Artificial intelligence in tourism marketing: an exploratory factor analysis of the 7Ps of marketing mix

The dependence on artificial intelligence (AI) is growing rapidly, impacting both businesses and individuals in many ways. In marketing, AI helps businesses better understand their customers by improving customer segmentation and optimizing marketing campaigns through data-driven insights. It also enhances the customer experience by providing personalized recommendations, which leads to higher engagement and satisfaction. Many experts believe that the AI developments we see today are just the beginning, and the technology will continue to evolve and bring even greater changes in the future.

Technological advancements, especially AI, have significantly changed how businesses operate, particularly in the service industry. AI has revolutionized the interplay between businesses and customers, improving the customer experience, by redefining the roles of service providers (van Doorn et al., 2016). For example, many restaurants now use AI-powered robots to automate service processes (Molinillo et al., 2023), while other businesses have started relying on chatbots and virtual assistants to deliver self-service options for customers (Ruan and Mezei, 2022). Additionally, AI allows companies to personalize services using big data, machine learning algorithms and help firms to better understand their customer preferences and offer customized experiences (Rane, 2023). Beyond improving customer interactions, AI also helps businesses reduce operational costs and increase profitability (Mishra et al., 2022). By automating tasks and making processes more efficient, AI is playing a key part in driving business growth and innovation within the service domain (Jabeen et al., 2021). As AI continues to transform marketing strategies, it is important to examine its footprint on key marketing mix elements and see how academicians, researchers and practitioners assess its effectiveness.

The tourism and hospitality industry, being highly service-oriented, is also being affected by AI’s growing role. With the integration of AI tools like intelligent booking systems, recommendation engines and real-time customer service agents, the traditional marketing strategies have undergone considerable change (Tussyadiah, 2020). AI enables tourism businesses to predict demand patterns, optimize pricing and deliver personalized travel experiences, leading to better customer retention and brand loyalty (Haleem et al., 2022). These changes demand a reevaluation of the traditional 7Ps marketing mix, through the lens of AI applications.

Moreover, the shift toward digitalization has accelerated after the Covid-19 pandemic, compelling tourism marketers to adopt digital and AI-driven tools not just for efficiency, but as a strategic necessity (Petropoulou et al., 2024; Kumar et al., 2024). As customer expectations are evolving, service providers must reconsider the marketing mix elements, so that it can be used to match this expectation. For example, “People” in the service mix now includes virtual agents; “Place” has expanded into virtual spaces and platforms; and “Promotion” increasingly relies on predictive analytics and real-time content optimization.

The growing body of literature around AI and marketing in tourism has been primarily focusing on conceptual discussions, isolated use cases and applications of AI within specific marketing functions (Plećaš and Paunović, 2024). There remains a gap in the existing literature to systematically examine this simultaneous impact of AI on the seven elements of marketing mix. Also, limited efforts have been made to operationalize and measure the impact of 7Ps using a validated scale. This gap in the existing literature limits the abilities of researchers and practitioners to properly assess the role of AI in reshaping marketing strategies.

Addressing this gap, the present study aims to make these three contributions. First, it reconceptualizes the traditional marketing mix by discussing how AI has changed it from static framework into more dynamic and data-driven system. Second, the study highlights the diminishing boundaries across different marketing mix elements. With AI and its associated strategies, there are simultaneous impact on multiple Ps, rather than each element of marketing mix functioning independently. Third and most importantly, this study will develop a measurement scale that empirically captures the impact of AI across the marketing mix elements in tourism context, providing a robust tool for future research and practical application.

Therefore, this manuscript aims to investigate and examine the dimensions of AI integration into the 7Ps of the marketing mix within the tourism sector using exploratory factor analysis (EFA). Understanding these factors can provide a more structured approach for both academic and practical purposes, enabling stakeholders to design AI-driven marketing strategies that are both effective and sustainable.

Marketing involves a series of actions and activities that leads to matching of customer needs with the company’s capabilities and offerings. As both needs and capabilities are constantly evolving, marketers must adapt through innovative strategies and frameworks. Marketing mix is one such framework that has been in use for some time now. This concept originated with McCarthy (1960), who first gave the 4Ps of marketing, i.e. Product, Price, Place and Promotion, which primarily focused on tangible goods. However, as services began to dominate global economies, the limitations of the 4Ps framework became apparent. In response, Booms and Bitner (1981) made additions to this model by incorporating People, Process and Physical Evidence, making it the 7Ps framework, offering a more nuanced approach suitable for service-oriented businesses. This 7Ps framework has since become foundational in services marketing and is widely used in both academic and practical domains.

The developments in the field of marketing were carried out by authors like Kotler, whose work shifted the emphasis on customer centric and holistic value creation concepts, while the operationalization of these principles is reflected in the extended 7Ps marketing mix framework (Kotler and Keller, 2016; Wirtz and Lovelock, 2021). The inclusion of people, processes and physical evidence highlights the importance of service characteristics like service delivery systems, employee interactions and tangible cues. In addition, the service literature conceptualizes services as a bundle of core and supplementary elements delivered through a structured process reinforcing the need for an integrated approach in managing the 7Ps.

Over time, scholars have challenged and reimagined the traditional marketing mix to reflect shifting consumer behaviors and technological advancements. Lauterborn (1990) argued that the 4Ps model was outdated because it emphasized the firm's perspective rather than that of the customer. He proposed the 4Cs model; Consumer wants and needs, Cost, Convenience and Communication to better align with consumer-centric marketing. Despite these innovations, questions persist about the adequacy of both the 4Ps and 4Cs frameworks in addressing complex, multi-stakeholder service environments. Peattie and Peattie (2003) pointed out that the existing models were insufficient to capture the experiential and intangible nature of services. Ivy (2008) also noted limitations of traditional frameworks in sectors like education marketing, where services are co-produced by consumers and providers. Extending this evolution, contemporary frameworks such as the 4A model which includes Acceptability, Affordability, Accessibility and Awareness further emphasize customer-centricity by focusing on how offerings are perceived, accessed and valued by consumers in dynamic markets (Sheth and Sisodia, 2018).

As digital technologies emerged, so did the models of marketing mix. Constantinides (2002) introduced the web-marketing mix concept popularly known as the 4S, which included Scope, Site, Synergy and System as its elements, to address the growing importance of web-based interactions. His model emphasized integration across digital touchpoints, web usability and technology infrastructure, recognizing that marketing had moved beyond physical domains. Similarly, Londhe (2014) critiqued existing frameworks as too static and proposed a four value marketing model consisting of, Valued Customer, Value to Customers, Value to Society and Value to the Marketer. This approach captured the ethical and holistic aspects of value exchange in modern marketing. In parallel, the 4E model (Experience, Exchange, Everyplace and Evangelism) emerged as a response to the increasing importance of engagement, omnichannel presence and customer advocacy in digital ecosystems (Epuran et al., 2015). Together, these modern frameworks reflect a clear shift from transactional marketing toward experience-driven, technology-enabled and relationship-oriented approaches, where value is co-created across multiple touchpoints, aligning closely with the demands of contemporary service and AI-driven marketing environments.

Recent research highlights the growing role of digital technologies in shaping tourism marketing, particularly through the rise of electronic word of mouth (eWoM). Studies based on the UTAUT framework show that factors such as performance expectancy, effort expectancy, social influence and facilitating conditions significantly drive eWoM engagement, influencing Promotion, People and Process elements of the 7Ps framework (Jan et al., 2023). This reinforces the view that eWoM is an outcome of technology-enabled interactions across the service marketing mix. At a broader level, bibliometric evidence demonstrates that tourism marketing research has evolved across multiple waves, with recent trends emphasizing digital transformation and data-driven strategies (Kozak and Correia, 2024). This shift aligns with the increasing integration of artificial intelligence across the 7Ps, indicating that modern tourism marketing is moving toward a more holistic, technology-driven and experience-oriented paradigm.

In the past few years, the application of AI has prompted further reconsideration of the marketing mix. Kumar et al. (2024) argue that AI has fundamentally transformed the “what,” “where” and “how” of marketing activities. From hyper-personalization and real-time analytics to automated service delivery, AI enables marketers to move beyond static frameworks and create dynamic, data-driven strategies. This shift aligns with the need for adaptive models that respond to fast-changing consumer expectations, especially in service sectors like tourism and hospitality.

Although several extensions and modifications of the marketing mix have been proposed, the 7Ps framework remains dominant in both academic and practical applications due to its simplicity and adaptability. Consequently, this study adopts the 7Ps framework while integrating AI-driven advancements to explore its evolving relevance in the tourism marketing context.

There are several emerging technologies using AI to deliver enhanced experience to customers. These includes service robots, virtual assistants, language translators, personalization systems, conversational systems and recommendation systems. These technologies may exist in independent physical devices or may be embedded in applications as computer programs to perform its intended functionality.

Wirtz et al. (2018) define service robots as system-based autonomous and adaptable interfaces that interact, communicate and deliver service to an organization’s customers. Wirtz et al. (2018) further categorizes service robots based on their design attributes. These robots can be Virtual (Siri, Alexa) or take physical representation (Pepper, Nao). It can also take human appearance (Sophia) or be non-humanoid (Roomba) and at last these robots can perform cognitive analytical tasks or emotional social tasks (reception robots). Virtual assistants have found its applications in numerous ways. It acts as a conversational interface for users to provide services as assistant to the user. This technology-driven assistants along with physical robots can perform simple tasks like welcoming guests, catering to house-keeping activities, information provisions, handling bookings and payments, etc. Henn-a Hotel in Japan introduced service robot in the form of dinosaur to handle front desk activities (Bhimasta and Kuo, 2019).

Tourists visiting foreign locations often face difficulties in understanding the local languages. There can be circumstances where they need to converse in local languages. Presently, travelers address this problem by hiring local guides. However, with the help of language translators like Google Translate, tourists can translate to and from local languages. Google translate also provides audio speech services wherein it can take a speech input in a particular language and translate the voice message into the target language, thereby conversing with the local people.

While using social media, we are familiar with instances where we get to see advertisements related to our past purchase interests or browsing history. The advertisements also come in the form of suggestions or product offerings as it is something that we are interested in. Pictures and catalogues have been a major source of information for tourists in the past to decide on their destination choice. With so many developments in social media and Internet usage, the amount of content that is being circulated is huge. Technologies like AI supported by big data are capable of tailoring and filtering contents based on users’ specific preferences. Personalization systems require large amount of information about users to provide customized service (Duan, 2024). Another problem faced by users is to choose among destinations from several options which keeps on growing. With the help of recommender systems, the options that best fit the user’s interests are shown to them.

Conversational systems allow users to engage in conversations either through chatbots or voice assistants. Chatbots interact with the help of text messages. Generally, these chatbots pop up when visiting a particular website or using an application. Voice assistants alike Siri, Alexa, Cortana, etc. require less effort from user to communicate. These systems are primarily used for searching information.

AI has significantly transformed marketing strategies by enhancing decision-making, optimizing processes and improving customer engagement. Within the marketing mix, AI has reshaped product, price, place, promotion, people, process and physical evidence, making them more efficient and customer centric.

AI has revolutionized product development by enabling businesses to analyze consumer preferences, predict trends and personalize offerings. Big data analytics helps companies tailor products to market needs, while machine learning accelerates product innovation by adapting to real-time feedback (Dekimpe, 2019). AI facilitates the creation of personalized travel itineraries, dynamic tour packages and customized hotel experiences based on individual preferences and behavior patterns. In addition, customer’s experience is enhanced with improved customer service, with the help of chatbots and virtual assistants providing continuous support (Sousa et al., 2024).

AI-driven pricing models allow companies to implement dynamic pricing strategies based on consumer behavior, demand fluctuations and competitor pricing. Algorithms analyze large datasets in real-time to set optimal prices, maximizing profitability while maintaining competitiveness (Mishra et al., 2022). Personalization in pricing has also become prevalent, where prices are based on consumer willingness to pay, online engagement and purchase history (Tomczyk et al., 2022). Machine learning enables businesses to automate price negotiations, making pricing strategies more efficient and data-driven (Oteri et al., 2023).

AI has optimized distribution and logistics, making product accessibility more efficient. Retailers leverage AI and its capabilities to improve customer convenience by providing individualized shopping experiences and automated inventory management (Aggarwal et al., 2024). Recommendation systems powered by AI helps customers in deciding, by suggesting suitable items that fits the customer’s need (Necula and Păvăloaia, 2023). Additionally, AI-powered robots and self-service technologies streamline in-store interactions, improving operational efficiency (Wirtz et al., 2018). In logistics, predictive analytics ensures timely deliveries and robust supply chain management which helps in reducing the associated risks and improve customer satisfaction (Aljohani, 2023).

AI has also brought about changes in marketing communications by personalizing real-time interactions. Businesses utilize AI to automate advertising, optimize media planning and execute targeted campaigns. Generative AI is playing an important part in creation of online contents like ad copy and social media posts, which enhances the engagement of audience (Zhang et al., 2023). Real-time emotion tracking and sentiment analysis allow brands to measure customer reactions and adjust promotional strategies accordingly (Olukemi et al., 2024). Further, AI enhances promotional efforts in tourism and hospitality industries by enabling destination marketing organizations and hotels to deliver hyper-targeted advertisements based on user preferences, travel history and behavioral data, increasing the effectiveness of campaigns (Barde et al., 2025).

The “People” element includes everyone involved in delivering a service who can shape how customers feel about it. Most often, this refers to employees, whose personality, appearance and behavior can strongly influence whether a service succeeds or fails (Nickson et al., 2005). In the past decade, companies have tried to improve efficiency by using technology instead of human workers. For a long time, this was hard to do in the service industry, because services usually need human interaction due to the nature of tasks. But with the rise of AI, things are starting to change due to factors like cost savings and improved efficiency, businesses are now beginning to replace some human roles with AI (Huang and Rust, 2021). However, many experts believe that not all services can be replaced by machines, some will always need human interaction, especially those involving empathy, communication and personal care (De Bruyn et al., 2020). Right now, AI is mostly helping or supporting human workers in service roles, but we don’t yet know exactly how this balance will look in the future (Rust, 2019).

AI has significantly streamlined service delivery processes, improving efficiency and customer satisfaction in the tourism and hospitality industry. Self-service technologies, such as automated check-ins and kiosks, have enabled faster processing and reduced the need for human intervention (Marinakou et al., 2023). Chatbots has further enhanced the travel experience by offering real-time support and personalized guidance (Gajdošík and Marciš, 2019). Kumar et al. (2025) explored the potential uses of AI robots in different hospitality settings for tasks such as payment processing, beverage service, guest reception and providing information about destinations and facilities. Their study highlighted a growing willingness among tourists to engage with robotic service providers over human staff for certain routine functions. Continuous developments in the field of AI are making the process dimension of service marketing more seamless, with an increasing number of technology-enabled interactions that prioritize speed, personalization and convenience.

Physical evidence includes tangible cues that aid in the delivery of services and shape customer perceptions. AI influences this dimension by enhancing both digital and physical touchpoints. In physical environments, smart displays, interactive kiosks and AI-guided signage provide contextual information and improve ambiance. In digital services, AI ensures a consistent brand experience through interface personalization, automated design optimization and dynamic content delivery. Augmented reality (AR) and virtual reality (VR) are transforming how tourists experience and perceive physical environments. AR enriches real-world settings with contextual information, enhancing cultural and historical engagement, while VR offers virtual previews of destinations and accommodations, helping travelers make informed decisions (Rane et al., 2023). AI personalizes these experiences by adapting content to user preferences and optimizing space design through data analytics.

The marketing mix framework was developed to help businesses effectively plan, implement and evaluate their marketing strategies. Traditionally, it has guided decision-making across the seven Ps. While these core elements have remained relevant, the strategies surrounding them have evolved significantly due to rapid technological advancements and shifting consumer behaviors. Most prior studies have examined AI in specific functional areas such as customer service automation, personalized recommendations or digital promotion without integrating these insights within a comprehensive marketing framework. Limited attention has been given to understanding how AI simultaneously influences multiple dimensions of the service marketing mix. For instance, earlier approaches relied heavily on personal selling and mass-market promotions, whereas today, AI-powered personalized advertisements and data-driven customer insights dominate marketing efforts. This transformation has rendered older marketing mix measurement scales inadequate for assessing modern strategies (Wichmann et al., 2021)

Despite the critical role of marketing mix in business success, traditional measurement models are struggling to keep pace with innovations like AI. AI-driven tools such as machine learning, predictive analytics and automation have redefined the 7Ps, by making them more dynamic and responsive to real-time consumer data (Mirwan et al., 2023). Consequently, conventional frameworks fall short in accurately evaluating AI-enhanced marketing strategies, leading to inefficiencies in decision-making and resource allocation. While existing research has examined various aspects of AI-driven marketing, there exists a significant gap in the development of updated measurement scales that reflect these evolving dynamics. This research work aims to fill this gap by exploring how AI has reshaped the measurement of marketing mix elements in service industries.

A self-administered survey was conducted using convenience sampling, with data collected directly from respondents in person. The data collection for this study was carried out between 20 Jan 2025 and 13th Feb 2025. The respondents for the study were Indian tourists and the point of data collection was across Sikkim, India. The questionnaire underwent face validity evaluation by two experts, leading to a final version with 46 items after refinement. A total of 210 questionnaires were distributed and after screening for incomplete responses, missing values, outliers, etc. 149 valid responses were considered for further analysis. Although, the number of responses used in the study can seem less but, prior studies have suggested that a sample size above 100 is acceptable for EFA, particularly when factor loadings are sufficiently high, and the model is theoretically grounded (Hair et al., 2019). Also, prior studies have indicated that exploratory studies with smaller sample sizes can yield stable factor solutions when communalities are high and variables are well-defined (Fabrigar et al., 1999). EFA was conducted to develop the measurement instrument for AI-integrated 7Ps of the services marketing mix. Further, reliability and validity tests for the scale were carried out using SPSS 20.

The demographic analysis of the sample reveals that females constituted a larger proportion (55%) compared to males (45%). The majority of respondents (61.1%) were in the 25–44 age group, followed by 28.9% in the 18–24 age group, reflecting a predominance of individuals in the working-age category. In terms of occupation, private sector employees made up the largest group (38.9%), followed by students (23.5%), government employees (20.1%) and self-employed individuals (17.4%). Income distribution shows that a significant portion (59.1%) of respondents had an annual income of less than 5 lakhs, suggesting a middle-to-lower income bracket. Regarding educational qualifications, an overwhelming majority (88.6%) were graduates, with 51% holding a bachelor's degree, 36.2% a master's degree and only two respondents possessing a PhD. These findings indicate a well-educated sample with a strong representation from the working-class population, particularly in the private sector.

To investigate the underlying structure of the data, EFA was performed across the 46-item proposed measurement scale for refinement and further analysis. The measurement items were developed by taking insights from the extensive review of prior literature on service marketing and AI use in tourism, as well as through the authors’ observations of AI applications in tourism industry. The conceptual inspiration for the constructs and item formulation were derived from established studies on services marketing (Al-Dmour et al., 2013) and from empirical studies on 7Ps of marketing (Siripipatthanakul et al., 2021). In addition to these studies, contemporary studies on AI and customer-related outcomes were also referred as the 7Ps were developed as technology-related items (Hariguna and Ruangkanjanases, 2024).

Principal component analysis (PCA) was employed as the extraction method, while Varimax rotation with Kaiser normalization was applied for factor rotation. The study employed both PCA and principal axis factoring (PAF) for complementary purposes. PCA was used for preliminary data reduction and as a diagnostic step to examine the overall variance structure of the dataset. However, PAF was employed due to the theory-driven nature of the study to validate the factors (Fabrigar et al., 1999; Costello and Osborne, 2005).

The items were organized into seven constructs (7Ps categories), with a minimum factor loading threshold set at 0.50. The number of factors was pre-decided (priori) based on the theoretical foundations of the 7Ps services marketing mix framework. Given the strong theoretical backing in the literature, the factor structure specified in advance to align with the 7 dimensions of marketing mix. EFA was employed not to determine the factor structure but to validate the underlying structure and assess the alignment of measurement items with their respective construct. This approach has been used in prior research as well, to confirm the dimensional structure rather than explore it (Hair et al., 2019).

Table 1 summarizes the results of the KMO measure of sampling adequacy and Bartlett’s test of sphericity. The KMO value, at 0.659, exceeds the acceptable threshold of 0.50, indicating that the data are suitable for factor analysis. Additionally, Bartlett’s test of sphericity produced a high chi-square value (1147.114) with a significance level of 0.000 and 231 degrees of freedom, confirming that the variables are sufficiently correlated to justify factor analysis.

Table 2 presents the results of the EFA. The analysis identified seven factors – product, price, place, promotion, people, process and physical evidence. Initially, 46 items were proposed for developing a reliable and valid measurement scale. However, after multiple iterations of item removal and refinement, a final 22-item scale was established. Items with factor loadings below 0.5 and those exhibiting cross-loadings were systematically eliminated based on both statistical and conceptual criteria. The eliminated items showed weak association with their respective construct while cross-loaded items were removed to ensure construct purity and dimensional clarity. This process enhanced the interpretability and reliability of the factor structure.

The PCA method was applied using Varimax rotation, which redistributed the variances, enhancing the interpretability of factor loadings and influencing the percentage of variance explained by each factor.

Table 2 displays the factor loadings, all of which are above 0.6, indicating strong relationships with their respective factors. The final 22-item scale was derived through nine iterations, during which 15 items were eliminated due to low factor loading values (<0.5), and 9 items were deleted due to significant cross loading across multiple factors, which would create ambiguity in construct interpretation. During the iterative process, several factors exhibited cross loadings. For instance, three factors: product, price and promotion showed cross loading across each other. These cross loadings can be theoretically interpreted due to convergence of AI-enabled services. Specifically, AI-driven personalization and recommendation systems often simultaneously influence product design (customized offerings), pricing (dynamic adjustments) and promotion (targeted communication). As a result, certain items would naturally show associations with multiple factors, reflecting on the integrated nature of AI applications in marketing. Likewise, overlap in items association was observed in process and place element as AI-powered digital platforms, not only facilitates access to tourism services (Place) but also streamline service delivery mechanisms such as booking, navigation and real-time assistance (Process). Eliminating weak and cross loaded items was necessary to ensure that each observed variable strongly and distinctly represented a single latent construct, thereby improving discriminant validity.

Among the identified factors, product had the highest eigenvalue (3.950) and accounted for 11.975% of the variance explained (VE). Also, the VE explained by other factors is shown in Table 2. Collectively, the first seven factors explained 68.018% of the total variance, which exceeds the 50% threshold, indicating a strong and meaningful factor structure. These results validate the effectiveness of varimax rotation in distinguishing clear and interpretable factors.

The extracted factors are not only statistically significant and valid but also conceptually meaningful in its representation which reinforces the applicability of the 7Ps framework in context of AI-enabled tourism services. The product factor reflects the role of AI in enhancing personalization, customization and diversity of tourism offerings. The price factor captures AI’s influence on value perception, affordability and dynamic pricing strategies. The place factor represents how AI improves accessibility and information availability across tourism destinations. The promotion factor highlights AI-enabled targeted communication, personalized recommendations and digital advertising effectiveness. The people factor reflects the integration of AI in customer service delivery, enhancing responsiveness, accuracy and user experience. The process factor emphasizes the efficiency gains in booking systems and service design enabled by AI technologies. Finally, the physical evidence factor captures AI’s influence on the tangible and digital environment, including infrastructure, service interfaces and overall experiential quality.

The Cronbach’s alpha values from the EFA results in Table 2 indicate good internal consistency through all factors, confirming the reliability of the AI-enhanced tourism measurement scale. The processes factor exhibits the highest reliability (α = 0.817), underscoring a solid comprehension of AI’s influence in the design and applications of different tourism-related tasks. Similarly, the product factor (α = 0.804) reinforces AI’s impact on personalized travel experiences. Other factors, including price (α = 0.746), place (α = 0.730), promotion (α = 0.729) and physical evidence (α = 0.723), demonstrate acceptable reliability. The people factor, with the lowest alpha (α = 0.701), still meets the acceptable threshold, indicating consistency in perceptions of AI-driven customer service. Although not explicitly presented in Table 2, the combined Cronbach’s alpha for all 22 items forming the AI-driven services marketing mix was found to be 0.762. Overall, these values confirm the robustness and reliability of the extracted factors, making them suitable for further analysis. These reliability values further support the internal consistency of the constructs identified through EFA, confirming their suitability for measuring AI-enabled tourism service dimensions.

Convergent validity evaluates whether the items within a factor are strongly correlated, suggesting that they measure the same underlying concept. Factor loadings serve as evidence of convergent validity, with a threshold of 0.50 for significance in a sample size of 120. As shown in Table 3, each of the seven factors – product, price, place, promotion, people, processes and physical evidence exhibit average loadings that exceed this threshold. This confirms strong convergent validity, as it indicates that the items within each factor are consistently measuring their respective constructs. The strong factor loadings across constructs also indicate that the retained items meaningfully represent their respective theoretical dimensions within the AI-driven tourism context.

Discriminant validity measures the degree to which factors are not strongly correlated with each other, confirming that each construct is clearly distinct from the others. Ideally, variables should exhibit strong correlations within their own factor rather than across different factors. A correlation value exceeding 0.70 would indicate significant shared variance between factors, which could compromise discriminant validity. The factor correlation matrix was obtained using PAF as the extraction method, followed by Promax rotation with Kaiser normalization. The correlation matrix in Table 4 confirms that no inter-construct correlation value exceeds 0.70, in fact the highest factor correlation value is 0.347 only, thereby establishing discriminant validity. This clear distinction among constructs further validates that each factor captures a unique dimension of the AI-enabled services marketing mix, minimizing conceptual overlap.

The EFA results highlight AI’s transformative impact on tourism by improving personalization, accessibility and efficiency across the 7Ps of the marketing mix. The high variance explained in product suggests that personalization is an important factor in shaping tourist experiences. AI also plays a vital role in pricing strategies, enabling competitive rates and affordability. The strong impact on place and promotion indicates that AI facilitates better trip planning and targeted marketing. Moreover, AI-driven customer service (People) enhances responsiveness, while process and physical evidence show that AI is optimizing booking systems and infrastructure. Beyond these empirical results, the findings show a fundamental shift in the logic of marketing from a firm-driven, static model to a dynamic, AI-driven and customer centric model (Song and Bonanni, 2024). The traditional view of marketing considered each element in the 7Ps as independent variables but, AI has transformed these elements into more adaptive and inter-dependent variables. The ability of AI- enabled technologies to monitor real-time data and continuously reshape pricing, offerings and communication strategies has been crucial in driving this transformation. Marketing is no longer about designing fixed long-term strategies but about orchestrating intelligent and responsive systems.

Along with the alignment to the traditional 7Ps framework, the EFA also revealed the emergence of conceptual convergence among these dimensions. For example, AI-enabled personalization simultaneously influence product (customized offerings), price (dynamic pricing) and promotion (targeted communication). Similarly, place and process elements are influenced by the seamless access and service delivery through unified digital interfaces. This suggests that AI is blurring the boundaries between the traditionally distinct marketing mix elements and transforming them into a more interconnected system. Building on this convergence, the present study makes a significant contribution by developing and validating a multidimensional measurement scale that shows the evolving and dynamic nature of services marketing mix. Unlike other studies that examine the AI applications in isolation, this study presents a holistic and empirically validated framework for understanding AI’s role across interconnected marketing dimension.

In line with the findings of this study, the literature review also revealed an emergent shift from static marketing elements to dynamic, data-driven strategies that respond to real-time consumer behavior (Legito and Andriani, 2023; Suherlan and Okombo, 2023). This aligns with broader industry trends where AI tools like predictive analytics, sentiment analysis and dynamic pricing are reshaping how tourism services are marketed and consumed. Furthermore, the notion that the marketing mix must evolve from a rigid, producer-centric model to a more fluid, customer-centric and technology-enabled framework. This has important implications for tourism marketers, who must rethink strategic priorities and invest in AI capabilities to maintain relevance and effectiveness in a rapidly digitizing landscape.

This study makes a few major theoretical contributions to the literature on marketing mix. Firstly, it empirically validates the relevance of 7Ps framework in AI-enabled context. The study reveals that marketing mix framework is still applicable but requires major reinterpretation. The marketing mix elements no longer operates in isolation. Secondly, marketing mix has become a dynamic, interconnected system rather than a set of independent elements. The instances of personalization influencing product, price and promotion at the same time reveals the need to view marketing mix from a different perspective. AI is evolving continuously and rapidly, and these developments will surely blur these lines more within the marketing mix framework. Thirdly, the study has contributed by developing a reliable and validated measurement scale that could be useful for future researchers to take the study on AI’s impact on marketing mix within the services industry further. Collectively, this study provides both methodological rigor and advancement in theoretical development in the evolving services marketing landscape driven by the use of AI-enabled applications.

This study explored the influence of AI on the marketing mix within the tourism industry, uncovering a reconfiguration of traditional 7Ps through exploratory factor analysis. The results indicate that AI does not merely enhance existing marketing strategies but fundamentally transforms how they are conceptualized and operationalized. For instance, tourism firms and managers should stop relying heavily on standardized packages and adopt AI-based recommendation engines to curate more dynamic and personalized tour packages. Also, modular itineraries can be introduced so that customers can customize experiences in real time. Managers or businesses can integrate recommendation engines into the firm’s website/applications to curate packages based on individual profiles of the customers. In addition to this, deploying dynamic pricing models that would make real-time adjustment based on demand, seasonality and customer behavior would significantly improve its pricing strategies. This would also enable businesses to offer personalized discounts and offers that would maximize both competitiveness and profitability.

Tourism firms should ensure seamless service delivery with the help of AI chatbots and virtual assistants, through a hybrid model where AI supports employees in delivering faster and accurate services. Managers must take help from AI to streamline processes to gain operational efficiency through automation and reduce friction to enhance overall customer experience. Moreover, managers and businesses should move toward building an integrated AI-enabled system that connects all the elements of 7Ps enabling real-time data-driven decision making and continuous service optimization.

Despite its contributions, this study is not without limitations. First, the sample size and geographic concentration may limit the broader applicability of the findings. As the data were primarily collected from a specific regional context, the applicability of results to international tourism markets remains uncertain. Future research should employ larger and more diverse samples across multiple regions or countries to enhance generalizability and enable cross-cultural comparisons of AI adoption in tourism marketing. Second, the research adopts an exploratory design using EFA, which while suitable for identifying latent constructs, does not confirm the structure or causality among variables. A confirmatory factor analysis (CFA) or structural equation modeling (SEM) in future studies would provide greater robustness to examine causal relationships among AI-driven marketing dimensions and customer outcomes. Third, the pace of AI advancement is extremely rapid. The tools and strategies considered relevant today may become obsolete or significantly altered in a short span, which limits the longitudinal relevance of the findings. Longitudinal studies must be conducted to track how AI-driven marketing practices are evolving over time and assess the stability and the adaptability of the 7Ps framework in dynamic technological environments. Lastly, respondent’s understanding of AI applications may vary, potentially influencing the reliability of their perceptions and ratings in the questionnaire. More in-depth qualitative insights, experimental studies or case studies could complement this limitation.

Ultimately, this research adds to the expanding body of literature advocating for a reimagined marketing mix model that reflects the realities of the digital and AI age. Future research should build on these foundations to develop refined, validated models that can guide strategic marketing decisions across diverse service industries.

Conceptualization: AP,

Methodology/Study design: AP, SK.

Software: AP.

Formal analysis: AP and SK.

Investigation: AP, SK, SC.

Writing – original draft: AP, SK.

Writing – review and editing: AP, SK, SC.

Visualization: AP, SK and SC.

Supervision: SK, SC.

Ethical approval has been taken from the university ethics committee.

The informed consent was taken from the participants while collecting the data.

Authors are thankful to the Loyola Institute of Business Administration (LIBA) for covering the APC fee for this work by publishing it as open access in their journal, Management Matters.