Next Article in Journal
Bridging Cultural Gaps: Insights from Communication in Migrant-Inclusive Cultural Initiatives
Previous Article in Journal
Clustering Residents’ Perception and Support of Urban Rally Tourism: The Sierra Morena Rally in Córdoba, Spain
Previous Article in Special Issue
Red Tourism and Youth Education: A Systematic Review of the Literature
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Societies
Volume 15
Issue 4
10.3390/soc15040082
societies-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Unlocking Tourist Motivations in a Smart Tourism Destination: An Application of the Push–Pull Theory

by
Sergio Nieves-Pavón
,
Natalia López-Mosquera
* and
Manuel Jesús Sánchez González
Departamento de Economía Financiera y Contabilidad, Facultad de Empresa Finanzas y Turismo, Universidad de Extremadura, 10071 Cáceres, Spain
*
Author to whom correspondence should be addressed.
Societies 2025, 15(4), 82; https://doi.org/10.3390/soc15040082
Submission received: 13 February 2025 / Revised: 21 March 2025 / Accepted: 25 March 2025 / Published: 27 March 2025
(This article belongs to the Special Issue Embodiment and Engagement of Tourism with Social Sustainability)
Browse Figure
Versions Notes

Abstract

:
This study employs the push–pull theory to analyze tourism motivations (push and pull), perceived risk (financial risk and perceived risk), perceived value, educational level and smartphone usage and their effect on willingness to pay (WTP) and electronic word-of-mouth (E-WOM) behavioural intentions in Smart Tourism Destinations (STDs). With a significant sample of 504 respondents in the STD of Cáceres, the push–pull theory is used to assess its impact on smartphone usage. Results reveal that motivations, financial risk, perceived value and educational level positively affect smartphone usage, directly influencing WTP and E-WOM. Managers are advised to prioritize privacy in mobile payments, implement security services against financial risks and promote specialized applications and encourage the personalization of offers through artificial intelligence. Understanding these variables, which explain 41.5% of WTP and 65.8% of E-WOM, provides a basis for strategic decision-making in STDs.

1. Introduction

Around 2.5 billion people around the world use mobile phones, mainly smartphones, to communicate with each other [1]. Smartphones have a number of characteristics (portable, smart, capable of multitasking, providing immediate responses, availability and maintaining constant connectivity) that allow for greater information availability by tourists [2]. However, global interconnectedness and the increasing use of technology and smartphones expose users to a variety of security and privacy risks [3].
All this technological progress has enabled the emergence of Smart Tourism Destinations (STDs) which are based on smart cities [4]. Thus, an STD can be understood as an innovative tourist destination, built on a state-of-the-art technology infrastructure that ensures the sustainable development of tourist areas, accessible to all, facilitates the interaction of visitors with their environment and integration into it, improving the quality of the tourist experience in the destination and raising the quality of life of the residents [5].
The popularity of smartphones has contributed to the development of mobile tourism services with the aim of improving the perceived quality of tourism information and services [6]. Given the constantly evolving expectations of tourists [7], different studies in the tourism field have concentrated their efforts on determining how tourists’ expectations condition the value that tourists perceive of the services offered [8,9,10,11] and how much they are willing to pay for them [12,13,14].
Different variables have been considered around the use of technology in tourism destinations. One of the most studied variables in the tourism literature is motivations; sometimes evaluated through a joint variable [15,16,17,18] and others, through the push and pull dimensions [19,20,21,22]. A number of studies have also addressed E-WOM to measure tourists’ willingness to give positive online feedback about the destination [23,24,25,26]. Finally, technology risks, which encompass financial, infrastructure, device and destination risks, stand out [3,27,28,29].
Numerous tourism-related studies have analyzed the use of socio-demographic variables, such as age, gender, educational attainment and income, to understand their impact on tourists’ behavioural intentions [30,31] and its role in technology development has even been highlighted [32,33]. However, they have rarely been addressed in a joint technology and tourism context [34,35].
In order to explain the underlying reasons that lead tourists to select certain destinations over other available alternatives, it is important to understand the reasons behind the choice of destinations [21], this study has selected the push–pull theory [36]. In turn, the synergy between the push–pull theory and various factors associated with technology, such as virtual reality, has been highlighted [37].
Taking the push–pull theory [36] as a reference, this study aims to address the lack of research in the tourism literature regarding the explanatory power of push and pull motivations, technological risks, perceived value and educational level of tourists in the use of smartphones and their consequent impact on E-WOM and WTP. Therefore, the aim of this research is to analyze whether smartphone adoption can actually have a positive impact on the management of Smart Tourism Destinations (STDs) by driving the generation of online recommendations (E-WOM) and stimulating tourists’ interest in purchasing additional services (WTP).
Understanding these variables and the relationships between them can help tourism managers adapt their products, services and marketing strategies to meet the needs and expectations of tourists. In particular, understanding push and pull motivations can help tourism managers to design experiences in applications or services that facilitate access to tourist information of interest or encourage the sharing of their experiences on social media. By considering technological risks, security measures can be implemented, and appropriate information and assistance can be provided to ensure a safe tourism experience and minimize tourism perceptions around costs and security breaches. In addition, a higher level of education can be associated with a greater ability to use tourism apps and take full advantage of smartphone functionalities. Understanding the educational level of tourists can help managers to tailor their services and provide information and support appropriate to the specific educational conditions of each tourist.
In summary, the main novelty of studying these factors is the ability of tourism managers to make more informed and strategic decisions that enhance the tourist experience and generate positive outcomes for STDs. In this sense, this paper aims to gain a deeper understanding of the factors that affect tourist behaviour in relation to mobile technology. By understanding these variables, tourism managers can tailor their services and marketing strategies to meet tourists’ needs and expectations more effectively.
In this study, the STD of the city of Cáceres (Spain) has been selected as the object of analysis. Cáceres initiated its transformation towards a smart city through its participation in the second phase of the smart city initiative called “Cáceres, Smart Heritage”.

2. Theorical Background

2.1. Push–Pull Theory

The framework of the push–pull theory initially proposed by Ravenstein [38] to analyze population migration and subsequently applied to the tourism field by Dann [39]. This theory postulates that the factors affecting the travel behaviour of tourists are divided into push and pull factors. Push factors are associated with tourism demand, encompassing motivations such as visits to well-known tourist sites, entertainment, relaxation and sporting activities. In contrast, pull factors refer to factors pertaining to tourism supply such as attractiveness of the destination, food, culture and facilities. [40,41]. Therefore, push–pull factors refer to two interconnected aspects of travellers’ motivation, both driven by their needs and interests and linked by emotion [42].
The push–pull theory has been analyzed, in a variety of contexts, to determine population migration between cities [43,44,45]. There is an entrepreneurial intention to undertake [29] or to deal with the effects of technology, communication and email coordination tasks [46]. In the field of tourism, the push–pull theory has received extensive attention and application. Several studies have contributed to the understanding of tourists’ motivations [47,48,49,50]. These studies have allowed for a deeper understanding of the factors influencing travellers’ behaviour, both from the perspective of tourism demand (push) and tourism supply (pull), thus consolidating the relevance and usefulness of the push–pull theory in the analysis of tourism phenomena [51].
The use of the push–pull theory as a theoretical basis for the present study is justified by its previous application in the tourism field, where it has been used to analyze tourists’ motivations regarding revisit behaviour at a destination [19], satisfaction [22] or recommendation to others [52]. At the same time, this theory has proven useful in technology contexts, assessing how it influences people’s behaviours [18] or e-commerce [53]. Therefore, its application is considered appropriate given the prominent role of technologies in tourism destinations through the use of smartphones and the importance attributed by tourists to the economic value of the services offered. Consequently, this research seeks to explore these intentions in conjunction with key variables such as technological risk, perceived value, E-WOM and WTP. This approach aims to provide a holistic view of the factors influencing tourists’ behaviour, considering the relationship between intrinsic and extrinsic motivations, perceptions of value and technological risks on smartphone usage in the destination and, finally, on E-WOM and WTP behavioural intentions.

2.2. Push–Pull Motivations and Smartphone Usage

Motivations, in the field of psychology and human behaviour, are defined as the forces that drive individual actions [54]. This conceptualization is commonly linked to the idea of need, which is the key to understanding human motivational behaviour [55]. In this context, intrinsic curiosity and the pursuit of acquiring novel information and experiences in a context of uncertainty can exert a motivational influence on an individual and induce them to act in a certain way [56].
Several researchers have pointed out that motivational push factors are based on Maslow’s hierarchy of needs theory [57]. These factors have been conceptualized as motivational drives or needs that emerge as a result of an imbalance or tension in the motivational system [36,58,59]. In the context of traveller behaviour, push motivational drives denote that initial actions are triggered by intrinsic desires that encompass diverse aspects such as the search for escape, rest and relaxation, social interaction, health and fitness concerns, the pursuit of adventurous experiences, the desire for prestige and the pursuit of knowledge expansion [60]. In essence, push motivational drives reflect individuals’ willingness to undertake or refrain from a particular action, such as choosing to undertake a trip over alternative activities in the field of tourism [22]. These impulses, triggered by internal needs, play a key role in the traveller’s decision-making process, influencing their choice of tourism activities and destinations.
Pull factors have been described as those that influence where when and how people travel [61], thus exerting a direct influence on the tourist’s choice of destination. In this context, facilities and services play a crucial role [62], taking as a reference the destination’s own characteristics, attractions or attributes [63]. Consequently, some studies have confirmed that factors such as “social opportunities and attractions”, “natural and cultural attractions” and “physical facilities and services” are important when choosing a destination [59,64,65]. Consequently, destination choice arises from tourists’ own evaluations of the destination’s attractions and their perceived usefulness [66].
Numerous research studies have investigated the relationships between push and pull motivations and tourists’ satisfaction and behavioural intentions [19,67,68] as well as the connection between motivations and technology use [18]. However, to date, there are no studies that have reflected the relationship between push and pull motivations and smartphone use. Therefore, when it comes to smartphone use, push and pull motivations may influence how tourists use their mobile devices during their visit to the STD. If a tourist has a push motivation to escape from routine, they are likely to use their smartphone to search for local activities or explore new places. On the other hand, if a tourist has a pull motivation related to tourist attractions, they are likely to use their smartphone to receive information about those places or share their experiences on social media. In addition, both motivations may influence tourists’ willingness to use specific services on their smartphones, such as tourist apps or booking services. If a tourist has a push motivation to experience new places or cultures, they are more likely to use tourist guides on their smartphone or translation apps. On the other hand, if a tourist has a pull motivation related to tourist attractions, they are more likely to use map apps or booking services to plan their visit. Therefore, push and pull motivations can influence tourists’ use of smartphones, as these motivations affect their needs and desires during their trips. This, in turn, can influence how they use their mobile devices and which specific services they use on their smartphones.
H1: 
Higher push motivations lead to increased smartphone usage.
H2: 
Stronger pull motivations lead to increased smartphone usage.

2.3. Technology Risk, Price Value, Education Level and Smartphone Usage

Today, tourists have adopted a more intensive use of technology, and destinations have responded to this trend through the implementation of STDs [69]. Thus, smartphones are mediating the experiences of tourists, making them more innovative [66]. However, this increased reliance on technology by tourists has given rise to a number of security vulnerabilities, increasing the possibility of attacks and data exposure. This has led to a tendency for people to be wary of certain websites and to give out their personal data [70].
Technological risks are understood as the consumer’s perception of the potential negative and uncertain outcomes associated with a transaction [27]. Research has shown that there is a negative relationship between technology risks and smartphone use [66,71].
In this study, in line with our dependent variables, we model technological risks through two constructs: financial risk, associated with tourists’ payment intentions, and device risk, associated with the use of mobile devices in the destination. Financial risk refers to the probability of experiencing unforeseen financial losses due to smartphone use, such as unexpected charges for mobile internet services [72]. Device risk, on the other hand, addresses risks associated with the performance and physical characteristics of the smartphone, such as product defects or malfunctions [73].
In the field of tourism, the assessment of technological risks associated with the use of smartphones has been addressed from various perspectives, albeit in a limited manner. For example, Dayour, Park and Kimbu [3] propose an integrated model of perceived risks that combines destination- and technology-related risk factors of backpackers towards smartphone use, or Li et al. [28], which takes into account the risks perceived by tourism businesses offering services in a destination. Based on these works, we believe that the higher the financial and device risk perceived by tourists, the lower their intention to use smartphones in the destination. Despite consumers’ understanding of the usefulness of smartphones, we believe that the perception of financial risk (additional expenses, such as roaming charges or mobile data charges abroad) and device risk (loss, theft or their personal data being compromised) will reduce their willingness to use them due to the intrinsic concerns associated with costs, security and lack of technological literacy. Tourism managers need to address these concerns and provide clear information and security measures to encourage confidence and adoption of mobile technology by tourists.
H3: 
A higher perception of financial risk reduces smartphone usage.
H4: 
A higher perception of device risk reduces smartphone usage.
Perceived value can be understood as the consumer’s overall evaluation of the usefulness of a product (or service) based on the perception of what is received and what is given in return [74]. Ultimately, perceived value refers to the customer’s perception of the tangible or intangible benefits they receive from a product or service, as well as their perception of whether the money or time they invest is equal to their perceived benefits. Uzir et al. [75] emphasize that perceived value is the result of the trade-off between perceived benefits and perceived costs. Perceived value is thus characterized as an idiosyncratic, experiential, contextual and meaning-laden concept [76].
In the destination context, perceived value influences the ability of the destination’s resources to generate economic benefits for the local tourism industry [77]. Several studies have highlighted that perceived value is a predictor of tourists’ behavioural intentions [78,79,80]. Although perceived value has been applied in the tourism field widely [8,81]; it has never been applied on an STD. Only a few studies have focused on the effect of perceived value on technology, specifically virtual reality [82,83].
Based on the above, it is likely that the value that tourists perceive about the services offered in the STD apps may influence smartphone use in the destination itself, as tourists who perceive greater value of the services and products offered in the destination through the apps will make greater use of smartphones during their visit to the STD. This implies making the most of the products and services and being willing to adapt them to technological changes and to tourists’ tastes and preferences.
H5: 
A higher perceived value of services in the destination leads to increased smartphone usage.
In the area of assessing educational attainment in relation to tourism, little research has been identified. A single study has addressed this specific socio-demographic characteristic by assessing Taiwanese domestic urban tourists during the pandemic, revealing significant differences according to education level [31]. On the other hand, education level has been studied in the context of ICT use. For example, the relationship in online training due to COVID-19 between different educational levels such as undergraduate and postgraduate has been evaluated, indicating that there are significant differences around the level of education [84] or to examine the role of level of education on users’ purchase intentions showing significant differences [85].
Although socio-demographic variables have been studied extensively in both tourism and device and technology use, there are no previous studies that have taken into account the level of education in a context that combines tourism and technology, such as an STD. Therefore, this paper seeks to explore how education affects the use of a mobile device during a visit to an STD. It is understood that those with a higher level of education will make greater use of technology. Consequently, as educational attainment increases, people are more likely to have greater access to technology and a greater understanding of its use. This may be because people with higher levels of education tend to have higher levels of digital literacy and technological skills. They may also have a greater awareness of the benefits of mobile technology in terms of access to information, communication and productivity. They are therefore more likely to use their smartphones more actively and frequently than less educated people. Finally, a higher level of education is associated with greater economic resources [86] which allows them to access smartphones and Internet services more easily. Therefore, educational level is positively related to increased smartphone use, as people with higher levels of education tend to have greater access to technology, a greater understanding of its use and a greater awareness of the benefits it offers.
H6a–e: 
A higher education level leads to increased smartphone usage.

2.4. Smartphone Usage, WTP and E-WOM

The use of smartphones has become widespread among users all over the world because of their ease of information searching [87]. In addition to being an effective medium for data collection, smartphones play a crucial role in virtually connecting tourists with their friends while exploring a destination [88,89]. In this sense, smartphones have become an essential support tool for tourists during their travels [90,91,92]. By providing functional and instant support, it helps visitors to visit the destination, make short-term decisions, and conduct transactions and activities on site [18].
E-WOM is defined as the dynamic and continuous process of information exchange between potential, current or former consumers regarding a product, service, brand or company, accessible to a multitude of people and institutions via the Internet [25]. This concept represents an evolution of traditional word-of-mouth and is distinguished by its diversity, breadth and scalability [93]. The E-WOM is shared through different networks and important platforms, thus social media is considered one of the most appropriate options [24]. The way of expressing E-WOM on the internet can be given in a multitude of ways either through text, images, videos or apps, making this content more enjoyable and attractive to other users [94].
Although the relationship between technology use and E-WOM has not yet been fully demonstrated, it is clearly associated with smartphone usage [26]. Smartphones facilitate and encourage the creation of content on the internet. Social networks, tourism applications and different forums and platforms allow tourists to share their experiences, recommendations and opinions about the destination in a quick and easy way. In addition to all this, the use of smartphones makes it possible to capture and share information in real time that can help other users with enriched content that helps the effectiveness and dissemination of information.
H7: 
More frequent smartphone usage leads to increased engagement in E-WOM.
On the other hand, WTP can be understood as the value that a person is willing or able to pay to obtain a service [95]. There is a predisposition on the part of tourists to assume additional costs in order to access specific activities [96].
WTP is a notorious topic in environmental studies [97,98,99,100]. WTP has also been highlighted in studies related to the tourism field. For example, it has been investigated how the use of different fonts affects travel preference and WTP, highlighting that when it is a relaxation trip the easy-to-read font increases these preferences, while when the trip is an adventure trip the difficult-to-read font increases WTP. Additionally, WTP has been assessed for having preferential access to tourist attractions, revealing that individuals with a greater aversion to waiting times exhibit a greater propensity to incur additional costs [14]. On the other hand, WTP in relation to the use of smartphone apps has also been highlighted in previous studies. For example, through a study related to WTP by self-designed and self-built apps [101] or through WTP by transport services via mobile apps [102].
However, there are no previous studies that have evaluated WTP in the context of an STD. Therefore, this paper addresses the relationship between smartphone use and WTP, as the use of smartphones facilitates faster payments, either physically through virtual cards or through payment gateways offered in apps. This makes tourists more willing to make payments for extra or personalized services. Consequently, the use of smartphones in the destination increases WTP due to the added value offered by smartphones, allowing to contract services and functions in a simpler, more accessible, personalized and efficient way.
H8: 
More frequent smartphone usage leads to an increased WTP.
The hypotheses formulated are integrated into the following model (Figure 1):

3. Methodology

3.1. Sample and Data Collection

The survey items are divided into four sections. The first section addresses demographic characteristics such as year of birth, gender, educational level, monthly income and type of company during the trip. The next sections contain items related to specific variables assessed on a Likert scale (1 = strongly disagree, 7 = strongly agree). The second section includes variables such as push and pull motivations and perceived risks (financial risk and device risk); the third section addresses variables such as smartphone usage and perceived value; the fourth section addresses the explanatory variables of WTP and E-WOM.
To ensure the validity of the items, a focus group was carried out with 30 experts in the field, who participated in the adaptation of the items to the characteristics of the STD in Cáceres. In addition, a pilot study was conducted with a small sample (n = 20) in order to make adjustments according to the feedback received. During the pilot study, it was verified that the factor loadings met the minimum threshold of 0.7 [103]. Table 1 presents the different variables used in this study.
The survey was carried out between April and June 2023 by means of personal surveys. A team of 4 trained professionals was available to answer any questions tourists might have. The staff was located at various tourist points and at the tourist office of the STD of Cáceres. Before conducting the survey, tourists were provided an explanation of the nature of an STD, informed about the privacy policy and data processing and asked if they used tourism apps. The survey was only administered to those who answered in the affirmative. In total, 540 responses were collected, and after eliminating incomplete responses, 504 responses were obtained, with a margin of error of 4.37% and a confidence level of 95%. Socio-demographic data are presented in Table 2.

3.2. Data Analysis

In this study, we used SPSS Statistics 27.0 and SPSS AMOS 28.0 software to perform statistical analysis and structural equation modelling. The analysis was carried out following the methodology provided by Anderson and Gerbing [105]. First, confirmatory factor analysis (CFA) was conducted followed by structural equation modelling (SEM) analysis to examine causal relationships.
The adequacy of the data was assessed by considering various fit indices. The chi-square statistic (χ2) and the degrees of freedom (df) were taken into account, with the χ2/df value being less than 5. In addition, indices such as the goodness-of-fit index (GFI), the comparative fit index (CFI) and the normalized fit index (NFI) were considered, with values above 0.80 and 0.90 being ideal, respectively. The robustness of the root mean square error of approximation (RMSEA) was also assessed, with a range between 0.05 and 0.08 being ideal.

4. Results

All the variables in the model show an adequate fit in terms of reliability, with a Cronbach’s alpha greater than 0.70 (Table 3), which indicates a high internal consistency of the constructs [106]. The convergent validity is satisfactory, as the average variance extracted (AVE) exceeds the threshold of 0.50 (Table 3) [107], confirming that the indicators explain a significant proportion of the variance of each construct. Similarly, discriminant validity is verified according to the criteria established in the literature (Table 4) [107], ensuring the independence between the constructs of the model. In addition, the EFA confirms the unidimensionality of the variables, which supports the theoretical structure of the model and guarantees its suitability as a measurement tool [108].
The CFA results revealed a solid fit between the model and the data (χ2 = 1335.434, df = 506, χ2/df = 2.64, CFI = 0.945, RMSEA = 0.05). The SEM model exhibits an acceptable fit (χ2 = 2181.160, df = 723, χ2/df = 3.017, CFI = 0.916, GFI = 0.814, RMSEA = 0.06). All relationships are presented in Table 5. With regard to the hypotheses formulated, it was evident that push motivations had a positive and significant effect on smartphone usage (β = 0.568; t = 6.102), which allowed H1 to be accepted. Similarly, pull motivations demonstrated a positive and significant influence (β = 0.199; t = 2.170), confirming H2.
In relation to technological risks, it was found that financial risk had a significant negative effect on smartphone usage (β = −0.104; t = −3.201), which supported H3; in contrast, device risk did not show a significant relationship (β = −0.009; t = −0.271), thus rejecting H4. In addition, it was observed that perceived value was positively and significantly associated with smartphone usage (β = 0.191; t = 5.821), confirming H5.
With regard to educational level, a positive and significant effect was found at all academic levels evaluated. Primary education (β = 0.125; t = 1.851), secondary education (β = 0.252; t = 2.041), bachelor’s degree (β = 0.235; t = 1.990), professional training (β = 0.457; t = 2.140) and university degree or higher (β = 0.417; t = 2.632) were significantly associated with smartphone use, which allowed us to accept H6a–e.
Finally, smartphone usage was positively and highly significantly related to both the intention to recommend the destination through electronic means (E-WOM) (β = 0.813; t = 20.477) and the willingness to pay for additional services (WTP) (β = 0.647; t = 16.264), which allowed us to accept H7 and H8. Overall, through R2, it was possible to verify that the model explained 65.8% of the variance in E-WOM and 41.5% in WTP.

5. Discussion and Implications

5.1. Discussion

If we focus on the influence that each of the independent variables has on smartphone use in the destination, we find that push motivations are a determining factor in smartphone use in the destination (H1). This indicates that the needs, interests and desires that lead tourists to take a trip influence the use of devices to search for information, share their experience or stay connected. This relationship has previously been supported by other authors [22,67]. On the other hand, similarly, we can find that pull motivations have a positive effect on smartphone use (H2). Therefore, the motivations that attract tourists to a destination influence how people use their smartphones during the trip, to consult information such as cultural experiences, capture visual memories or explore gastronomy and tourist sites. This relationship has been previously argued in other studies [18,19].
Regarding the following relationship, we can find that financial risk has a negative effect on the use of smartphones in the destination (H3), this is in line with other studies [3,28]. All this indicates that tourists perceive a risk in using paid services in the destination and therefore inhibits the use of the devices. On the other hand, the relationship between device risk and smartphone use has not been tested (H4), although other studies have confirmed it [3].
It can also be observed that perceived value has a positive effect on smartphone use (H5). This relationship has been tested before [82,83]. Thus, individuals who perceive that the services offered in the STD are fairly priced and provide value for money are more likely to use smartphones during their visit to the destination.
Finally, with regard to the relationship between educational level and smartphone use, we have found that the more educated tourists are, the more they use smartphones (H6). This relationship has been previously argued for in other contexts [32,109,110] but never through the combination of technology and tourism and tourists’ willingness to use smartphones in the destination. This finding suggests that people with a higher educational background opt to use smartphones to obtain information during their visit to the destination.
If we focus on the relationship between smartphone use and the dependent variables, we find a positive relationship between smartphone use and E-WOM (H7), a relationship that has been previously advocated [26].
Finally, we can observe a positive relationship with WTP (H8), a relationship that has been previously supported in other studies [14,102]. This tells us that individuals using mobile devices in the STD show a higher willingness to pay for additional services.
In addition to these findings, our results suggest that regional differentiation may play an important role. Specifically, local contextual factors—such as the technological infrastructure available in the STD of Cáceres, cultural attitudes towards technology and the socio-economic conditions of the region—could be influencing the observed differences in smartphone usage and the corresponding behavioural intentions. These regional characteristics might strengthen or moderate the relationships posited in our hypotheses. For example, a robust local infrastructure and a culture that embraces digital innovation may enhance the effect of push and pull motivations on smartphone usage, thereby also impacting E-WOM and WTP more strongly than in regions with less advanced conditions.

5.2. Theorical Implications

On a theoretical level, this research contributes to the field of tourism, and specifically to smart tourism, by integrating motivational factors from two perspectives—push and pull—along with factors related to the risks associated with smartphone use and educational level. This study is based on the application of the push–pull theory [38,39] as a theoretical foundation, allowing for a comprehensive analysis of the various decision-making areas of tourists, as well as their influence on smartphone use and the behavioural intentions of E-WOM and WTP. By examining these elements together, the research contributes to the existing literature, revealing what motivates tourists to visit a destination, what drives or inhibits smartphone use, what encourages them to recommend a destination, or what induces them to pay for certain services at a destination.
Furthermore, this study deepens the application of the push–pull motivation model by incorporating variables such as financial risk, device risk, and how educational level affects smartphone use, providing a greater understanding of tourist behaviour in a technology-driven environment.
In conclusion, our study through the push–pull motivations theory shows that the components (push and pull motivations, education level, perceived value, financial risk, device risk, and smartphone usage) explain 41.5% of WTP and 65.8% of E-WOM. All this demonstrates the suitability of applying this theory in the context of an STD. Understanding these factors contributes significantly to the identification of key elements that influence tourists’ behavioural intentions, providing valuable information for the effective management and promotion of sustainable tourism destinations.

5.3. Managerial Implications

The findings have significant implications for STD managers and businesses operating in STDs, highlighting the importance of considering privacy in mobile payments, device usage, the motivations that drive tourists to visit an STD and the perceived value of the services offered. A holistic understanding of tourists’ behavioural intentions and trends in a STD can contribute to strengthening, inhibiting or encouraging WTP and E-WOM behavioural intentions.
In this context, it is strategically suggested that managers of such destinations implement the development of specialized apps. These apps could focus on enhancing the personal motivations of tourists by offering, for example, cultural guides and platforms that highlight tourist sites and gastronomic services. In addition, it is proposed to consider the integration of advanced technologies, such as augmented reality, into tourism applications. This integration would allow providing contextualized interactive experiences with the destination, thus seeking to motivate tourists to explore the place in a deeper and more meaningful way. These initiatives could contribute to enriching the tourist experience and promote greater interaction with the sustainable destination’s tourism resources.
To mitigate financial risks and encourage the use of smartphones in an STD, it is recommended to implement security services, such as recognized security standards and seals. These seals can provide tourists with the assurance that their financial transactions are being conducted in secure environments, which could increase confidence in using mobile technologies for transactions during their visit to the sustainable tourism destination. Additionally, the integration of specific emergency services to address fraud exposure by other tourists in the STD is suggested. These services could be incorporated into mobile applications developed by sustainable destination managers, allowing tourists to quickly report possible fraudulent activities or risky situations. This measure would not only contribute to improving perceived safety but would also strengthen the image of a destination committed to the protection and well-being of its visitors.
Thus, it is recommended that STD managers and local businesses implement strategies offering personalized promotions and discounts, as well as real-time creation of tourism packages. In this regard, the use of technologies such as artificial intelligence (AI) can be particularly beneficial, as it enables real-time analysis of tourist information and provides specific recommendations based on personal preferences, length of stay, or available budget. This approach fosters the continuous use of smartphones to access these offers, while simultaneously enhancing the visitor’s overall experience.
Additionally, destination managers should recognize and adapt to the trend of tourists with higher levels of education adopting mobile technologies. Implementing educational applications that include historical, cultural, or destination-specific tourism information can be especially appealing to this segment. Incorporating enriching content and interactive experiences, such as virtual tours or gamification elements, will facilitate deeper visitor immersion in the destination. Likewise, designing targeted digital promotion campaigns for this audience—highlighting the destination’s advanced technological features and providing personalized information through mobile platforms—is recommended.
In parallel, fostering the organic exchange of information and experiences requires destination managers to integrate social media into tourism applications. This way, visitors can easily share their experiences and generate real-time content. To encourage such behaviour, destination officials could offer rewards or special promotions to those who post reviews and positive feedback, thereby increasing the destination’s visibility and reputation in the digital environment.
Finally, businesses operating in STDs should invest in strategies that maximize the perceived value of the experiences offered. One promising approach involves designing multisensory experiences that leverage the capabilities of mobile devices to create interactive content, thereby enhancing the sense of quality and excitement. Likewise, offering personalized and exclusive services—such as virtual guided tours, preferential access to tourist attractions, or special discounts at local establishments—is advisable. These initiatives not only enrich the tourist’s overall experience but also stimulate visitors’ willingness to pay for additional services, thus contributing to the sustainable growth of these destinations.

6. Conclusions

This study highlights the importance of integrating several variables, such as push and pull motivations, financial and device risks, educational attainment and perceived value. These variables are identified as factors influencing smartphone use in the STD. In addition, smartphone usage is found to have a significant influence on willingness to pay (WTP) and online recommendations (E-WOM).
In light of the results obtained, it is suggested that STD managers and the companies offering services in the STD should carry out different strategies to inhibit the financial risks associated with the destination, encourage and enhance motivations and improve the quality and services offered in the STD in order to enhance the WTP and E-WOM of the STD. This could be achieved by offering customized plans, active promotion on social networks, AI support and the introduction of innovative services that add value to the tourist experience.
With regard to the limitations identified in this study, it is important to note that our research has been restricted to a single Smart Tourism Destination (STD) in Cáceres, which may affect the external validity and generalizability of our findings. Moreover, the study focused primarily on technology linked to the use of smartphones, thereby not fully exploring the potential impacts of other emerging technologies on the tourist experience. Finally, since our analysis has been predominantly quantitative, some of the emotional, cultural and social dimensions influencing tourist behaviour might not have been fully captured.
Future studies should replicate and extend our model across multiple tourism destinations in diverse geographic and cultural contexts to enhance the generalizability of the results. Additionally, it would be beneficial to examine the role of emerging technologies—such as augmented reality, virtual reality and artificial intelligence—in conjunction with smartphone usage, and to explore how these innovations further influence tourist decision-making, WTP and E-WOM. Finally, adopting complementary qualitative or mixed-methods approaches could provide richer insights into the emotional, cultural and social factors that drive tourist behaviour, thereby deepening our understanding of these complex phenomena.

Author Contributions

Conceptualization, S.N.-P. and N.L.-M.; methodology, S.N.-P. and N.L.-M.; software, S.N.-P. and N.L.-M.; validation, S.N.-P. and N.L.-M.; formal analysis, S.N.-P., N.L.-M. and M.J.S.G.; investigation, S.N.-P. and N.L.-M.; resources, S.N.-P. and N.L.-M.; data curation, S.N.-P. and N.L.-M.; writing—original draft preparation, S.N.-P., N.L.-M. and M.J.S.G.; writing—review and editing, S.N.-P., N.L.-M. and M.J.S.G.; visualization, S.N.-P., N.L.-M. and M.J.S.G.; supervision, S.N.-P., N.L.-M. and M.J.S.G.; project administration, N.L.-M.; funding acquisition, S.N.-P., N.L.-M. and M.J.S.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The information you provide will be treated confidentially and used solely for the stated purpose. No data will be disclosed to third parties unless legally required. Data processing is carried out in accordance with Regulation (EU) 2016/679 on Data Protection and with Organic Law 3/2018 on the Protection of Personal Data and the guarantee of digital rights.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Oviedo-Trespalacios, O.; Nandavar, S.; Newton, J.D.A.; Demant, D.; Phillips, J.G. Problematic Use of Mobile Phones in Australia…Is It Getting Worse? Front. Psychiatry 2019, 10, 105. [Google Scholar] [CrossRef] [PubMed]
  2. Okazaki, S. Fundamentals of Mobile Marketing: Theories and Practices; Peter Lang: New York, NY, USA, 2012; ISBN 978-1-4331-1561-5. [Google Scholar]
  3. Dayour, F.; Park, S.; Kimbu, A.N. Backpackers’ Perceived Risks towards Smartphone Usage and Risk Reduction Strategies: A Mixed Methods Study. Tour. Manag. 2019, 72, 52–68. [Google Scholar] [CrossRef]
  4. Jovicic, D.Z. From the Traditional Understanding of Tourism Destination to the Smart Tourism Destination. Curr. Issues Tour. 2017, 22, 276–282. [Google Scholar] [CrossRef]
  5. Lopez de Avila, A. Smart Destinations: XXI Century Tourism. In Proceedings of the ENTER2015 Conference on Information and Communication Technologies in Tourism, Lugano, Switzerland, 3–6 February 2015. [Google Scholar]
  6. Goh, D.H.; Lee, C.S.; Ang, R.P.; Lee, C.K. Determining Services for the Mobile Tourist. J. Comput. Inf. Syst. 2010, 51, 31–40. [Google Scholar]
  7. Buhalis, D.; Amaranggana, A. Smart Tourism Destinations. In Information and Communication Technologies in Tourism 2014, Proceedings of the International Conference, Dublin, Ireland, 21–24 January 2014; Xiang, Z., Tussyadiah, I., Eds.; Springer International Publishing: Cham, Switzerland, 2014; pp. 553–564. [Google Scholar]
  8. Caber, M.; Albayrak, T.; Crawford, D. Perceived Value and Its Impact on Travel Outcomes in Youth Tourism. J. Outdoor Recreat. Tour. 2020, 31, 100327. [Google Scholar] [CrossRef]
  9. Choi, H.C.; Huang, S.; Choi, H.; Chang, H. The Effect of Flight Attendants’ Physical Attractiveness on Satisfaction, Positive Emotion, Perceived Value, and Behavioral Intention. J. Hosp. Tour. Manag. 2020, 44, 19–29. [Google Scholar] [CrossRef]
  10. Elshaer, A.; Huang, R. Perceived Value within an International Hospitality Learning Environment: Antecedents and Outcomes. J. Hosp. Leis. Sport Tour. Educ. 2023, 32, 100429. [Google Scholar] [CrossRef]
  11. Shah, A.M.; Yan, X.; Shah, S.A.A.; Ali, M. Customers’ Perceived Value and Dining Choice through Mobile Apps in Indonesia. Asia Pac. J. Mark. Logist. 2020, 33, 1–28. [Google Scholar] [CrossRef]
  12. Boden, J.; Maier, E.; Wilken, R. The Effect of Credit Card versus Mobile Payment on Convenience and Consumers’ Willingness to Pay. J. Retail. Consum. Serv. 2020, 52, 101910. [Google Scholar] [CrossRef]
  13. Booth, H.; Mourato, S.; Milner-Gulland, E.J. Investigating Acceptance of Marine Tourism Levies, to Cover the Opportunity Costs of Conservation for Coastal Communities. Ecol. Econ. 2022, 201, 107578. [Google Scholar] [CrossRef]
  14. Hernandez-Maskivker, G.-M.; Nicolau, J.L.; Ryan, G.; Valverde, M. A Reference-Dependent Approach to WTP for Priority. Tour. Manag. 2019, 71, 165–172. [Google Scholar] [CrossRef]
  15. Liang, S.; Schuckert, M.; Law, R.; Masiero, L. The Relevance of Mobile Tourism and Information Technology: An Analysis of Recent Trends and Future Research Directions. J. Travel Tour. Mark. 2017, 34, 732–748. [Google Scholar] [CrossRef]
  16. Rejón-Guardia, F.; Rialp-Criado, J.; García-Sastre, M.A. The Role of Motivations and Satisfaction in Repeat Participation in Cycling Tourism Events. J. Outdoor Recreat. Tour. 2023, 43, 100664. [Google Scholar] [CrossRef]
  17. Song, M.; Wang, Y.; Guo, R. How Do I Remind You? The Combined Effect of Purchase Motivation and Reminding Message Content on Tourism Consumers’ Verification Behavior. J. Hosp. Tour. Manag. 2023, 57, 133–142. [Google Scholar] [CrossRef]
  18. Tan, W.-K. The Relationship between Smartphone Usage, Tourist Experience and Trip Satisfaction in the Context of a Nature-Based Destination. Telemat. Inform. 2017, 34, 614–627. [Google Scholar] [CrossRef]
  19. Bayih, B.E.; Singh, A. Modeling Domestic Tourism: Motivations, Satisfaction and Tourist Behavioral Intentions. Heliyon 2020, 6, e04839. [Google Scholar] [CrossRef]
  20. Caber, M.; Albayrak, T. Push or Pull? Identifying Rock Climbing Tourists’ Motivations. Tour. Manag. 2016, 55, 74–84. [Google Scholar] [CrossRef]
  21. Li, F.; Qi, H. An Investigation of Push and Pull Motivations of Chinese Tourism Doctoral Students Studying Overseas. J. Hosp. Leis. Sport Tour. Educ. 2019, 24, 90–99. [Google Scholar] [CrossRef]
  22. Wong, B.K.M.; Musa, G.; Taha, A.Z. Malaysia My Second Home: The Influence of Push and Pull Motivations on Satisfaction. Tour. Manag. 2017, 61, 394–410. [Google Scholar] [CrossRef]
  23. Abubakar, A.M.; Ilkan, M.; Meshall Al-Tal, R.; Eluwole, K.K. eWOM, Revisit Intention, Destination Trust and Gender. J. Hosp. Tour. Manag. 2017, 31, 220–227. [Google Scholar] [CrossRef]
  24. Erkan, I.; Evans, C. The Influence of eWOM in Social Media on Consumers’ Purchase Intentions: An Extended Approach to Information Adoption. Comput. Hum. Behav. 2016, 61, 47–55. [Google Scholar] [CrossRef]
  25. Ismagilova, E.; Dwivedi, Y.K.; Slade, E.; Williams, M.D. Electronic Word of Mouth (eWOM) in the Marketing Context; SpringerBriefs in Business; Springer International Publishing: Cham, Switzerland, 2017; ISBN 978-3-319-52458-0. [Google Scholar]
  26. Verma, D.; Dewani, P.P.; Behl, A.; Dwivedi, Y.K. Understanding the Impact of eWOM Communication through the Lens of Information Adoption Model: A Meta-Analytic Structural Equation Modeling Perspective. Comput. Hum. Behav. 2023, 143, 107710. [Google Scholar] [CrossRef]
  27. Im, I.; Kim, Y.; Han, H.-J. The Effects of Perceived Risk and Technology Type on Users’ Acceptance of Technologies. Inf. Manag. 2008, 45, 1–9. [Google Scholar] [CrossRef]
  28. Li, J.; Feng, Y.; Li, G.; Sun, X. Tourism Companies’ Risk Exposures on Text Disclosure. Ann. Tour. Res. 2020, 84, 102986. [Google Scholar] [CrossRef]
  29. Martínez-Cañas, R.; Ruiz-Palomino, P.; Jiménez-Moreno, J.J.; Linuesa-Langreo, J. Push versus Pull Motivations in Entrepreneurial Intention: The Mediating Effect of Perceived Risk and Opportunity Recognition. Eur. Res. Manag. Bus. Econ. 2023, 29, 100214. [Google Scholar] [CrossRef]
  30. Cheah, J.-H.; Amaro, S.; Roldán, J.L. Multigroup Analysis of More than Two Groups in PLS-SEM: A Review, Illustration, and Recommendations. J. Bus. Res. 2023, 156, 113539. [Google Scholar] [CrossRef]
  31. Liu, S.-T. Urban Tourist Profiles during the Pandemic in Taiwan: A Multigroup Analysis. Heliyon 2023, 9, e14157. [Google Scholar] [CrossRef] [PubMed]
  32. Abbasi, A.Z.; Shamim, A.; Ting, D.H.; Hlavacs, H.; Rehman, U. Playful-Consumption Experiences and Subjective Well-Being: Children’s Smartphone Usage. Entertain. Comput. 2021, 36, 100390. [Google Scholar] [CrossRef]
  33. Park, Y.; Lee, S. Gender Differences in Smartphone Addiction and Depression among Korean Adolescents: Focusing on the Internal Mechanisms of Attention Deficit and Self-Control. Comput. Hum. Behav. 2022, 136, 107400. [Google Scholar] [CrossRef]
  34. Jiménez-Barreto, J.; Rubio, N.; Campo, S. Destination Brand Authenticity: What an Experiential Simulacrum! A Multigroup Analysis of Its Antecedents and Outcomes through Official Online Platforms. Tour. Manag. 2020, 77, 104022. [Google Scholar] [CrossRef]
  35. Palos-Sanchez, P.; Saura, J.R.; Velicia-Martin, F.; Cepeda-Carrion, G. A Business Model Adoption Based on Tourism Innovation: Applying a Gratification Theory to Mobile Applications. Eur. Res. Manag. Bus. Econ. 2021, 27, 100149. [Google Scholar] [CrossRef]
  36. Dann, G.M.S. Anomie, Ego-Enhancement and Tourism. Ann. Tour. Res. 1977, 4, 184–194. [Google Scholar] [CrossRef]
  37. Zhang, J.; Luximon, Y.; Li, Q. Seeking Medical Advice in Mobile Applications: How Social Cue Design and Privacy Concerns Influence Trust and Behavioral Intention in Impersonal Patient–Physician Interactions. Comput. Hum. Behav. 2022, 130, 107178. [Google Scholar] [CrossRef]
  38. Ravenstein, E.G. The Laws of Migration. J. Stat. Soc. Lond. 1885, 48, 167–235. [Google Scholar] [CrossRef]
  39. Dann, G.M.S. Tourist Motivation an Appraisal. Ann. Tour. Res. 1981, 8, 187–219. [Google Scholar] [CrossRef]
  40. Jafari, J.; Honggen, X. Encyclopedia of Tourism; Springer: Cham, Switzerland, 2020; Volume 29, pp. 885–886. [Google Scholar] [CrossRef]
  41. Kim, S.-S.; Lee, C.-K. Push and Pull Relationships. Ann. Tour. Res. 2002, 29, 257–260. [Google Scholar] [CrossRef]
  42. Wang, L.; Li, X.; Zhu, H.; Zhao, Y. Influencing Factors of Livestream Selling of Fresh Food Based on a Push-Pull Model: A Two-Stage Approach Combining Structural Equation Modeling (SEM) and Artificial Neural Network (ANN). Expert Syst. Appl. 2023, 212, 118799. [Google Scholar] [CrossRef]
  43. Jiang, Y.; Zhang, J.; Jin, X.; Ando, R.; Chen, L.; Shen, Z.; Ying, J.; Fang, Q.; Sun, Z. Rural Migrant Workers’ Intentions to Permanently Reside in Cities and Future Energy Consumption Preference in the Changing Context of Urban China. Transp. Res. Part D Transp. Environ. 2017, 52, 600–618. [Google Scholar] [CrossRef]
  44. Niu, F. A Push-Pull Model for Inter-City Migration Simulation. Cities 2022, 131, 104005. [Google Scholar] [CrossRef]
  45. Tartakovsky, E.; Patrakov, E.; Nikulina, M. Factors Affecting Emigration Intentions in the Diaspora Population: The Case of Russian Jews. Int. J. Intercult. Relat. 2017, 59, 53–67. [Google Scholar] [CrossRef]
  46. Shih, H.-P. Technology-Push and Communication-Pull Forces Driving Message-Based Coordination Performance. J. Strateg. Inf. Syst. 2006, 15, 105–123. [Google Scholar] [CrossRef]
  47. Lin, J.; Zhang, B.; Feng, J.; Yi, Z.; Zhang, H.; Luo, M.; Zhong, Z.; Zhao, F. Determining Food Tourism Consumption of Wild Mushrooms in Yunnan Provence, China: A Projection-Pursuit Approach. Heliyon 2023, 9, e14638. [Google Scholar] [CrossRef] [PubMed]
  48. Lyu, J.; Huang, H.; Mao, Z. Middle-Aged and Older Adults’ Preferences for Long-Stay Tourism in Rural China. J. Destin. Mark. Manag. 2021, 19, 100552. [Google Scholar] [CrossRef]
  49. Mody, M.; Day, J.; Sydnor, S.; Jaffe, W.; Lehto, X. The Different Shades of Responsibility: Examining Domestic and International Travelers’ Motivations for Responsible Tourism in India. Tour. Manag. Perspect. 2014, 12, 113–124. [Google Scholar] [CrossRef]
  50. Yousaf, S.; Liu, Y.; Feite, L. Dark Personality Traits and Sensation-Seeking Tourist Behaviors. Is There a Link? A Preliminary Investigation of Chinese Tourists. Tour. Manag. Perspect. 2023, 47, 101119. [Google Scholar] [CrossRef]
  51. Sun, X.; Xu, H. Lifestyle Tourism Entrepreneurs’ Mobility Motivations: A Case Study on Dali and Lijiang, China. Tour. Manag. Perspect. 2017, 24, 64–71. [Google Scholar] [CrossRef]
  52. Xu, J.; Chan, S. A New Nature-Based Tourism Motivation Model: Testing the Moderating Effects of the Push Motivation. Tour. Manag. Perspect. 2016, 18, 107–110. [Google Scholar] [CrossRef]
  53. Tsai, P.-H.; Tang, J.-W. Consumers’ Switching Intention towards E-Commerce Platforms’ Store-to-Store Pickup Services: The Application of the Extended PPM Model. J. Retail. Consum. Serv. 2023, 75, 103535. [Google Scholar] [CrossRef]
  54. Schiffman, G.L.; Kanuk, L.L. Consumer Behavior: Pearson International Edition; Recording for the Blind & Dyslexic: Princeton, NJ, USA, 2009. [Google Scholar]
  55. Pizam, A.; Mansfeld, Y. Consumer Behavior in Travel and Tourism; Psychology Press: Hove, UK, 1999; ISBN 978-0-7890-0610-3. [Google Scholar]
  56. Sharpley, R.A.J.; Stone, P.R. (Re)presenting the Macabre: Interpretation, Kitschification and Authenticity. In The Darker Side of Travel: The Theory and Practice of Dark Tourism; Channel View Publications: Bristol, UK, 2009; pp. 109–128. ISBN 978-1-84541-115-2. [Google Scholar]
  57. Mayo, E.J.; Jarvis, L.P. The Psychology of Leisure Travel. Effective Marketing and Selling of Travel Services; CBI Publishing Company, Inc.: Boston, MA, USA, 1981. [Google Scholar]
  58. Iso-Ahola, S.E. Toward a Social Psychological Theory of Tourism Motivation: A Rejoinder. Ann. Tour. Res. 1982, 9, 256–262. [Google Scholar] [CrossRef]
  59. Kim, S.S.; Lee, C.-K.; Klenosky, D.B. The Influence of Push and Pull Factors at Korean National Parks. Tour. Manag. 2003, 24, 169–180. [Google Scholar] [CrossRef]
  60. Uysal, M.; Jurowski, C. Testing the Push and Pull Factors. Ann. Tour. Res. 1994, 21, 844–846. [Google Scholar] [CrossRef]
  61. Mill, C.; Morrison, A.M. The Tourism System: An Introductory Text; Prentice Hall Internacional Editorial: Englewood Cliffs, NJ, USA, 1985. [Google Scholar]
  62. Kim, K.; Oh, I.-K.; Jogaratnam, G. College Student Travel: A Revised Model of Push Motives. J. Vacat. Mark. 2007, 13, 73–85. [Google Scholar] [CrossRef]
  63. Klenosky, D.B. The “Pull” of Tourism Destinations: A Means-End Investigation. J. Travel Res. 2002, 40, 396–403. [Google Scholar] [CrossRef]
  64. Fakeye, P.C.; Crompton, J.L. Image Differences between Prospective, First-Time, and Repeat Visitors to the Lower Rio Grande Valley. J. Travel Res. 1991, 30, 10–16. [Google Scholar] [CrossRef]
  65. Hu, Y.; Ritchie, J.R.B. Measuring Destination Attractiveness: A Contextual Approach. J. Travel Res. 1993, 32, 25–34. [Google Scholar] [CrossRef]
  66. Kim, M.-K.; Chang, Y.; Wong, S.F.; Park, M.-C. The Effect of Perceived Risks and Switching Barriers on the Intention to Use Smartphones among Non-Adopters in Korea. Inf. Dev. 2013, 31, 258–269. [Google Scholar] [CrossRef]
  67. Khuong, M.N.; Ha, H.T.T. The Influences of Push and Pull Factors on the International Leisure Tourists’ Return Intention to Ho Chi Minh City, Vietnam—A Mediation Analysis of Destination Satisfaction. Int. J. Trade Econ. Financ. 2014, 5, 490. [Google Scholar] [CrossRef]
  68. Vada, S.; Filep, S.; Moyle, B.; Gardiner, S.; Tuguinay, J. Welcome Back: Repeat Visitation and Tourist Wellbeing. Tour. Manag. 2023, 98, 104747. [Google Scholar] [CrossRef]
  69. Celdran Bernabeu, M.A. New Scenarios for the Planning and Management of Tourist Destinations. The Smart Tourism Destinations Approach. Bol. La Asoc. Geogr. Esp. 2018, 79, 2727. [Google Scholar]
  70. Awad, N.F.; Krishnan, M.S. The Personalization Privacy Paradox: An Empirical Evaluation of Information Transparency and the Willingness to Be Profiled Online for Personalization. MIS Q. 2006, 30, 13–28. [Google Scholar] [CrossRef]
  71. Lee, M.-C. Factors Influencing the Adoption of Internet Banking: An Integration of TAM and TPB with Perceived Risk and Perceived Benefit. Electron. Commer. Res. Appl. 2009, 8, 130–141. [Google Scholar] [CrossRef]
  72. Featherman, M.S.; Pavlou, P.A. Predicting E-Services Adoption: A Perceived Risk Facets Perspective. Int. J. Hum.-Comput. Stud. 2003, 59, 451–474. [Google Scholar] [CrossRef]
  73. Mitchell, V. Consumer Perceived Risk: Conceptualisations and Models. Eur. J. Mark. 1999, 33, 163–195. [Google Scholar] [CrossRef]
  74. Zeithaml, V.A. Consumer Perceptions of Price, Quality, and Value: A Means-End Model and Synthesis of Evidence. J. Mark. 1988, 52, 2–22. [Google Scholar] [CrossRef]
  75. Uzir, M.U.H.; Al Halbusi, H.; Thurasamy, R.; Thiam Hock, R.L.; Aljaberi, M.A.; Hasan, N.; Hamid, M. The Effects of Service Quality, Perceived Value and Trust in Home Delivery Service Personnel on Customer Satisfaction: Evidence from a Developing Country. J. Retail. Consum. Serv. 2021, 63, 102721. [Google Scholar] [CrossRef]
  76. Vargo, S.L.; Lusch, R.F. Institutions and Axioms: An Extension and Update of Service-Dominant Logic. J. Acad. Mark. Sci. 2016, 44, 5–23. [Google Scholar] [CrossRef]
  77. Modica, P.; Uysal, M. (Eds.) The Various Faces of Competitiveness in Tourism: A Survey of Existing Literature; CABI: Wallingford, UK, 2016; ISBN 978-1-78064-542-1. [Google Scholar]
  78. Cronin, J.J.; Brady, M.K.; Hult, G.T.M. Assessing the Effects of Quality, Value, and Customer Satisfaction on Consumer Behavioral Intentions in Service Environments. J. Retail. 2000, 76, 193–218. [Google Scholar] [CrossRef]
  79. Ramseook-Munhurrun, P.; Seebaluck, V.N.; Naidoo, P. Examining the Structural Relationships of Destination Image, Perceived Value, Tourist Satisfaction and Loyalty: Case of Mauritius. Procedia—Soc. Behav. Sci. 2015, 175, 252–259. [Google Scholar] [CrossRef]
  80. Zhang, M.; Zhao, L.; Zhang, Y.; Liu, Y.; Luo, N. Effects of Destination Resource Combination on Tourist Perceived Value: In the Context of Chinese Ancient Towns. Tour. Manag. Perspect. 2021, 40, 100899. [Google Scholar] [CrossRef]
  81. Huang, Y.-C. Low-Cost Airlines Not So Low-Cost—Exploring the Relationships among Travel Motivation, Service Quality and Satisfaction: The Moderating Roles of Perceived Value. Res. Transp. Bus. Manag. 2023, 49, 101008. [Google Scholar] [CrossRef]
  82. Jiang, S.; Scott, N.; Tao, L. Antecedents of Augmented Reality Experiences: Potential Tourists to Shangri-La Potatso National Park, China. Asia Pac. J. Tour. Res. 2019, 24, 1034–1045. [Google Scholar] [CrossRef]
  83. Vishwakarma, P.; Mukherjee, S.; Datta, B. Travelers’ Intention to Adopt Virtual Reality: A Consumer Value Perspective. J. Destin. Mark. Manag. 2020, 17, 100456. [Google Scholar] [CrossRef]
  84. Tang, Y.M.; Chen, P.C.; Law, K.M.Y.; Wu, C.H.; Lau, Y.; Guan, J.; He, D.; Ho, G.T.S. Comparative Analysis of Student’s Live Online Learning Readiness during the Coronavirus (COVID-19) Pandemic in the Higher Education Sector. Comput. Educ. 2021, 168, 104211. [Google Scholar] [CrossRef]
  85. Kizgin, H.; Jamal, A.; Dwivedi, Y.K.; Rana, N.P. The Impact of Online vs. Offline Acculturation on Purchase Intentions: A Multigroup Analysis of the Role of Education. J. Bus. Res. 2021, 130, 724–735. [Google Scholar] [CrossRef]
  86. Marrero-Rodríguez, R.; Morini-Marrero, S.; Ramos-Henriquez, J.M. Tourism Jobs in Demand: Where the Best Contracts and High Salaries Go at Online Offers. Tour. Manag. Perspect. 2020, 35, 100721. [Google Scholar] [CrossRef]
  87. Verkasalo, H.; López-Nicolás, C.; Molina-Castillo, F.J.; Bouwman, H. Analysis of Users and Non-Users of Smartphone Applications. Telemat. Inform. 2010, 27, 242–255. [Google Scholar] [CrossRef]
  88. Lee, K.; Lee, H.R.; Ham, S. The Effects of Presence Induced by Smartphone Applications on Tourism: Application to Cultural Heritage Attractions. In Information and Communication Technologies in Tourism 2014, Proceedings of the International Conference, Dublin, Ireland, 21–24 January 2014; Xiang, Z., Tussyadiah, I., Eds.; Springer International Publishing: Cham, Switzerland, 2014; pp. 59–72. [Google Scholar]
  89. Molz, J.G. Travel Connections: Tourism, Technology and Togetherness in a Mobile World; Routledge: London, UK, 2012; ISBN 978-0-203-12309-6. [Google Scholar]
  90. Gretzel, U. Travel in the Network: Redirected Gazes, Ubiquitous Connections and New Frontiers. In Post-Global Network and Everyday Life (Digital Formations); Levina, M., Kien, G., Eds.; Peter Lang Inc.: Lausanne, Switzerland, 2010; pp. 41–58. [Google Scholar]
  91. Wang, D.; Park, S.; Fesenmaier, D.R. The Role of Smartphones in Mediating the Touristic Experience. J. Travel Res. 2012, 51, 371–387. [Google Scholar] [CrossRef]
  92. Wang, D.; Xiang, Z.; Fesenmaier, D.R. Adapting to the Mobile World: A Model of Smartphone Use. Ann. Tour. Res. 2014, 48, 11–26. [Google Scholar] [CrossRef]
  93. Cheung, C.M.K.; Thadani, D.R. The Impact of Electronic Word-of-Mouth Communication: A Literature Analysis and Integrative Model. Decis. Support Syst. 2012, 54, 461–470. [Google Scholar] [CrossRef]
  94. Tapanainen, T.; Dao, T.K.; Nguyen, T.T.H. Impacts of Online Word-of-Mouth and Personalities on Intention to Choose a Destination. Comput. Hum. Behav. 2021, 116, 106656. [Google Scholar] [CrossRef]
  95. Abas, A. Valuation of Visitor Perception of Urban Forest Ecosystem Services in Kuala Lumpur. Land 2023, 12, 572. [Google Scholar] [CrossRef]
  96. Sreen, N.; Tandon, A.; Jabeen, F.; Srivastava, S.; Dhir, A. The Interplay of Personality Traits and Motivation in Leisure Travel Decision-Making during the Pandemic. Tour. Manag. Perspect. 2023, 46, 101095. [Google Scholar] [CrossRef]
  97. Hemkemeier, T.A.; Almeida, F.C.R.; Sales, A.; Klemm, A.J. Repair Mortars with Water Treatment Plant Sludge (WTPS) and Sugarcane Bagasse Ash Sand (SBAS) for More Eco-Efficient and Durable Constructions. J. Clean. Prod. 2023, 386, 135750. [Google Scholar] [CrossRef]
  98. López-Mosquera, N. Gender Differences, Theory of Planned Behavior and Willingness to Pay. J. Environ. Psychol. 2016, 45, 165–175. [Google Scholar] [CrossRef]
  99. López-Mosquera, N.; García, T.; Barrena, R. An Extension of the Theory of Planned Behavior to Predict Willingness to Pay for the Conservation of an Urban Park. J. Environ. Manag. 2014, 135, 91–99. [Google Scholar] [CrossRef]
  100. Ureta, J.C.; Motallebi, M.; Vassalos, M.; Seagle, S.; Baldwin, R. Estimating Residents’ WTP for Ecosystem Services Improvement in a Payments for Ecosystem Services (PES) Program: A Choice Experiment Approach. Ecol. Econ. 2022, 201, 107561. [Google Scholar] [CrossRef]
  101. Fink, L.; Geldman, D. The Effects of Consumer Participation in Product Construction and Design on Willingness to Pay: The Case of Software. Comput. Hum. Behav. 2017, 75, 903–911. [Google Scholar] [CrossRef]
  102. Ho, C.Q.; Hensher, D.A.; Mulley, C.; Wong, Y.Z. Potential Uptake and Willingness-to-Pay for Mobility as a Service (MaaS): A Stated Choice Study. Transp. Res. Part A Policy Pract. 2018, 117, 302–318. [Google Scholar] [CrossRef]
  103. Byrne, B.M. Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming, 3rd ed.; Routledge: New York, NY, USA, 2016; ISBN 978-1-315-75742-1. [Google Scholar]
  104. Balamoorthy, S.; Chandra, B. The Influence of Intrinsic and Extrinsic Motivational Factors on E-WOM Behaviour: The Role of Psychological Impact during the Time of COVID-19 Crisis. Heliyon 2023, 9, e13270. [Google Scholar] [CrossRef]
  105. Anderson, J.C.; Gerbing, D.W. Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychol. Bull. 1988, 103, 411–423. [Google Scholar] [CrossRef]
  106. Nunnally, J.C.; Bernstein, I.H. Psychometric Theory, 3rd ed.; McGraw-Hill: New York, NY, USA, 1994. [Google Scholar]
  107. Fornell, C.; Larcker, D.F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  108. Hair, J.F.; Black, W.C.; Babin, B.J.; Anderson, R.E. Multivariate Data Analysis; Cengage Learning EMEA: Andover, UK, 2019; ISBN 978-1-4737-5654-0. [Google Scholar]
  109. Amez, S.; Baert, S. Smartphone Use and Academic Performance: A Literature Review. Int. J. Educ. Res. 2020, 103, 101618. [Google Scholar] [CrossRef]
  110. Nayak, J.K. Relationship among Smartphone Usage, Addiction, Academic Performance and the Moderating Role of Gender: A Study of Higher Education Students in India. Comput. Educ. 2018, 123, 164–173. [Google Scholar] [CrossRef]
Societies 15 00082 g001
Figure 1. SEM model.
Figure 1. SEM model.
Societies 15 00082 g001
Table 1. Variables used in the study and the reference literature.
Table 1. Variables used in the study and the reference literature.
Scales VariablesReferences
Push motivations[PusM01] Desire to appreciate natural resources[19]
[PusM02] The need to acquire knowledge about a tourist destination
[PusM03] Interest in visiting a place you have not visited before
[PusM04] The desire to experience new and different lifestyles
[PusM05] The need for physical and mental relaxation
[PusM06] The need to find emotions
[PusM07] The yearning to reconnect with spiritual roots
[PusM08] The desire to increase my social interaction
[PusM9] The need to visit friends and relatives
[PusM10] The desire to be away from home
Pull motivations[PulM01] To get to know places that are cultural heritage references[19]
[PulM02] The need for safety and security
[PulM03] Enjoying traditional food
[PulM04] Contemplating beautiful landscapes
[PulM05] Visiting shops/markets
[PulM06] The desire to contemplate traditional and cultural arts
Smartphone usage[SU01] Social networking (e.g., Facebook, Instagram) to share my travel experiences with others[18]
[SU02] Instant messaging and phone calls to share my travel experiences with others
[SU03] Obtain transport information
[SU04] Get travellers’ experiences and advice
[SU05] Obtain general information about the destination and its tourist attractions.
[SU06] Obtain street and tourist route maps
E-WOM[E-WOM01] I intend to publish my travel experience on social mobile applications.[104]
[E-WOM02] I will try to publish my visit experience on social mobile applications.
[E-WOM03] I plan to publish my travel experience on social mobile applications.
Financial risk[FR01] I feel that using a smartphone to travel in Cáceres may cause me to incur unnecessary expenses.[3]
[FR02] I am concerned that excessive mobile internet charges may be levied while using my smartphone in Cáceres.
[FR03] I feel that using my smartphone to make payments during my trip may expose me to potential fraud.
Device risk[PR01] I am worried about my smartphone being stolen during my trip.[3]
[PR02] I am worried that I might be physically attacked for possessing a smartphone during my trip.
[PR03] I think holding my smartphone in public is a danger while travelling
Perceived value[PV01] The services offered in local tourism applications are reasonably priced.[83]
[PV02] The services offered in local tourism applications are good value for money.
[PV03] The services offered in local tourism applications are good value for money compared to other tourist destinations.
Table 2. Socio-demographic components.
Table 2. Socio-demographic components.
Sample Characteristics
GenderFemale 49.6%; Male 50.4%.
Age18 to 29 years: 35.2%; 30 to 39 years: 18.3%; 40 to 49 years: 16.9%; 50 to 59 years: 18.45%; 60 to 69 years: 11.11% 70 or more years: 0.2%.
Travel companionsAlone: 9.3%; Partners: 34.1%; Families: 35.5%; Friends: 21.0%.
EducationHigh school or below: 8.5%; Bachelor’s degree: 6.0%; Vocational training: 11.7%; Master’s degree or above: 73.8%.
Monthly incomeLess than EUR 1000: 45.0%; EUR 1000–3000: 50.4%; More than EUR 3000: 4.6%.
Table 3. Compositive reliability Cronbach’s alpha and AVE results.
Table 3. Compositive reliability Cronbach’s alpha and AVE results.
ConstructCompositive ReliabilityCronbach’s AlphaAVE
Smartphone Usage0.9350.9340.706
E-WOM0.9440.9430.849
Push Motivations0.9530.9540.671
Pull Motivations0.8950.8940.588
Financial Risk0.8900.8900.729
Device Risk0.9150.9130.783
Perceived Value0.8960.8960.742
Table 4. Latent correlation matrix *.
Table 4. Latent correlation matrix *.
(1)(2)(3)(4)(5)(6)(7)
Smartphone Usage (1)0.840
E-WOM (2)0.8200.921
Push Motivations (3)0.7650.7620.819
Pull Motivations (4)0.7410.6820.8960.767
Financial Risk (5)−0.306−0.2230.200−0.2240.792
Device Risk (6)−0.172−0.123−0.085−0.1210.7320.885
Perceived Value (7)0.5110.4030.4140.426−0.318−0.1470.862
* bold = average variance extracted.
Table 5. Standardized path estimates and hypotheses testing.
Table 5. Standardized path estimates and hypotheses testing.
Hypothesis (H)PathsPath Coefficientst-Statisticp-ValueResult
H1:Push Motivations → Smartphone usage0.5686.102***supported
H2:Pull motivations → Smartphone usage0.1992.170**supported
H3:Financial risk → Smartphone usage−0.104−3.201**supported
H4:Device risk → Smartphone usage−0.009−0.271n.s.not supported
H5:Perceived value → Smartphone usage0.1915.821***supported
H6a:Primary education → Smartphone usage0.1251.851*supported
H6b:Secondary education → Smartphone usage0.2522.041**supported
H6c:Bachelors → Smartphone usage0.2351.990**supported
H6d:Professional training → Smartphone usage0.4572.140**supported
H6e:University degree or higher → Smartphone usage0.4172.632**supported
H7:Smartphone usage → E-WOM0.81320.477***supported
H8:Smartphone usage → WTP0.64716.264***supported
* p < 0.010; ** p < 0.05; *** p < 0.001; n.s.—not significant.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Nieves-Pavón, S.; López-Mosquera, N.; Sánchez González, M.J. Unlocking Tourist Motivations in a Smart Tourism Destination: An Application of the Push–Pull Theory. Societies 2025, 15, 82. https://doi.org/10.3390/soc15040082

AMA Style

Nieves-Pavón S, López-Mosquera N, Sánchez González MJ. Unlocking Tourist Motivations in a Smart Tourism Destination: An Application of the Push–Pull Theory. Societies. 2025; 15(4):82. https://doi.org/10.3390/soc15040082

Chicago/Turabian Style

Nieves-Pavón, Sergio, Natalia López-Mosquera, and Manuel Jesús Sánchez González. 2025. "Unlocking Tourist Motivations in a Smart Tourism Destination: An Application of the Push–Pull Theory" Societies 15, no. 4: 82. https://doi.org/10.3390/soc15040082

APA Style

Nieves-Pavón, S., López-Mosquera, N., & Sánchez González, M. J. (2025). Unlocking Tourist Motivations in a Smart Tourism Destination: An Application of the Push–Pull Theory. Societies, 15(4), 82. https://doi.org/10.3390/soc15040082

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop