Abstract
Purpose
This study analyzes how digital technologies collaboration, and technological capabilities affect tourism products' advantage and supply chain resilience via virtual integration and customer service capabilities.
Design/methodology/approach
To achieve the goals of this study, a digital transformation model was formulated based on the real option theory (ROT) and digital competencies perspective. Data were collected from travel agencies in Taiwan. This study uses the partial least square structural equation modeling (PLS-SEM) technique to analyze the research model, and 384 samples were collected from travel agencies for analysis.
Findings
The research results point out that digital technology collaboration and technical capabilities affect virtual integration and customer service capabilities; customer service capabilities should also be regarded as key influencing variables to improve tourism product advantages and supply chain flexibility.
Originality/value
This study shares a unique perspective on the digital transformation model, which includes antecedents, mediators and moderators, to construct the critical effects for analyzing the tourism products' advantage and supply chain resilience.
Keywords
Citation
Ku, E.C.S. (2024), "Tourism digital transformation and future supply chain competition: an integrated perspective on real options theory and digital competencies", Journal of Tourism Futures, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JTF-10-2023-0232
Publisher
:Emerald Publishing Limited
Copyright © 2024, Edward C.S. Ku
License
Published in Journal of Tourism Futures. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Digital transformation is one of the leading innovative approaches for tourism enterprises to solve operational issues and strategic opportunities (Sánchez and Oskam, 2022; Woolley and Lim, 2023); the digital transformation of tourism enterprises has solved operational issues and strategic opportunities and leveraged data analytics to gain insights into customer behavior, preferences, and market trends, enabling better decision-making.
This study aims to understand how using and collaborating with digital technologies affects the advantages of tourism products and the ability to address tourism supply chain challenges. The gaps between the study and prior research include prior research focused on the digital marketing of tourism enterprises (Dewantara et al., 2022; Gutierriz et al., 2023); that research needs more exploration into the impact of digital transformation on tourism supply chain competition. On the other hand, many studies have emphasized that digital transformation is an essential strategy for tourism enterprises facing the threats and challenges of the epidemic (Sánchez and Oskam, 2022; Tang and Huang, 2023). Furthermore, few studies have discussed the role of technology interoperability in the advantages of tourism products and supply chain resilience; technology compromise will influence the digital transformation of tourism enterprises.
Continuing the research scope, the study will discuss the advantages of tourism products and the resilience of the tourism supply chain. Specifically, this study will explore and analyze the following issues:
What factors related to enterprises' digital competence can affect travel agencies' tourism product advantage and tourism supply chain resilience?
Does technology interoperability moderate travel agencies' tourism product advantage and tourism supply chain resilience?
Digital transformation is enterprises using new digital technologies to achieve significant business or process improvements (Kraus et al., 2022). Previous studies stated that tourism enterprises use digital technologies to accelerate disruption at the industrial and social levels (Tsou and Chen, 2023); digital technologies play a nucleus role in digital transformation.
Digital transformation enables travel agencies to offer a more customer-centric and personalized experience (Ivanova et al., 2022). Accordingly, real options theory (ROT) is based on a financial perspective; it analyzes managers' perspectives when evaluating investment decisions (Sharma et al., 2022); the digital transformation of travel agencies requires substantial amounts of capital and mature collaboration in digital technologies (Tsou and Chen, 2023); travel agencies should evaluate their technological capabilities (Cheng et al., 2023) in digital transformation.
Digital technologies transactions are significant for travel agencies that operate multi-channel services (Huang et al., 2022; Ku, 2023b) within the tourism supply chain; the emergence of digital technologies enables suppliers to establish virtual integration (Jean et al., 2020) and attach great importance to customer service capabilities (Abadie et al., 2023) based on the perspective of virtual competence. Furthermore, technology interoperability and data processing capabilities are critical aspects that enterprises need to consider (Ali et al., 2022b) when adopting digital transformation.
To achieve the goals of this study, a digital transformation model based on ROT and digital competencies was proposed. Data were collected from travel agencies in Taiwan. This study uses the partial least square structural equation modeling (PLS-SEM) technique to analyze the research model; research findings will help tourism businesses carefully consider digital transformation strategies.
2. Literature review
2.1 Theoretical background
2.1.1 Real options theory and digital transformation
ROT provides a powerful and persuasive theoretical perspective on technology investment (Singh et al., 2017); the theory focuses on helping enterprises make correct decisions under uncertainty (Wu and Ku, 2024). Likewise, government policies, investment in technologies, and technological turbulence will affect the digital transformation of tourism enterprises.
ROT emphasizes identifying the ability to process multiple pieces of information to implement the selected option effectively (Irwin et al., 2022; Salvoldi and Brock, 2023). Previous research has pointed out that digital transformation requires mature digital technologies collaboration (Tsou and Chen, 2023), and tourism enterprises should evaluate their technological capabilities and calculate the benefit-cost ratio (Cheng et al., 2023) to formulate better digital transformation strategies.
2.1.2 Digital competencies of digital transformation
Digital competencies can be broadly defined as the ability of members of an organization to confidently and critically (Alam et al., 2018); digital technologies interact with consumers and provide services by travel agencies (Calvaresi et al., 2023; Huang et al., 2022; Ku, 2023a), inter-organizational systems (IOS) collaborative tourism industry conducts more information technology-based cross-organizational remote collaboration (Ku, 2023b) and responds to real-time market changes.
The emergence of digital technologies enables suppliers to establish virtual integration (Jean et al., 2020), and digital transformation attaches significant importance to customer service capabilities (Abadie et al., 2023) because it is of great significance to the multi-channel service operation management of travel agencies.
2.2 Hypotheses development
2.2.1 Digital technologies collaboration
Digital technology collaboration embodies information exchange and transactions across organizations, distributed ledgers, and shared infrastructure (Tsou and Chen, 2023). Verstegen et al. (2019) stated that enterprises' use of digital technologies is reflected in at least three aspects: enterprises use new digital technologies to achieve corporate goals and the processes of digitally innovative enterprises.
The impact of digitalization included internal and external collaboration in innovation activities; Moschko and Blazevic (2023) argued that internal partnership refers to the internal integration of digital technologies and will affect cooperation, representation, and contribution of the company’s innovation activities, Eslami et al. (2023) stated that external collaboration creates value and innovation through the application of digital technologies in the context of digitalization.
2.2.2 Virtual integration
Virtual integration includes the concepts of user intention and virtual governance, and it has two aspects: coordination and cooperation (Jean et al., 2020); virtual integration in the context of digital transformation refers to the seamless integration of various digital technologies, processes, and data across an organization (Jean et al., 2020) to enhance collaboration, efficiency, and innovation.
Past research has pointed out that the relationship between travel agencies' digital technologies collaboration and their virtual integration is closely related to the evolving landscape of the tourism industry (Rashed and Mutis, 2023; Runck et al., 2022) and digital technologies will reshape the future of tourism industry (Elia et al., 2020; Hamann-Lohmer et al., 2023).
Virtual integration will also moderate the impact of digital technologies on customer-supplier relationships in the tourism supply chain; by utilizing digital technologies, travel agencies can provide tourists with a more convenient and personalized service; the following hypothesis is proposed:
Digital technologies' collaboration with enterprises is positively associated with their virtual integration.
2.2.3 Customer service capabilities
Customer service capabilities can be seen as an enterprise’s ability to meet customer needs through its existing service portfolio under digital competition (Sok et al., 2018); they are also essential to service marketing to assess the service representatives they supervise (Ali et al., 2022a; Bani-Melhem et al., 2021).
Previous research has stated that enterprises can improve customer service capabilities and productivity by using digital technologies (Adhiatma et al., 2023; Almunawar and Anshari, 2022); for example, Halpern et al. (2021) pointed out that using digital technologies at critical stages of the airport journey can enhance passengers' travel experience.
Digital technology collaboration using IOS will provide real-time updates, help tourists through chat or messaging apps, and continually gather feedback to improve travel services. We argued that digital technologies will enhance the customer service capabilities of travel agencies and are essential to assist such customer engagement and aggregate market knowledge effectively. This leads to hypothesis 2:
Enterprises' digital technologies collaboration is positively associated with their customer service capabilities.
2.2.4 Technological capabilities
Technological capabilities refer to the skills, knowledge, expertise, and resources possessed by organizations or societies to effectively use and develop technology for various purposes (Ku, 2022b). This will help the enterprise collect consumer knowledge and share market transaction information through different cooperation channels (Abdelaziz et al., 2023; Wu and Ku, 2024), which can increase enterprises’ capabilities to integrate the collaborative relationship.
The relationship between travel agents' technological capabilities and their virtual integration is integral to transforming the tourism industry (Bhattacharya et al., 2022; Chen et al., 2023). Enterprises' technological capabilities are increasingly considered critical (Hadjielias et al., 2022; Romero et al., 2023), and their virtual integration is increasingly regarded as critical for maintaining a long-term joint competitive advantage.
Digital transformation of travel agencies are embracing and harnessing technologies to provide a seamless, customer-focused travel experience. Furthermore, travel agencies can leverage virtual collaboration tools to communicate with tourists through various digital channels by using data analytics to gain insights into customer behavior, market trends, and booking patterns. This leads to hypothesis 3:
Technological capabilities of enterprises are positively associated with their virtual integration.
Collaborative technology and knowledge sharing between enterprises is fundamental, especially the core planning and control processes between cooperative enterprises; this will enable managers to cope with the technical complexities of tourism business operations (Hadjielias et al., 2022; Romero et al., 2023). Likewise, Hadjielias et al. (2022) found that tourism enterprises leverage digital technologies to generate and deliver customer value through customer service agility while coping with inherent tensions.
The more advanced their technological capabilities, the more effectively travel agencies can bridge the digital and physical aspects of travel, creating a cohesive and convenient virtual journey for their tourists; likewise, technological capabilities allow travel agents to access vast information about travel options, destinations, and customer preferences, and travel agencies need technological capabilities to share and connect with other partner companies in the market to achieve inter-organizational interactions. This leads to hypothesis 4:
The technological capabilities of enterprises are positively associated with their customer service capabilities.
2.2.5 The tourism products advantage
Advantages in tourism products refer to the superiority and uniqueness of the product in terms of quality and efficiency compared with other products in the tourism market (Cui and Wu, 2017); highly innovative tourism products will have more advantages when tourists feel they are more suitable and meet their needs.
Virtual integration represents the integration of suppliers through digital technologies to achieve closer supply chain collaboration and replace ownership with partnership (Asamoah et al., 2021; Tang and Zhang, 2022); prior studies have pointed out that virtual integration is significantly related to tourism product selection (Zhang et al., 2023). Virtual customer integration also provides customers with the experience of participating in the new product development process (Kulkov et al., 2023), which is conducive to innovative product advantages.
Virtual integration in the tourism industry will enhance the customer experience, offering a broader range of services, personalizing offerings, providing real-time information, reducing costs, and improving adaptability. In a highly competitive tourism market, travel agencies that leverage virtual integration are more likely to succeed and outperform their rivals. This leads us to our hypothesis 5.
Virtual integration of enterprises is positively associated with their tourism product advantage.
2.2.6 Tourism supply chain resilience
Tourism supply chain resilience is defined as the operational capabilities of enterprises within the supply chain system to return to their original state or shift to a new, more ideal state after being disrupted (Ghaderi et al., 2023; Mandal and Dubey, 2020); eliminating any waste and reducing costs for tourism enterprises will bring greater flexibility and resilience to the tourism supply chain in a complex environment and comply with the lean supply chain paradigm.
The benefits of virtual integration drive enterprises to construct rational choices in the supply chain, further ensuring the maintenance of willingness to share knowledge and enhancing the supply chain’s resilience (Chen and Huang, 2023); Sheng and Saide (2021) stated that enterprises' big data analysis through virtual integration had become a key strategy to achieve the viability of the tourism supply chain.
Virtual integration allows tourism enterprises to have real-time visibility into their supply chains. Virtual integration equips tourism enterprises with the tools and capabilities to respond proactively and effectively to disruptions while enhancing their overall supply chain resilience; by leveraging digital technologies and integrated systems, businesses in the tourism sector can better prepare for, withstand, and recover from unforeseen events and challenges. Therefore, this leads to hypothesis 6:
Virtual integration of enterprises is positively associated with tourism supply chain resilience.
Prior research argued that exceptional customer service can differentiate tourism products from others in the market (Ko et al., 2023), high-quality customer service creates a positive and memorable experience for tourists (Moliner-Tena et al., 2023; Zhou et al., 2023), and indicated customer service capabilities of enterprises and their competitive advantage in the tourism industry is highly significant (Lee et al., 2022).
Travel agencies' customer service capabilities offer tourists personalized recommendations, itineraries, and services; a knowledgeable and well-trained customer service travel agency can provide valuable information and guidance to tourists. This leads to hypothesis 7:
Customer service capabilities of enterprises are positively associated with their tourism product advantage.
Customer service capabilities often involve collecting and analyzing data about customer preferences and behaviors. These analytical competencies can be applied to supply chain data (Buhalis et al., 2019; Zhang et al., 2019); customer service capabilities will significantly contribute to the resilience of the tourism supply chain (Liu et al., 2022), aiding in making data-driven decisions that enhance resilience.
The adaptability of customer service capabilities extends to the tourism supply chain, where the flexibility to adjust to disruptions or changing market conditions can enhance resilience. Travel agencies with solid customer service capabilities are better positioned to navigate and recover from supply chain disruptions, contributing to their overall resilience. Thus, hypothesis 8 is proposed as follows:
Customer service capabilities of enterprises are positively associated with tourism supply chain resilience.
Competitive tourism products are often designed to operate efficiently and adapt quickly to changing market conditions (Kim et al., 2016); these attributes can translate into more agile and adaptable supply chain practices (Vives and Ostrovskaya, 2023), helping tourism businesses respond to disruptions more effectively.
A competitive advantage may allow tourism businesses to work with a broader range of suppliers, and competitive tourism products are technologically advanced. These technologies can improve supply chain management and communication, leading us to our hypothesis 9.
The advantage of tourism products is positively associated with resilience in the supply chain.
2.2.7 The moderating effects of technology interoperability
Technology interoperability refers to the ability of different information communication systems (Hsu et al., 2019) and electronic data applications to communicate, exchange data, and use existing information exchange capabilities; Hsu et al. (2019) stated that digital technology interoperability refers to the degree to which new digital technologies will be integrated with existing internal and external knowledge components of the enterprise. High interoperability means that enterprises can achieve many service innovations and new services.
Technology interoperability ensures that different systems, applications, and platforms used for virtual integration can seamlessly exchange data (Bokolo, 2022; Jnr, 2023); this enables tourism businesses to access and utilize a wide range of tourism information (Priporas and Vellore-Nagarajan, 2022); likewise, technology interoperability allows real-time information sharing between different components of the virtual integration system (Bokolo, 2022; Lo et al., 2019), which mediates the relationship between virtual integration for competitive advantage.
Technology interoperability enables real-time information sharing between different components of the virtual integration system; it ensures that tourism businesses can respond quickly and efficiently to changing market conditions, which can be a competitive advantage for tourism products. Thus, the hypothesis 10 is proposed:
Technology interoperability moderates the impact of virtual integration on the tourism products advantage.
Moreover, technology interoperability allows the tourism supply chain as needed (Kumar et al., 2023; Wong et al., 2023); interoperable systems make it easier to gather and analyze data from various sources (Pierdicca et al., 2019; Solmaz et al., 2019), and IOS provides consistent and effective data conversion for tourism enterprises in the tourism supply chain (Ku, 2022a, b, 2023b); transactional information can provide travel agencies with development products and efficiencies in pursuit of new markets.
Interoperable systems can aid in coordinating supply chain activities, from procurement to distribution, by ensuring that information flows smoothly; this coordination can increase the reliability and responsiveness of the supply chain, contributing to a competitive advantage. Thus, hypothesis 11 is formulated:
Technology interoperability moderates the impact of virtual integration on tourism supply chain resilience.
Technology interoperability ensures that customer service systems and databases seamlessly share and update information (Leung and Loo, 2022); it makes it easier to gather and analyze customer data (Weng and Hsu, 2020) and reduce operational costs (Chaturvedi and Binkley, 2021); customer service travel agencies make data-driven decisions, improve service quality, and develop strategies that set tourism products apart from competitors.
Customer service agents can access real-time data about customer preferences, past interactions, and needs to provide more personalized and effective service. This enhanced service quality can differentiate tourism products and create a competitive advantage. Hypothesis 12 is formulated.
Technology interoperability moderates the impact of customer service capabilities on the tourism products advantage.
Technology interoperability ensures that customer service and supply chain teams can communicate seamlessly (Bommu et al., 2023; Islam et al., 2023), it is essential to efficiently coordinate responses to supply chain disruptions (Rai et al., 2022); furthermore, technology interoperability can support adapting supply chain processes to changing conditions or disruptions.
Technology interoperability is a critical factor in moderating the impact of customer service capabilities on tourism supply chain resilience, and it ensures that customer service and supply chain partners can communicate, share data, adapt to changing conditions, make informed decisions, and respond effectively to disruptions for the digital transformation of the tourism industry. This leads to hypothesis 13.
Technology interoperability moderates the impact of customer service capabilities on tourism supply chain resilience.
3. Research methodology
3.1 Research model
Based on the perspective of ROT and digital competencies, the study will draw the relationship between virtual integration, customer service capabilities, tourism product advantage, and tourism supply chain resilience. Figure 1 identifies the above key constructs and central relationships examined in the study.
3.2 Instrument development
We adopted structural descriptions from operational definitions proposed in the existing literature. According to Boudreau et al. (2001) recommendations, researchers should validate their instruments even if measurement items are adopted from the literature; thus, with four items of digital technologies collaboration adapted and modified from Tsou and Chen (2023), technological capabilities scales adopted from Ku (2022b), virtual integration operationalized with three items adopted by Bryan Jean et al. (2020), customer service capabilities with six items operationalized based on Sok et al. (2018), tourism products advantage, with four items operationalized from Cui and Wu (2017), and tourism supply chain resilience scales modified from Ghaderi et al. (2023). Lastly, technology interoperability was modified from Hsu et al. (2019) study using four items. Table 1 summarizes the survey items of this study.
Survey items featured a Likert 7-point rating scale for respondents to evaluate (1 = completely disagree, 7 = completely agree). This study invited two professors in the field of tourism management and two management information system experts to participate in the pilot test, using a double translation protocol to correct the measurement content and ensure its validity. Next, twenty-five employees with experience in travel agencies were invited to pre-test the Chinese version of the questionnaire, re-identifying faces and content validity and confirming appropriate minor wording corrections to measurements.
3.3 Sampling procedure and data collection
To reach our research purposes, the organization’s technological capabilities are an essential factor (Ku, 2022b); likewise, the level of analysis in this study is the firm level. Therefore, we invited the person in charge of the travel agencies' information system to be the survey participants.
Based on statistics from the Tourism Bureau in Taiwan https://www.taiwan.net.tw/statistics; There are 2,800 travel agencies which can be classified into three categories: accounting for 4.15% of wholesalers, 87.87% of travel agencies-direct sales, and 7.98% of retailers; according to our research purpose, EMSs are adapted by travel agencies-direct sales, and wholesalers, operate ERP and stratified random sampling was adopted in the sampling method.
Thus, the priority participant we select to mail the research’ questionnaire is the manager of the travel agency; otherwise, considering the classified of the travel agencies, we have confirmed the critical person who is responsible for EMSs or ERP of travel agencies in advance to use the IOS and indicate that the key person responsible for the systems’ activities should be delivered to answer. In total, 1,000 travel agencies were used as the sample of the mailing questionnaire, and 384 completed questionnaires were received (return rate of 38.4%).
4. Analysis and results
4.1 Demographics of samples
Sample characteristic analysis displays that 75.8% of the travel agencies-direct sales and 55% of travel agencies employed over 51 staff. Among the participants, 54.4% had accepted IOS above 11 years; Table 2 lists the characteristics of the samples in this study.
4.2 Common method bias (CMB) and endogeneity
CMB is likely to occur when independent and dependent variables are measured simultaneously in a survey (Chin et al., 2012); measurement survey items must be tested for CMB before analyzing the research model (Kock et al., 2021); Harmon’s single-factor test method was applied to perform CMB analysis (Baumgartner et al., 2021), which revealed an explained covariance of 19.12%, it showed that no CMB was found in seven structures in this study for the seven underlying structures, indicating that no CMB was identified in this study.
In addition, cross-sectional data may lead to model misspecification, and variation within exogenous variables may be endogenous to the model (Guide and Ketokivi, 2015). The Ramsey Regression Equation Specification Error Test (RESET) test was used to evaluate the endogeneity of the proposal model (Ramsey and Ramsey, 2006); the result represented was not an issue in the study.
4.3 Measurement model
The PLS-SEM technique (SmartPLS software Version 4.1) was applied in this study (Raza et al., 2024). First, the validity and reliability of the survey items were evaluated, and factor loadings helped quantify the extent to which each observed variable loads onto or is associated with each factor. In the model, the loading of each item should be higher than the discrimination threshold of 0.70 (Li et al., 2022). The reliability metric is considered the overall reliability of the acquisition, and each composite reliability (CR) of the structure must exceed the minimum standard of 0.70. Convergent validity computes the average variance extracted (AVE) for each measure concerning its latent factor. A high AVE value (typically above 0.5) indicates that the measure shares more variance with its latent factor than with measurement error (Dahl et al., 2023). Table 3 lists the validity of the measurement model.
Three indexes were used to assess discriminant validity (Becker et al., 2023), including the cross-loading of the measurement model, Fornell-Lacker criterion (Henseler et al., 2015), and heterotrait–monotrait ratio (HTMT) method (Radomir and Moisescu, 2020); cross-loadings must be above the threshold of 0.7 to be considered acceptable for the model (Yuan et al., 2023), as appeared in Table 4. Moreover, all Fornell-Lacker criterion and HTMT values are significantly lower than 1 with a confidence interval of 95% (Table 5); the Fornell-Lacker criterion, HTMT ratio, and the analysis results show that the research model has good convergent validity.
4.4 Structural model
The evaluation model goodness-of-fit (GOF) is measured by the coefficient of determination (R-squared) (Hair et al., 2019); Table 6 presents the results of GOF. Statistics R-squared over 0.30, and the variance calculated for 41.7% of virtual integration, 49.2% of customer service capabilities, 40.6% of tourism products advantage, and 56.7% of tourism supply chain resilience; in addition, the effect size (f2) was from 0.00 to 0.316, which were less than 0.33 of critical recommended value (Ibarra-Cisneros and Hernandez-Perlines, 2020); as exhibited in Table 7, it shows that the research model has good interpretability.
Variance inflation factor (VIF) assesses whether there is collinearity between constructs (Assaf and Tsionas, 2021). Moreover, it suggested that the VIF estimates of the research model were lower than 3.3 (Goodhue et al., 2017); the results pointed that the VIF was between 1.27 and 2.23, which shows that there is no collinearity problem in this study; moreover, five indexes identified the predictive accuracy of the structural model (Yusif et al., 2020), as proved in Table 8; indicates that the research model has a good fit.
5. Discussion and implications
This study demonstrates that digital technologies collaboration impact on virtual integration (t = 3.581*, p < 0.05) and customer service capabilities (t = 10.596 ***, p < 0.001), that technological capabilities impact virtual integration (t = 8.010 **, p < 0.01) and customer service capabilities (t = 5.858 **, p < 0.01) were supported. Compared to past research, the results of the study are compatible with those of Adhiatma et al. (2023) and Almunawar and Anshari (2022) works; digital technologies collaboration and technological capabilities are two significant factors that lead to tourism supply chain resilience; in addition, the virtual integration impact on the tourism products advantage (t = 8.010 **, p < 0.01) was supported, but the impact on tourism supply chain resilience (t = 0.126, p > 0.05) was not supported in the study. This finding is similar to Hadjielias et al. (2022) and Romero et al. (2023). In addition, the role of technology interoperability in moderate virtual integration affects the tourism products advantage (t = 1882, p > 0.05) was not supported, but the impact on tourism products advantage (t = 2.51*, p < 0.05) was supported; likewise, its moderate customer service capabilities effect on tourism products advantage (t = 1.569, p > 0.05) was not supported and impact on tourism supply chain resilience (t = 2.457 *, p < 0.05) was supported. As Pierdicca et al. (2019) and Solmaz et al. (2019) stated, interoperability allows different components of the tourism supply chain, such as booking systems, payment gateways, and reservation systems, to work together seamlessly. This integration leads to streamlined and efficient operations of tourism enterprises. Finally, Table 9 lists the hypothesis testing results.
5.1 Implications for research
This study makes three contributions to the field of digital transformation and management of information systems. First, it supplements the tourism digital transformation and future supply chain competition by combining the ROT and digital competencies, thereby unveiling two significant factors that affect the tourism products' advantage and tourism supply chain resilience through the digital transformation for travel agencies (Figure 1). Hence, the research is one of the few studies that consider the moderating effect of technological interoperability on digital transformation and the factors that can enhance tourism supply chain resilience. Second, there are clear benefits of adopting digital technologies; tourism digital transformation will increase revenue, reduce operational costs, improve customer satisfaction, and increase market competitiveness for tourism enterprises. Virtual integration and customer service capabilities should also be significant influencing variables in improving tourism products' competitive advantage and supply chain resilience. Third, this study shares a unique perspective on the digital transformation model, specifying that technology interoperability plays a crucial role in the tourism supply chain by enhancing communication, efficiency, and collaboration among various stakeholders.
5.2 Implications for practice
The study’s findings highlight the importance of digital transformation in the application of digital technologies to the digital transformation of travel agencies and further propose specific practice topics for them.
First, travel agencies' digital transformation could involve operational inefficiencies, customer service challenges, or marketing difficulties. Based on our findings of H1 and H2, digital technology collaboration for travel agencies involves identifying innovative solutions that can improve their operations, enhance customer experiences, and increase efficiency.
For travel agencies, through IOS, tourists can seamlessly and promptly reserve tourism products from digital travel agencies. The advantageous functions of digital technologies collaboration will enhance customer interaction management, lead tracking, and personalized marketing efforts of travel agencies; future managers should take advantage of the competitive advantages of digital transformation; travel agencies will use data to identify customer preferences and trends and optimize digital marketing strategy.
Second, technological capabilities emphasize that managers stay informed about the latest market knowledge and technology trends that benefit travel agencies. This may include advances in data analytics and customer relationship management. Based on our findings of H3 and H4, using data analysis tools to extract insights, identify customer preferences, and optimize marketing strategies has become an essential technical capability for travel agencies.
Managers should gain insight into successful innovations in travel agencies. Learn how technologies change travel agencies and determine where to differentiate yourself in the tourism supply chain. Likewise, in the future, travel agencies must continue to invest in and monitor the development of digital technology capabilities, collect, and analyze market data for evaluation through the digital transformation process, leverage the benefits of digital transformation, and make rapid adjustments to strategies to achieve continuous improvement.
Third, virtual integration for travel agencies involves recommending integrating various digital technologies and online services to create a seamless, interconnected tourist experience. Virtual integration can enhance customer service, streamline operations, and increase the agency’s competitiveness (H5); however, virtual integration enables global suppliers to coordinate inventory levels electronically, which will improve in the future.
Conversely, virtual integration also allows global suppliers of travel agencies to monitor the quality and changes of tourism products electronically; in the future, travel agencies will be more capable of forecasting product demand with partners in the IOS synchronously while integrating and cooperating with global suppliers.
Fourth, travel agencies must improve customer service during the digital transformation to enhance customer experience and maintain competitiveness in the tourism market. According to the findings of H7, solving travel agencies' specific customer service problems has become one of the critical factors for the success of travel agencies' digital transformation; unique customer service planning satisfies travel agencies to improve customer service capabilities effectively.
In the future, travel agencies must develop and implement a system that allows tourists to communicate, interact, and provide feedback through omnichannel. Moreover, they must enhance their digital capabilities in handling tourist issues promptly throughout the tourism supply chain, better help customers solve problems related to service provision, and make tourists satisfied with the service.
Finally, technology interoperability is critical to ensuring that various systems and technologies can work together seamlessly as part of travel agencies' digital transformation (H10-H13). In digital transformation, travel agencies recommend integrating existing systems (booking engines, CRMs, payment gateways) through well-documented APIs to achieve data exchange.
Moreover, digital transformation requires promoting standardized data formats and protocols to ensure seamless data exchange between systems. Therefore, supply chain members must jointly develop data exchange standards to make digital transformation successful in the future.
Digital transformation emphasizes the importance of data-based analytics for enterprises to understand customer behavior, optimize marketing efforts, and make data-driven decisions. Future digital transformation strategies for travel agencies include understanding the evolving landscape of the travel industry and producing innovative solutions to stay competitive and meet changing customer expectations. In addition, travel agencies need to understand emerging technologies and technological intelligence and analyze evolving consumer behavior and global events to shape the future advantages of the industry through digital transformation.
6. Conclusions
As travel agencies leverage collaborative digital technologies, they can enhance the features, accessibility, and overall appeal of their products, contributing to a competitive edge in the tourism supply chain; the research suggests that travel agencies with advanced technological capabilities are better equipped to increase tourism product advantage and build resilient tourism supply chains; these capabilities may include real-time data analytics, predictive modeling, and adaptive technologies that enable businesses to respond swiftly to disruptions and uncertainties in the tourism environment.
The study highlights that digital technologies facilitate virtual integration, allowing for seamless communication and collaboration across different supply chain nodes. This integration enhances coordination, efficiency, and adaptability when facing challenges. Moreover, the findings underscore the significance of customer service capabilities in the tourism industry. Businesses that provide excellent customer service through digital channels are more likely to attract and retain customers. The interplay between digital technologies collaboration, technological capabilities, virtual integration, and customer service ability creates a holistic impact on the overall competitiveness of travel agencies.
6.1 Limitations and future recommendations
This study identifies seven factors from theory and existing literature that are likely to change with context and the digital transformation of travel agencies. For example, when the context of the study is to understand the impact of technology transfer due to globalization on supply chain operations, the adoption rate of digital technologies collaboration may be an essential factor to be included in the list of factors. Simultaneously, future research can discuss the stage performance of digital transformation from different theories, including punctuated equilibrium and digital government evolution models that can provide different insights. Likewise, we discussed the moderating effects of technology interoperability, which significantly affects tourism supply chain resilience; future research can increase research on Introducing compromise as a critical construct for digital innovation.
Figures
Figure 1
Research model
Items in survey
| Constructs with references | |
|---|---|
| Digital Technologies Collaboration (DTC) was adapted from Tsou and Chen (2023) | |
| DTC1 | Our company can process transaction information in a short time through digital technologies |
| DTC2 | Our company’s information system encrypts customers' personal information using a decentralized ledger |
| DTC3 | The digital technologies of our company’s infrastructure align with market standards and practices |
| DTC4 | Our management team will develop a digital transformation strategy based on digital technology choices |
| Technological Capabilities (TC) was adopted from Ku (2022a, b) | |
| TC1 | IOS helps us gain market knowledge about our customers, suppliers, and competitors |
| TC2 | Market knowledge embedded in our IOS database |
| TC3 | Our data using the iOS solution is correct |
| TC4 | We update systems such as intranet and electronic bulletin boards based on IOS to facilitate sharing information and knowledge |
| TC5 | We invested in IOS to capture and manage real-time customer information and feedback |
| TC6 | We use the IOS of the supply chain system |
| TC7 | Compared to our competitors, we use higher-quality iOS resources |
| TC8 | We use IOS to fully capture individual customer history, purchasing activity, and transaction issues |
| TC9 | IOS assists our company in being able to differentiate the profitability of our clients |
| Virtual integration (VI) adopted by Bryan Jean et al. (2020) | |
| VI1 | We handle order processing and invoicing electronically with our global suppliers |
| VI2 | We electronically monitor the quality of our products with our global supply partners |
| VI3 | We coordinate inventory levels electronically with our global suppliers |
| VI4 | Relying on IOS under the supply chain, we work with global suppliers to forecast and plan tourism products |
| VI5 | Demand forecasting and planning for tourism market development with global suppliers are always available in our information system |
| Customer Service Capabilities (CSC) was adapted from Sok et al. (2018) | |
| CSC1 | We will deal with tourists' problems promptly so that tourists are satisfied with our services |
| CSC2 | We can provide timely solutions to tourists' current travel problems |
| CSC3 | We reliably resolve issues related to guest service received from iOS |
| CSC4 | We will listen carefully to tourists' opinions and take appropriate actions to address their concerns about tourism services |
| CSC5 | I will pay attention to tourists' questions about their experiences with tourism services and then use information systems to respond appropriately |
| CSC6 | My abilities enable me to assist clients with travel service delivery questions better |
| Tourism Products Advantage (TPA) adapted from Cui and Wu (2017) | |
| TPA1 | The quality of tourism products offered by our company is superior to that of our competitors |
| TPA2 | Our company provides tourism products that satisfy tourists better than our competitors |
| TPA3 | Tourism products bring unique benefits to tourists |
| TPA4 | The tourism products we offer our tourists outperform our competitors |
| Tourism Supply Chain Resilience (TSCR) was adapted from was adapted from Ghaderi et al. (2023) | |
| TSCR1 | My company can quickly recover its activities during a service disruption |
| TSCR2 | My company can adapt to respond positively to operational disruptions |
| TSCR3 | Our company has the appropriate information equipment to respond quickly to environmental disturbances |
| TSCR4 | Our company is well-equipped to respond to temporary financial needs |
| TSCR5 | Our company has the best capabilities to respond positively to the consequences of market changes |
| Technology Interoperability (TIO) was adapted from Hsu et al. (2019) | |
| TIO1 | Enabled two distributed processes to share selective data |
| TIO2 | Enhanced coordination among distributed process operations |
| TIO3 | Separated communication models of clients from those of servers |
| TIO4 | Made explicit the common properties of interfaces and reduced the mapping task |
Sample description (384)
| Characteristics | Number | Percentage (%) | |
|---|---|---|---|
| Classified of travel agent | Wholesaler | 93 | 24.2 |
| Travel agencies-direct sales | 291 | 75.8 | |
| Employees | Under 10(and = 10) employees | 35 | 9.0 |
| 11–50 | 99 | 36.2 | |
| 51–100 | 132 | 25.7 | |
| Over 101 employees | 118 | 30.1 | |
| Total assets (NT$) | Less than 30 million | 162 | 42.2 |
| 30 million – 100 million | 184 | 47.9 | |
| Over 100 million | 38 | 9.9 | |
| Experience of adapting | Under 10 (and = 10) years | 175 | 45.6 |
| IOS (years) | Above 11 years | 209 | 54.4 |
Source(s): The author’s work, derived from the statistical analysis of this study (SPSS)
Descriptive statistics of constructs
| Constructs | Cronbach’s alpha | rho_A | CR | AVE |
|---|---|---|---|---|
| DTC | 0.917 | 0.918 | 0.941 | 0.801 |
| TC | 0.938 | 0.939 | 0.948 | 0.669 |
| VI | 0.907 | 0.908 | 0.931 | 0.729 |
| CSC | 0.945 | 0.946 | 0.956 | 0.786 |
| TPA | 0.957 | 0.958 | 0.969 | 0.886 |
| TSCR | 0.912 | 0.912 | 0.934 | 0.739 |
| TIO | 0.896 | 0.901 | 0.928 | 0.762 |
Note(s): CR: Composite Reliability; AVE: Average Variance Extracted
DTC stands for Digital Technologies Collaboration; TC for Technological Capabilities; VI for Virtual Integration; CSC for Customer Service Capabilities; TPA for Tourism Products Advantage; TSCR for Tourism Supply Chain Resilience; and TIO for Technology Interoperability
Source(s): The author’s work, derived from the statistical analysis of this study (SmartPLS, Version 4)
Cross-loadings analysis
| Item | DTC | TC | VI | CSC | TPA | TSCR | TIO |
|---|---|---|---|---|---|---|---|
| DTC1 | 0.879 | 0.546 | 0.488 | 0.536 | 0.289 | 0.498 | 0.400 |
| DTC2 | 0.919 | 0.544 | 0.472 | 0.613 | 0.360 | 0.451 | 0.426 |
| DTC3 | 0.906 | 0.503 | 0.423 | 0.617 | 0.424 | 0.424 | 0.419 |
| DTC4 | 0.875 | 0.497 | 0.450 | 0.607 | 0.404 | 0.426 | 0.416 |
| TC1 | 0.521 | 0.833 | 0.517 | 0.477 | 0.298 | 0.569 | 0.383 |
| TC2 | 0.492 | 0.846 | 0.523 | 0.446 | 0.263 | 0.582 | 0.354 |
| TC3 | 0.468 | 0.827 | 0.478 | 0.449 | 0.261 | 0.465 | 0.313 |
| TC4 | 0.436 | 0.815 | 0.501 | 0.444 | 0.272 | 0.473 | 0.350 |
| TC5 | 0.525 | 0.808 | 0.494 | 0.472 | 0.292 | 0.551 | 0.355 |
| TC6 | 0.439 | 0.801 | 0.502 | 0.438 | 0.252 | 0.533 | 0.308 |
| TC7 | 0.460 | 0.814 | 0.491 | 0.463 | 0.311 | 0.532 | 0.353 |
| TC8 | 0.477 | 0.822 | 0.559 | 0.548 | 0.445 | 0.519 | 0.396 |
| TC9 | 0.475 | 0.793 | 0.506 | 0.488 | 0.336 | 0.527 | 0.379 |
| VI1 | 0.421 | 0.492 | 0.840 | 0.466 | 0.269 | 0.634 | 0.522 |
| VI2 | 0.328 | 0.526 | 0.848 | 0.380 | 0.249 | 0.635 | 0.437 |
| VI3 | 0.468 | 0.547 | 0.863 | 0.490 | 0.337 | 0.632 | 0.471 |
| VI4 | 0.493 | 0.545 | 0.851 | 0.566 | 0.395 | 0.606 | 0.466 |
| VI5 | 0.465 | 0.542 | 0.867 | 0.512 | 0.343 | 0.612 | 0.459 |
| CSC1 | 0.559 | 0.479 | 0.466 | 0.872 | 0.558 | 0.400 | 0.463 |
| CSC2 | 0.551 | 0.496 | 0.485 | 0.893 | 0.546 | 0.430 | 0.478 |
| CSC3 | 0.610 | 0.493 | 0.539 | 0.882 | 0.570 | 0.451 | 0.539 |
| CSC4 | 0.595 | 0.541 | 0.492 | 0.885 | 0.562 | 0.499 | 0.471 |
| CSC5 | 0.618 | 0.529 | 0.542 | 0.890 | 0.536 | 0.529 | 0.500 |
| CSC6 | 0.593 | 0.520 | 0.491 | 0.895 | 0.523 | 0.495 | 0.470 |
| TPA 1 | 0.354 | 0.309 | 0.316 | 0.571 | 0.940 | 0.278 | 0.400 |
| TPA 2 | 0.378 | 0.367 | 0.354 | 0.566 | 0.935 | 0.273 | 0.464 |
| TPA 3 | 0.383 | 0.356 | 0.370 | 0.588 | 0.956 | 0.287 | 0.435 |
| TPA 4 | 0.437 | 0.375 | 0.370 | 0.607 | 0.935 | 0.309 | 0.471 |
| TSCR1 | 0.365 | 0.515 | 0.627 | 0.415 | 0.209 | 0.847 | 0.421 |
| TSCR2 | 0.425 | 0.546 | 0.657 | 0.425 | 0.216 | 0.890 | 0.425 |
| TSCR3 | 0.430 | 0.553 | 0.610 | 0.463 | 0.283 | 0.847 | 0.434 |
| TSCR4 | 0.486 | 0.569 | 0.610 | 0.491 | 0.317 | 0.844 | 0.463 |
| TSCR5 | 0.452 | 0.593 | 0.632 | 0.482 | 0.289 | 0.870 | 0.442 |
| TIO1 | 0.405 | 0.359 | 0.459 | 0.452 | 0.362 | 0.426 | 0.852 |
| TIO2 | 0.405 | 0.309 | 0.463 | 0.486 | 0.389 | 0.387 | 0.893 |
| TIO3 | 0.402 | 0.435 | 0.516 | 0.493 | 0.444 | 0.500 | 0.886 |
| TIO4 | 0.410 | 0.402 | 0.480 | 0.485 | 0.437 | 0.449 | 0.859 |
Note(s): DTC stands for Digital Technologies Collaboration; TC for Technological Capabilities; VI for Virtual Integration; CSC for Customer Service Capabilities; TPA for Tourism Products Advantage; TSCR for Tourism Supply Chain Resilience; TIO for Technology Interoperability
Source(s): The author’s work, derived from the statistical analysis of this study (SmartPLS, Version 4)
Fornell–Larcker criterion and heterotrait-monotrait at ratio
| Item | Constructs | DTC | CSC | TPA | TC | TIO | TSCR | VI |
|---|---|---|---|---|---|---|---|---|
| Fornell-Larcker criterion | DTC | 0.895 | ||||||
| CSC | 0.664 | 0.886 | ||||||
| TPA | 0.413 | 0.620 | 0.841 | |||||
| TC | 0.584 | 0.576 | 0.374 | 0.818 | ||||
| TIO | 0.464 | 0.550 | 0.471 | 0.435 | 0.873 | |||
| TSCR | 0.502 | 0.529 | 0.305 | 0.646 | 0.508 | 0.860 | ||
| VI | 0.512 | 0.568 | 0.375 | 0.622 | 0.551 | 0.730 | 0.854 | |
| Heterotrait-Monotrait at ratio | DTC | |||||||
| CSC | 0.711 | |||||||
| TPA | 0.439 | 0.651 | ||||||
| TC | 0.629 | 0.609 | 0.391 | |||||
| TIO | 0.512 | 0.596 | 0.504 | 0.468 | ||||
| TSCR | 0.550 | 0.569 | 0.327 | 0.698 | 0.559 | |||
| VI | 0.559 | 0.610 | 0.400 | 0.672 | 0.610 | 0.803 |
Note(s): DTC stands for Digital Technologies Collaboration; TC for Technological Capabilities; VI for Virtual Integration; CSC for Customer Service Capabilities; TPA for Tourism Products Advantage; TSCR for Tourism Supply Chain Resilience; TIO for Technology Interoperability
Source(s): The author’s work, derived from the statistical analysis of this study (SmartPLS, Version 4)
Measure the goodness of fit (GOF): R square
| Items | R square | R square adjusted |
|---|---|---|
| Virtual Integration | 0.421 | 0.417 |
| Customer Service Capabilities | 0.494 | 0.492 |
| Tourism Products Advantage | 0.414 | 0.406 |
| Tourism Supply Chain Resilience | 0.573 | 0.567 |
Source(s): The author’s work, derived from the statistical analysis of this study (SmartPLS, Version 4)
f Square the effect size (f2)
| Item | DTC | CSC | PA | TC | TIO | TSRC | VI |
|---|---|---|---|---|---|---|---|
| DTC | 0.302 | 0.058 | |||||
| CSC | 0.250 | 0.043 | |||||
| PA | 0.007 | ||||||
| TC | 0.107 | 0.274 | |||||
| TIO | 0.035 | 0.023 | |||||
| TSRC | |||||||
| VI | 0.000 | 0.316 |
Note(s): DTC stands for Digital Technologies Collaboration; TC for Technological Capabilities; VI for Virtual Integration; CSC for Customer Service Capabilities; TPA for Tourism Products Advantage; TSCR for Tourism Supply Chain Resilience; TIO for Technology Interoperability
Source(s): The author’s work, derived from the statistical analysis of this study (SmartPLS, Version 4)
Model fit summary
| Items | Saturated model | Estimated model |
|---|---|---|
| SRMR | 0.046 | 0.077 |
| d_ULS | 1.517 | 4.120 |
| d_G | 0.849 | 0.923 |
| Chi-square | 1899.474 | 1940.424 |
| NFI | 0.859 | 0.856 |
Source(s): The author’s own work, derived from the statistical analysis of this study (SmartPLS, Version 4)
Results of Hypothesis testing
| Hypotheses | t value | Results |
|---|---|---|
| (H1) Digital Technologies Collaboration → Virtual Integration | 3.581* | Supported |
| (H2) Digital Technologies Collaboration → Customer Service Capabilities | 10.596*** | Supported |
| (H3) Technological Capabilities → Virtual Integration | 8.010 ** | Supported |
| (H4) Technological Capabilities → Customer Service Capabilities | 5.853** | Supported |
| (H5) Virtual Integration → Tourism Products Advantage | 7.473** | Supported |
| (H6) Virtual Integration → Tourism Supply Chain Resilience | 0.126 | Not Supported |
| (H7) Customer Service Capabilities → Tourism Products Advantage | 10.039*** | Supported |
| (H8) Customer Service Capabilities → Tourism Supply Chain Resilience | 3.272 * | Supported |
| (H9) Tourism Products Advantage → Tourism Supply Chain Resilience | 1.607 | Not Supported |
| (H10) Technology Interoperability x Virtual Integration → Tourism Products Advantage | 1.882 | Not Supported |
| (H11) Technology Interoperability x Virtual Integration → Tourism Supply Chain Resilience | 2.510* | Supported |
| (H12) Technology Interoperability x Customer Service Capabilities → Tourism Products Advantage | 1.569 | Not Supported |
| (H13) Technology Interoperability x Customer Service Capabilities → Tourism Supply Chain Resilience | 2.457* | Supported |
Note(s): *p < 0.05, **p < 0.01, ***p < 0.001
Source(s): The author’s work, derived from the statistical analysis of this study (SmartPLS, Version 4)
References
Abadie, A., Roux, M., Chowdhury, S. and Dey, P. (2023), “Interlinking organizational resources, AI adoption and omnichannel integration quality in Ghana's healthcare supply chain”, Journal of Business Research, Vol. 162, 113866, doi: 10.1016/j.jbusres.2023.113866.
Abdelaziz, M.A.A., Wu, J.N., Yuan, C.W. and Ghonim, M.A. (2023), “Unlocking supply chain product and process innovation through the development of supply chain learning capabilities under technological turbulence: evidence from Egyptian SMEs”, Journal of Manufacturing Technology Management, Vol. 34 No. 5, pp. 793-819, doi: 10.1108/jmtm-11-2022-0395.
Adhiatma, A., Fachrunnisa, O. and Nurhidayati and Rahayu, T. (2023), “Creating digital ecosystem for small and medium enterprises: the role of dynamic capability, agile leadership and change readiness”, Journal of Science and Technology Policy Management, Vol. 14 No. 5, pp. 941-959, doi: 10.1108/jstpm-12-2020-0171.
Alam, K., Erdiaw-Kwasie, M.O., Shahiduzzaman, M. and Ryan, B. (2018), “Assessing regional digital competence: digital futures and strategic planning implications”, Journal of Rural Studies, Vol. 60, pp. 60-69, doi: 10.1016/j.jrurstud.2018.02.009.
Ali, A., Ali, S.M. and Xue, X.F. (2022a), “Motivational approach to team service performance: role of participative leadership and team-inclusive climate”, Journal of Hospitality and Tourism Management, Vol. 52, pp. 75-85, doi: 10.1016/j.jhtm.2022.06.005.
Ali, O., Shrestha, A., Ghasemaghaei, M. and Beydoun, G. (2022b), “Assessment of complexity in cloud computing adoption: a case study of local governments in Australia”, Information Systems Frontiers, Vol. 24 No. 2, pp. 595-617, doi: 10.1007/s10796-021-10108-w.
Almunawar, M.N. and Anshari, M. (2022), “Digital enabler and value integration: revealing the expansion engine of digital marketplace”, Technology Analysis and Strategic Management, Vol. 34 No. 7, pp. 847-857, doi: 10.1080/09537325.2021.1926967.
Asamoah, D., Agyei-Owusu, B., Andoh-Baidoo, F.K. and Ayaburi, E. (2021), “Inter-organizational systems use and supply chain performance: mediating role of supply chain management capabilities”, International Journal of Information Management, Vol. 58, 102195, doi: 10.1016/j.ijinfomgt.2020.102195.
Assaf, A.G. and Tsionas, M. (2021), “Testing for collinearity using Bayesian analysis”, Journal of Hospitality & Tourism Research, Vol. 45 No. 6, pp. 1131-1141, doi: 10.1177/1096348021990841.
Bani-Melhem, S., Shamsudin, F.M., Abukhait, R.M. and Quratulain, S. (2021), “Paranoid personality and frontline employee's proactive work behaviours: a moderated mediation model of empathetic leadership and perceived psychological safety”, Journal of Service Theory and Practice, Vol. 31 No. 1, pp. 113-135, doi: 10.1108/jstp-05-2020-0104.
Baumgartner, H., Weijters, B. and Pieters, R. (2021), “The biasing effect of common method variance: some clarifications”, Journal of the Academy of Marketing Science, Vol. 49 No. 2, pp. 221-235, doi: 10.1007/s11747-020-00766-8.
Becker, J.M., Cheah, J.H., Gholamzade, R., Ringle, C.M. and Sarstedt, M. (2023), “PLS-SEM's most wanted guidance”, International Journal of Contemporary Hospitality Management, Vol. 35 No. 1, pp. 321-346, doi: 10.1108/ijchm-04-2022-0474.
Bhattacharya, S., Agnihotri, A., Yannopoulou, N. and Sakka, G. (2022), “Technological knowledge and internationalization: evidence from India”, International Marketing Review, Vol. 39 No. 3, pp. 509-528, doi: 10.1108/imr-02-2021-0082.
Bokolo, A.J. (2022), “Exploring interoperability of distributed Ledger and Decentralized Technology adoption in virtual enterprises”, Information Systems and E-Business Management, Vol. 20 No. 4, pp. 685-718, doi: 10.1007/s10257-022-00561-8.
Bommu, S., Kumar, M.A., Babburu, K., Srikanth, N., Thalluri, L.N., Ganesh, G.V., Gopalan, A., Mallapati, P.K., Guha, K., Mohammad, H.R. and Kiran, S.S. (2023), “Smart city IoT system network level routing analysis and blockchain security based implementation”, Journal of Electrical Engineering and Technology, Vol. 18 No. 2, pp. 1351-1368, doi: 10.1007/s42835-022-01239-4.
Boudreau, M.C., Gefen, D. and Straub, D.W. (2001), “Validation in information systems research: a state-of-the-art assessment”, MIS Quarterly, Vol. 25 No. 1, pp. 1-16, doi: 10.2307/3250956.
Buhalis, D., Harwood, T., Bogicevic, V., Viglia, G., Beldona, S. and Hofacker, C. (2019), “Technological disruptions in services: lessons from tourism and hospitality”, Journal of Service Management, Vol. 30 No. 4, pp. 484-506, doi: 10.1108/josm-12-2018-0398.
Calvaresi, D., Ibrahim, A., Calbimonte, J.P., Fragniere, E., Schegg, R. and Schumacher, M.I. (2023), “Leveraging inter-tourists interactions via chatbots to bridge academia, tourism industries and future societies”, Journal of Tourism Futures, Vol. 9 No. 3, pp. 311-337, doi: 10.1108/jtf-01-2021-0009.
Chaturvedi, A. and Binkley, D. (2021), “Web service slicing: intra and inter-operational analysis to test changes”, IEEE Transactions on Services Computing, Vol. 14 No. 3, pp. 930-943, doi: 10.1109/tsc.2018.2821157.
Chen, P.K. and Huang, X. (2023), “Enhancing supply chain resilience and realizing green sustainable development through the virtual environment of the metaverse”, Sustainable Development, Vol. 32 No. 1, pp. 438-454, doi: 10.1002/sd.2663.
Chen, S., Yang, R.Z., Ma, L., Xu, M.F. and Zhou, Y.F. (2023), “Hybrid optimization-based cutting simulation for soft objects”, Computer-Aided Design, Vol. 163, 103561, doi: 10.1016/j.cad.2023.103561.
Cheng, X.S., Xue, T., Yang, B. and Ma, B.J. (2023), “A digital transformation approach in hospitality and tourism research”, International Journal of Contemporary Hospitality Management, Vol. 35 No. 8, pp. 2944-2967, doi: 10.1108/ijchm-06-2022-0679.
Chin, W.W., Thatcher, J.B. and Wright, R.T. (2012), “Assessing common method bias: problems with the ulmc technique”, MIS Quarterly, Vol. 36 No. 3, pp. 1003-1019, doi: 10.2307/41703491.
Cui, A.S. and Wu, F. (2017), “The impact of customer involvement on new product development: contingent and substitutive effects”, Journal of Product Innovation Management, Vol. 34 No. 1, pp. 60-80, doi: 10.1111/jpim.12326.
Dahl, A.J., Peltier, J.W. and Swan, E.L. (2023), “Anticipatory value-in-use in early-stage digital health service transformations: how consumers assess value propositions before and after abrupt, exogenous shocks”, Journal of Business Research, Vol. 163, 113910, doi: 10.1016/j.jbusres.2023.113910.
Dewantara, M.H., Gardiner, S. and Jin, X. (2022), “Travel vlog ecosystem in tourism digital marketing evolution: a narrative literature review”, Current Issues in Tourism, Vol. 26 No. 19, pp. 3125-3139, doi: 10.1080/13683500.2022.2136568.
Elia, G., Margherita, A. and Passiante, G. (2020), “Digital entrepreneurship ecosystem: how digital technologies and collective intelligence are reshaping the entrepreneurial process”, Technological Forecasting and Social Change, Vol. 150, 119791, doi: 10.1016/j.techfore.2019.119791.
Eslami, M.H., Pellegrini, L., Lazzarotti, V., Puliga, G., Manzini, R. and Boer, H. (2023), “The effect of knowledge collaboration on innovation performance: the moderating role of digital technologies”, International Journal of Technology Management, Vol. 91 Nos 3-4, pp. 162-189, doi: 10.1504/ijtm.2022.10050401.
Ghaderi, Z., Shakori, H., Bagheri, F., Hall, C.M., Rather, R.A. and Moaven, Z. (2023), “Green supply chain management, environmental costs and supply chain performance in the hotel industry: the mediating role of supply chain agility and resilience”, Current Issues in Tourism, pp. 1-17, doi: 10.1080/13683500.2023.2223911.
Goodhue, D.L., Lewis, W. and Thompson, R. (2017), “A multicollinearity and measurement error statistical blind spot: correcting for excessive false positives in regression and PLS”, MIS Quarterly, Vol. 41 No. 3, pp. 667-684, doi: 10.25300/misq/2017/41.3.01.
Guide, V.D.R. and Ketokivi, M. (2015), "Notes from the Editors: redefining some methodological criteria for the journal”, Journal of Operations Management, Vol. 37 No. 1, pp. V-VIII, doi: 10.1016/s0272-6963(15)00056-x.
Gutierriz, I., Ferreira, J.J. and Fernandes, P.O. (2023), “Digital transformation and the new combinations in tourism: a systematic literature review”, Tourism and Hospitality Research, doi: 10.1177/14673584231198414.
Hadjielias, E., Christofi, M., Christou, P. and Drotarova, M.H. (2022), “Digitalization, agility, agility, and customer value in tourism”, Technological Forecasting and Social Change, Vol. 175, 121334, doi: 10.1016/j.techfore.2021.121334.
Hair, J.F., Risher, J.J., Sarstedt, M. and Ringle, C.M. (2019), “When to use and how to report the results of PLS-SEM”, European Business Review, Vol. 31 No. 1, pp. 2-24, doi: 10.1108/ebr-11-2018-0203.
Halpern, N., Mwesiumo, D., Budd, T., Suau-Sanchez, P. and Bråthen, S. (2021), “Segmentation of passenger preferences for using digital technologies at airports in Norway”, Journal of Air Transport Management, Vol. 91, 102005, doi: 10.1016/j.jairtraman.2020.102005.
Hamann-Lohmer, J., Bendig, M. and Lasch, R. (2023), “Investigating the impact of digital transformation on relationship and collaboration dynamics in supply chains and manufacturing networks-A multi-case study”, International Journal of Production Economics, Vol. 262, 108932, doi: 10.1016/j.ijpe.2023.108932.
Henseler, J., Ringle, C.M. and Sarstedt, M. (2015), “A new criterion for assessing discriminant validity in variance-based structural equation modeling”, Journal of the Academy of Marketing Science, Vol. 43 No. 1, pp. 115-135, doi: 10.1007/s11747-014-0403-8.
Hsu, H.Y., Liu, F.H., Tsou, H.T. and Chen, L.J. (2019), “Openness of technology adoption, top management support and service innovation: a social innovation perspective”, Journal of Business and Industrial Marketing, Vol. 34 No. 3, pp. 575-590, doi: 10.1108/jbim-03-2017-0068.
Huang, H.Y., Ku, E.C.S. and Chen, C.D. (2022), “Cloud infrastructure enhancing product competitive advantage of tourism SMEs on online consumption values of tourists”, Business Process Management Journal, Vol. 28 No. 4, pp. 1146-1163, doi: 10.1108/bpmj-05-2022-0246.
Ibarra-Cisneros, M.A. and Hernandez-Perlines, F. (2020), “Entrepreneurial orientation, absorptive capacity and business performance in SMEs”, Measuring Business Excellence, Vol. 24 No. 4, pp. 417-429, doi: 10.1108/mbe-09-2019-0091.
Irwin, K., Gilstrap, C., Drnevich, P. and Sunny, M. (2022), “The acquisition of capabilities: how firms use dynamic and ordinary capabilities to manage uncertainty”, Journal of Management and Organization, Vol. 28 No. 3, pp. 564-586, doi: 10.1017/jmo.2022.23.
Islam, M.M., Nooruddin, S., Karray, F. and Muhammad, G. (2023), “Internet of things: device capabilities, architectures, protocols, and smart applications in healthcare domain”, IEEE Internet of Things Journal, Vol. 10 No. 4, pp. 3611-3641, doi: 10.1109/jiot.2022.3228795.
Ivanova, R., Skrobotova, O., Polyakova, I., Karaseva, G. and Strelnikova, M. (2022), “Study on digitalisation of the tourism industry in the regions of the Russian federation”, International Journal of Computer Science and Network Security, Vol. 22 No. 8, pp. 385-391.
Jean, R.J.B., Kim, D., Lien, Y.C. and Ro, S. (2020), “The moderating effect of virtual integration on intergenerational governance and relationship performance in international customer-supplier relationships”, International Marketing Review, Vol. 37 No. 3, pp. 579-592, doi: 10.1108/imr-03-2019-0102.
Jnr, B.A. (2023), “Enabling interoperable distributed ledger technology with legacy platforms for enterprise digitalization”, Enterprise Information Systems, Vol. 18 No. 1, doi: 10.1080/17517575.2023.2255979.
Kim, M., Lee, S.K. and Roehl, W.S. (2016), “The effect of idiosyncratic price movements on short- and long-run performance of hotels”, International Journal of Hospitality Management, Vol. 56, pp. 78-86, doi: 10.1016/j.ijhm.2016.04.006.
Ko, C.C., Liu, C.Y. and Liu, C.M. (2023), “Social sustainable development green brand value in education impact strategic alliance investment decision evaluation”, Frontiers in Energy Research, Vol. 10, doi: 10.3389/fenrg.2022.1050603.
Kock, F., Berbekova, A. and Assaf, A.G. (2021), “Understanding and managing the threat of common method bias: detection, prevention and control”, Tourism Management, Vol. 86, 104330, doi: 10.1016/j.tourman.2021.104330.
Kraus, S., Durst, S., Ferreira, J.J., Veiga, P., Kailer, N. and Weinmann, A. (2022), “Digital transformation in business and management research: an overview of the current status quo”, International Journal of Information Management, Vol. 63, 102466, doi: 10.1016/j.ijinfomgt.2021.102466.
Ku, E.C.S. (2022a), “Developing business process agility: evidence from inter-organizational information systems of airlines and travel agencies”, Journal of Air Transport Management, Vol. 103, 102247, doi: 10.1016/j.jairtraman.2022.102247.
Ku, E.C.S. (2022b), “Technological capabilities that enhance tourism supply chain agility: role of E-marketplace systems”, Asia Pacific Journal of Tourism Research, Vol. 27 No. 1, pp. 86-102, doi: 10.1080/10941665.2021.1998162.
Ku, E.C.S. (2023a), “Anthropomorphic chatbots as a catalyst for marketing brand experience: evidence from online travel agencies”, Current Issues in Tourism, pp. 1-20, doi: 10.1080/13683500.2023.2289592.
Ku, E.C.S. (2023b), “Role of inter-organizational systems in driving tourism businesses forward in the post-COVID-19 new normal”, Journal of Business and Industrial Marketing, Vol. 38 No. 11, pp. 2471-2484, doi: 10.1108/jbim-06-2022-0255.
Kulkov, I., Tsvetkova, A. and Ivanova-Gongne, M. (2023), “Identifying institutional barriers when implementing new technologies in the healthcare industry”, European Journal of Innovation Management, Vol. 26 No. 4, pp. 909-932, doi: 10.1108/ejim-02-2021-0093.
Kumar, A., Shankar, A., Tiwari, A.K. and Hong, H.J. (2023), “Understanding dark side of online community engagement: an innovation resistance theory perspective”, Information Systems and E-Business Management, doi: 10.1007/s10257-023-00633-3.
Lee, C.H., Wu, C.H. and Jong, D. (2022), “Understanding the impact of competitive advantage and core competency on regional tourism revitalization: empirical evidence in Taiwan”, Frontiers in Psychology, Vol. 13, 922211, doi: 10.3389/fpsyg.2022.922211.
Leung, R. and Loo, P.T. (2022), “Co-creating interactive dining experiences via interconnected and interoperable smart technology”, Asian Journal of Technology Innovation, Vol. 30 No. 1, pp. 45-67, doi: 10.1080/19761597.2020.1822748.
Li, J., Saide, S., Ismail, M.N. and Indrajit, R.E. (2022), “Exploring IT/IS proactive and knowledge transfer on enterprise digital business transformation (EDBT): a technology-knowledge perspective”, Journal of Enterprise Information Management, Vol. 35 No. 2, pp. 597-616, doi: 10.1108/jeim-08-2020-0344.
Liu, W., Choi, T.M., Niu, X.Q., Zhang, M. and Fan, W.G. (2022), “Determinants of business resilience in the restaurant industry during the COVID-19 pandemic: a textual analytics study on an O2O platform case”, IEEE Transactions on Engineering Management, pp. 1-14, doi: 10.1109/TEM.2022.3187986.
Lo, Y.S., Yang, C.Y., Chien, H.F., Chang, S.S., Lu, C.Y. and Chen, R.J. (2019), “Blockchain-Enabled iWellChain framework integration with the national medical referral system: development and usability study”, Journal of Medical Internet Research, Vol. 21 No. 12, p. e13563, doi: 10.2196/13563, available at: https://preprints.jmir.org/preprint/13563
Mandal, S. and Dubey, R.K. (2020), “Role of tourismITadoption and risk management orientation on tourism agility and resilience: impact on sustainable tourism supply chain performance”, International Journal of Tourism Research, Vol. 22 No. 6, pp. 800-813, doi: 10.1002/jtr.2381.
Moliner-Tena, M.A., Monferrer-Tirado, D., Estrada-Guillen, M. and Vidal-Meliá, L. (2023), “Memorable customer experiences and autobiographical memories: from service experience to word of mouth”, Journal of Retailing and Consumer Services, Vol. 72, 103290, doi: 10.1016/j.jretconser.2023.103290.
Moschko, L. and Blazevic, V. (2023), "Managing digitization of industrial incumbents through innovation-oriented leadership”, Industrial Marketing Management, Vol. 113, pp. 232-242, doi: 10.1016/j.indmarman.2023.06.009.
Pierdicca, R., Paolanti, M. and Frontoni, E. (2019), “eTourism: ICT and its role for tourism management”, Journal of Hospitality and Tourism Technology, Vol. 10 No. 1, pp. 90-106, doi: 10.1108/jhtt-07-2017-0043.
Priporas, C.V. and Vellore-Nagarajan, D. (2022), “New-normal market entry mode for pharmaceuticals: an Internet of things (IoT) market entry framework stemming from COVID-19”, International Marketing Review, Vol. 40 No. 5, pp. 906-935, doi: 10.1108/imr-12-2021-0363.
Radomir, L. and Moisescu, O.I. (2020), “Discriminant validity of the customer-based corporate reputation scale: some causes for concern”, Journal of Product and Brand Management, Vol. 29 No. 4, pp. 457-469, doi: 10.1108/jpbm-11-2018-2115.
Rai, A., Tang, X.L., Yin, Z.T. and Du, S. (2022), “Gaining customer loyalty with tracking information quality in B2B logistics”, Journal of Management Information Systems, Vol. 39 No. 2, pp. 307-335, doi: 10.1080/07421222.2022.2063552.
Ramsey, P.P. and Ramsey, P.H. (2006), “Robust testing of level changes in interrupted time-series analysis”, Journal of Statistical Computation and Simulation, Vol. 76 No. 10, pp. 913-923, doi: 10.1080/10629360500109069.
Rashed, A. and Mutis, I. (2023), “Trends of integrated project delivery implementations viewed from an emerging innovation framework”, Engineering Construction and Architectural Management, Vol. 30 No. 3, pp. 989-1014, doi: 10.1108/ecam-06-2021-0516.
Raza, S.A., Khan, K.A. and Qamar, B. (2024), “Understanding the influence of environmental triggers on tourists' pro-environmental behaviors in the Pakistan's tourism industry”, Journal of Tourism Futures, Vol. 10 No. 1, pp. 38-67, doi: 10.1108/JTF-12-2021-0269.
Romero, I., Fernández-Serrano, J. and Cáceres-Carrasco, R. (2023), “Technological capabilities and global value chains: insights from Spanish SMEs in the hotel industry”, Journal of Hospitality and Tourism Insights, Vol. 7 No. 1, pp. 583-601, doi: 10.1108/JHTI-08-2022-0362.
Runck, B.C., Joglekar, A., Silverstein, K.A.T., Chan-Kang, C., Pardey, P.G. and Wilgenbusch, J.C. (2022), “Digital agriculture platforms: driving data-enabled agricultural innovation in a world fraught with privacy and security concerns”, Agronomy Journal, Vol. 114 No. 5, pp. 2635-2643, doi: 10.1002/agj2.20873.
Salvoldi, R. and Brock, D.M. (2023), “International alliance structure and effectiveness: evidence from law firms”, Journal of Management and Organization, pp. 1-27, doi: 10.1017/jmo.2023.14.
Sánchez, N.H. and Oskam, J. (2022), “A ‘new tourism cycle’ on the Canary Islands: scenarios for digital transformation and resilience of small and medium tourism enterprises”, Journal of Tourism Futures, doi: 10.1108/JTF-04-2022-0132.
Sharma, K., Jain, M. and Dhir, S. (2022), “Analysing the impact of artificial intelligence on the competitiveness of tourism firms: a modified total interpretive structural modeling (m-TISM) approach”, International Journal of Emerging Markets, Vol. 17 No. 4, pp. 1067-1084, doi: 10.1108/ijoem-05-2021-0810.
Sheng, M.L. and Saide, S. (2021), “Supply chain survivability in crisis times through a viable system perspective: big data, knowledge ambidexterity, and the mediating role of virtual enterprise”, Journal of Business Research Vol. 137, pp. 567-578, doi: 10.1016/j.jbusres.2021.08.041.
Singh, R., Mindel, V. and Mathiassen, L. (2017), “IT-Enabled revenue cycle transformation in resource-constrained hospitals: a collaborative digital options inquiry”, Journal of Management Information Systems, Vol. 34 No. 3, pp. 695-726, doi: 10.1080/07421222.2017.1373005.
Sok, K.M., Sok, P., Snell, L. and Qiu, P.P. (2018), “Does transformational leadership always matter in frontline service roles?”, Journal of Service Theory and Practice, Vol. 28 No. 6, pp. 733-751, doi: 10.1108/jstp-03-2018-0053.
Solmaz, G., Wu, F.J., Cinllo, F., Kovacs, E., Santana, J.R., Sánchez, L., Sotres, P. and Munoz, L. (2019), “Toward understanding crowd mobility in smart cities through the Internet of things”, IEEE Communications Magazine, Vol. 57 No. 4, pp. 40-46, doi: 10.1109/mcom.2019.1800611.
Tang, R. and Huang, J.H. (2023), “The influence of digital transformation on the resilience of tourism companies under the shock of the COVID-19 pandemic”, Asia Pacific Journal of Tourism Research, Vol. 28 No. 8, pp. 827-840, doi: 10.1080/10941665.2023.2276469.
Tang, Y. and Zhang, P. (2022), “The impact of virtual integration on innovation speed: on the view of organizational information processing theory”, Journal of Organizational and End User Computing, Vol. 34 No. 1, pp. 1-20, doi: 10.4018/JOEUC.298702.
Tsou, H.T. and Chen, J.S. (2023), “How does digital technology usage benefit firm performance? Digital transformation strategy and organisational innovation as mediators”, Technology Analysis and Strategic Management, Vol. 35 No. 9, pp. 1114-1127, doi: 10.1080/09537325.2021.1991575.
Verstegen, L., Houkes, W. and Reymen, I. (2019), “Configuring collective digital-technology usage in dynamic and complex design practices”, Research Policy, Vol. 48 No. 8, 103696, doi: 10.1016/j.respol.2018.10.020.
Vives, A. and Ostrovskaya, L. (2023), “Exploring the impact of hotel attributes and services on price and revenue: a hedonic pricing model approach with a focus on internal and external segmentation and repeat customers”, Tourism Economics, Vol. 30 No. 4, pp. 813-843, doi: 10.1177/135481662312003.
Weng, T.C. and Hsu, C.T. (2020), “Applying smarter commerce analysis for click and mortar of integration platform in the retail industry”, International Journal of Services Technology and Management, Vol. 26 No. 5, pp. 440-453, doi: 10.1504/ijstm.2020.109428.
Wong, L.W., Tan, G.W.H., Lee, V.H., Ooi, K.B. and Sohal, A. (2023), “Psychological and system-related barriers to adopting blockchain for operations management: an artificial neural network approach”, IEEE Transactions on Engineering Management, Vol. 70 No. 1, pp. 67-81, doi: 10.1109/tem.2021.3053359.
Woolley, K. and Lim, S. (2023), “Interpersonal consequences of joint food consumption for connection and conflict”, Social and Personality Psychology Compass, Vol. 17 No. 6, doi: 10.1111/spc3.12748.
Wu, S.H. and Ku, E.C.S. (2024), “Aligning restaurants and artificial intelligence computing of food delivery service with product development”, Journal of Hospitality and Tourism Technology, doi: 10.1108/JHTT-10-2023-0322.
Yuan, K.H., Wen, Y. and Tang, J.S. (2023), “Sensitivity analysis of the weights of the composites under partial least-squares approach to structural equation modeling”, Structural Equation Modeling-A Multidisciplinary Journal, Vol. 30 No. 1, pp. 53-69, doi: 10.1080/10705511.2022.2106487.
Yusif, S., Hafeez-Baig, A., Soar, J. and Teik, D.O.L. (2020), “PLS-SEM path analysis to determine the predictive relevance of e-Health readiness assessment model”, Health and Technology, Vol. 10 No. 6, pp. 1497-1513, doi: 10.1007/s12553-020-00484-9.
Zhang, Y., Gao, J., Cole, S.T. and Ricci, P. (2019), “Beyond accessibility: empowering mobility-impaired customers with motivation differentiation”, International Journal of Contemporary Hospitality Management, Vol. 31 No. 9, pp. 3503-3525, doi: 10.1108/ijchm-08-2018-0663.
Zhang, J., Xu, Y.H., Dong, L.F. and Long, Q.Q. (2023), “Gratification matters? An explorative study of antecedents and consequence in livestream shopping”, Industrial Management and Data Systems, Vol. 123 No. 6, pp. 1649-1669, doi: 10.1108/imds-08-2022-0513.
Zhou, G.F., Liu, Y., Hu, J.J. and Cao, X.P. (2023), “The effect of tourist-to-tourist interaction on tourists? Behavior: the mediating effects of positive emotions and memorable tourism experiences”, Journal of Hospitality and Tourism Management, Vol. 55, pp. 161-168, doi: 10.1016/j.jhtm.2023.03.005.
Further reading
Noorizadeh, A., Kuosmanen, T. and Peltokorpi, A. (2021), “Effective purchasing reallocation to suppliers: insights from productivity dynamics and real options theory”, International Journal of Production Economics, Vol. 233, 108002, doi: 10.1016/j.ijpe.2020.108002.
Corresponding author
About the author
Edward C.S. Ku is Professor in the Department of Travel Management at the National Kaohsiung University of Hospitality and Tourism. He received a Ph.D. from the Graduate School of Business Administration, National Central University. His research interests include electronic commerce, supply chain management, knowledge management and information systems applied in travel management. His papers have been published in Current Issues in Tourism, Internet Research, Online Information Research, The Service Industries Journal, Service Business, Journal of Air Transport Management, Journal of Hospitality and Tourism Research, International Journal of Tourism Research, Journal of Travel and Tourism Marketing, International Journal of Hospitality Management.