing the interactions between different fields in scientific research (Cobo et al., 2011 ), allowed us to discover the main concepts treated by the field.

Figure  1 shows the results of our keyword analysis. The circles represent each sub-period, with the number of keywords in that sub-period indicated inside the circle (37 in the first period and 23 in the second one). The arrow between consecutive sub-periods represents the number of keywords shared between them (20) and, in parentheses, we also report the Stability Index (overlap fraction, 0,87). The upper-incoming arrow represents the number of new keywords of the sub-period (2) and, finally, the upper-outcoming arrow represents the keywords that are not present (i.e., discontinued) in the next sub-period (17) (Cobo et al., 2011 ). The significant overlap between the two sub-periods indicates that most concepts remain relevant across both periods.

Source Authors’ compilation based on Data from the WoS Core Collection

Overlapping map.

Figure  2 shows the results of co-word analysis where clusters of keywords (and their interconnections) were obtained. These clusters were considered as themes. Each research theme obtained in this process was characterized by two parameters: “density” (which can be understood as a measure of the theme’s development) and “centrality” (which can be understood as a measure of the importance of a theme in the development of the entire research field analyzed).

Strategic Diagram period 1.

In Fig.  2 , the themes are placed in four groups according to the quadrant where they are located. Overall, a strategic diagram in a two-dimensional space can be built by plotting themes according to their centrality and density rank values. In the present study, strategic diagrams are shown in Figs.  2 and 3 for each period, respectively; the numbers within the circles correspond to the number of articles included in the analysis.

Big Data–Networks graph (Period 1). Source Authors’ compilation based on Data from the WoS Core Collection

Within a theme, the keywords and their interconnections draw a network graph, called a thematic network. Each thematic network is labelled using the name of the most significant keyword in the associated theme (usually identified by the most central keyword of the theme). Figures  4 and 5 (Period 1) and 6 (Period 2) show the network graphs constructed with the central themes for each period.

Technologies–Networks graph (Period 1). Source Authors’ compilation based on Data from the WoS Core Collection

Strategic diagram (Period 2). Source Authors’ compilation based on Data from the WoS Core Collection

Coming back to Fig.  2 , following Cobo et al. ( 2011 ), the themes in the upper-right quadrant are both well developed and important for the structuring of a research field. These are generally known as the motor-themes of the specialty, as they present strong centrality and high density. The placement of themes in this quadrant implies that the corresponding themes are related externally to concepts applicable to other themes that are conceptually closely related. Themes in the upper-left quadrant have well developed internal ties, but unimportant external ties; therefore, they are of only marginal importance for the field. These themes are very specialized and peripheral in character. Themes in the lower-left quadrant are both weakly developed and marginal. The themes of this quadrant have low density and low centrality, mainly representing either emerging or disappearing themes. In the lower-right quadrant, we find themes that can be considered foundational and cross-cutting due to their low density and high centrality.

In Period 1 (2000–2020), Big Data emerged as a central, well-developed theme (Fig.  2 ), signifying its rise as a key instrument in decision making and business strategy within the tourism industry (Fig.  4 ). The graph depicts “Big Data” as intricately linked to both BI and BA, signifying its integral role in the development and application of these disciplines in tourism. The observed connections between “Big Data” and other thematic areas such as “Hospitality”, “Decision Making”, and “Smart Destination” imply that the analysis and use of extensive data sets are essential for informed decision-making processes and the enhancement of smart destinations in the hospitality industry. The use of technologies also appears to be a secondary central theme (see Fig.  1 ), increasingly standing out for its connections to innovation, use of information systems, assessment of consumer experiences and achievement of smart cities (see Fig.  4 ). Online Reviews also emerged as a relevant theme, which may point to the growing impact of user opinions. As a cross-cutting theme, the influence of culture in the tourism industry remains consistent.

In Period 2 (2021–2023), the results shown in the strategic diagram (Fig.  3 ) indicate that Decision Support Systems gained prominence, exhibiting an increase in centrality and suggesting a greater reliance on analytical tools for making strategic decisions. Furthermore, Business Intelligence maintained its position as an established theme, reflecting its ongoing impact on deriving actionable insights from large volumes of data. The interconnection between Decision-Support Systems (Fig.  6 ) and other concepts such as Hospitality, Framework, Experiences and Tourism Industry highlights the integration of advanced analytics across all facets of tourism.

Decision-support-systems-Networks graph (Period 2). Source Authors’ compilation based on Data from the WoS Core Collection

We also constructed a longitudinal theme map by grouping the two periods (Fig.  7 ). Continuous lines in the map represent a conceptual link, while broken lines represent linked themes that share keywords that differ from their respective names. Finally, the diameter of the sphere corresponds to the number of documents retrieved in each theme.

Longitudinal theme map. Source Authors’ compilation based on Data from the WoS Core Collection

Figure  7 suggests that, with the transition from Period 1 to Period 2, there occurred a shift from a focus on Big Data and technologies to a greater emphasis on the practical applications of these data through decision support systems and BI strategies. The increasing complexity of networks and the emergence of new nodes depict a tourism industry that is in constant adaptation to the innovations and demands of the more connected and data-driven era. Indeed, tourism gets increasingly informed by personalized experiences and optimized management, guided by the intelligence gleaned from the analysis of complex data and diverse sources of information, including online reviews and cultural factors.

The results of our bibliometric literature review revealed the complexity of BI and BA within the tourism industry, marking out their characteristics and areas where they converge and complement each other. Upon reviewing the current body of literature on BI and BA in tourism, we observed a shortage of research on the subtle roles of BI and BA in tourism. Seeking to bridge this gap in the literature, we thoroughly analyzed 12 key articles on BI and BA in tourism. The results of our Gioia analysis are discussed in the next section.

Analyzing BI in tourism with the Gioia methodology

Our qualitative analysis of the data using the Gioia methodology revealed a modulation in strategic decision-making processes within the tourism sector attributable to BI. The results indicated a profound impact of BI on strategic, operational, and competitive dimensions of tourism. These findings highlighted how BI facilitates a more dynamic approach to demand forecasting and decision making that accommodates the rapid changes in the tourism environment. Moreover, the results showed how BI promotes operational improvements, placing a premium on customer-centric strategies to enhance the tourism experience. The integrative function of BI emerged as a key driver for knowledge management, fostering competitive practices well aligned with global sustainability goals. The visual representation in Fig.  8 and accompanying analysis provide further detail on how BI acts as a linchpin to drive end-to-end growth, equipping the travel industry with the tools for strategic foresight, operational optimization, and a sustainable future.

Data structure for BI obtained using the Gioia methodology (Gioia et al., 2013 ) Source Authors’ compilation from selected articles

Figure  8 shows the data structure as the result of the application of the Gioia methodology to BI in tourism and demonstrates BI’s substantial impact on strategic planning, operational enhancements, and sustainable competitive practices.

Based on these findings, it can be inferred that BI is instrumental in refining tourism demand forecasts and shaping adaptable decision-making processes. BI streamlines operations, placing customer preferences at the forefront, thus optimizing the overall tourist event experience. Furthermore, BI’s integrative capabilities contribute to knowledge management, aiding the tourism industry’s progression towards more responsible and sustainable practices. In summary, our qualitative results allow us to conclude that BI is a pivotal element in steering the tourism industry towards comprehensive growth, underlining its critical role in operational optimization, strategic foresight and sustainability.

Analyzing BA in tourism with the Gioia methodology

Our qualitative analysis on the influence of BA on decision-making in the tourism sector using the Gioia methodology was somewhat more extensive due to the increased complexity of the articles included in the sample. The results of this analysis unraveled the intricate web of applications and implications of BA in the tourism sector (see Fig.  9 ). The identified aggregate dimensions provided a multifaceted panorama of how BA reshapes tourism.

Data structure for BA obtained using the Gioia methodology (Gioia et al., 2013 ). Source Authors’ compilation from selected articles

The strategic application of BA is the cornerstone supporting innovation and addressing challenges within the sector. Adapting BA strategically improves decision making and paves the way for significant advancements and effective management of challenges. This strategic foundation drives innovation in customer engagement, which emerges as a key outcome.

By leveraging data to enhance interactions and experiences, BA enables a more personalized and dynamic approach to customers. Furthermore, with regard to challenges and opportunities in data use, the path to innovation is marked by both. These critical elements directly influence how tourism organizations strategically apply BA and make progress in customer engagement. Paramount aspects centrally implied in the efficacy of BA are the successful management of data privacy and the development of specialized skills. The culmination of these dimensions is observed in the evolution and continuous improvement of tourism services. The successful integration of BA strategies, coupled with innovation in customer engagement and overcoming challenges, leads to a tangible transformation in the services offered, reflecting the vital role of BA in the tourism sector’s ongoing innovation. Together, these intertwined dimensions highlight a narrative of progress and adaptation in tourism, where BA acts as a catalyst for strategic change, customer-centered innovation, challenge overcoming, and service evolution. Taken together, the results of our qualitative analysis of BA using the Gioia methodology reflect the significant and multifaceted influence of BA in tourism, paving the way for greater personalization, enhanced engagement, and more informed decision making, all within the context of a constantly evolving and adapting tourism sector.

In recent years, surpassing their status as mere technological tools, BI and BA have emerged as strategic assets. In the present study, we conducted an in-depth exploration of the intersection of BI and BA within the tourism sector, and the results of our analysis contribute to the current understanding of distinct yet complementary roles of BI and BA in enhancing strategic orientation, customer engagement, operational efficiency and sustainability within the tourism sector.

Our results are largely consistent with the findings previously reported in the literature on the use of digital technologies for efficiency, innovation, and sustainability. For instance, similarly to our conclusions, Lyulyov et al.’s ( 2024 ) study on the integration of e-governance and e-business in sustainable development argued that enhancing efficiency and fostering innovation are key areas that need be pursued. Both e-governance and e-business leverage digital technologies to optimize processes, improve decision-making, and promote sustainable practices. This synergy is evident in their alignment with global sustainability goals and in contributing to a more informed, efficient, and customer-centric approach in their respective sectors.

Furthermore, in our analysis on BI and BA in tourism using the SciMAT tool, we found a significant change in publications before 2020 and from 2020 onwards, reflecting the tourim’s adaptation to digitalization. This result is broadly similar to Ribeiro-Navarrete et al.’s ( 2023 ) observations on the evolution in academic production, with a significant change happening in pre-pandemic and post-pandemic publications in the literature on digitalization of cooperatives.

Applying BI and BA in the tourism sector opens new avenues to enhance service quality and customer interaction. The proposed perspectives suggest a more tailored approach, where BI and BA are used not just as tools for data processing and analysis, but rather become integral to creating a dynamic, responsive, and customer-centric tourism experience. By integrating these technologies, tourism businesses can gain deeper insights into customer preferences and behaviors, allowing for a more personalized service delivery and a more engaging customer journey. Furthermore, the use of BI and BA can lead to the development of innovative strategies to attract and retain customers, offering a competitive edge in an increasingly digital marketplace. Therefore, the implementation of these technologies should be viewed not just as a technical upgrade, but as a strategic move towards a more informed, agile, and customer-oriented tourism industry.

Furthermore, if we compare the results of the present study with Saura et al. ( 2023b ) study on advances in digital technology, data-driven decision making, combination of methodologies, improvement of interaction and customer satisfaction, we find that, despite the fact that the central themes in both studies are the use of digital technologies and data analysis in the improvement of services, our article focuses specifically on the tourism sector, with a particular emphasis on the roles of BI and BA, while Saura et al. ( 2023b ) has a broader perspective on digital service models in various industries. In addition, the present paper on BI and BA focuses more on distinguishing the roles and impacts of BI and BA in tourism, while Saura et al. ( 2023b ) analyzes the broader application of digital technologies in improving the customer experience in services. Finally, the present study incorporates and details in a novel way the use of the Gioia methodology for the qualitative analysis applied to BI and BA in the tourism sector.

Our results reveal that BI is focused on technological integration and operational enhancement and thus is concerned with the systematic merging of various technological tools and platforms to streamline data collection, analysis, and reporting. This integration, as also demonstrated in several previous studies (e.g., Khan & Quadri, 2012 ; Viitanen & Pirttimaki, 2006 ), involves harmonizing disparate systems, including but not limited to customer relationship management (CRM) software, booking engines and social media analytics tools, in order to create a cohesive BI framework. In its turn, BA excels in predictive analysis and direct customer engagement through data, using the insights gained from BI to improve the efficiency and effectiveness of operational processes in tourism. As also indicated in previous research (DuttaRoy & DuttaRoy, 2016 ; Vahn, 2014 ), this could include optimizing resource allocation, enhancing customer service, tailoring marketing strategies based on customer data, and improving the overall business performance. Accordingly, our findings reinforce extant literature while providing new insights into the strategic application of BI and BA in tourism.

Specifically, we identified that strategic orientation in both fields emphasizes the importance of long-term development and market adaptation, with BA focusing more on strategic decision-making and innovation. This aligns with previous studies emphasizing the importance of strategic planning in the evolution of tourism (e.g., Vukotić & Vojnović, 2016 ). With regard to customer engagement, BA stands out for its ability to personalize customer experiences through detailed data analysis, whereas BI focuses on optimizing systems that influence customer service—an approach also observed in previous research (Shende & Panneerselvam, 2018 ).

Regarding operational and service enhancement, BI and BA share the common goal of improving service quality, albeit with a different focus: while BI focuses on operational processes, BA prioritizes data-driven service development. This reflects trends noted in previous studies on the importance of operational efficiency and service innovation in tourism (Hollebeek & Rather, 2019 ; Jiménez-Zarco et al., 2011 ).

Furthermore, sustainability emerges as a critical issue in the current industry, with BI facilitating sustainable practices through effective knowledge management. While none of the papers included in our dataset directly refers to sustainability in conjunction with BA, our results may suggest that BA advocates for more dynamic and innovative sustainable innovations. In this context, very few other publications are currently available. However, some articles would confirm these conclusions, as was the case of the few publications suggesting that the vast amount of data generated on social media by tourists regarding their travel experiences could serve as a valuable source of open innovation (Saura et al., 2023a ). For instance, the use of social Big Data might facilitate innovation processes supporting the development of sustainable tourism experiences in a destination (Del Vecchio et al., 2018b ).

Overall, an increasing number of publications point to the relevance of advanced data analysis technologies for understanding and responding to complex challenges, whether they study the economic impacts of extreme weather phenomena, as in Saura et al. ( 2023c ), or the evolution of practices in the tourism sector, as we did the present study. In both cases, the ability to process and analyze large volumes of data becomes an essential tool for innovation and strategic adaptation in an ever-changing world.

Conclusions

In the present study, we used a dual research methodology to explore how BI and BA serve as distinct yet interconnected facets in the tourism industry. The major research question addressed in this study was as follows: In what ways do Business Intelligence and Business Analytics manifest as distinct yet interrelated elements in the tourism sector? Through an in-depth analysis of 12 scientific articles extracted from the WoS Core Collection database, significant insights were garnered. The selected papers were subjected to a comprehensive bibliometric analysis that was initially conducted to determine the current landscape of the field. The findings presented here offer a deep understanding of the roles and interplay between BI and BA in tourism.

While sharing common goals, BI and BA differ in their approaches to those goals. While BI integrates and optimizes systems for improved efficiency and decision support, BA is dynamic and focuses more on leveraging data for direct customer engagement and innovative service transformation. These results highlight a complementary relationship, where BI provides a robust infrastructure and BA offers agility and responsiveness in the data-driven landscape of tourism. The selected methodological framework allowed us to draw upon a broad and diverse range of existing academic insights, enhancing the understanding of the current state of BI and BA in tourism.

The results of the present study advance the theoretical framework of tourism management by integrating and differentiating the roles of BI and BA. Our findings also add depth to existing management theories by showcasing how data-driven approaches can be applied to improve decision making, strategic planning and customer relations in tourism.

Furthermore, in the present study, we addressed the gap in the literature—namely, the scarcity of a comparative analysis of BI and BA within the tourism sector. On exploring the unique and combined effects of BI and BA, we highlighted areas previously unexamined in academic research. Accordingly, our findings provide a more holistic understanding of how data-driven strategies shape tourism industry practices. Viewed from this perspective, several key components regarding methodological aspects should be noted. Regarding data-driven inquiry, we used an inductive approach, which allowed themes and theories to naturally emerge from the data; with regard to narrative analysis, our data were organized into a coherent narrative, transitioning from raw data to more abstract themes and theoretical constructs; dealing with systematic thematic coding, several initial themes were directly identified from participant terms and then developed into broader themes; besides, methodology application supported detailed descriptions of phenomena, capturing the complexity of the qualitative data, In addition, our analysis was rooted in the perspectives of the original scientific studies. Accordingly, before moving to researcher-driven interpretations, we engaged in reflexivity, critically examining our role and potential biases in the data analysis process; visual models were extracted to illustrate the relationships between concepts and themes; and finally, this approach facilitated the development of new theoretical insights by organizing data into themes and dimensions.

Through a detailed exploration of the role of BI/BA in tourism decision-making using the Gioia methodology, we identified key themes that are presented in the Results section with visual aids such as thematic maps and comparative tables for clarity. These tools directly linked our findings to our research objectives, so that in our analysis of BI, we observed its notable influence on strategic and operational decision making in the tourism sector. According to our results, BI enhances demand forecasting and adaptability, especially in response to rapid changes in the tourism environment. The data also revealed the important role of BI in promoting operational improvements and customer-centric strategies, which is essential for enhancing tourism experience. Of note, BI’s integrative function emerged as a key factor in knowledge management, supporting sustainable and competitive practices within the industry.

Similarly, the results of our analysis of BA highlighted its strategic application in fostering innovation and effectively managing challenges in the tourism sector. Indeed, BA enables a more personalized approach to customer interaction, leveraging data to improve customer experiences. Identifying challenges and opportunities in data use proved critical in influencing BA’s strategic application in tourism organizations. Accordingly, the successful integration of BA strategies results in a continuous service improvement, thereby underscoring BA’s vital role in the ongoing innovation and transformation of tourism services.

Taken together, the results of the present study highlight the multifaceted and significant impact of BI and BA in shaping the strategic, operational, and customer-centric aspects of the tourism industry.

Practical implications

A major distinction between the use of BI and BA tools in the tourism sector concerns their different focus on both management and the organization’s strategy. To start with BI helps to understand past performance and manage the present one based on concrete facts; accordingly, using BI, one can better understand past trends and obtain indicators such as sales performance, customer behaviors, hotel occupancy, and forth. These indicators can meaningfully inform decision making to optimize hotel operations or improve marketing. As for the tools used within BI, these typically include structured data and software allowing one to create dashboards, reports, and data visualizations. In its turn, BA uses both structured and semi-structured data, such as customer reviews and social media comments, with more emphasis on complex analyses, be them predictive and prescriptive. BA is essential for anticipating changes in the market and strategical planning—for instance, it can predict the demands of certain tourist destinations, or market trends, thereby facilitating proactive and strategic decisions. BA also incorporates advanced tools such as statistical analysis, data mining, and machine learning.

Hence, for industry practitioners, an integration of BI and BA offers a comprehensive approach: BI for foundational data management and technological infrastructure, on the one hand, and BA for dynamic, data-driven decision-making, on the other hand. Tourism businesses can leverage BI for operational efficiencies and BA for customer-centric strategies and service innovation. Our findings also suggest, in order to fully leverage BI and BA for effective tourism management, there is a need for investment in technological infrastructure. In this respect, our findings offer a strategic roadmap for tourism professionals to effectively integrate BI and BA, offering a guide for implementing these technologies to maximize service quality and innovation in customer strategies.

Furthermore, Gioia’s methodological diagram for BI in tourism reveals a structured approach through which BI empowers the tourism sector. It highlights how strategic integration and forecasting, driven by BI, are critical to adapting to the dynamic nature of tourism, with the integration of data from various sources so as to improve demand analysis and agility in decision making. Technological advancements facilitated by BI are a breakthrough in data integration and a means to promote operational excellence through customer-centric strategies, allowing tourism companies to stay competitive. The operational efficiency improvements highlighted in the model reflect the role of BI in streamlining processes, optimizing resource allocation, and overall enrichment of visitor experiences. BI’s contribution to sustainability and knowledge management indicates its potential to foster sustainable tourism practices, improving the overall competitiveness of the sector. Said differently, BI acts as a conduit for innovation, strategic foresight, and sustainable development within the tourism industry, which makes its applications far-reaching, beyond mere data analysis, and capable of re-shaping the very structure of tourism sector management solutions.

Likewise, the concept diagram resulting from applying the Gioia methodology in the study of BA in the tourism sector clarifies the channels through which BA influences management solutions within it. Specifically, it shows how BA facilitates strategic decision-making, enabling predictive modelling and demand forecasting that support robust marketing strategies and service personalization. Data gleaned from online reviews and comments is key to refining customer satisfaction analysis, thus leading to improvements in service quality. In addition, the diagram indicates the importance of addressing data privacy and the need for expertise in handling it to effectively use and manage Big Data. Collectively, these pathways contribute to the evolution of tourism services through data analytics, which manifests itself in more personalized service offerings and recommendation systems. This progression shows BAs as analytical tools and as integral components in creating a more responsive and data-driven tourism industry that prioritizes customer engagement and operational excellence.

Theoretical implications

The present study contributes to the theoretical understanding of how BI and BA intersect and complement each other in the tourism sector, offering novel insights into their distinct yet synergistic roles. Our results broaden the scope of tourism management theories to include data-driven approaches for both operational and strategic planning. A new contribution to the theoretical understanding of BI and BA in the tourism sector is that, via clearly delineating the distinct roles and applications of BI and BA, the present study existing theoretical models of tourism management.

Yet another novel aspect of this study is the innovative use of secondary data from scientific articles in the Web of Science database, applied through the Gioia methodology. This approach extends the traditional use of this methodology by setting a precedent of using extensive academic databases to synthesize and analyze existing knowledge.

Collectively, our findings emphasize the importance of BI and BA in strategic orientation, customer engagement, and sustainability, thereby enriching the theoretical framework within which tourism management operates. Furthermore, our methodological innovation of data use adds a new dimension to academic research, offering a robust, comprehensive approach to understanding complex interdisciplinary phenomena in the tourism industry.

As argued by Saura et al. ( 2024 ), the adaptability and flexibility of small- and medium-sized enterprises, coupled with technology, enables enterprises to quickly respond to unforeseen challenges, emphasizing the importance of cybersecurity, government support, and digital tools to improve business processes. If we consider these conclusions in the context of the present study, we can reveal the following broader theoretical implication: in the modern business landscape, regardless of the specific industry, the effective use of technology and data analysis is an indispensable tool needed to manage complexity and implement innovation. This idea translates into the need for continuous adaptation and integration of digital tools into business strategies to improve efficiency, customer engagement, and overall resilience. These insights suggest a growing trend towards reliance on data-driven decision making and technological advancements in different sectors, reshaping theoretical frameworks and business practices alike.

Similar theoretical implications concerning the key role of advanced data analytics and technology in revolutionizing business strategies were made by Saura et al. ( 2024 ) in a study on data-mining analytics of Twitter-based user-generated content in operations management (OM), suggesting a transformative impact of digital innovation in this field. These conclusions resonate with the present study’s focus on the synergistic roles of BI and BA in improving tourism management through data-driven approaches. Both studies highlight how the new digital technologies can revolutionize operational and strategic planning, from automation and forecasting to customer engagement and sustainability in tourism. The use of various data sources, such as Twitter UGC and academic databases, also reflects a broader business trend: the imperative to use technology and data analytics to manage complexity and drive innovation across various industries. This convergence suggests a shift towards data-driven decision-making and technological advancements, reshaping theoretical frameworks and business practices.

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Business Administration Department, Faculty of Economics and Business Sciences, Rey Juan Carlos University, Paseo de los Artilleros s/n. 28032, Vicálvaro, Madrid, Spain

Montserrat Jiménez-Partearroyo

Financial Economics and Accounting Department, Faculty of Economics and Business Sciences, Rey Juan Carlos University, Paseo de los Artilleros s/n. 28032, Vicálvaro, Madrid, Spain

Ana Medina-López

College of Healthcare Management and Economics, Gulf Medical University, Ajman, UAE

Sudhir Rana

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Correspondence to Ana Medina-López .

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Jiménez-Partearroyo, M., Medina-López, A. & Rana, S. Business intelligence and business analytics in tourism: insights through Gioia methodology. Int Entrep Manag J (2024). https://doi.org/10.1007/s11365-024-00973-7

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Accepted : 22 March 2024

Published : 12 April 2024

DOI : https://doi.org/10.1007/s11365-024-00973-7

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