Investigating the determinants and process of destination management
system (DMS) implementation
Anh T. P. Le
Taylor’s University–Lakeside Campus, Selangor Darul Ehsan, Malaysia
Puvaneswaran Kunasekaran and S. Mostafa Rasoolimanesh
Taylor’s University–Lakeside Campus, Selangor Darul Ehsan, Malaysia and Center of Research and Innovation in Tourism (CRiT), Taylor’s University,
Subang Jaya, Malaysia
Neethiahnanthan AriRagavan
Taylor’s University–Lakeside Campus, Selangor Darul Ehsan, Malaysia, and
Toney K. Thomas
Mahatma Gandhi University, Kerala, India
Abstract
Purpose–This study aims to propose a comprehensive model to help understand factors influencing the intention to participate in a destination management system (DMS) amongst tourism stakeholders in Vietnam which are considered as the determinants of the successful implementation of the system.
Design/methodology/approach–A survey was conducted to investigate key stakeholders’opinions of participating in a DMS. In total, 301 questionnaires were used for analysis. Partial least squares structural equation modelling (PLS-SEM) was used to assess the measurement and structural models of the study.
Findings–Factors that influence various tourism stakeholders’intention to participate in the national DMS in Vietnam have been identified and examined. The results identify the important predictors of the tourism stakeholders’participation in the DMS including information quality, DMS operator readiness, government regulations and technology awareness. Interestingly, technology awareness was ascertained as a significant mediator for the relationship between performance expectancy, social influence, technology competency, competitive pressure and the intention to participate in the DMS.
Originality/value–This study has a unique theoretical contribution by developing a comprehensive model to predict the intention to participate in a DMS amongst tourism stakeholders with the modification and combination of three theoretical models and frameworks: the unified theory of acceptance and use of technology (UTAUT) model, technology–organisation–environment (TOE) framework and updated DeLone and McLean information systems (D&M IS) success model. It is expected to be a useful reference source for tourism management departments that want to develop DMSs in Vietnam. This model also can be used as an initial investigation for DMS implementation studies at other destinations.
KeywordsDestination management system (DMS), UTAUT, TOE, D&M IS success, Tourism stakeholder, Destination management
Paper typeResearch paper
Destination management system
This work was supported by Taylor’s University through its ASEAN TOURISM SCHOLARSHIP Programme. The authors would like to thank ASEAN Tourism Research Association (ATRA), Vietnam National Administration of Tourism (VNAT) and Taylor’s University that has given the authors the opportunity to carry out this research. The authors would also like to thank all those respondents who participated in this study. The authors acknowledge the helpful comments and supports from anonymous reviewers, guest editors of the special issue of Knowledge Management in Tourism:
paradigms, approaches and methods and the editors of Journal of Organizational Change Management.
This research is part of a thesis which was submitted as partial fulfilment to meet requirements for the degree of Doctor of Philosophy at Taylor’s University.
Funding:This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0953-4814.htm
Received 22 November 2020 Revised 11 January 2021 Accepted 11 January 2021
Journal of Organizational Change Management
© Emerald Publishing Limited 0953-4814 DOI10.1108/JOCM-11-2020-0352
Introduction
Information and communication technology (ICT) has become a critical factor for successful tourism destinations (UNWTO, 2018). The destination’s strategic positioning and competitiveness will be improved as the benefits from tourism will be maximised if advanced ICTs, especially destination management systems (DMSs) are adopted (Buhaliset al., 2011). DMSs help destinations have a more powerful and competitive advantage in the future as they become more and more complex based on emerging technologies over the years (Buhalis and Wagner, 2013). DMSs have become fundamental to smart tourism destinations (Benckendorffet al., 2019) that ensure maximising value for all stakeholders (Buhalis, 2019).
All stakeholders’needs in the destination are harmonised by DMSs. In addition, they enhance internal communication with partners and external communication with consumers. They foster the management and marketing of the destination (Martinset al., 2013).
DMSs can be defined as“inter-organisational systems”(Chen and Sheldon, 1997, p. 159), often presented as “a dynamic web-based platform” (Est^ev~ao et al., 2011, p. 163) that
“consolidate and distribute a comprehensive range of tourism products through a variety of channels and platforms, generally catering for a specific region, and supporting the activities of a Destination Management Organisation (DMO) within that region. DMSs attempt to utilise a customer-centric approach to manage and market the destination as a holistic entity, typically providing strong destination-related information, real-time reservations, destination management tools and paying particular attention to supporting small and independent tourism suppliers”(Horan and Frew, 2007, p. 63). DMSs“educate and make the community aware of the current situation and problems of the destination, and then, provide a platform for gathering, consolidating and synthesising different stakeholders’voices for designing and implementing tourism development strategies”(Sigala, 2011, p. 107).
DMSs are emerging as a notable ICT solution for destination marketing organisations (DMOs) that are in charge of managing tourist destinations which are“some of the most difficult entities to manage and market, due to the complexity of the relationships of local stakeholders”(Sautter and Leisen, 1999 as cited inBuhalis, 2000, p. 98) and“considered as an example of dynamic and adaptive (to the external environment) complex system” (Baggio and Valeri, 2020, p. 6).Valeri and Baggio (2020a)assert that the way information and knowledge spread between all stakeholders in a destination is one of the factors that have the greatest influence on tourism development. It even“has a great impact on possible competitive advantages that a destination and its elements can have and on how actions are planned”(Valeri and Baggio, 2020a, pp. 7–8). In this case, DMSs support DMOs to perform their day-to-day operations more actively, efficiently and effectively by integrating all information about resources, products and services of the destination in one place (Seggitur and CICtourGUNE, 2014).
In Vietnam, the importance of ICT in tourism development has been realised since the early time of Internet innovation (Vietnam National Administration of Tourism [VNAT], 2018a).
However, the level of ICT adoption in Vietnam tourism is still lagging in terms of developing a DMS for the country. Currently, the Vietnam National Administration of Tourism (VNAT) websites have not been dynamic DMS websites (VNAT, 2018b). They show limited services to stakeholders, lack proper reservation and purchase functions and have a fragmented database (Vu, 2018). VNAT websites lack information on the market trend which is most useful for tourism businesses. These factors have hindered Vietnam from improving its quality of tourism service, tourism marketing and management as well as reaping maximum benefits from tourism and assuring sustainable development (VNAT, 2018a). It has been found that a comprehensive DMS is the most suitable tool to address the current Vietnamese tourism’s limitations and weaknesses to enhance tourism development in the country (VNAT, 2018c).
The VNAT is pursuing to develop a system that can harmonise all stakeholders’benefits and enhance tourism development in Vietnam (Interviewed with Chief of Office of VNAT, T. Vu
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2019, personal communication, 5 June). One of the major tasks and solutions that have been proposed in the master plan on ICT applications for Vietnamese tourism in the period of 2018– 2020 and vision to 2025 is developing tourism information systems (ISs) and applications associated with the digital Vietnamese knowledge system project (VNAT, 2018b). When a DMS is integrated with a knowledge management system, it will help“to respond more effectively to customers’needs and to target them with appropriate offerings”(Belbalyet al., 2004, p. 338).
Especially, with supplier- and customer-based knowledge generation,“customer demand is satisfied in an intelligent and collaborative manner and the destination offer will continuously be improved, based on customer needs and market knowledge”(H€opkenet al., 2011, p. 428).
Therefore, developing a DMS is integral to Vietnam. However, the lack of stakeholders’ participation in the national IS is one of the major obstacles in developing a comprehensive tourism IS for Vietnam (Interviewed with Director of Tourism Information Technology Center, A. Le 2019, personal communication, 25 June).
The importance of DMSs for a destination is undeniable as all stakeholders in the destination are the main beneficiaries of a DMS, including potential and current travellers, tourism products and services providers, domestic and international travel agents, public sector, local communities, information technology (IT) providers and investors (Buhalis and Spada, 2000; Sigala, 2014). However, many researchers reported that “the successful implementation of a DMS is a matter openly complex”(Martinset al., 2013, p. 52). It was found that DMS projects failed or were challenging to succeed because governmental tourism departments often developed the system by themselves with a lack of participation of other stakeholders (Estev~aoet al., 2014). Moreover,“the adoption and diffusion of a technological innovation are linked to the number of users who have profitably adopted it, and up to now, this seems to be a very weak point”(Valeri and Baggio, 2020b, p. 5).
In addition, despite the vast literature on DMSs, the lack of applying specific models or frameworks for these studies has been revealed. Past DMS studies were often in the form of case studies, mainly focussed on the advantages of DMSs to destinations or the prerequisites or barriers to DMS implementation (Estev~ao et al., 2014). To study the issues of DMS application and management more comprehensively, multiple theories, perspectives and study fields should be applied (Sigala, 2013). Furthermore, it is meaningful to apply the quantitative method for complexity studies in hospitality management which has the involvement of many stakeholders (e.g. DMS)“as it offers the possibility for modelling and simulation”(Valeri and Baggio, 2020c, p. 3).
Based on the foregoing discussions, this study utilises multiple theories aiming to propose a comprehensive model to predict the intention to participate in the national DMS amongst tourism stakeholders in Vietnam. The proposed framework will be empirically validated by quantitative data to understand the actual factors that influence stakeholders’intention of participation in a DMS.
Conceptual development
A total of three theoretical frameworks and models have been used in this study, including the technology–organisation–environment (TOE) framework, the updated DeLone and McLean information systems (D&M IS) success model and the unified theory of acceptance and use of technology (UTAUT).
The UTAUT has been selected as the underpinning model for the study as“the variance explained by UTAUT is quite high for behavioral research”(Venkateshet al., 2003, p. 470).
However, for a better understanding of technology acceptance and usage,Venkateshet al.
(2003, p. 470) suggested that “further work should attempt to identify and test additional boundary conditions of the model in an attempt to provide an even richer understanding of technology adoption and usage behavior”. As a result, the original UTAUT model is often
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amended to better suit the actual context (Zuiderwijket al., 2015). The extensions of UTAUT have significantly contributed to the theory of technology acceptance and use (Venkatesh et al., 2016). Besides,Dwivediet al.(2019)suggested the need to reconsider the moderators and the relationships proposed in the original model to avoid omitting useful information.
Furthermore, Chao (2019) found that many researchers who had applied the UTAUT
“suggested that increasing the number of external variables can enhance this model’s ability to predict the acceptance of IT”(p. 2).
Additionally,Oliveira and Martins (2011)indicated that new technology adoption is more complex, and hence, studies need to use more than one theoretical model to get a better understanding of its phenomenon. Furthermore, previous studies advocated that the small and medium-sized tourism enterprises’(SMTEs) participation in DMS was influenced by not only technological factors but also other factors such as (inter)-organisational and collaboration (Sigala, 2013). Therefore, the TOE framework has been integrated with the UTAUT model in this study.
Furthermore, to provide a holistic model, the updated D&M IS success model has been selected to integrate into the research framework. This is because to enhance the IS success rate, it is vital to take the level of quality into account. A total of three dimensions of the model that affect users’intention to use such as system quality, information quality and service quality (DeLone and McLean, 2003) have been combined in the research model. They are considered as IS success antecedents influencing the tourism stakeholders’ intention to participate in the DMS.
Besides, DMS has been present for a long time; however, the knowledge about it is still unclear (Aurelien and Herinandrianina, 2014). In Vietnam, the notion of DMSs is new to tourism stakeholders. Therefore, in the initial stage of the DMS implementation, this study attempts to provide a better understanding of the influence of stakeholders’awareness of DMSs on their intention to participate in the system.
Accordingly, the variables defined in the research model have been conceptualised based on the context of the study area and relevant literature concerning technology acceptance and use as well as DMS implementation and management as follows.
Technology awareness which is also known as technology cognisance is “the user’s knowledge about the capabilities of technology, its features, potential use, and cost and benefits” (Nambisan et al., 1999, p. 372). One of the main challenges for the successful implementation of DMSs in destinations is the insufficient awareness of technologies (Buhalis and Wagner, 2013; Ndou and Petti, 2007). The different perceptions of various DMS stakeholders about the role, the operation and the performance of a DMS that affect their expectation of benefits from joining the DMS“had restrained them from participating in the DMS”(Sigala, 2013, p. 1014).Aurelien and Herinandrianina (2014)revealed that if DMOs were fully aware of the benefits of the DMS, they would decide to adopt the DMS quickly. Hence, the following hypothesis is proposed:
H1. Technology awareness has a positive effect on the intention to participate in a DMS.
To get a better understanding of how interventions work, a mediator has been introduced in the research framework (MacKinnon, 2012). Therefore, the mediating effects of technology awareness between predictors and outcome will be examined in this study. Hereinafter, the indirect effect of technology awareness will be hypothesised along with the direct effect of relationships’hypotheses.
Performance expectancy is “the degree to which an individual believes that using a particular technology will help him or her to attain gains in job performance”(Venkatesh et al., 2003, p. 447). In this study, it is believed that users with higher expectations that the national DMS will help their organisation’s performance such as increasing the number of bookings; expanding markets (Sigala, 2014) and reducing seasonality, costs of IT,
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distribution, commission (Buhalis and Spada, 2000) will be more willing to participate in the system. Therefore, the following hypotheses are suggested:
H2a. Performance expectancy has a positive effect on the intention to participate in a DMS.
H2b. Technology awareness mediates the relationship between performance expectancy and the intention to participate in a DMS.
Effort expectancy,one of the main constructs of the UTAUT which refers to the ease of use of the IS; however, to predict behavioural intention by facilitating conditions, effort expectancy should not be in the model because the effect of facilitating conditions to behavioural intention was captured by effort expectancy (Venkatesh et al., 2003). Therefore, effort expectancy has been omitted from the research model.
Social influenceis “the degree to which an individual perceives that important others believe he or she should use the new system”(Venkateshet al., 2003, p. 475). In this study, social influence refers to the importance of others’ beliefs that the organisation should participate in the DMS, especially the influence of top management from the public sector. It is believed that the support from the heads of the national tourism organisation (here known as Vietnam National Administration of Tourism – VNAT) will encourage organisations to participate in the national DMS. So, the following hypotheses are proposed:
H3a. Social influence has a positive effect on the intention to participate in a DMS.
H3b. Technology awareness mediates the relationship between social influence and the intention to participate in a DMS.
Facilitating conditionsare“the degree to which an individual believes that organisational and technical infrastructure exists to support the use of the system”(Venkatesh et al., 2003, p. 453). In this study, facilitating conditions include technological, organisational and environmental factors that are believed to facilitate the tourism stakeholders to participate in the DMS.
Technology infrastructurerefers to the extent to which the fundamental technologies will be provided by the DMS operator, including the fundamental functions of a comprehensive DMS that the DMS operator needs to implement. It is believed that having a system with proper functions could attract more stakeholders to participate in the system. Hence, the following hypotheses are proposed:
H4a. Technology infrastructure has a positive effect on the intention to participate in a DMS.
H4b. Technology awareness mediates the relationship between technology infrastructure and the intention to participate in a DMS.
Technology competencerefers to the technology readiness of the organisation that intends to participate in the national DMS. The higher technology readiness of organisations may derive from organisations that have paid more attention to technology innovation adoption and implementation. Hence, the following hypotheses are proposed:
H5a. Technology competence has a positive effect on the intention to participate in a DMS.
H5b. Technology awareness mediates the relationship between technology competence and the intention to participate in a DMS.
DMS operator readiness(known as organisational readiness), which includes the readiness of financial and technical resources plays a significant role in the adoption of IT innovations of
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the organisation (Amini and Bakri, 2015). In this study, DMS operator readiness is considered as an external organisational facilitator. It is believed that the DMS operator with proper readiness to implement DMS including policy, financial and technical skills will facilitate and encourage stakeholders to participate in the system. Therefore, the following hypotheses are proposed:
H6a. DMS operator readiness has a positive effect on the intention to participate in a DMS.
H6b. Technology awareness mediates the relationship between DMS operator readiness and the intention to participate in a DMS.
Cooperationof all stakeholders in a destination is one of the most important factors for the success of DMS implementation (Buhalis and Deimezi, 2004).Est^ev~aoet al.(2011)revealed that the success of the DMS development was related to the level of cooperation amongst stakeholders in the destination. For example, in the case of VisitBritain, the success of the implementation was because it had paid special attention to communicating with DMOs at all levels and ensured the strong commitment from the largest number of stakeholders amongst public and private sectors (Guthrie, 2008). It can be assumed that the increase in the level of cooperation including cooperation with the DMS operator, public–private partnerships, harmonisation of interests of stakeholders at the destination (Sigala, 2013) increases will lead to an increase in stakeholders’participation intention. Hence, the following hypotheses are proposed:
H7a. Cooperation has a positive effect on the intention to participate in a DMS.
H7b. Technology awareness mediates the relationship between cooperation and the intention to participate in a DMS.
Trainingis a facilitator that influences users’intention to adopt innovation that leads to the successful implementation of the information management system (Maina and Nzuki, 2015).
Training is one of the most common organisational factors that affect the adoption of ISs (Bedardet al., 2008).Guthrie (2008)stated that VisitBritain has demonstrated that a strong investment in stakeholder training has brought success to the project. In this study, training is the extent to which stakeholders will be trained in terms of knowing how to productively use the system as well as know about the system’s functions and benefits and subsequently, they will be more willing to participate in the system. Therefore, the following hypotheses are proposed:
H8a. Training has a positive effect on the intention to participate in a DMS.
H8b. Technology awareness mediates the relationship between training and the intention to participate in a DMS.
Government regulationsor regulatory support refers to government initiatives and policies that facilitate the adoption of IT in organisations (Hameed and Counsell, 2012).Buhalis and Spada (2000)suggest that incentives will encourage tourism organisations to use and adopt DMSs. Governments can support tourism organisations to adopt IS technologies through planning, regulation and incentives (Eraqi and Abd-Alla, 2008). Hence, the following hypotheses are proposed:
H9a. Government regulations have a positive effect on the intention to participate in a DMS.
H9b. Technology awareness mediates the relationship between government regulations and the intention to participate in a DMS.
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Competitive pressurerefers to the extent to which the organisation is being pressured by its competitors in the industry (Oliveira and Martins, 2010). Wagaw and Mulugeta (2018) advocated that users pay more intention to use ICT in tourism when they believe that their competitive advantages will be significantly improved by technology. Hence, the following hypotheses are proposed:
H10a. Competitive pressure has a positive effect on the intention to participate in a DMS.
H10b. Technology awareness mediates the relationship between competitive pressure and the intention to participate in a DMS.
System qualityis the extent to which users perceive the quality of the system (Fianuet al., 2018). Easy to use, navigate and operate, user-friendly and reliable are the key features to measure system quality (DeLone and McLean, 2003). It is believed that a system with high quality will encourage customers to use it (DeLone and McLean, 2004). Therefore, the following hypotheses are proposed:
H11a. System quality has a positive effect on the intention to participate in a DMS.
H11b. Technology awareness mediates the relationship between system quality and the intention to participate in a DMS.
Information qualityrefers to accurate, updated, complete, relevant and consistent content (DeLone and McLean, 2003). The high quality of the data and content strongly affects the success of the ISs (Chen and Sheldon, 1997;Martinset al., 2013).Buhalis and Spada (2000)and Chen and Sheldon (1997)suggested that a system with comprehensive, updated, accurate, reliable, multilingual and standard information will encourage more people to use it. This will lead to tourism organisations having more intention to participate in the system. Hence, the following hypotheses are proposed:
H12a. Information quality has a positive effect on the intention to participate in a DMS.
H12b. Technology awareness mediates the relationship between information quality and the intention to participate in a DMS.
Service qualityis the extent to which the IS service provider provides (service or support) to the users of the system (Petteret al., 2013). According toDeLone and McLean (2004), service quality has become much more important than before because users are now likely to be customers. If we support users poorly, we will lose them as customers. Hence, the following hypotheses are proposed:
H13a. Service quality has a positive effect on the intention to participate in a DMS.
H13b. Technology awareness mediates the relationship between service quality and the intention to participate in a DMS.
Methodology
All items used to measure the constructs in the conceptual framework were adopted and adapted from previous studies. The proposed conceptual framework is shown inFigure 1. Each item was measured using a five-point Likert scale, with 1 referring to strongly disagree and 5 referring to strongly agree. The questionnaire was developed in English and then translated into the Vietnamese language. The back-translation method was used to verify the correctness of the translation (Brislin, 1970 as cited inBorreroet al., 2014). The validity and reliability of the research instrument was tested by using expert opinions and pilot test techniques.
This study mainly focussed on two key groups of stakeholders which are tourism management departments and tourism-related businesses that represent both public and private sectors in the destination. They are considered to have a significant influence in the
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initial stage of the DMS implementation. Hard copies of the questionnaire were distributed to respondents who had attended international and national tourism conferences, forums and tourism fairs in Vietnam. In addition, soft copies of the questionnaire and online survey link were emailed to tourism organisations in the industry list that the researcher’s office had contacted before. Data were collected from April to September 2019. The respondents were industry and business managers or senior staff. One respondent represented each organisation in responding to the questionnaire.
In total, 900 questionnaires were distributed, and 339 of the total questionnaires were answered. From the 339 returned questionnaires, after the initial screening process, 301 were used for further analysis. A total of 38 responses were removed from the sample because they were straight-lining patterns and outliers.
The minimum required sample size for the study has been estimated by using the inverse square root method is 160 and 146 by using the gamma-exponential method with the minimum absolute significant path coefficient of 0.197, the significant level at 0.05 and the power level of 0.8 (Kock and Hadaya, 2018). Hence, 301 respondents have satisfied the minimum required sample size to test the research model. Partial least squares structural equation modelling (PLS-SEM) was used to assess the research model for this study.
According toHairet al.(2019), PLS-SEM is an appropriate method for the analysis since it tests a theoretical framework with predictive purpose. It is suitable for exploratory research in terms of theory development. In addition, this method is most suitable for a complex research model that includes many constructs and relationships, especially the inclusion of formative construct(s).
The results of Harman’s single-factor test revealed that the maximum covariance explained by a single factor solution was 27.29%, indicating that common method variance (CMV) was not a problem in the present data set (Podsakoffet al., 2003).
Technology infrastructure
Technology competence DMS operator
readiness
Cooperation
Training
Government regulations Competitive pressure
Social Influence
Performance Expectancy
Technology Awareness
Intention to participate in the national
DMS
System Quality
Information Quality
Service Quality H4a
H3a H2a
H5a
H6a H7a H8a
H9a
H10a H11a H12a H13a
H1 H4b
H5b H6b H7b H8b
H9b H10b
H2b H3b
H11b H12b H13b
T E C H
O R G
N V E
UTAUT
Updated IS Success Figure 1.
Conceptual framework of the study
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Analysis and findings Measurement model assessment
Table 1 shows the results after deleting the three unsatisfied items of performance expectancy (PE1, PE2 and PE5). It revealed that the loading for all items almost exceeded the suggested threshold value of 0.7, ranging from a lower bound of 0.679 to an upper bound of 0.948. In total, three items that had loadings lower than 0.7 but higher than 0.6 and the constructs’average variance extracted (AVE) values higher than 0.5 were retained in the construct. Hence, the indicator reliability of the items used for the model assessment was satisfied (Hairet al., 2017).Table 1also shows that all constructs had AVE values ranging
Items Loading AVE CR rho_A Cronbach’s alpha
Performance PE3 0.702 0.548 0.829 0.732 0.728
expectancy PE4 0.774
PE6 0.782
PE7 0.699
Social SI1 0.864 0.648 0.880 0.830 0.817
influence SI2 0.846
SI3 0.803
SI4 0.697
System SQ1 0.794 0.578 0.872 0.841 0.821
quality SQ2 0.767
SQ3 0.779
SQ4 0.679
SQ5 0.777
Service SEVQ1 0.803 0.602 0.883 0.864 0.836
quality SEVQ2 0.721
SEVQ3 0.806
SEVQ4 0.728
SEVQ5 0.817
Cooperation COP1 0.682 0.591 0.878 0.835 0.827
COP2 0.832
COP3 0.763
COP4 0.784
COP5 0.776
Government GR1 0.836 0.761 0.905 0.843 0.843
regulations GR2 0.896
GR3 0.883
Training TRA1 0.888 0.849 0.944 0.929 0.911
TRA2 0.927
TRA3 0.948
Technology TC1 0.888 0.837 0.939 0.903 0.902
competency TC2 0.905
TC3 0.931
Competitive CP1 0.867 0.743 0.897 0.832 0.827
pressure CP2 0.856
CP3 0.863
Technology TA1 0.866 0.774 0.911 0.859 0.854
awareness TA2 0.825
TA3 0.859
Intention to INTEN1 0.920 0.826 0.935 0.895 0.895
participate INTEN2 0.910
INTEN3 0.883
Note(s): Items removed: indicator items are below 0.5: PE1, PE2 and PE 5
Table 1.
Reflective measurement model
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from 0.548 to 0.849, which confirmed that the convergent validity of all items was satisfied.
The composite reliability (CR) of each construct for this study ranged from 0.829 to 0.944. In addition, the smallest value of Cronbach’s alpha was 0.728 and rho_A was 0.732. Thus, the results indicated that the items satisfied internal consistency reliability (Rasoolimanesh et al., 2019).
Table 2shows that all heterotrait–monotrait (HTMT) ratio values were below 0.85, which confirmed that the discriminant validity of the construct was satisfied (Hairet al., 2019).
Table 3shows the results of variance inflation factor (VIF) values for composite constructs including information quality, technology infrastructure and DMS operator readiness, all were below the value of 5, which indicated that collinearity was not an issue for the composite
CP COP GR INT PE SERVQ SI SQ TA TC TRA
Competitive pressure
Cooperation 0.305 Government
regulations
0.358 0.577 Intention to
participate in DMS
0.550 0.428 0.453
Performance expectancy
0.463 0.491 0.392 0.436 Service quality 0.268 0.741 0.485 0.262 0.432 Social
influence
0.622 0.404 0.476 0.553 0.496 0.291 System
quality
0.288 0.537 0.456 0.354 0.637 0.745 0.349 Technology
awareness
0.566 0.207 0.254 0.605 0.475 0.134 0.530 0.222 Technology
competency
0.519 0.208 0.283 0.476 0.391 0.121 0.697 0.255 0.420 Training 0.265 0.530 0.600 0.302 0.455 0.572 0.495 0.507 0.227 0.238
Items Outer weights VIF
Information IQ1 0.191* 1.571
quality IQ2 0.354* 1.531
IQ3 0.336* 1.441
IQ4 0.218* 1.598
IQ5 0.243* 1.457
Technology TI1 0.213* 1.546
infrastructure TI2 0.279* 2.009
TI3 0.308* 2.073
TI4 0.212* 1.665
TI5 0.269* 1.570
DMS operator TR1 0.263* 2.068
readiness TR2 0.297* 2.418
TR3 0.293* 2.282
TR4 0.334* 1.741
Note(s):*Significance atp< 0.01 Table 2.
Discriminant validity (heterotrait–monotrait –HTMT)
Table 3.
Composite measurement model
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constructs of the study. Also, the outer weights of all composite indicators were significant at a level of 1%, which ensured the significance and relevance of the composite indicators (Hair et al., 2017).
Structural model assessment
There are 12 independent variables and one mediator variable in this study. The result showed that all VIF values were lower than 3.3 (in the range from 1.579 to 2.795), confirming that the collinearity issue was not a concern for this study (Rasoolimaneshet al., 2017).
The results of the SEM show that the values ofR2were 0.487 and 0.367 for the intention to participate in a DMS and technology awareness, respectively (Figure 2). Accordingly, the important predictors that altogether explainedR2of 48.7% variance in tourism stakeholders’ intention to participate in DMS. The R2 with a value of 0.487 demonstrates strong explanatory power (Benitezet al., 2020).
The predictive relevance of the model (Q2) for the intention was 0.617, which is higher than 0, indicating that all exogenous variables had a predictive ability on the intention to participate in DMS (Hairet al., 2017). The findings revealed that the predictive relevance of the model reached a high level at 61.7% and confirmed the research proposed model (Benitez et al., 2020).
Table 4shows that five proposed hypotheses of the direct effect were significant at least at the level of 0.05. However, only four of them were supported, which areH1,H6a,H9aand H12athat were significant at least at the level of 0.05 and had expected sign directions (i.e.
positive in this study), consist of a path coefficient value (β) ranging from 0.116 to 0.368 and the confidence intervals bias-corrected did not include 0 in between (Hair et al., 2017).
Therefore, the results confirmed the significant direct effects of technology awareness, information quality, DMS operator readiness and government regulations on the intention to participate in the national DMS.
Furthermore, the effects of technology awareness as a mediator on the relationship between the predictors of the model and the outcome (the intention to participate in the national DMS) were assessed based onZhaoet al.(2010)’s procedure (Gannonet al., 2021).
Table 4shows the significant indirect effects of performance expectancy, social influence, technology competency and competitive pressure on the intention to participate in the national DMS through technology awareness at least at the level of 0.05 and had the same sign directions (i.e. positive) with the direct effects and the confidence intervals bias-corrected did not include 0 in between (Hairet al., 2017). Accordingly,H2b,H3b,H5bandH10bwere supported.
Discussion
Technology awareness was found to be significantly and positively related to the intention to participate in the DMS. This implies that tourism stakeholders who have a higher level of awareness of the technology will have more intention to participate in the DMS. Consistent with this finding are studies that confirmed technology awareness significantly influencing behavioural intention to use point-of-sale terminal (Abubakar and Ahmad, 2014) and awareness of cloud computing significantly positively influencing cloud computing adoption (Senarathnaet al., 2018).
The findings showed that information quality had a significant positive effect on the intention to participate in the DMS. This is in line with previous literature that claimed that information quality positively affected intention to reuse in the paid m-learning applications context (Wanget al., 2019), the enhancement of information quality of DMOs’destination
Destination
management
system
TI1 TI2 TI3 TI4 TI5 TC1 TC2 TC3 TR1 TR2 TR3 TR4 COP1 COP2 COP3 COP4 COP5 TRA1 TRA2 TRA3 GR1 GR2 GR3 CP1 CP2 CP3
0.213 0.279 0.308 0.212 0.269 0.891 0.920 0.933 0.263 0.297 0.293 0.334 0.682 0.832 0.763 0.784 0.776 0.836 0.886 0.8830.878 0.941 0.943 0.867 0.856 0.863
A Technology Infrastructure Technology Competence DMS operator Readiness
A Cooperation Training Governement Regulations Competitive Pressure
SI1SI2SI3SI4 0.8640.8460.8030.697 Social Influence
Performance Expectation SQ1SQ2SQ3SQ4SQ51Q11Q21Q31Q41Q5SEVQ1SEVQ2SEVQ3SEVQ4SEVQ5
0.019 0.114 0.312 0.007 0.095 0.087 0.051 0.251 0.055 0.093 0.308 0.019 –0.107 0.119 0.092 0.057
PE3PE4PE6PE7 0.7020.7740.7820.699 0.173 0.082
0.211 –0.015
0.3670.901 0.880 0.859 Technology Awareness 0.368 –0.032–0.006 0.937 0.911 0.879
0.487
TA1 TA2 TA3 INT1 INT2 INT3 0.116–0.168
Intention to participate in the DMS 0.7940.7670.7790.6790.777System Quality
0.1910.3540.3360.2180.243Information Quality 0.8030.7210.8060.7280.817Service Quality
A
Figure 2.
Measurement model
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HypothesisDirect/indirecteffectBetaSEt-valuep-valuesConfidenceinterval(95%) biascorrectedSupported H1Techawareness→intentiontoparticipateinDMS0.3680.0616.0610.000[0.260;0.465]Yes H2aPerformanceexpectancy→intentiontoparticipateinDMS0.0150.0560.2640.396[0.124;0.067]No H2bPerformanceexpectancy→technology awareness→intentiontoparticipateinDMS0.0780.0253.0850.001[0.043;0.126]Yes H3aSocialinfluence→intentiontoparticipateinDMS0.0820.0601.3670.086[0.021;0.173]No H3bSocialinfluence→technologyawareness→intentionto participateinDMS0.0640.0292.2310.013[0.023;0.115]Yes H4aTechnologyinfrastructure→intentiontoparticipateinDMS0.0070.0580.1210.452[0.097;0.091]No H4bTechnologyinfrastructure→technology awareness→intentiontoparticipateinDMS0.0070.0250.2860.387[0.027;0.053]No H5aTechnologycompetency→intentiontoparticipateinDMS0.0870.0581.5020.067[0.009;0.171]No H5bTechnologycompetency→technology awareness→intentiontoparticipateinDMS0.0420.0231.8240.034[0.007;0.082]Yes H6aDMSoperatorreadiness→intentiontoparticipateinDMS0.2510.0723.4990.000[0.126;0.357]Yes H6bDMSoperatorreadiness→technologyawareness→intention toparticipateinDMS0.1150.0343.4250.000[0.179;0.067]No(different sign) H7aCooperation→intentiontoparticipateinDMS0.0930.0691.3540.088[0.029;0.198]No H7bCooperation→technologyawareness→intentionto participateinDMS0.0350.0261.3420.090[0.003;0.082]No H8aTraining→intentiontoparticipateinDMS0.1070.0711.5140.065[0.242;0.003]No H8bTraining→technologyawareness→intentiontoparticipate inDMS0.0190.0250.7430.229[0.017;0.065]No H9aGovernmentregulations→intentiontoparticipateinDMS0.1190.0502.4000.008[0.040;0.202]Yes H9bGovernmentregulations→technology awareness→intentiontoparticipateinDMS0.0200.0220.9070.182[0.015;0.057]No H10aCompetitivepressure→intentiontoparticipateinDMS0.0920.0601.5460.061[0.000;0.200]No H10bCompetitivepressure→technologyawareness→intentionto participateinDMS0.1130.0313.6540.000[0.072;0.171]Yes H11aSystemquality→intentiontoparticipateinDMS0.0570.0730.7800.218[0.051;0.187]No H11bSystemquality→technologyawareness→intentionto participateinDMS0.0070.0260.2650.395[0.038;0.048]No (continued)
Table 4.
Structural model results
Destination
management
system
HypothesisDirect/indirecteffectBetaSEt-valuep-valuesConfidenceinterval(95%) biascorrectedSupported H12aInformationquality→intentiontoparticipateinDMS0.1160.0701.6490.050[0.002;0.229]Yes H12bInformationquality→technologyawareness→intentionto participateinDMS0.0120.0310.3860.350[0.075;0.029]No H13aServicequality→intentiontoparticipateinDMS0.1680.0722.3260.010[0.309;0.073]No(different sign) H13bServicequality→technologyawareness→intentionto participateinDMS0.0020.0300.0720.471[0.049;0.050]No Table 4.
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website was very important as it affected website usefulness that influenced potential tourists to continue the intention to use the website (Chunget al., 2015).
DMS operator readiness is hypothesised as an external facilitator of DMS participation intention amongst tourism stakeholders. The result revealed that it had a significant positive influence on a tourism stakeholder’s intention to participate in the DMS. It implies that tourism stakeholders are critically concerned about the DMS operator’s capability in implementing the system. This relationship could be attributed to the fact that in order to prepare for a successful implementation of DMS, DMS operators must possess sufficient financial, technical and human resources readiness to enhance a tourism stakeholder’s participation intention. In line withFoon and Fah (2011),Bhuasiriet al.(2016)andAbdallah et al.(2018), facilitating conditions in this study is the readiness of DMS operator which were empirically confirmed to be a significant factor predicting the intention to participate in the DMS by tourism stakeholders. Tourism stakeholders would have a stronger intention to participate in a DMS when they see the strong readiness of DMS operators in terms of financial, human and technical resources.
Government regulations were found to be significantly and positively influencing a stakeholder’s intention to participate in the DMS. This finding is in line with previous studies that disclosed the influence of government regulations in encouraging IT innovation by firms, as one of the important factors in the adoption of cloud computing for small and medium enterprises (SMEs) (Amini and Bakri, 2015) and government support had a significant and strong positive relation to ICT adoption in SMEs of service sectors in Malaysia (Alam and Mohammad Noor, 2009).
Furthermore, the findings highlighted the mediating role of technology awareness. The results showed the significant indirect effects of performance expectation, social influence, technology competency and competitive pressure on the intention to participate in the national DMS through technology awareness, whereas their direct effects on the intention were not significant. These findings revealed that DMS implementation in Vietnam was still in the initial stage and tourism stakeholders were yet to know deeply about what benefits could be obtained when participating in the system; people in the industry have yet to mention much about the system; tourism stakeholders were yet to prepare properly in technical competency and use technology extensively for building competitive advantage.
Therefore, stakeholders with higher performance expectancy, stronger social influence, technology competency and competitive pressure tend to have higher technology awareness, which then subsequently results in higher participation intention.
Interestingly, the findings further revealed that technology awareness serves as a competitive mediator between DMS operator readiness and the intention to participate in the DMS since both the indirect effect and the direct effect are significant but point in opposite directions (Hairet al., 2017). This implies that higher levels of DMS operator readiness increase the intention to participate in the DMS directly but decrease technology awareness, which in turn“suggests that another mediator may be present whereby the indirect effect’s sign equals that of the direct effect”(Hairet al., 2017, p. 235).
Contrary to expectations, system quality, service quality, technology infrastructure, cooperation and training did not significantly positively influence the intention to participate in the DMS either directly or indirectly.
The findings indicated that service quality had a significant negative effect on the intention to participate in the DMS. This infers that even though the DMS operator and developer do not provide sufficient service quality, tourism stakeholders still show significant intention to participate in the DMS. This contrasts withSenarathnaet al.(2018) who found that quality of service has a significant positive influence on SMEs’adoption of cloud computing technology. Additionally, tourism stakeholders may see the features related to the technology infrastructure as the fundamentals of the system that the DMS developer
Destination
management
system
must cover when implementing the system. They just need to commit to participating in the system. Therefore, these factors might not be significantly relevant to the stakeholders. It is also probably because Vietnamese tourism stakeholders’level of awareness of the vital roles of system quality and technology infrastructure associated with DMS participation is low as the system is new. Therefore, these factors may find a significant positive influence on the stakeholders’participation intentions in the study of the next stage when the system has been implemented.
Surprisingly, training was found to be insignificantly and negatively affecting the intention to participate in the DMS. This implies that training and the intention to participate in the DMS are not associated with each other. This contrasts with the findings ofNdou and Petti (2007)and Sigala (2009) as cited inMartinset al.(2013), which indicates that lack of training is one of the main barriers to adopt DMS by SMEs in tourism destinations.
Furthermore, although the current study indicated that cooperation was not significantly influencing the intention to participate in the DMS by stakeholders, this factor should be further investigated. This is because previous studies showed that lack of cooperation and reliable relationship are the main barriers to the successful implementation of DMS. The relationship between stakeholders and DMS operators, the cooperation between SMTEs and the public–private partnerships are vital for the successful development and implementation of DMS (Buhalis and Deimezi, 2004). Especially, having a reliable relationship between the stakeholders and the DMS operator is vital for increasing DMS adoption (Buhalis and Wagner, 2013;Sigala, 2013).
Conclusion
The results of the study confirmed that the proposed model was theoretically and statistically valid. The important predictors of the tourism stakeholders’ participation in the DMS included technology awareness, information quality, DMS operator readiness and government regulations. The findings highlighted the significant indirect effects of performance expectancy, social influence, technology competency and competitive pressure on the intention to participate in the national DMS through technology awareness, whereas their direct effects on the intention were not significant.
This study contributes to DMS literature by proposing and examining a comprehensive model of enhancing successful DMS implementation. In total, three theoretical models and frameworks, including the UTAUT model, the TOE framework and the updated D&M IS success model, have been integrated into one diagram. It is believed that this combination would achieve a better understanding of the level of IT acceptance and adoption phenomenon, especially for the complex technology of an interorganisational system such as a DMS. Furthermore, the revelation of the significant indirect effects of the influencing factors on the intention to participate in the DMS through technology awareness has a substantial contribution to the theoretical aspect of the study.
The findings of the study have significant practical implications for tourism authorities (at national, provincial and local levels), DMS operators and DMS developers to enhance the successful implementation of the national DMS in Vietnam.
A tourism authority (specifically in this study, the Vietnam National Administration of Tourism–VNAT) should focus on issuing regulations that facilitate stakeholders to join the system, especially raising the awareness levels of tourism stakeholders regarding the DMS to enhance their intention to participate in the system. In addition, to assure the successful implementation of the system, special attention should be paid to the social influence of VNAT on the stakeholders. As the findings show that stakeholders with greater social influence are more aware of the system and more willing to participate in the
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DMS and those with higher technology awareness are more likely to become participants of the DMS.
For DMS operators to ensure the success of the national DMS implementation, in the context of this study, the Tourism Information Technology Center–TITC, that is the current webmaster of official Vietnam tourism websites, needs to properly prepare issues such as policy for the DMS implementation, efficient management, skilled labour force and efficient financial resources. In addition, the results show that service quality had a significant negative influence on the intention to participate in a DMS. It implies that this factor is critical even though they had not yet determined the stakeholders’participation intention. Therefore, DMS operators should consider this issue when the system is implemented.
For DMS developers, the system should be designed based on the stakeholders’ expectations of their performance when they participate in the DMS. Especially, DMS developers should pay special attention to the quality of the information of the system which has a significant influence on the tourism stakeholders’intention to participate in the system.
The information of the destination is widely distributed to visitors via the DMS which is also considered as the official website of the destination (Estev~aoet al., 2014). Therefore, useful and reliable information on the website is more important than ever as it creates the first impression of the destination to visitors and improves their perception of the destination image (Molinillo et al., 2018). Notably, destination image has been found to significantly affect both directly and indirectly tourist visit intention in post-coronavirus disease 2019 (COVID-19) crisis recovery (Ahmadet al., 2020). Hence, tourism destination stakeholders need to work closely together to provide“ethical, responsible, and accurate information about the real situation and the health system’s responses”during the recovery phase of the COVID-19 pandemic’s time (Chemliet al., 2020, p. 1). It is of critical importance as information through various information and communication channels has a significant impact on the awareness of potential outbound travellers for future travelling after this unprecedented time (Chemliet al., 2020).
Although the study did not find a significant influence of the technological factors (i.e.
system quality and technology infrastructure) on the intention to participate in the DMS, a proper system with high-quality features needs to be provided in order to ensure sustainable and long-term development of the system. In addition, DMS developers should, together with the tourism authorities and DMS operators, provide updated information on technology innovations (i.e. DMS) to destination stakeholders to increase their awareness about the system and enhance their participation in the system. Proper information on innovations will help tourism organisations actively integrate innovations in their organisations’operations to enhance competitive advantage as well as take the best benefits from the latest innovations. As a result, they will focus on enhancing technology competency, increasing expectations on their organisations’performance when participating in the system and noticing more about competitive pressure. These will increase their awareness of the system, and then they will be more likely to participate in the system, which will contribute to the successful DMS implementation.
There are several limitations to this study. The study investigates the intention of tourism stakeholders’participation in the national DMS only from the perspective of the supply side.
It is also imperative to research the tourists’demands when using the system. In addition, this study employs a cross-sectional study as it is in the initial stage of DMS implementation.
Hence, to measure the long-term effect of DMS participation amongst tourism stakeholders, a longitudinal study could be applied. Furthermore, multigroup analysis (MGA) should be employed to investigate the opinions of different groups in their decisions to participate in the national DMS. Based on that, the national tourism authority, national DMS operator as well as DMS developer will be able to enhance the successful implementation of the national DMS by formulating appropriate policies and strategies to facilitate all stakeholders’participation
