Aligning agility and proactive market orientation to get ready for Industry 4.0: stop lagging, start logging (In)

Over the last few years, the Fourth Industrial Revolution (I4.0) has driven constant technological progress, leading to innovations such as cloud computing (Hsu, 2024), Artificial Intelligence (AI) (Hwang et al., 2024), the Internet of Things (IoT), and automation (Rodrigues et al., 2024). These technologies have brought about radical changes, with a profound and disruptive impact on organizations, transforming their traditional practices and redefining industry standards (Castelo-Branco et al., 2022). As companies become increasingly dependent on digital solutions, they must know how to effectively take advantage of them, strengthen their technological knowledge, and optimize their digital assets (Ed-Dafali et al., 2023). In other words, they must be able to adapt to technological change to guarantee their long-term survival.

In the context of Industry 4.0, numerous plans, programs and policies seek to promote the digitization of industries. One example is the Digital Decade for Europe, a European Commission policy program designed to coordinate the digital development of Europe by 2030. In the hotel sector, a recent study shows significant potential for growth in digitalization efforts, as more than 80% of hotels in Spain still have room to fully integrate digital solutions into their operations (Fes, 2023). In 2024, the Spanish accommodation sector reached a basic level of digitalization (SEGITTUR, 2024), which represents an opportunity for further progress. This data reflects that digital change is advancing at different rates within the sector and that many establishments are well on their way to improving their processes.

Given the constant technological development, being prepared for these changes is key (Van Huy et al., 2024), and effective management of digital change can make a difference in the competitiveness and innovation of the sector. While readiness for digital change requires agility (Cubillas-Para et al., 2024), agility alone may not be sufficient. Although it allows businesses to respond quickly to market shifts, long-term success demands a more strategic approach. This approach involves actively seeking out market opportunities and aligning internal capabilities with emerging stakeholders’ needs, ensuring that the knowledge gained through agility drives innovation and fosters sustainable competitive advantages, rather than merely prompting reactive adjustments.

Regardless of the growing interest in the digital progress of service industries, the existing literature has largely overlooked the hospitality sector’s readiness for Industry 4.0 (e.g. Stentoft et al., 2021; Zhao et al., 2023a, b; Ostadi et al., 2024), particularly regarding the influence that organizational capabilities make exert in shaping this process (Rodrigues et al., 2024). Most prior studies have concentrated on technological drivers or provided descriptive accounts of implementation barriers (Caputo et al., 2019; Del Giudice et al., 2022), with limited empirical research connecting organizational agility (OA) and proactive market orientation (PMO) to I4.0 readiness in hotels (Christou et al., 2023). This study addresses that gap by proposing and testing a novel conceptual model in which OA is positioned not only as a driver of PMO but also as a key enabler of hotel readiness for digital change. Consequently, the study poses the following research question (RQ1):

A quantitative study with a sample of 198 hotels was conducted in southeast of Spain, analyzing the data using SmartPLS4 software (Ringle et al., 2024). The results confirm that organizational agility and proactive market orientation are key factors in preparing hotels for Industry 4.0 and contribute significantly to research on hotel RI4.0. By validating these relationships in the underexplored context of Spanish hotels, this study contributes to the dynamic capabilities literature by demonstrating that agility enhances anticipatory market behavior and strategic responsiveness to technological change (de Diego and Almodóvar, 2022).

Moreover, this paper offers the first empirical evidence of the mediating role of PMO in the OA-RI4 relationship, thereby highlighting the dynamic interplay between internal capabilities and strategic orientation in shaping digital readiness. Theoretically, this mediating effect extends the dynamic capabilities framework by positioning PMO as a bridging mechanism through which agility translates into digital readiness, offering a nuanced understanding of how organizational learning and strategic sensing co-evolve (Jaworski and Kohli, 1996). These findings have important implications for scholars interested in analyzing digital change, suggesting that capability development and market foresight are intertwined processes essential to I4.0 adaptation in service-intensive environments. From the managerial point of view, the study contributes to reinforcing the value of creating companies with decentralized structures that are agile and proactive in the face of change. It also stresses the relevance of investing in market research tools that enable hotels to detect and respond to emerging trends before they cause business disruption.

The Fourth Industrial Revolution, often referred to as Industry 4.0, I4.0, or 4IR, was first mentioned in 2016 at the World Economic Forum, representing a step beyond Industry 3.0 (Im and Kim, 2022). It is characterized by integrating intelligent technologies into modern operational and manufacturing processes (Muhammad et al., 2022). I4.0 involves radical innovations (Castelo-Branco et al., 2022) driven by cutting-edge technologies like cyber-physical systems (CPS), the Internet of Things (IoT), advanced robotics (Rana et al., 2025), business analytics (Gumbo et al., 2023), and AI (Vena-Oya et al., 2024). These technologies are embedded into organizations, people, and assets through enhanced connectivity (Stentoft et al., 2021). Industry 4.0 operates on fundamental principles such as interoperability, virtualization, decentralization, real-time data collection and analysis, service orientation, and modularity (Rodrigues et al., 2024).

I4.0 technologies are integrated in order to automate business operations, reduce human effort, and increase value for customers and enterprises (Belhadi et al., 2022; Ed-Dafali et al., 2023). These technologies enable real-time asset monitoring, providing updated knowledge about customers and the organization (Picazo Rodríguez et al., 2024), allowing decision-makers to make more informed judgments (Ed-Dafali et al., 2023). The advancement of I4.0 technologies has affected economic activity in all sectors (Krasyuk et al., 2022), including tourism (Vena-Oya et al., 2024). According to Rodrigues et al. (2024), I4.0 technologies have triggered the beginning of the Tourism 4.0 era, defined as “the new tourism value eco-system based on the high-tech service production paradigm” (Pencarelli, 2020, p. 457). This era requires companies to enhance their technological capabilities and optimize their digital resources (Ed-Dafali et al., 2023) to maintain their competitive edge in the market (Ostadi et al., 2024).

Despite the economic efforts to digitize the European and Spanish business fabric, only 15.93% of Spanish hotel establishments are highly digitized, and a mere 7.3% utilize digital booking tools (Fes, 2023). These figures highlight the need to understand the factors that can help Spanish hotels adapt to this technological shift. It should be noted that the main challenges of implementing Industry 4.0 are the company’s intention to experiment with I4.0, having employees with the necessary competencies, and possessing enough knowledge to assess the significance of technology (Stentoft et al., 2021). For organizations to meet these challenges, they must take a proactive approach to adopting new technologies as a means of achieving their strategic objectives (Ed-Dafali et al., 2023). In this context, I4.0 readiness refers to an organization’s willingness to accept and integrate I4.0-related technologies and use them effectively to optimize processes, improve performance, and gain a competitive advantage in the marketplace (Ed-Dafali et al., 2023; Stentoft et al., 2021).

Organizational dynamic capabilities refer to a firm’s ability to deploy, coordinate, and manage resources effectively (Aboelmaged and Hashem, 2019) to adapt to environmental changes, innovate, and maintain a competitive edge (García-Pérez et al., 2019; Teece et al., 1997). Leveraging dynamic capabilities to gain a competitive advantage is crucial for organizations (Ostadi et al., 2024) to respond to market demands, technological advancements, and operational challenges (García-Pérez et al., 2019; Helfat et al., 2007; Kogut and Zander, 1992).

One of these capabilities is known as Organizational Agility (OA). OA refers to an organization’s ability to identify unexpected changes in the environment (AlNuaimi et al., 2022) and adapt to them swiftly and innovatively (Cegarra-Navarro et al., 2016) by reconfiguring resources, strategies, and processes (Arsawan et al., 2022). It also reflects the ability to identify, acquire, and apply relevant knowledge quickly and efficiently (Cegarra-Navarro et al., 2016). Following these definitions, we understand OA in the context of our study as the hotels’ ability to identify and effectively respond to market changes.

In a context marked by rapid market changes, developing a strategic orientation is key to leading change (Morimura and Sakagawa, 2023). Proactive Market Orientation (PMO) is a strategic approach that aims to satisfy customers’ future and latent needs (Herhausen, 2016). PMO aligns with the marketing exploration function in which the organization adopts an “opportunity-driven” mindset emphasizing innovation and the search for new possibilities.

In this study, we argue that agile hotels can take advantage of market opportunities and prepare to adapt to any changes in their business environment. Therefore, we believe that OA supports and enables an approach based on market proactivity by allowing hotels to respond quickly to market changes. Both emphasize a forward-looking, opportunity-seeking approach. We argue that OA is a driver that enables companies to effectively implement and adjust their strategic orientation as market conditions evolve. Despite the potential relationship between agility and proactive market orientation, no studies have empirically investigated this issue. This represents an important gap in the literature, as understanding the interplay between these two variables could yield valuable insights that help prepare hotels for I4.0. Practical evidence of such a relationship is Spotify (Šmite et al., 2023). Spotify has adopted agile structures, demonstrating that its ability to reconfigure strategies, structures, and technologies has enabled it to adapt quickly to changes, stay ahead of the competition, capture new markets, and continually enhance the user’s experience. As a result, the company has identified emerging opportunities and developed a model known as the Spotify model, which is based on an agile methodology (Šmite et al., 2023). We propose our first hypothesis based on this case, arguing that OA enhances a company’s ability to capitalize on emerging opportunities by improving its capacity to identify and anticipate market changes and latent needs.

OA also helps organizations enhance their capacity to adapt to technological changes and innovate. For instance, Guo et al. (2023) analyzed the mediating role of OA on the innovation performance of new Chinese digital companies. Rabal Conesa et al. (2024) showed that OA has a positive influence on green products and process eco-innovation. In their study of Spanish manufacturing companies, Cubillas-Para et al. (2024) revealed the influence of OA on digital transformation, providing avenues for proactive change and facilitating adaptability to structural change. Similarly, the results of Hadjielias et al.’s (2022) study highlighted the role of OA in tourism organizations, facilitating their use of digital technologies.

I4.0 has a disruptive impact on organizations and business models (Castelo-Branco et al., 2022). It marks the end of established patterns, challenging managers to adapt to an increasingly complex and changing business environment by adopting new perspectives and paradigms (Pencarelli, 2020). Many organizations have failed in their digitalization efforts because the mere adoption of technology falls short (AlNuaimi et al., 2022). As Vial (2019) stated, leaders should ensure that the organizational structure and the company’s employees are agile enough to respond to the disruptions caused by digital technologies. Based on this, we argue that hotels’ agility influences their readiness for Industry 4.0.

Industry 4.0 creates a dynamic and constantly evolving environment, which pushes companies to become innovative and anticipate technological and market changes (Ed-Dafali et al., 2023). This ability is closely linked to PMO, as both share a focus on experimentation, the search for opportunities, and the identification of future needs (Zhao et al., 2023a, b). Therefore, we argue that PMO is crucial to embracing innovation, adopting new technologies, and maintaining a competitive advantage in the era of I4.0, as it promotes value creation through the development of new business models and the analysis of emerging markets (Zhao et al., 2023a, b). Although some studies have explored the relationship between PMO and aspects of innovation and adaptation to new technologies, the relationship between PMO and RI4.0 has not been studied.

Previous literature has demonstrated that organizations with a PMO develop a greater capacity to comprehend the market (Zhao et al., 2023a, b). This, in turn, strategically and dynamically prepares them for technological and market changes derived from I4.0 (Ostadi et al., 2024), overcoming one of the main barriers related to understanding the strategic importance of I4.0 technologies (Stentoft et al., 2021). In this study, we argue that a proactive market approach enables hotel managers to broaden their knowledge base, enhancing their ability to prepare for I4.0. More specifically, we argue that hotels with a proactive market orientation are aware of what is happening outside the organization (Ed-Dafali et al., 2023), which strategically prepares them to be more receptive and better prepared for I4.0 (Pencarelli, 2020). Based on this, we propose our last hypothesis.

Figure 1 shows the proposed model.

The study population comprises Spanish hotels operating in southeastern Spain. Tourism is this area is a cornerstone of the country’s economy, contributing substantially to its gross domestic product (Lascu et al., 2018). This territory was selected because of its critical importance to the national tourism industry. The Spanish National Institute of Statistics recorded more than 11 million international visitors to southeastern Spain in 2023, demonstrating its relevance as the focal point of this study. Before data collection, we conducted a pilot test to enhance the logical flow and comprehensibility of the survey instrument. This preliminary step aimed to ensure that the survey questions were clear, relevant, and aligned with the objectives of the study (Henseler et al., 2016; Podsakoff et al., 2003). Eight people participated in the pilot test: three hotel managers, two engineers working on digital transformation and I4.0 technologies, and three academics with experience in digital transformation. Their feedback led to minor modifications in the survey’s wording and sequencing to improve clarity. Incorporating such diverse perspectives helped us to identify ambiguities and refine the instrument to improve the quality and reliability of the data (Henseler et al., 2014; Podsakoff and Organ, 1986).

A fieldwork company gathered the data through an online questionnaire. They were previously trained to introduce the research and questions to the participants. The study participants were hotel managers, as they are key informants within tourism organizations and participate in the strategic decision-making process (Christou et al., 2023). Their participation in the study was voluntary, and we informed them about the anonymity of their answers. We also indicated there were no correct or incorrect responses, and they should answer honestly to avoid bias (Podsakoff et al., 2003). Finally, we introduced them to the key I4.0 technologies before they answered the questions. The data collection period lasted four months, from March to June 2024. We compared the 100 early and 98 late responses on Industry 4.0 readiness to address potential non-response bias. The independent t-test revealed no significant differences between the early and late responses (p-value: 0.638), which verified the absence of response bias. Finally, we estimated the minimum sample size required. According to Cohen (1992), the minimum sample size should be 90 for a significance level of 5% and power of 80%.

We considered the potential common method variance (CMV), as only one respondent from each organization was chosen to participate in our questionnaire (Podsakoff and Organ, 1986). Harman’s single-factor test was used to assess the likelihood of CMV using SPSS software. The results confirm the absence of CMV problems, as the percentage of the total variance explained by the first factor is 42.31%, below the 50% threshold (Podsakoff et al., 2012). Subsequently, for robustness, we followed the Measured Latent Marker Variable (MLMV) approach to detect CMV problems post hoc, as suggested for PLS-SEM models (Chin et al., 2013). To do so, we included the blue intention variable (Miller and Simmering, 2023). The results show that the difference in the R² values across all our endogenous variables is less than 10% when the respondent’s blue intention is removed (see Table 1). Thus, we confirm that there is no single-factor bias for most covariance (Chin et al., 2013; Podsakoff et al., 2003). Finally, we analyzed common method bias following Kock’s (2015) approach. Given that the VIF values of the inner model resulting from a complete collinearity test are lower than 3.3, our model can be considered free of common method bias (Kock, 2015).

All the constructs were measured using a 7-point Likert scale.  Appendix shows the research constructs and items used to measure the research variables. The items used to measure organizational agility were adapted from the scales of Cegarra-Navarro et al. (2016) and AlNuaimi et al. (2022). OA was measured through 4 items that reflect the organization’s ability to adapt to changes in its business environment by adjusting its production capacity, implementing decisions quickly, searching for ways to redesign the organization, and considering changes as opportunities. We adapted the scale proposed by Morimura and Sakagawa (2023) to measure PMO through 6 items that describe a business approach focused on proactivity towards customers’ needs. The items reflect the efforts of the organization to anticipate the evolution of the market and its customers’ needs, discover latent unexpressed needs, innovate, and extrapolate key trends to foresee what users might need in the future. Finally, our dependent variable, RI4.0, was measured by adapting the items to the scale proposed by Ed-Dafali et al. (2023). Four items focused on intangible resources were used to ensure they were aligned with the needs of digital change. These items reflect the organizational knowledge resource base, organizational support for employees, employees’ skills, and employees’ motivation to work with I4.0 technologies.

We used the PLS-SEM methodology since our variables are composites estimated in Mode A, and a particular dependency exists among the indicators that define each latent variable. We use this methodology because it is well-suited for analyzing complex relationships among hidden variables, and it works well even with small sample sizes (Hair et al., 2017; Henseler et al., 2016). PLS-SEM is especially useful in exploratory research, making it a good choice for studying new ideas, such as hotel RI4.0. It also allows researchers to examine measurement and structural models simultaneously, ensuring that the results are valid and reliable (Chin, 1998; Hair et al., 2019). These benefits match our aim of testing theories and identifying the main factors that influence RI4.0.

Internal consistency reliability was evaluated by analyzing the indicator loads, composite reliability, multicollinearity, and convergent and discriminant validity tests (see Table 2). The results indicate that the factor loads meet the required minimum of 0.7, all being significant (p-value: 0.000). Cronbach’s alpha and the composite reliability indicators are greater than 0.7 in all the constructs, which confirms internal consistency reliability (Cepeda-Carrion et al., 2019). In addition, we verify convergent validity, as the Average Variance Extracted (AVE) of each construct exceeds 0.5 (Fornell and Larcker, 1981). Results confirm no collinearity problems, as the Variance Inflation Factors (VIFs) range from 1.428 to 3.878 (Hair et al., 2017).

Subsequently, we assessed the discriminant validity of the constructs used in the model following the Fornell-Larcker criterion and Heterotrait-Monotrait ratio (HTMT) of Henseler et al. (2016). As Table 3 shows, the square root of the AVE is greater than the correlation coefficient between the competent and all the distinct variables for each latent variable. Moreover, the threshold value of the HTMT ratio is less than 1 in all the constructs, confirming the discriminant validity of the constructs (Henseler et al., 2014).

We assessed the significance of the path model relationship in the measurement and structural models via bootstrapping analysis with 10,000 subsamples (bias-corrected and accelerated bootstrapping, two-tailed test). We calculated the fit indices of the saturated and estimated models to further enhance the robustness of the casualization of the PLS-SEM analysis (Benitez et al., 2020). As shown in Table 4, the results show that both models have good fit indices, as the Standardized Root Mean Square Residual (SRMR), the Unweighted Least Squares (d_ULS), and Geodesic (d_G) discrepancies are below the 99%-quantile of the bootstrap discrepancies (Hi99) (Benitez et al., 2020).

We conducted a bootstrapping analysis with 10,000 subsamples to assess the significance level, path coefficients, and confidence intervals of the relationships we proposed in the hypotheses. As can be seen in Table 5, there is a positive relationship between (1) organizational agility and proactive market orientation, supporting H1 (coefficient: 0.758; p-value: 0.000); (2) organizational agility and readiness for Industry 4.0, supporting H2 (coefficient: 0.208; p-value: 0.031); and (3) proactive market orientation and readiness for Industry 4.0, supporting H3 (coefficient: 0.293; p-value: 0.002). We also carried out a post hoc indirect effect analysis to analyze the indirect effect of organizational agility on readiness for Industry 4.0 through market orientation (Preacher and Hayes, 2008). As the intervals determined by the bootstrapping do not contain zero value, PMO partially mediates the relationship between OA and RI4.0. This partial mediating effect is a key finding of our study, indicating that hotels’ PMO partially channels the agility needed to effectively anticipate and respond to technological advancements and industry shifts. Consequently, these results fully support hypotheses H1, H2 and H3.

Finally, we evaluated the predictive ability of the structural model with the cross-validated redundancy index (Q2) (Hair et al., 2022). Q2 is greater than zero in all the cases, indicating that the structural model has predictive capacity (Chin, 1998).

Although Europe’s Digital Agenda is implementing policies to encourage the digitalization of the hotel sector, the latest report from 2024 revealed that Spanish accommodation still has a basic level of digitalization. Despite the slower-than-expected progress, hotel readiness for Industry 4.0 remains an underexplored area in management research (Rodrigues et al., 2024). This study addresses pressing research gaps identified by Caputo et al. (2019) and Del Giudice et al. (2022), who call for deeper insights into how business models evolve in response to digital transformation. While previous literature has identified the importance of organizational readiness for Industry 4.0 (Stentoft et al., 2021), limited empirical work has examined the specific internal capabilities that drive this readiness, especially dynamic capabilities such as organizational agility and strategic orientations like proactive market orientation (Ostadi et al., 2024). The current study responds to these calls by empirically validating a novel conceptual framework in which OA and PMO significantly predict readiness for Industry 4.0 (RI4.0), thereby offering a direct answer to RQ1. Below, we elaborate on the main theoretical contributions and practical implications of this study.

The results of the study confirm H1, indicating that organizational agility enables hotels to better detect latent market needs before they evolve into industry standards. The ability to dynamically reconfigure resources, structures, and strategies empowers agile hotels to anticipate change and respond proactively, fostering organizational innovation. These findings contribute to the ongoing discussion in the literature on the Spotify model (Šmite et al., 2023), illustrating how agile organizational structures can facilitate the early identification of market opportunities and the rapid adaptation needed to maintain a competitive edge in dynamic environments. This extends the theoretical understanding of dynamic capabilities by demonstrating their practical application in service-intensive environments, specifically within the tourism sector.

The results of this study extend the theoretical understanding of organizational agility as a key driver of digital adaptability in the context of Industry 4.0 (H2). Consistent with AlNuaimi et al. (2022), Cegarra-Navarro et al. (2016), Cubillas-Para et al. (2024) and Hadjielias et al. (2022), our study confirms that organizational agility serves as a critical enabler of digital adaptability, enhancing firms’ absorptive and transformative capacities (Teece et al., 1997; Helfat et al., 2007). This contributes to the theoretical discourse on Industry 4.0 by showing that agility functions as a dynamic capability that mitigates organizational inertia and facilitates continuous reconfiguration in response to technological disruptions. Likewise, the study contributes to the existing literature as the first to analyze this relationship in the tourism sector with a specific focus on RI4.0. It addresses Stentoft et al.’s (2021) and Zhao et al.’s (2023a, b) call for research providing an understanding of the critical organizational factors that underpin successful preparation for I4.0 within the tourism sector. By conceptualizing organizational agility as an antecedent of resilience and innovation in digital ecosystems, this study provides a novel theoretical framework for understanding the way service-oriented firms can strategically leverage agility to achieve sustained competitive advantage in the era of Industry 4.0.

Finally, our findings confirm that proactive market orientation is a fundamental driver of hotel readiness for Industry 4.0 (H3). While prior studies have established that proactive market-oriented firms anticipate market changes and shape demand patterns (Zhao et al., 2023a, b; Ed-Dafali et al., 2023), our research adds to the literature by positioning proactive market orientation as a strategic orientation that enhances hotel digital readiness. Furthermore, aligning with Morimura and Sakagawa (2023), the study confirms that proactive market orientation also partially mediates the relationship between OA and I4.0 readiness. This mediating role reinforces the co-evolution of organizational learning and strategic sensing capabilities, as emphasized in dynamic capabilities literature (Jaworski and Kohli, 1996; Ostadi et al., 2024). By validating these mechanisms within the underexplored context of Spanish hotels, our research extends the work of Christou et al. (2023) and contributes to a more integrated understanding of the role of internal capabilities and forward-looking market behavior exert on enhancing digital readiness in complex, service-intensive environments.

This study highlights the importance of hotels having organic organizational structures that are not overburdened by bureaucracy. I4.0 requires flexible and agile structures, capable of reacting quickly to changes in the environment (Rabal Conesa et al., 2024). One way to achieve this is by implementing a decentralized decision-making system, allowing workers to make decisions on the spot without waiting for approval from senior management (Šmite et al., 2023). For example, if a supplier does not deliver on time, workers can make immediate decisions, such as looking for alternative suppliers.

Our results show that a proactive market orientation is crucial to meet the challenges of I4.0. Hotels that adopt a proactive approach to the market are constantly aware of industry trends and changes in customer needs and expectations. By being updated on what is happening around them, they can identify opportunities and challenges in advance, enabling them to adjust their strategies. This market orientation must be addressed in all the company’s positions and levels (from the receptionist, who is in direct contact with customers, to the manager who attends trade fairs). All these parties must be aware of the importance of analyzing all the information they receive from their environment. To achieve this, hotels could develop an ambidextrous culture, as it allows all employees to be prepared for exploratory roles and take calculated risks, generating a positive mindset toward change (Moreno-Luzon et al., 2024). They should also invest in market research, customer feedback mechanisms, and cross-functional collaboration with a focus on all their business environment to develop a more proactive and customer-centric strategic orientation. In this regard, analyzing customers’ needs is essential but not enough: they should also analyze their competitors and the rest of stakeholders to get valuable information about the demand and supply chain.

Employees’ skills are key to effectively adapting structures (Gumbo et al., 2023). Therefore, regular training programs should be conducted to equip staff with the skills and knowledge needed to handle change. Fostering a culture of continuous learning and open communication encourages employees to seek solutions or escalate issues when necessary. To this end, organizations can implement organizational debriefing sessions for team learning (Hebles et al., 2023). Group debriefing is a team-oriented technique designed to foster collective learning among employees, promoting creative and agile responses to challenges. By sharing the experiences and insights of employees within the organization, it enhances performance in organizational processes (Hebles et al., 2023). Moreover, these sessions can help detect latent needs in the market, as the points of view of employees from all departments are considered. Finally, to effectively respond to unexpected changes, hotel managers should develop contingency plans to achieve adaptability to change. This involves identifying potential risks, such as supply chain disruptions, and preparing tailored strategies to address them (Parajuli et al., 2017). Managers should routinely review and update these plans to keep pace with evolving market conditions.

The results of the study have confirmed all the proposed hypotheses. However, the study has some limitations that should be considered. First, the sample comprises exclusively hotels in southeastern Spain, an area that mainly receives beach and sun tourism from Europe. Therefore, to generalize the results, it is necessary to verify whether the proposed relationships hold in the rest of Spain and other countries. Moreover, this study is quantitative. Future research should adopt a qualitative approach to explore the analyzed factors in depth and discover other potential drivers that contribute to Industry 4.0 readiness. Another limitation is that our work has only focused on identifying two key factors (OA and PMO). Future research should broaden this focus and identify other factors that influence the phenomenon under study, such as economic aspects, other strategies, or barriers specific to the hotel sector (i.e. innovation culture, infrastructure availability, and cybersecurity risks). Future research should also focus on examining which factors may influence hotel agility, as this would provide a more holistic view of the situation of these hotels in the context of Industry 4.0. Furthermore, we are now transitioning to the era of Industry 5.0. Future studies should investigate whether our findings remain applicable when assessing Industry 5.0 readiness and consider other variables that may affect this readiness. Finally, this study represents a snapshot of a dynamic and evolving process, which presents certain limitations. Future research should explore additional critical concepts such as over-tourism and seasonality to provide a more comprehensive understanding.

Funding: This work was supported by the Ministerio de Universidades, Gobierno de España (Ministry of Universities, Spanish Government) (FPU20/05986).

• (1)

  OA1. We have the capacity to quickly adapt production to fluctuations in demand.

• (2)

  OA2. We quickly implement decisions to address market changes.

• (3)

  OA3. We continually seek ways to reinvent or redesign our organization.

• (4)

  OA4. We see market changes as opportunities for fast capitalization.

• (1)

  PMO1. We help our customers anticipate developments in their markets.

• (2)

  PMO2. We continually seek to discover additional needs that our customers are not aware of.

• (3)

  PMO3. We incorporate solutions to unexpressed customer needs into our new products and services.

• (4)

  PMO4. We innovate even at the risk of making our own products obsolete.

• (5)

  PMO5. We look for opportunities in areas where customers have difficulty expressing their needs.

• (6)

  PMO6. We extrapolate key trends to get an idea of what users in the current market will need in the future.

• (1)

  RI1. We have the necessary knowledge of new digital technologies to evaluate their importance for our company.

• (2)

  RI2. My organization provides the necessary support to assess and work with new digital technologies.

• (3)

  RI3. Our employees have the appropriate skills to work with new digital technologies.

• (4)

  RI4. Our employees have motivation to evaluate and work with new digital technologies.