Competitive intelligence in tourism: charting future directions through bibliometric analysis

Abstract

Purpose

This study explores the crucial role of competitive intelligence (CI) in the tourism sector’s strategic decision-making. CI has significantly transformed the tourism sector through new insights and sophistication in data analysis and strategic planning. The rise in tourism-related competition, due to new destinations, varied tourist preferences and sustainability emphasis, makes competitive intelligence essential for understanding future market trends and making informed strategic choices.

Design/methodology/approach

Utilising PRISMA techniques for bibliometric analysis, the study examines literature from 1998 to 2023 (WoS), focusing on service innovation, customer experience management and sustainable strategies. It presents an analysis of the evolution of CI in tourism, its impact, influential works and future research directions.

Findings

Findings show that the multidisciplinary nature of CI in tourism is further evidenced by studies on quality cues, travellers’ information needs and the utilisation of big data. Future studies need to understand both global trends and regional specifics, as shown in investigations of spatial-temporal tourism dynamics.

Originality/value

This study represents a novel contribution to the field of tourism research by offering a comprehensive bibliometric analysis of CI literature from 1998 to 2023. It uniquely integrates service innovation, customer experience management and sustainable strategies within the context of CI, highlighting its multidisciplinary impacts and evolution. These insights collectively emphasise the need for future innovation and a comprehensive understanding of the global-local nexus to inform future tourism research and practice.

Citation

Aljaafreh, N.A.A., De-Pablos-Heredero, C. and Orea-Giner, A. (2024), "Competitive intelligence in tourism: charting future directions through bibliometric analysis", Journal of Tourism Futures, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JTF-01-2024-0015

1. Introduction

The tourism sector, known for its economic contribution and support of diverse socio-economic groups, including women and youth (Nepal and Sapkota, 2019), is dynamic and resilient. However, it continually faces challenges from changing consumer preferences, technological advancements, and global economic shifts (Sigala, 2018; Gössling et al., 2020; Higgins-Desbiolles, 2021). In this evolving landscape, competitive intelligence (CI) has become essential. Competitive intelligence (CI) is a pivotal strategic tool that empowers organizations to anticipate market dynamics and enhance their strategic positioning against competitors (ref). In the context of the tourism industry, CI transcends mere data collection, evolving into a crucial element of strategic management that directly influences organizational competitiveness (Maluleka and Chummun, 2023). CI aids in understanding market dynamics, responding to competitive pressures, and making strategic decisions (Jasim et al., 2020), enabling businesses to stay ahead of trends and adapt to market changes. Moreover, with the growing emphasis on sustainable and responsible tourism, CI’s role in gathering data on market trends and environmental and social impacts is vital, supporting the alignment of business strategies with sustainable goals (Casado-Salguero and Quintero, 2016; Domashova and Zasypkina, 2021).

Recent bibliometric analyses on tourism competitiveness, such as those conducted by Gomezelj (2016), have highlighted the evolving nature of competitive strategies in response to global economic shifts, technological advancements, and changing consumer behaviours. These studies often reveal a trajectory of increasing sophistication in competitive strategies, integrating sustainability and technological innovation as core components. In service innovation, which is crucial for tourism, CI identifies innovation drivers and climates (Wooder and Baker, 2012; Garcia and Calantone, 2002; Olshavsky and Spreng, 1996; Spohrer and Maglio, 2008; Ismail, 2005). Customer experience management, a key to success, also depends on CI for managing interactions and stakeholder engagement (Lemon and Verhoef, 2016; Forbes, 2020; Kandampully et al., 2018; Hwang and Seo, 2016). CI’s role extends to monitoring trends, competitors, and consumer preferences, influencing service innovation and delivery (Gomezelj, 2016; Martin-Rios and Ciobanu, 2019; Ostrom, 2010), and it is crucial for developing sustainable strategies and organisational growth (Dishman and Calof, 2008; Casado-Salguero et al., 2019; Nasiri and Mozafari, 2015). Despite its importance, there is not enough of thorough studies on CI’s role in the tourism industry (Buhalis and Amaranggana, 2015; Sigala, 2017, 2020). This leaves a significant gap in understanding how technology can improve competitiveness via CI (Buhalis and Amaranggana, 2015). Additionally, there is a lack of understanding of CI’s real-world impact on tourism business performance.

To fill this gap, this study aims to thoroughly analyse the role of Competitive Intelligence (CI) in strategic decision-making within the tourism industry. The research is based on PRISMA techniques to conduct a bibliometric analysis from January 1998 to May 2023 of literature spanning diverse aspects of CI in tourism, including trends, methodologies, and impacts included in WoS database. Specifically, it focuses on service innovation, customer experience management, and sustainable strategies within the sector. This study addresses three research questions:

RQ1.

What is the conceptual structure of research focused on competitive intelligence in the tourism sector?

RQ2.

How do the trends, methodologies, and impacts of competitive intelligence research in the tourism sector compare with each other?

RQ3.

What are the potential future directions for research in the field of competitive intelligence within the tourism sector?

The outcome of this research will provide a detailed understanding of how CI is being utilised in the tourism sector for supporting strategic decision-making, offering valuable insights for academics, industry practitioners, and policy makers involved in tourism development and management.

This study articulates critical implications for practitioners operating within the competitive landscape of the tourism industry, particularly through its examination of Competitive Intelligence (CI). The detailed analysis of CI applications provided herein assists practitioners in understanding how strategic intelligence is essential not only for responding to immediate market trends but also for integrating sustainable tourism practices that align with global sustainability goals (Farsari, 2023). For example, the study’s findings on the role of CI in enhancing strategic adaptability underscore its significance in fostering an agile response to competitive and environmental pressures, which is crucial for maintaining a sustainable competitive advantage (Casado-Salguero and Quintero, 2016). For research students, this study serves as an advanced guide to employing bibliometric analysis in tourism studies, specifically those focused on competitive intelligence. The integration of bibliometric methods to dissect the evolution and impact of CI practices offers a methodological blueprint for students interested in the dynamics of strategic decision-making within tourism (Zupic and Čater, 2015). Moreover, the study’s exploration of interdisciplinary themes enriches its academic value, presenting a model for how quantitative analyses can be effectively paired with qualitative insights to produce a comprehensive view of industry trends. These aspects are designed to prepare students for tackling complex research questions that require a nuanced understanding of both data analysis and thematic interpretation (Van Eck and Waltman, 2014).

2. Literature review

This literature review delves into the multifaceted role of CI in the tourism industry, exploring how it aids in strategic decision-making, impacts service innovation, enhances customer experience, and contributes to the sustainable growth of organisations.

2.1 Competitive intelligence

Competitive Intelligence (CI) has become increasingly critical in the modern, dynamic business world. It integrates market analysis and strategic planning to secure a competitive edge, as described by Adidam et al. (2012) and Colakoglu (2011). CI adopts an ethical and systematic methodology to collect and interpret external information, focusing on competitors and market dynamics, which are essential for strategic decision-making. This point is emphasised by Myburgh (2004) and Attaway (1998). CI is instrumental in identifying opportunities and threats, enabling businesses to make strategic adaptations, as noted by Nwokah and Ondukwu (2009) and Rousch and Senti (2001). Particularly in the tourism industry, the systematic and ethical approach of CI is crucial for anticipating market shifts and maintaining a competitive edge, a fact highlighted by Beal (2000) and Tanev and Bailetti (1998). Differing from traditional market research, CI focuses on interpreting information to foresee competitors' strategies and market prospects, as Cloutier (2013) and Jaworski et al. (2002) point out. Conducted ethically and legally, CI relies on publicly accessible information for ongoing monitoring and analysis, a practice especially important in sectors like tourism, which face challenges related to sustainability and ethical considerations, as discussed by Agarwal (2006) and Attaway (1998).

The primary objective of CI is to enhance strategic decision-making by helping in devising strategies for improved market positioning and foreseeing competitor manoeuvers. This aspect is elaborated by Dishman and Calof (2008) and Viviers et al. (2005). CI entails gathering data from various sources, analysing it, interpreting it into actionable insights, and disseminating these within the organisation, a process that is key in industries like tourism. This multi-faceted process is detailed by sources such as Ezenw et al. (2018), Priporas et al. (2005), Albescu et al. (2008), Amiri et al. (2017), Calof et al. (2017), and Rousch and Senti (2001). CI significantly steers strategic direction and decision-making, becoming a linchpin of business success, as noted by Maritz and Du Toit (2018) and Wright and Calof (2006). In the tourism sector, CI facilitates informed decisions on product development, marketing, and service offerings, as discussed by Dishman and Calof (2008) and Adidam et al. (2012). It enables the prediction of market trends, the identification of external threats and risks, and the provision of evidence-based insights for decision-making, an aspect explored by López-Robles et al. (2019), Nwokah and Ondukwu (2009), Cloutier (2013), Jaworski et al. (2002), Beal (2000), and Qiu (2008).

The strategic insights provided by CI are fundamental for long-term sustainability and growth. They are helpful in risk mitigation, crisis management, and long-term planning and enhance organisational agility and flexibility. This is particularly important in the tourism industry, where external factors can rapidly change the market landscape, as indicated by Vedder and Guynes (2001), Rousch and Senti (2001), Agarwal (2006), Attaway (1998), Myburgh (2004), Colakoglu (2011), Dukić et al. (2016), and Negash and Gray (2008). CI plays a crucial role in identifying sustainable practices and adapting to evolving consumer preferences, ensuring businesses are well-prepared for both current and future challenge.

Competitive intelligence has previously been used in other industries (Lee et al., 2021), but its application in tourism has been limited (Buhalis and Amaranggana, 2015; Sigala, 2017, 2020). However, the tourism industry’s information-intensive nature and increased competition have prompted organisations to implement competitive intelligence strategies (Tulungen et al., 2021; Lee et al., 2021). Other industries have successfully used competitive intelligence to improve their competitiveness (Tulungen et al., 2021; Lee et al., 2021), and the tourism industry can benefit from the same strategies, particularly in the evolving landscape of sustainable tourism, which is heavily influenced by the integration of Sustainable Development Goals (SDGs), aimed to promote socioeconomic development while mitigating environmental impact. A recent bibliometric analysis by Fauzi (2023) underscores this progression by examining 479 journal publications to trace the influence of SDGs on sustainable tourism practices. This study reveals how the research trajectory in sustainable tourism has expanded significantly since the introduction of the SDGs in 2015, offering a structured framework for assessing the impact of tourism on global sustainability objectives. Such insights are crucial as they highlight the broadening scope of research and implementation strategies that align tourism development with global sustainability mandates (Fauzi, 2023).

2.2 Competitive intelligence and tourism industry

Competitive Intelligence (CI) has emerged as an indispensable tool in the strategic management of the tourism industry, providing companies with a sustainable competitive advantage in an ever-evolving landscape. As noted by Bethapudi (2013) and Buhalis and O'Connor (2005), its importance is particularly evident in dynamic sectors like the Spanish hotel industry, underscoring the need to comprehend both environmental and organisational factors in strategy development. Furthermore, the adaptability of CI is crucial for sustainable growth, especially in the context of increasing emphasis on sustainability, as highlighted by Fuchs et al. (2013) and Höpken et al. (2015). The influence of CI is not limited to the tourism sector; it extends to various other areas, such as healthcare. In private hospitals, for instance, the Analytic Hierarchy Process (AHP) is employed to prioritize CI elements. This approach is also invaluable in the tourism industry, where decision-making is multifaceted and involves numerous stakeholders, as pointed out by Korte et al. (2013) and Sebele (2010).

In the realm of tourism, CI encompasses both market and strategic intelligence. Market intelligence focuses on understanding customer needs and marketing strategies, while strategic intelligence monitors the competitive strategies and dynamics within the industry, as Xu (2010) and Misso et al. (2018) have indicated. The primary objectives of CI in this field are to expand market reach, evaluate business opportunities, and identify potential threats. This is particularly relevant in some areas like Iran, where the principles of CI, though underexplored, are highly applicable, as observed by Nasri (2011) and Korte et al. (2013). Additionally, environmental and organisational characteristics play a significant role in the application of CI in tourism. For instance, in the hotel industry, factors such as the number of competitors, size, star rating, and certifications like ISO are critical in determining the scope of CI activities. Evans (2016) and Viviers et al. (2005) suggest that intensive CI efforts are associated with more effective implementation and significant impact on strategic decisions. Amiri et al. (2017) and Calof et al. (2017) show that CI not only creates a fair and honest business environment, but it also reduces uncertainty, which helps managers make better decisions in areas where market and technology change often.

Technology, particularly text mining, also plays a pivotal role in enhancing CI, enabling sustainable competitive advantages in areas like hotel reservations and restaurant management. Strategic employment of information technology amplifies the benefits derived from CI, as discussed by Maritz and Du Toit (2018) and Qiu (2008). Organisational characteristics such as size, chain affiliation, star rating, and quality certifications significantly influence CI efforts, impacting resource allocation and the breadth of CI activities, as indicated by Wright and Calof (2006) and Dishman and Calof (2008). In summary, CI in the tourism industry is dynamic and multifaceted, indispensable for informed decision-making, strategic planning, and maintaining a competitive edge. Its application ranges from analysing market trends and competitor strategies to leveraging technological advancements and adapting to organisational and environmental changes. As the tourism industry continues to evolve, the role of CI in navigating its complexities and seizing new opportunities becomes increasingly vital.

Smart Destinations represent a progressive integration of technology in the tourism sector, enhancing competitiveness and sustainability through advanced competitive intelligence practices. This concept encapsulates the use of information and communication technologies (ICTs) to improve destination management, visitor experience, and economic and environmental sustainability (Gretzel et al., 2015). The emergence of Smart Destinations highlights how technological innovations are increasingly pivotal in crafting competitive strategies within the tourism industry.

The application of technologies such as big data analytics, the Internet of Things (IoT), and artificial intelligence in Smart Destinations provides a sophisticated infrastructure for gathering, analysing, and operationalizing data. This technological framework significantly augments the capabilities of competitive intelligence by offering real-time insights into consumer behaviour, market trends, and operational efficiencies (Boes et al., 2016). For instance, the implementation of smart systems in destinations can track visitor patterns, optimize resource management, and enhance personalized marketing strategies, directly contributing to the competitive positioning of the destination.

3. Methodology

In line with contemporary methodological approaches within tourism research, this study draws upon bibliometric techniques to examine the integration of competitive intelligence in sustainable tourism strategies. Similar to the approach taken by Fauzi (2023), our analysis utilizes document citation analysis, co-citation analysis, and keyword co-occurrence to map the scholarly landscape and identify evolving trends within the field. Fauzi’s comprehensive bibliometric study not only highlights the pivotal themes but also maps the future trajectory of sustainable tourism research, providing a benchmark for methodological rigour in exploring the interplay between tourism practices and sustainability goals (Fauzi, 2023).

The study was aimed to conduct an in-depth analysis of Competitive Intelligence (CI) within the tourism sector, focusing on trends, methodologies, and the impact of CI on strategic decision-making. To achieve this, it followed a bibliometric analysis based on a systematic approach to data collection and analysis (Sigala et al., 2021; Liu et al., 2022).

3.1 Bibliometric analysis

The PRISMA technique (Moher et al., 2009), to conduct an exhaustive analysis of Competitive Intelligence (CI) within the tourism industry (Figure 1) has been used for the bibliometric analysis. The PRISMA framework, initially developed to improve the reporting of systematic reviews and meta-analyses, offers a rigorous method for identifying, evaluating, and integrating research findings (Moher et al., 2009). This method completes and replicates research study identification, selection, and assessment (Moher et al., 2009). A transparent screening technique selects research by pre-defined inclusion and exclusion criteria after a full database search (Moher et al., 2009). A PRISMA flowchart indicates the number of studies found, included, and excluded, enhancing clarity. Standardising data extraction and synthesis from selected studies reduced bias (Moher et al., 2009). This approach is consistent with recent trends in tourism studies that emphasize methodological rigour and transparency in literature reviews (Hall, 2011; Xiao and Smith, 2016). This bibliometric analysis utilises science mapping to gain a thorough comprehension of the structural and dynamic elements of the CI field within the tourist industry. Science mapping techniques like co-citation and keyword co-occurrence analyses are essential for identifying and visualizing key research themes and their interconnections (Molina-Collado et al., 2022). This method helps identify research clusters, trends, and the evolution of scientific domains, which are crucial for comprehensive bibliometric analysis (Chen, 2017).

3.1.1 Data acquisition

Selecting between the Web of Science (WoS) and Scopus databases for research often hinges on study scope, journal coverage, and database features. WoS is favoured for its stringent selection criteria and coverage of high-quality, peer-reviewed journals that adhere to rigorous research standards, making it ideal for studies requiring authoritative and impactful insights. This selectivity benefits research aimed at exploring well-established fields, as WoS prioritizes long-standing journals, noted for contributing foundational knowledge (Mongeon and Paul-Hus, 2016). WoS also offers significant historical depth, with archives dating back to 1900, valuable for bibliometric and trend analyses that track long-term field developments (Archambault et al., 2009). Additionally, WoS’s Journal Citation Reports provide detailed citation data, aiding in evaluating journal impacts and supporting studies that assess research influence across the academic community (Buela-Casal and Zych, 2012). Moreover, WoS excels in specialized indexing and search capabilities that facilitate precise article retrieval based on citations, authors, and topics—key for studies analysing citation networks and identifying major field contributors (Falagas et al., 2008). Despite Scopus’s broader coverage and higher publication counts, WoS’s focus on high-impact, authoritative journals and its robust, integrated citation analysis tools make it more suited for quality-centric research (Meho and Yang, 2007; Franceschet, 2010). Considering the previous facts, WoS was selected for its particularly advantageous for bibliometric research requiring detailed, quality-focused, and historically rich data sources, thus supporting comprehensive and authoritative literature examinations within specific fields.

The search criteria included the keywords “competitive intelligence”, “strategic decision” and “Business Intelligence” linked to “Business Tourism” or “Tourism Industry.” This approach ensured the relevance and specificity of the data collected for the study. Papers were collected if the titles, author-provided keywords, or abstracts included the specified search keywords. In terms of inclusion criteria, the study filtered the results to include only open access documents, such as articles, proceeding papers, or review articles. Open access articles were selected considering that this model aligns with the principles of open science, promoting transparency, reproducibility, and the free exchange of knowledge. This fosters a more collaborative and open research environment, facilitating the verification of results and the reuse of data for further research (McKiernan et al., 2016). Data acquisition was done in June 2023. The publication years considered spanned from January 1998 to May 2023, offering a comprehensive view of the literature over 3 decades and enabling the study to track the evolution and trends in CI within the tourism sector. During this stage, duplicates and irrelevant records were removed to refine the dataset.

Next, the study moved to the eligibility assessment stage, where 5,565 records were carefully evaluated against specific inclusion criteria. This involved scrutinizing abstracts and, when necessary, full texts, to ensure each study’s relevance to CI and its influence on strategic decision-making in the tourism industry. Finally, 1,418 records were excluded for not closely aligning with the topic, leaving 4,147 studies for the final analysis. These selected studies, representing the most pertinent and informative literature on CI and strategic decision-making in tourism, formed the foundation for the subsequent bibliometric analysis. Figure 2 summarises the process considering the PRISMA flow chart.

3.1.2 Analysis

Initially the number of publications over time in the area of CI within tourism is traced, revealing the field’s developmental trajectory and growing interest. This bibliometric analysis employed statistical tools to illustrate the developmental trajectory and increasing scholarly interest in the field.

An exploratory quantitative analysis is developed to obtain the number of publications by year, principal keywords and author contributions using Excel. Besides, a keyword co-ocurrence Network Analysis using the top 50 most frequently cited keywords from our dataset was created using Python 3.8. This network, represented by nodes (keywords) and edges (keyword co-occurrences), visualises the interconnections among key research themes, highlighting dominant topics and potential interdisciplinary linkages. This was augmented by an innovation and co-citation analysis, employing bibliometric network analysis techniques to identify significant research that integrates diverse themes and methodologies, thereby contributing to the advancement of the field of CI in tourism.

4. Results and discussion

4.1 Number of publications by year, principal journals and keyword co-ocurrence network analysis

The initial years (1998–2004) witnessed a modest number of publications, indicating that this was a nascent area of research. The significant growth in publications from 2005 onwards, with a peak in 2022 (745 publications), highlights the growing recognition of competitive intelligence as a key factor in strategic decision-making in the tourism sector. Figure 3 shows the evolution of publications from 1998 to March 2023. This suggests an increasing academic and practical interest in competitive intelligence within the tourism industry. Literature in this domain has consistently underscored the critical role of competitive intelligence in enabling businesses to adjust to and leverage global trends (Moya-Martínez and Del Pozo-Rubio, 2021; Foris et al., 2021).

The keyword analysis conducted in this study provides valuable insights into the focal points of Competitive Intelligence research in the tourism sector. The most frequent keyword, “IMPACT” (330), suggests a significant emphasis on the effects within the field. “MANAGEMENT” (321) follows closely, indicating a focus on organisational aspects. The prominence of “MODEL” (243) and “tourism” in its various forms (216, 164, 133) highlights the importance of theoretical frameworks. The keyword “COVID-19” (188) reflects the pandemic’s critical impact on tourism research. Other notable keywords include “PERFORMANCE” (201), “BUSINESS INTELLIGENCE” (161), and “SATISFACTION” (145), which collectively point to a diverse range of research interests encompassing operational efficiency, data-driven insights, and customer experience. In total, these keywords appear 2,102 times, demonstrating their centrality in recent tourism studies. Figure 4 represents the results obtained from the keyword co-ocurrence Network Analysis using the top 50 most frequently cited keywords from our dataset.

The evolution of research fields and methodological trends in the study of competitive intelligence within the tourism industry has undergone significant development, progressing from foundational to more diversified and nuanced approaches. Initially, research in this field concentrated on establishing fundamental principles and frameworks. For example, early investigations may have focused on understanding the types of intelligence relevant to tourism businesses and methods for collecting and analysing data, similar to the foundational work by Moya-Martínez and Del Pozo-Rubio (2021), who examined the financing of SMEs in the Spanish tourism sector during the financial crisis, offering valuable insights into the economic aspects of competitive intelligence (Moya-Martínez et al., 2021). Over time, research has expanded both in terms of topics and methodologies. Recent studies have delved into complex areas such as the integration of competitive intelligence with technological tools like big data analytics and the impact of global events like the COVID-19 pandemic on the competitive landscape of tourism. For instance, Prasetyo et al. (2022) conducted a case study on the effect of the COVID-19 pandemic on religious tourism, providing valuable insights into crisis management within the tourism sector (Prasetyo et al., 2022).

4.2 Article co-citation analysis

The analysis of the top Innovation co-cited articles in the field of competitive intelligence in tourism reveals a diverse range of topics and methodological approaches, reflecting the multifaceted nature of the sector (Table 1). On the one hand, one prominent area of focus is the financing of SMEs in tourism, as highlighted by Moya-Martínez et al. (2021). With an empirical analysis methodology, this paper underscores the critical role of funding in fostering innovation within small and medium tourism enterprises, a vital component of the industry. On the other hand, the absence of co-citations for the recent work of Koliouska and Andreopoulou (2023) on e-tourism for sustainable development, using a conceptual framework, suggests an emerging interest in how digital transformation can spur sustainable innovation in tourism. The impact of the COVID-19 pandemic is another significant theme, illustrated by Prasetyo et al. (2022) through a case study on religious tourism. This research provides valuable insights into the pandemic’s disruptive effects on specific tourism sectors, emphasizing the need for resilient and adaptive strategies. Similarly, Foris et al. (2021) explore the technological dimension, particularly the role of Global Distribution Systems (GDS) in tourism, pointing out how technology can aid in navigating challenges like the pandemic and contribute to the diffusion of innovation in the sector. In summary, each of these articles contributes to a broader understanding of competitive intelligence in tourism, emphasizing the importance of diverse approaches—from empirical and case studies to technological analysis and conceptual frameworks—in addressing the complex challenges and opportunities in this dynamic field. This variety not only enriches the academic discourse but also offers practical insights for stakeholders in the tourism industry, guiding strategic decisions and innovation initiatives.

4.3 Top innovation research with high betweenness centrality analysis

The analysis of the top innovation research with high betweenness centrality in competitive intelligence within the tourism sector, as illustrated in Table 2, provides insight into influential research that acts as a bridge connecting various topics and methodologies in this field. Moya-Martínez and Del Pozo-Rubio (2021), with a betweenness centrality of 0.21, explore the financing of SMEs in the Spanish tourism sector during the 2008 financial crisis. Their case study approach in business and economics sheds light on lessons learned, offering valuable insights for similar future challenges. Moreover, the work by Koliouska and Andreopoulou (2023), scoring 0.24 in betweenness centrality, investigates e-tourism for sustainable development through alternative tourism activities. Their conceptual analysis bridges technology with environmental sustainability, highlighting the role of digital innovation in promoting sustainable tourism practices.

Prasetyo et al. (2022), with a centrality of 0.22, focus on the impact of the COVID-19 pandemic on religious tourism in Iraq. Their survey methodology provides an understanding of the pandemic’s effects on a specific tourism niche, adding to the knowledge of crisis management in tourism. Furthermore, Foris et al. (2021), with a centrality of 0.21, review GDS technology in the context of the pandemic. This study emphasizes the importance of technological solutions in navigating and adapting to crisis situations in tourism. Van Nuenen’s (2019) qualitative study, with a centrality of 0.18, examines the production of locality on peer-to-peer platforms, contributing to understanding how technology shapes tourism experiences and local tourism dynamics. Moreover, Kahyalar et al. (2023) score 0.23 in centrality, explore the relationship between tourism and the shadow economy. Their econometric analysis provides insights into economic aspects often overlooked in conventional tourism studies.

These studies, with their high centrality scores, demonstrate their role as pivotal works in connecting diverse research themes, methodologies, and focus within the field of competitive intelligence in tourism. They collectively highlight critical areas such as crisis response, sustainable development, technological innovation, and economic analysis, providing comprehensive insights that consider both academic research and industry practices.

4.4 Research topics and future research agenda

After analysing the co-occurrence network, top co-cited articles, and high betweenness centrality innovation research, the findings were leveraged to identify research gaps and propose future research directions (Table 3). These suggestions focus on the application of CI in the tourism industry, aiming to address the current gaps and enhance the industry’s innovation and sustainability efforts.

5. Conclusion

The integration of big data and analytical methods has profoundly transformed the sector, endowing competitive intelligence with novel insights and capabilities. These technological advancements have called for an enhanced level of sophistication in data analysis and strategic planning within the tourism sector (Xiong et al., 2019; Kong, 2023). Additionally, recent times have seen an intensification of competition within the tourism industry. The rise of new travel destinations, the diversification of tourist preferences, and an increasing emphasis on sustainable practices have heightened the industry’s competitiveness. Competitive intelligence has become an indispensable tool for companies to comprehend their competitors, discern emerging patterns, and execute well-informed strategic choices (Zimmerhackel et al., 2019; Cheng et al., 2023). This study shows that the escalation in publications concerning competitive intelligence in the tourism field from 1998 to 2023 mirrors the augmented acknowledgement of its criticality. The marked increase in scholarly publications since 2005, culminating in a zenith in 2022, accentuates the progressively pivotal role of competitive intelligence within the tourism sector (RQ1). This trend closely aligns with the challenges posed by globalization, technological progress, and increased competition in the tourism industry. Keyword analysis reveals key research focuses on competitive intelligence within tourism, emphasizing “IMPACT”, “MANAGEMENT”, and theoretical models, with notable attention to “COVID-19”, operational efficiency, and customer experience, as evidenced by their cumulative 2,102 occurrences in recent studies (RQ2). Tables 1 and 2 show the most influential and innovative research works in the field of competitive intelligence and the tourism industry, highlighting the contributions of Moya-Martínez et al. (2021), Prasetyo et al. (2022), Moya-Martínez and Del Pozo-Rubio (2021), and Foris et al. (2021). The aggregated knowledge from these scholarly works highlights the imperative of competitive intelligence in steering the strategic decision-making processes in this dynamic and continuously evolving sector.

Looking ahead, the intersections of competitive intelligence, sustainable tourism, and SDGs present a fertile ground for future research. As identified in the bibliometric analysis by Fauzi (2023), the co-occurrence of keywords related to these themes points to burgeoning areas of interest and investigation. Future studies should consider these evolving trends to anticipate changes in tourism strategies and their impacts on sustainability goals. This forward-looking perspective is essential for developing robust frameworks that support sustainable growth within the tourism industry while adhering to the comprehensive objectives outlined in the SDGs (Fauzi, 2023).

5.1 Future of competitive intelligence in tourism research

Moving from bibliometric data analysis and future research paths to new keywords might help you identify fascinating new competitive intelligence topics in the tourism sector (RQ3). These tendencies, together with the importance of interdisciplinary approaches, new technology, and sustainability practices, guide future studies. For instance, Chan’s (2021) conceptual model for the post-COVID-19 tourism business shows how the pandemic devastated the tourism industry and led to a move towards post-pandemic recovery methods. Salisu et al. (2021) examined tourist industry adoption of business intelligence systems during the pandemic, highlighting technology as a response tactic. Kong’s (2023) study on real-time processing systems and the Internet of Things (IoT) in tourism shows the tourism industry’s technology evolution. Similarly, Xiong et al. (2019) analysed China’s online retail growth and its effects on tourism, emphasising the growing importance of digital platforms. Shark tourism’s economic benefits indicate the growing importance of sustainability in tourist studies (Zimmerhackel et al., 2019). Tourism ecological efficiency and sustainable development by Cheng et al. (2023) support this theme. Competitive intelligence in tourism is multidisciplinary, according to studies on quality cues and visitors' information needs (Jang et al., 2021) and big data in tourism (Latinovic et al., 2016). The spatial-temporal evolution characteristics and influencing elements of tourism in a regional context show the need of understanding global trends and regional particular (Chi et al., 2022). Liu et al. (2023) discuss extracting tourism focus points from online text data, emphasising regional tourism dynamics. These studies show that tourism competitive intelligence research is dynamic and complex. Innovative thinking, technology and sustainability integration, and a grasp of global and local trends are needed to impact tourism research and practice.

The current study’s focus on CI in tourism has shown some key findings, but it also shows the need to expand to adjacent thematic areas. This growth is necessary to comprehend market dynamics and the tourism industry’s complex issues and tactics, including those encountered by SMEs. To enhance the study, future research could include tourist technology adoption, which shapes competitive tactics and operational efficiency. Including digital marketing techniques and online customer behaviour could provide a more holistic understanding of how CI is driving corporate performance in the digital age (Gretzel et al., 2015). Tourism competitiveness economics, such as market access, pricing strategies, and financial sustainability, may reveal how SMEs and larger entities overcome financial challenges and use CI for growth and resilience (Buhalis and Law, 2008). By examining these associated areas, the study can make more contributions to academic research and tourism management practice.

5.2 Practical implications

Tourism workforce may benefit from CI technologies by incorporating them into market analysis, consumer behaviour tracking, and decision-making processes to improve strategic planning and competition. For example, in the context of destination management, CI systems may use competition pricing, customer reviews, and social media sentiment to alter offerings in real time, enabling for dynamic pricing and targeted marketing campaigns. In sustainable tourism, experts might utilise CI to track environmental trends, policy changes, and visitor behaviour data, making it easier to plan eco-friendly activities that correspond with market demand and regulatory frameworks. Furthermore, CI technologies may assist professionals in identifying developing market prospects, such as speciality tourist sectors (e.g. wellness or adventure tourism), allowing them to predict trends and capitalise on first-mover advantages.

5.3 Limitations

In this study, certain limitations are inherent in the methodology employed. The research was constrained by its reliance on specific keywords, which might have limited the scope and potentially excluded relevant studies not encompassing these terms. Additionally, the focus on open-access documents may have omitted insights from non-accessible sources, thus restricting the diversity of the literature reviewed. The timeframe, spanning from January 1998 to May 2023, while extensive, may not encompass the very latest developments in the field.

The bibliometric approach employed in this study is predominantly quantitative, which involve statistical analyses of publication patterns, citation counts, and the mapping of keyword co-occurrences. However, it is important to acknowledge that while these methods provide a robust framework for measuring the influence and scope of research within the field, they also facilitate the extraction of qualitative insights. Such insights emerge through the interpretative analysis of citation contexts and the thematic associations among cited works. This dual capability enables bibliometric methodologies to not only quantify the impact of research but also to uncover underlying thematic and conceptual trends that inform the qualitative dimensions of competitive intelligence in tourism (Fauzi, 2023).

Figures

Figure 1

Research systematic review process based on PRISMA technique

Figure 2

PRISMA flow chart

Figure 3

Number of publications by year

Figure 4

Results from the keyword co-ocurrence network analysis

Table 1

Top innovation co-cited articles

Area	Author/Date	Co-citation frequency	Topic	Methodology	Research focus
CI in Tourism	Moya-Martínez et al. (2021)	2	Financing of SMEs in tourism	Empirical Analysis	Innovation Evaluation
CI in Tourism	Koliouska and Andreopoulou (2023)	0	E-Tourism for sustainable development	Conceptual Framework	Innovation Creation
CI in Tourism	Prasetyo et al. (2022)	2	Impact of COVID-19 on religious tourism	Case Study	Crisis Impact Analysis
CI in Tourism	Foris et al. (2021)	1	Role of GDS technology in tourism	Technology Analysis	Innovation Diffusion
CI in Tourism	Van Nuenen (2019)	11	Locality on peer-to-peer platforms	Qualitative Analysis	Social Impact Evaluation
Competitive Intelligence in Tourism	Moya-Martínez and Del Pozo-Rubio (2021)	2	The financing of SMEs in the Spanish tourism sector at the onset of the 2008 financial crisis: Lessons to learn?	Case Study	Business and Economics; Social Sciences – Other Topics
Competitive Intelligence in Tourism	Koliouska and Andreopoulou (2023)	0	E-Tourism for Sustainable Development through Alternative Tourism Activities	Conceptual Analysis	Science and Technology – Other Topics; Environmental Sciences and Ecology
Competitive Intelligence in Tourism	Prasetyo et al. (2022)	2	Impact of the COVID-19 pandemic on religious tourism amongst Muslims in Iraq	Survey	Religion
Competitive Intelligence in Tourism	Foris et al. (2021)	1	Exploring Solutions and the Role of GDS Technology in Crossing the Current Pandemic Context in Tourism	Technology Review	Social Sciences – Other Topics
Competitive Intelligence in Tourism	Van Nuenen (2019)	11	The production of locality on peer-to-peer platforms	Qualitative Study	Social Sciences – Other Topics
Competitive Intelligence in Tourism	Kahyalar et al. (2023)	0	Tourism and the shadow economy: Long-run and short-run relationships in developed and underdeveloped countries	Econometric Analysis	Business and Economics; Social Sciences – Other Topics
Competitive Intelligence in Tourism	Andayani et al. (2022)	1	Prediction model for agro-tourism development based on local wisdom of Samin community	Model Development	Agriculture
Competitive Intelligence in Tourism	Azmi et al. (2023)	3	Innovative and Competitive: A Systematic Literature Review of Tourism and Hospitality Industry	Literature Review	Science and Technology – Other Topics; Environmental Sciences and Ecology
Competitive Intelligence in Tourism	Dolnicar (2023)	3	Tourist behaviour change for sustainable consumption: A literature review	Literature Review	Social Sciences – Other Topics
Competitive Intelligence in Tourism	Li and Zhang (2022)	2	Spatial-temporal evolution and influencing factors of tourism economy efficiency in China	Quantitative Study	Environmental Sciences and Ecology
Competitive Intelligence in Tourism	Stojanović et al. (2021)	2	Effects of Price Competitiveness on Tourism Performance Under Different Economic Conditions	Quantitative Study	Social Sciences – Other Topics
Competitive Intelligence in Tourism	Ngo and Vu (2021)	3	Can customer relationship management create customer agility and superior firms' performance?	Survey	Business and Economics

Source(s): Own elaboration

Table 2

Top innovation research with high betweenness centrality analysis

Area	Author/Date	Betweenness centrality	Topic	Methodology	Research focus
Competitive Intelligence in Tourism	Moya-Martínez and Del Pozo-Rubio (2021)	0.21	The financing of SMEs in the Spanish tourism sector at the onset of the 2008 financial crisis: Lessons to learn?	Case Study	Business and Economics; Social Sciences – Other Topics
Competitive Intelligence in Tourism	Koliouska and Andreopoulou (2023)	0.24	E-Tourism for Sustainable Development through Alternative Tourism Activities	Conceptual Analysis	Science and Technology – Other Topics; Environmental Sciences and Ecology
Competitive Intelligence in Tourism	Prasetyo et al. (2022)	0.22	Impact of the COVID-19 pandemic on religious tourism amongst Muslims in Iraq	Survey	Religion
Competitive Intelligence in Tourism	Foris et al. (2021)	0.21	Exploring Solutions and the Role of GDS Technology in Crossing the Current Pandemic Context in Tourism	Technology Review	Social Sciences – Other Topics
Competitive Intelligence in Tourism	Van Nuenen (2019)	0.18	The production of locality on peer-to-peer platforms	Qualitative Study	Social Sciences – Other Topics
Competitive Intelligence in Tourism	Kahyalar et al. (2023)	0.23	Tourism and the shadow economy: Long-run and short-run relationships in developed and underdeveloped countries	Econometric Analysis	Business and Economics; Social Sciences – Other Topics
Competitive Intelligence in Tourism	Andayani et al. (2022)	0.19	Prediction model for agro-tourism development based on local wisdom of Samin community	Model Development	Agriculture
Competitive Intelligence in Tourism	Azmi et al. (2023)	0.28	Innovative and Competitive: A Systematic Literature Review of Tourism and Hospitality Industry	Literature Review	Science and Technology – Other Topics; Environmental Sciences and Ecology
Competitive Intelligence in Tourism	Dolnicar (2023)	0.29	Tourist behaviour change for sustainable consumption: A literature review	Literature Review	Social Sciences – Other Topics
Competitive Intelligence in Tourism	Li and Zhang (2022)	0.18	Spatial-temporal evolution and influencing factors of tourism economy efficiency in China	Quantitative Study	Environmental Sciences and Ecology

Source(s): Own elaboration

Table 3

Research gaps and future research lines

Themes	Potential future research lines
Business intelligence and competitiveness	Using business intelligence tools, like predictive analytics and decision support systems, to craft data-driven strategies that enhance competitiveness and market positioning
Governance and competitiveness	Implementing AI-enabled governance models for tracking and boosting tourism resilience, especially in crisis management and recovery
Tourism and the shadow economy	- Investigating the shadow economy’s effect on tourism development and its interplay with sustainability - Deploying advanced econometric modelling and data analytics to uncover the impact of the shadow economy on tourism sustainability and competitiveness
Artificial intelligence (AI) and big data	- Role of AI in enhancing tourism decision-making and competitiveness - Using AI-driven social media analytics to understand and shape responsible tourist behaviour and promote sustainability initiatives - Utilising big data analytics to improve service quality, personalisation, and overall tourism experiences
Sustainability and tourism	- Utilising smart city technologies and IoT to facilitate the sharing economy and promote resource-efficient, sustainable tourism solutions - Exploring innovative financing mechanisms for SMEs within the tourism sector to support sustainable growth - Analysing shifts in tourist behaviour toward sustainable consumption patterns - The use of social media to promote sustainable tourism and influence responsible tourist behaviour
Hospitality and innovation	- The impact of technological innovations on hospitality management, especially in the context of sustainability and resilience - Exploring the integration of Internet of Things (IoT) and AI-driven automation to streamline operations and improve guest experiences while ensuring sustainability

Source(s): Own elaboration