Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7, 2297-2310 2025 Publisher: Learning Gate DOI: 10.55214/2576-8484.v9i7.9156 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 28 May 2025; Revised: 14 July 2025; Accepted: 17 July 2025; Published: 29 July 2025 * Correspondence: akraja@ubt.edu.al The impact of smart tourism on improving business performance Ambra Kraja1*, Irini Goga2, Ana Kapaj3 1,3Department of Mathematics and Informatics Agriculture University of Tirana Koder Kamez, Tirane, Albania; akraja@ubt.edu.al (A.K.). 2Department of Economic Sciences‘Aleksander Moisiu’ University, Durres, Albania. Abstract: This study investigates the impact of smart tourism on improving business performance, highlighting the increasing role of digital transformation in reshaping the tourism industry. As advancements in information and communication technologies (ICT), big data, and social media accelerate, tourism businesses and destinations are adopting smart tourism strategies to enhance operational efficiency, visitor satisfaction, and competitiveness. Smart tourism integrates data-driven technologies—such as sensor networks, real-time analytics, and interactive platforms—into destination planning and service delivery. A comprehensive literature review was conducted to identify the predominant methodologies and conceptual frameworks within the domain of smart tourism. Special emphasis was placed on the role of smart destination strategy and the influence of digital tools on tourist experiences and post-visit behaviors. Findings reveal that smart tourism technologies contribute significantly to the creation of efficient, personalized, and engaging tourism services, ultimately strengthening both visitor loyalty and business performance. The study concludes that adopting smart tourism is a strategic imperative for destinations and tourism enterprises seeking sustainable growth in an increasingly connected environment. These insights offer valuable implications for tourism stakeholders aiming to utilize smart technologies and digital platforms to enhance performance and meet evolving tourist expectations. Keywords: Information and communication technology and Big Data, Smart Destination Strategy, Smart tourism, Social media. 1. Introduction Digitalisation has had a great impact on tourism all over the world, becoming a key element of the competitiveness for companies in this sector. It is well known and accepted that the competitive advantages of tourism products are closely related to the natural resources of different countries, but also to several other factors where we would single out communication. In a few decades the tourism market reached a level of digital communication where social networks play a dominant role in the decision-making of tourists. Tourists are increasingly connected to the internet, utilising a lot of tourism-related mobile apps, accessing real-time news, transport and online booking platforms, and utilising social networks for a wide range of tourism services. Smart tourism, also known as intelligent tourism or e-tourism, refers to the integration of information and communication technologies (ICTs) into the tourism industry to enhance service quality, efficiency, and personalization [1]. Smart is generally defined as intelligent, which is the ability to learn and think quickly and show good judgement [2]. This integration includes tools such as Internet of Things (IoT), cloud computing , artificial intelligence (AI) , big data analytics, mobile apps, and augmented/ virtual reality, enabling both tourists and service providers to make smarter decisions [3]. Tourists now engage with destinations through real time apps, interactive maps, and digital booking systems, and contribute user-generated content on social platforms-transforming from passive https://orcid.org/0000-0002-1240-9909 https://orcid.org/0000-0002-2406-2164 2298 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 2297-2310, 2025 DOI: 10.55214/2576-8484.v9i7.9156 © 2025 by the authors; licensee Learning Gate consumers to active co-creators [4]. In parallel, destinations benefit from smart governance, dynamic pricing, visitor flow control and sustainability monitoring [5]. The benefits of smart tourism are evident. Studies show improvements in traveler satisfaction, perceived value and destination loyalty due to smart technologies [6]. Furthermore, predictive analytics and decision support systems allow tourism operators to anticipate demand, personalize services, and improve operational efficiency [7]. Notably, the COVID-19 pandemic accelerated the adoption of smart technologies such as contactless services, virtual tours, and digital health passports—emphasising resilience and safety in tourism [8]. As destinations recover, the importance of data-driven innovation becomes even more crucial. This literature review aims to explore the prospects of smart tourism by examining relevant studies and research in the field. By analysing the existing literature, we can gain insights into the potential benefits, challenges, and future directions of smart tourism. The concept of smart tourism is experiencing rapid development along with the information and communication technology (ICT) revolution. ICT has enabled tourism companies to be “smarter” in terms of improving performance and competitiveness in various aspects, such as business, human resources (HR), information processing, customer service, and management. Smart tourism, also known as intelligent tourism or e-tourism, refers to the integration of information and communication technologies (ICTs) into the tourism industry. ICTs currently play an important role in any area of society specifically in any of those making up the tourist sector. The digital technological revolution and the impacts of ICTs in the tourism sector highlight the importance of investing in the most appropriate and current technological applications in companies providing tourist services. Moreover, according to Buhalis and O'Connor [9] ICTs incorporate a whole series of electronic instruments that facilitate the operational and strategic management of institutions and companies in matters such as information and knowledge management, as well as communication and interaction with interest groups (stakeholders). We are indubitably facing a digital revolution that has substantially modified tourism management [1]. The tourism sector, one of the biggest sectors globally [10] is a crucial component of the social and economic activity of a plethora of countries, as it generates job openings and business opportunities [11]. The tourism industry steadily increases its growth yearly, with tourist arrivals having reached the amount of 1.19 billion in 2015, from 528 million in 2005, while it is expected to grow even more than 1.8 billion by 2030. 2017 statistics indicate that the tourism industry contributed a tremendous amount of approximately 2.57 trillion U.S. dollars, creating millions of jobs worldwide [12]. In the last six years, there have been several research efforts, concepts, challenges and concerns discussed in Smart Tourism-related papers. 2. Literature Review The term Smart Tourism refers to the application of information and communication technology to develop innovative tools and approaches to improve tourism. Smart Tourism relies on core technologies such as ICT, mobile communication, computing, artificial intelligence, and virtual reality. These technologies enable destinations to collect, process and analyse data from infrastructure, user behaviour and social networks in real time, improving efficiency, sustainability, and service personalisation. It supports integrated efforts in a destination to find innovative ways to collect and use data derived from physical infrastructure, social connectivity, organisational resources and users combined with advanced technologies to increase efficiency, sustainability, and experiences. The rapid growth of the importance of Information and Communication Technology (ICT), and specifically e-business, is a development that needs to be taken seriously and made a priority. Not only the Internet and Web 1.0 but also Web 2.0 and the entire social networking environment have had a major impact and have largely changed the tourism industry [13]. At the same time, technological infrastructures such as sensors, cloud computing services, smartphones, radio frequency identification (RFID), and Wi-Fi can also have a vital role in the 2299 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 2297-2310, 2025 DOI: 10.55214/2576-8484.v9i7.9156 © 2025 by the authors; licensee Learning Gate development of smart tourism [14]. ICT plays a crucial role in the competitiveness of tourism organisations and destinations. It “empowers” consumers to identify, personalise, and purchase tourism products and supports the globalization of the industry by providing the means to develop, manage, and distribute offers worldwide [15]. A smart tourism destination should be an innovative place accessible to all visitors who can experience an improved, more interactive, and higher-quality journey [16]. When aiming to create a “Smart Tourism” Destination, a few challenges arise such as how to personalise the content presented to a user, what are the most appropriate sources for collecting data, how this data should be extracted i.e. implicitly or explicitly, privacy issues, etc. [17]. Albania is known as an attractive, authentic and hospitable tourist destination in Europe, based on the sustainable use of natural, cultural and historical potentials, easily accessible from international markets. Over the years, there have been many problems associated with tourism planning, lack of capacity, problems related to quality of services, deficiencies in infrastructure, lack of coordination between central and local government, uncoordinated investments by private entrepreneurs and the missing role of the Ministry of Tourism and Urban Development [18]. In Albania, the tourism sector has become one of the most important sectors of economic development, especially in recent years when attention has increased in the international arena. The tourism sector has begun to adopt smart tourism technologies, though penetration remains lower than the EU average [19]. For example, mobile services, IoT integration, and data- driven applications are increasingly used for marketing, reputation management and forecasting tourist expenditures [20]. The financial and social impact has begun to be experienced, when seeing that almost all figures in the tourism components show growth. Surveys among Albanian SMEs- primarily hotels, travel agencies, and tour operators- show widespread use of basic ICT tools such as online booking. Wi- Fi and websites, though higher-level digitalisation (advanced analytics, integrated platforms) remain limited due to financial constraints and lack of professional training. External shocks such as the 2019 earthquake and the COVID-19 pandemic exposed vulnerabilities in traditional tourism models, while also spurring growth in digital promotion, e-tourism platforms, and remote service delivery in Albania [21]. For instance, mobile and web-based platforms were launched for smarter promotion to foreign markets, and service standardisation improved due to increased digital awareness [21]. As a result of the great opportunities that our country offers in the field of tourism, technology has played an important role, especially for promotion to foreigners. The standardisation of services has come because of awareness of development opportunities but also because of constant demands and models borrowed from other countries, where digital transformation also undoubtedly plays a role. The use of ICT in different sectors of the Albanian economy has begun to be realised rapidly, improving economic and financial performance and expanding the range of services that are offered to users. ICT has been used massively, also a sector which in Albania brings an important impact on GDP it is the tourism sector. However, it is noted that the use of ICT in this sector is at levels lower than in other countries of Europe as well as the region. The use of ICT and the Internet in the tourism sector in the world but also in Albania has enabled the creation of a new business environment, the global distribution of information as a very important element for the tourism sector and the creation of new channels of distribution, transforming the tourism sector globally [22]. 3. Material and Methods This study employs a mixed methods approach to collect and analyse data on the tourism sector in Albania, particularly over recent years. Both secondary and primary data sources were used to ensure a comprehensive and triangulated analysis [23]. 3.1. Secondary Data Collection Secondary data were obtained from reputable international and national databases, including the World Bank, INSTAT and EUROSTAT, as well as previous academic studies and official government 2300 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 2297-2310, 2025 DOI: 10.55214/2576-8484.v9i7.9156 © 2025 by the authors; licensee Learning Gate reports related to tourism and ICT in Albania. These sources provided baseline indicators of tourism growth, infrastructure development, and digital integration [24, 25]. 3.2. Primary Data Collection Primary data were collected using a combination of surveys and semi-structured interviews with key stakeholders in the Albanian tourism sector. Respondents included: • Tourists, • Rural tourism service providers, • Sector experts, • Municipality officials in the selected areas. The use of both open-ended and closed-ended questions allowed for deeper exploration of tourism perceptions, service quality, and digital readiness, while also generating data that could be statistically analysed. The main instrument was a structured questionnaire, designed to assess tourist behaviour, preferences, and perceived needs, particularly in the context of ICT and rural tourism. To ensure reliable sampling, tourist agencies, local tour operators, and accommodation providers were contacted. The geographic focus was on Tirana and Shkodra, regions recognised for their increasing contribution to rural and agrotourism in Albania. 3.3. Target Groups and Analytical Strategy The target groups consisted of agrotourists and agribusiness operators active in the study regions. The analytical strategy depended on the type of data: • Quantitative analyses (using descriptive statistics and frequency distributions) were applied to structured responses. • Qualitative analyses (using thematic coding) were applied to interpret open-ended responses. This methodology is aligned with established tourism research practices, ensuring that the findings contribute meaningfully to the discourse on Smart Tourism development in Albania. 4. Results of the Study 4.1. Descriptive Analysis of Tirana Tourists To analyse the typology of tourists interviewed in the two cities that are part of the study, we began collecting their socio-economic data. Below is the distribution of tourists for Tirana according to their gender. Table 1. Gender Distribution of Tourists in Tirana. Description Frequency Percent Valid Percent Cumulative Percent Female 126 46.7 46.7 46.7 Male 144 53.3 53.3 100.0 Total 270 100.0 100.0 As shown in Table 1, out of 270 respondents, 53.3% were male and 46.7% were female. This near- equal gender distribution provides a balanced perspective for the subsequent demographic analyses. Continuing the demographic breakdown, we present a cross-tabulation of gender and age of Tirana tourists in Table 2. 2301 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 2297-2310, 2025 DOI: 10.55214/2576-8484.v9i7.9156 © 2025 by the authors; licensee Learning Gate Table 2. Cross-tabulation of Gender and Age of Tourists in Tirana. Age Total 18-25 26-36 37-47 48-58 58+ F 6 33 46 36 5 126 M 8 43 56 29 8 144 Total 14 76 102 65 13 270 As Table 2 illustrates, the 37-47 age group represents the largest segment of tourists, accounting for 102 out of 270 respondents. These findings indicate that the tourism market in Tirana is predominantly made up of middle-aged adults between 26 and 47 years old, suggesting that tourism services and marketing strategies should be tailored to meet the preferences and expectations of this demographic. Moreover, the relatively balanced gender representation across age categories reinforces the importance of inclusive tourism experiences that appeal to both male and female travellers. Table 3. Cross-tabulation of Age and Income of Tourists in Tirana. Description Income Total 250-400€ 400-650€ 650-800€ 800€+ Age 18-25 0 1 7 6 14 26-36 1 12 34 29 76 37-47 5 14 37 46 102 48-58 4 4 37 20 65 58+ 0 3 3 7 13 Total 10 34 118 108 270 Table 3 above shows that the age group 37-47 emerges as the most financially affluent, with 46 respondents reporting a monthly income above 800€ and 37 respondents earning between 650-800. The data suggest that tourists aged 37-47 not only dominate in terms of numbers but also in spending power - making them a strategic demographic for tourism planning, premium services, and targeted marketing in Tirana. Figure 1. Civil Status Distribution of Tourists in Tirana. 0 20 40 60 80 100 120 140 Single Divorced Married Widow 97 31 137 5 35.9 11.5 50.7 1.9 Civil Status Frequency Percent 2302 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 2297-2310, 2025 DOI: 10.55214/2576-8484.v9i7.9156 © 2025 by the authors; licensee Learning Gate Figure 1 illustrates the civil status of tourists visiting Tirana, based on a sample of 270 respondents. The data indicates that the majority of tourists are married, accounting for 137 individuals, which represents 50.7% of the total sample. This is followed by single tourists, who make up 35.9%. The dominance of married individuals may suggest a preference for Tirana as a travel destination among couples or families. Figure 2. Interest of Tirana Tourists in Smart Tourism. Figure 2 illustrates the level of interest in the Smart Tourism concept among tourists visiting Tirana. Out of a total of 270 respondents, 66.3% expressed a positive interest in Smart Tourism, while 33.7% reported no interest. This result indicates a generally favourable perception of Smart Tourism among tourists, suggesting a growing awareness and demand for technology-enhanced travel experiences. The significant majority in favour implies that digital infrastructure, mobile applications, smart guides, and data - driven services could play a vital role in improving the visitor experience in Tirana. Table 4. Gender Distribution of Tourists in Shkodra. Description Frequency Percent Valid Percent Cumulative Percent Female 152 46.1 46.1 46.1 Male 178 53.9 53.9 100.0 Total 330 100.0 100.0 4.3. Descriptive Analysis Shkodra Tourists Table 4 presents the gender composition of tourists surveyed in Shkodra, based on a total of 330 respondents. The results indicate that 53.9% of the tourists were male, while 46.1% were female, reflecting a slight male majority. The balanced representation between male and female tourists supports the importance of gender inclusive tourism strategies, ensuring that both groups are equally considered in the design of tourism services, promotional content and travel experiences. Understanding the gender dynamics of tourist flows is essential for local stakeholders, as it informs 2303 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 2297-2310, 2025 DOI: 10.55214/2576-8484.v9i7.9156 © 2025 by the authors; licensee Learning Gate decision-making on marketing approaches, service costumisation, and infrastructure development tailored to traveller needs. Table 5. Gender and Attraction toward Smart Tourism (ST) among Shkodra Tourists. Description Attraction of ST Total No Yes Gender Female 48 104 152 Male 58 120 178 Total 106 224 330 Table 5 examines the relationship between gender and the level of interest in Smart Tourism (ST) among tourists in Shkodra. Out of 330 respondents, 224 individuals expressed interest in Smart Tourism, while 106 indicated no interest. Among those who showed interest, 120 were male and 104 were female, indicating that both genders are similarly receptive to smart tourism experiences, though males slightly outnumber females in this regard. These results suggest a broad appeal of Smart Tourism across genders, reinforcing its potential as a strategic development area for tourism operators and policymakers in Shkodra. The high level of interest from both male and female tourists supports continued investment in digital tools, smart services, and innovative experiences that enhance convenience, personalisation, and connectivity during travel. Table 6. Preference for travelling with an agency among Shkodra Tourists. Travel with agency Description Frequency Percent Valid Percent Cumulative Percent No 203 61.5 61.5 61.5 Yes 127 38.5 38.5 100.0 Total 330 100.0 100.0 Table 6 presents data regarding the use of travel agencies among tourists in Shkodra. The results indicate that a majority of tourists (61.5%) prefer to travel independently, while 38.5% choose organised travel through agencies. This finding suggests a prevailing tendency toward individual or self-directed tourism experiences, possibly driven by greater flexibility, personalised planning, and the influence of digital tools such as booking apps and online reviews. However, a notable portion still relies on travel agencies, which may reflect preferences for convenience, curated experiences, or lack of digital access or familiarity. For tourism stakeholders, this split highlights the importance of maintaining both digital self-service platforms and agency-based services, ensuring accessibility and satisfaction across diverse tourist preferences. 2304 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 2297-2310, 2025 DOI: 10.55214/2576-8484.v9i7.9156 © 2025 by the authors; licensee Learning Gate Figure 3. Use of Electronic Payment among Tourists. Figure 3 displays tourists responses regarding the use of electronic payment methods during their stay. Out of 330 total respondents, 233 individuals reported using electronic payments, while 97 indicated they did not. This means approximately 70.6% of tourists embrace digital payment options, while 29.4% still rely on traditional payment methods such as cash. The high adoption of electronic payments highlights a significant shift toward digitally enabled travel experiences, consistent with global trends in smart tourism. Furthermore, the remaining portion of tourists who do not use electronic payments may represent specific demographics or regions with limited digital infrastructure or preferences for privacy and simplicity. Addressing this group`s needs while promoting wider digital adoption could support a more inclusive smart tourism strategy. 4.4. Descriptive Analysis for Agritourism Administrators Table 7. Gender and Age Distribution of Agritourism Administrators. Table 7 summarises the demographic profile of agritourism administrators, based on 55 respondents. The data reveal a clear gender imbalance, with male administrators comprising 72.7% of the total, while females account for only 27.3%. Age-wise, the 26-35 group is the most represented, followed by the 36-45 and 46-55 age groups. Notably, the majority of both male and female administrators are concentrated in the younger age bracket (26-35), suggesting a relatively youthful workforce involved in managing agritourism activities. This demographic structure may reflect a new generation of entrepreneurs engaging in rural tourism, possibly influenced by trends in sustainability, digital innovation and local development. 97 233 330 Electronic Payment No Yes Total Description Age Total 26-35 36-45 46-55 Gender Male 26 8 6 40 Female 8 6 1 15 Total 34 14 7 55 2305 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 2297-2310, 2025 DOI: 10.55214/2576-8484.v9i7.9156 © 2025 by the authors; licensee Learning Gate Figure 4. Relationship between Attraction to Smart Tourism and Perceived Quality Improvement. Figure 4 explores the association between tourists attraction to Smart Tourism and their perception of its impact on quality improvement in tourism services. The results indicate that the more attracted tourists are to ST, the more likely they are to perceive a positive improvement in quality. This trend suggests a strong positive correlation between Smart Tourism attraction and quality perception. Tourists who recognise and value ST innovations are more inclined to associate them with enhanced service delivery, personalisation, efficiency, and overall travel satisfaction. In this study, two hypotheses were tested to see how tourists with different socio-economic characteristics react towards smart tourism. We also gave an important focus to how smart tourism affects business performance. Below is an analysis of these hypotheses. Hypothesis 1: Tourists with different socio-economic characteristics approach smart tourism differently. Dependent variable: “Approach to ST” will be measured as a Dummy variable. Independent variables: Socio-economic characteristics such as “Family Income level” (quantitative), “Age” (quantitative), “Civil status” (qualitative) and “Level of Education” (qualitative). Model proposed based on the dependent variable: Binary Logistic. Table 8. Binary Logistic Regression Analysis of Socio-Economic Characteristics Influencing Tourists Approach to Smart Tourism. B SE Wald Sig. Step 1 Age 0.061 0.005 145.7 0.000* Income 0.149 0.178 0.699 0.403 Civil status -0.318 0.123 6.723 0.022* Gender 1.214 0.167 103.1 0.000* Level of Education 1.314 0.267 131.1 0.001* Based on the results of the model, income does not result in affecting the Approach to ST. While gender, age, civil status and level of education are important parameters. Hypothesis 2/a: Smart tourism positively affects the performance of tourism sector 2306 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 2297-2310, 2025 DOI: 10.55214/2576-8484.v9i7.9156 © 2025 by the authors; licensee Learning Gate Dependent variable: “Financial Performance” will be measured as a quantitative variable Independent variables: Application of ST, Online reservations, social media (dummy variables) Model proposed based on the dependent variable: Multiple Linear Regression Table 9. Multiple Linear Regression Statistics for the Impact of Smart Tourism on Financial Performance. Regression Statistics Multiple R 0.886231 R Square 0.785406 Adjusted R Square 0.74964 Standard Error 6.634991 The Multiple R value of 0.886 indicates a strong positive correlation between the application of smart tourism components and financial performance. The R Square value of 0.785 reveals that approximately 78.5% of the variance in financial performance is explained by the model. These results support Hypothesis 2/a, confirming that the use of smart tourism practices (digital booking systems, online visibility, and smart technologies) positively impacts the financial performance tourism businesses. The statistical strength of the model suggests that investments in smart tourism infrastructure and services may yield tangible economic benefits for tourism operators. Table 10. ANOVA Results for the Effect of Smart Tourism Practices on Financial Performance. ANOVA F Significance F Regression 21.95975 0.003374 Residual Total Table 11. Dependent variable: Financial Performance. Coefficients t Stat P-value Intercept -1.31467 -0.1437 0.890445 Application of ST 4.541333 4.686123 0.003374* Online reservations 0.023433 9.434098 0.002311* Social media 0.304321 21.00732 0.000* Based on the results of the regression analysis, financial performance is significantly and positively influenced by all three smart tourism-related variables. The model is statistically robust, and the variables are all strongly supported with low p-values. This confirms Hypothesis 2/a, suggesting that smart tourism adoption is a key driver of business success in the tourism sector. 5. Smart Tourism Using Tam Model The Technology Acceptance Model (TAM) was developed by Davis [26] as a framework to explain and predict user behaviour regarding information technology adoption [26]. Based on Davis [26] model and supported by previous empirical studies, this research identifies key factors influencing tourists decisions to use smart travel applications in Tirana and Shkodra. In particular, the model highlights “ease of use”—defined as the degree of personal confidence in using an application- as a significant factor contributing to perceived user freedom and satisfaction [27]. 2307 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 2297-2310, 2025 DOI: 10.55214/2576-8484.v9i7.9156 © 2025 by the authors; licensee Learning Gate 6. Variables in the Model and Their Measurement Decision to use (VP = mean VP1,VP2, VP3, VP4) 1. In general, the smart travel forms makes me feel satisfied (VP1) 2.Using smart travel apps is my right decision (VP2) 3. I enjoy using smart travel apps for my activities (VP3) 4. I will continue to use the smart travel application in future (VP4) To measure the variables in the questionnaire, a Likert scale (1-5) was used. The main variables were calculated as the arithmetic mean of the subvariables for each case. A convenient sampling method was used to survey 600 tourists using the smart travel applications in Albania. Data were collected by questionnaire, coded and processed by SPSS software. Perceived ease of use (LP = LP1, LP2, LP3) I have learn how to use the application easily (LP1) When interacting with the Smart application I find it easy to understand (LP2) I find the smart travel app easy to use (LP3) Perceived usefulness (PP = PP1, PP2, PP3) I find the use of smart tourism apps very helpful and useful (PP1) Smart tourism application increases the efficiency of travel activities, can be used anytime, anywhere (PP2) Overall, I consider the smart tourism app a valuable service to me (PP3) Technological barriers (BT = mean BT1, BT2) I find installing smart travel application difficult (BT1) Differences from traditionl travel forms affect your us of smart travel (BT2) Information security (SI = mean SI1, SI2) I am afraid my personal information will be collected (SI1) Logging in to smart travel websites or apps with personal information affects your intention to use the app (SI2) Service quality (CSh = mean CSh1, CSh2, CSh3) The application provider fulfills its commitment to quality of service (CSh1) The provider will provide the service as expected (CSh2) The smart tourism app provider always provides service quickly (CSh3) Information quality (CI = mean CI1, CI2, CI3) The information provided from the smart tourism application is accurate (CI1) Easily refer to the parameters of the smart tourism application through different forms whenever you want (app on phone, web, ...) (CI2) H3: Perceived Ease of Use (LP) and Perceptual usefulness (PP) have a positive effect on the decision to use (VP) H4: Technological barrier (BT) has the opposite effect of usage decision (VP) H5: Information security (SI) and Information quality (CI) have a positive impact on the decision to use (VP) H6: Service quality (CSh) has a positive impact on the decision to use (VP) Based on the above hypotheses, we derive the following model: Table 12. Pearson correlation test results (Correlations). VP LP PP BT SI VP Pearson Correlation 0.366** 0.602** 0.461** 0.509** Sig. (2- tailed) 0 0 0 0 N 600 600 600 600 600 2308 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 2297-2310, 2025 DOI: 10.55214/2576-8484.v9i7.9156 © 2025 by the authors; licensee Learning Gate Table 13. Model summary results. Model R R Square Adjusted R Square F test Sig. F test Durbin Watson 1 0.802 0.643 0.608 38.65 0.000 1.768 Note : a. Predictors: (Constant), LP, PP, BT, SI, CSh, CI. b. Dependent Variable: VP. Table 14. ANOVA variance analysis results. Decision to use (VP) Perceived Ease of Use (LP) Perceptual usefulness (PP) Technological barrier (BT) Information security (SI) Service quality (CSh) Information quality (CI) Model Sum of Squares Df Mean Square F test Sig. F test Regression 137.805 6 22.96 298.1 0.0000a Residual 45.73 593 0.077 Total 183.535 599 Note: Dependent Variable: VP. Predictors: LP, PP, BT, SI, CSh, CI. Sig. F test is 0.000 < 0.05, showing the regression model is consistent with the overall variables. Regresion analysis - Coefficients of the Model Table 15. Regression Coefficients for TAM- Based Predictors of Tourists Intention to Use Smart Tourism Services. Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta LP Perceived ease of use B Std. Error Beta PP Perceived usefulness 0.141 0.053 0.114 2.641 0.0102 BT Technological barriers 0.221 0.073 0.201 3.022 0.0044 SI Information security 0.453 0.104 0.399 4.320 0.0003 CSh Service quality 0.750 0.251 0.710 2.988 0.0211 CI Information quality 0.213 0.039 0.199 5.435 0.0000 Note: Dependent Variable: VP Decision to Use. Source: SPSS data analysis results. Decision to use (VP) = f (Perceived Ease of Use (LP), Perceptual usefulness (PP), Technological barrier (BT), Information security (SI), Service quality (CSh), Information quality (CI) The Sig values of each variable shows that all variables affect the VP dependent variable. Specifically, the influence of the factors on the VP is as follows: VP = 0.141LP + 0.221PP + 0.453BT + 0.750SI + 0.213CSh + 0.503CI 7. Conclusion and Discussion In this study, 600 tourists were randomly selected in two different cities, Tirana and Shkodra. The results have shown that tourists today are very attracted to ST as well as to electronic payments. This is because of the time saving and lower costs. The path towards ST has been evidenced in the large increase in the number of tourists in Albania, where the government itself has called it the engine of the country's economy. From the results of the survey for the city of Tirana, of 270 total respondents, 46.7% are female and 53.3% male. 37 interviewees aged 37-47 and 48-58 have a monthly income of over 800 euros, which means they are more willing to pay for a tourist package. 72.2% of tourists are satisfied with the smart tourism service. 70.6 % of tourists use electronic payment. From the results of the survey of Shkodra, of 330 total respondents, 46.1% are female and 53.9% male. 68.5% of Shkodra tourists are 25-45 years old. From 330 respondents, 224 are attracted to smart tourism and 106 are not. 2309 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 2297-2310, 2025 DOI: 10.55214/2576-8484.v9i7.9156 © 2025 by the authors; licensee Learning Gate 233 tourists use electronic payment because it is more useful than cash payment and faster. 61.5% of tourist don’t use agency travel, and 38.5% are agreeing to book with agency travel. Finally, the interest in ST presented a satisfying relationship with the socio-demographic variables. Gender is an important parameter in the model (sig. =0.000). Age is an important parameter in the model (sig. = 0.000). Civil Status is an important parameter in the model (sig. = 0.022). Income is not an important parameter in the model (sig. = 0.403), so it does not affect tourists choices to purchase tourist packages. Level of Education is an important parameter in the model (sig. = 0.001). Application of Smart Tourism is an important parameter in the model (sig. = 0.003374) Online reservation is an important parameter in the model (sig. = 0.002311) Social media is an important parameter in the model (sig. = 0.000). Smart tourism is a multi-dimensional concept today everywhere in the world, not only for the contributions it makes to the government of Albania and tourism service companies but also for the permanent implementation of ICTs in the daily use of individuals and tourists. The application of smart tourism in areas other than urban areas is very complex [28]. The facilities that businesses offer every day to domestic and foreign tourists are increasing the impact of smart tourism even in rural areas of the country. 8. Recommendation and Practical Implications Online applications for rural destinations that are well designed and complete in their services (information, maps, resource guides, real-time reservations) should be the first approach for the smart rural destination. Use data collected from tourists to create highly personalised travel experiences. For example, customised itineraries, personalised recommendations, and real-time alerts based on individual preferences can help travellers make the most of their visit. Utilise smart devices, apps, and augmented reality (AR) to enhance the tourists journey. Smart tourism requires collaboration among government bodies, tourism organisations, local businesses, and technology providers to ensure the effective use of technology and infrastructure. Governments and tourism organisations should create policies that support the integration of technology into the tourism sector. Destinations should collect and analyse large datasets to improve decision-making and better predict future trends. This data can be used to optimise resource allocation, enhance marketing strategies, and refine tourism services. Given the reliance on personal data in smart tourism, it's crucial to prioritise the security and privacy of tourists information. These recommendations aim to create a more efficient, personalised and sustainable tourism experience, helping destinations harness the full potential of smart tourism technologies. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Copyright: © 2025 by the authors. 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