Exploring factors influencing smart tourism destination visiting behaviors in a historic country: a theory of e-consumption behavior

Abstract

Purpose

This study aims to explore factors that influence tourists’ visiting behaviors towards smart tourism destinations (STDs) by extending the theory of planned behavior.

Design/methodology/approach

Partial least squares structural equation modeling (PLS-SEM) technique is used to test the proposed hypotheses by analyzing 413 usable responses that are collected through personal interviews. Moreover, data are collected using structured interviews and analyzed by SmartPLS, 3.3.3.

Findings

This study results reveal that STD visit intentions significantly influence STD visiting behaviors among international and domestic tourists in Bangladesh. Moreover, the results show that e-attitude, resource efficiency norms and perceived environmental sustainability have significant impacts on STD visit intentions.

Practical implications

The study findings indicate that destinations’ electronic flowcharts of places, usage of low impact and biodegradable materials and history and culture presented in the forms of games and stories increase travelers’ motivation to visit STDs.

Originality/value

The study provides empirical evidence to support the importance of factors enhancing travelers’ STD visiting behaviors by integrating e-attitude, resource efficiency norms, perceived environmental sustainability and STD visit intentions.

研究目的

研究人員藉著把計劃行為理論伸延開去,去探索是哪些因素影響著到訪智慧旅遊目的地的旅遊行為。

研究設計/方法/理念

研究人員以偏最小平方法的結構方程模型 (PLS-SEM) 技術去測試其提出之假設,方法是透過分析取自個人訪談413個可用的回應。這些數據均以結構化訪談方式收集,繼而以 SmartPLS 3.3.3 來進行分析。

研究結果

研究結果顯示,就於孟加拉的國際和國內旅客而言,到訪智慧旅遊目的地的目的,顯著地影響著智慧旅遊目的地的旅遊行為。研究結果亦顯示,到訪智慧旅遊目的地的目的和意願,會顯著地受電子態度、資源效率規範和感知環境可持續性所影響。

實務方面的啟示

研究結果表明了目的地的處所電子流程圖、低影響和能進行生物降解材料的使用,以及以遊戲和故事形式展示的歷史和文化,均會提昇旅客到訪智慧旅遊目的地的動機。

研究的原創性/價值

研究人員透過結合電子態度、資源效率規範、感知環境可持續性和到訪智慧旅遊目的地的目的,給我們提供了經驗性證據,以確認增強及完善影響著旅客到訪智慧旅遊目的地的旅遊行為的因素至為重要。

Citation

Hasan, A.A.-T., Rahman, M.M. and Gani, M.O. (2024), "Exploring factors influencing smart tourism destination visiting behaviors in a historic country: a theory of e-consumption behavior", European Journal of Management and Business Economics, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/EJMBE-03-2024-0081

1. Introduction

Since the world’s tourists have been growing rapidly, promoting smart tourism destinations (STDs) has been inspired by changes in their behavioral patterns and extensive use of digital technologies since the last decade (Buckley et al., 2015). With the incremental rapid growth of this sector, understanding tourists’ choice behaviors (visiting behaviors) towards STDs has become crucial. However, the factors that may influence tourists’ STD visiting behaviors are not well defined yet.

To address these undefined factors, a behavioral model may be developed to explore factors that necessarily affect tourists’ visiting behaviors towards STDs because today’s young generation (Generation-Z), the main customers of tourism industry, expects a recreational space that has integrated smart technologies in optimizing consumption and producing resources for benefiting both travelers and industry operators (Kumar and Dahiya, 2017). However, traditional tourism destinations have failed to meet the desire of the young generation. As a result, STDs have got an opportunity to be popular among tourists. STDs are innovative destinations that integrate smart technologies to optimize consumption and resource production for benefiting business, citizens and government (Mandić and Kennell, 2021). It focuses on unique geographical features of destinations that associate information technology for seamless access of customers’ value-added services for responding to customers’ needs before, during and after a trip (Tavitiyaman et al., 2021). It is a logical evolutionary development of traditional tourism, which is built upon modern technology infrastructure, promoting sustainable and accessible development of tourist areas designed to improve visitors’ experiences and enhance quality of life of residents. Due to the unique features, visitors’ traveling rate towards STDs has grown exponentially across the world. However, research efforts for promoting STDs by exploring factors influencing tourists’ STD visiting behaviors are scarce.

Besides, the term “smart tourism” has been quite fashionable due to the extensive growth of information and communication technology (ICT). This development of ICT has led to the birth of smart tourism, and thus, smart tourism has become the most critical factor in the tourism industry. The tourism industry has contributed approximately 5.81 trillion US$ to the global economy in 2021, increasing by about one trillion US$ from 2020 (Suyunovich and Jaxongirovna, 2023). Coupled with the global economic contribution, tourism industry has contributed approximately Bangladeshi Taka (BDT)809.6 billion (4.7% of the total gross domestic products (GDP)) to the national GDP of Bangladesh, and it is expected that this contribution will be increased by 6.40% per annum to BDT1596.0 billion (5% of total GDP) in 2026 (Hafsa, 2020). Besides, Bangladesh received 6.50 lakh (650 thousand) foreign visitors in 2023 compared to 1.35 lakh (135 thousand) visitors in 2021 (Siddiqui, 2023), greater than 292%. The number of domestic tourists has increased approximately to 2 crores (200 hundred thousand) in 2022 compared to 1 crore (100 hundred thousand) in 2021 (The Bangladesh Monitor, 2023). Thus, it is argued that careful design and development of tourism destinations can covert Bangladesh’s natural resources into economic capital.

Given the significance of the tourism industry, promoting STDs has been crying need for enhancing both the tourism industry and economic growth of a historic country like Bangladesh. However, the previous empirical studies have mainly focused on environmental importance and destinations’ attributes of STDs. As such, Cavalheiro et al. (2020) explain that STDs promote a country’s environmental, socio-cultural, economic and political values; Dabeedooal et al. (2019) note that smart tourism has significant contributions to sustainable urban development; and Mandić and Garbin Praničević (2019) explain that public and private amenities, destinations’ attractions, image and character and price attract travelers to visit STDs. Besides, tourists’ attitude, subjective norms and perceived behavioral control have strong effects on STD revisit intentions (Torabi et al., 2022); Chen and Hung (2016) explain that resource saving tendency impacts an individual’s green product acceptance intentions; and Yoo et al. (2017) identify that utilitarian and hedonic motivations, perceived enjoyment and information quality are related to intentions towards gamified smart tourism applications. However, tourists’ psychological factors (i.e. e-attitude, resource efficiency norms and perceived environmental sustainability) that may influence them to visit a historic country like Bangladesh remain unexplored. To the best of the authors’ knowledge, scholars have not conducted research yet on understanding how e-attitude, resource efficiency norms, environmental sustainability perceptions and smart tourism visiting intentions influence tourists’ smart tourism visiting behaviors.

Therefore, this study has proposed the theory of electronic consumption behavior (TeCB), incorporating e-attitude, resource efficiency norms and perceived environmental sustainability by extending theory of planned behavior (TPB) to predict tourists’ STD visiting behaviors in Bangladesh. Specifically, this study attempts to (1) investigate the causal effects of e-attitude, resource efficiency norms and perceived environmental sustainability on STD visit intentions and (2) examine the causal effect of STD visit intentions on STD visiting behaviors (see Figure 1). The rest of this study is arranged as follows: review of literature is detailed in Section 2; Section 3 details methodology; results of this study are discussed in Section 4 and conclusions and implications of the study are discussed in Section 5.

2. Literature review

Research interest in smart tourism has been growing since the extensive growth of urban smart infrastructure and ICT development at the beginning of 21st century (Bastidas-Manzano et al., 2021). The applications of smart technologies in integrating urban infrastructure, the development and use of mobile applications technologies, widespread use of social media and virtual reality and an increase in live videos have offered the opportunities for sharing tourists’ experiences across the world. Thus, today’s tourists can receive the pervasive tour information before, during and after a trip regarding a particular tourist destination, and technologically literate tourists are more likely to visit smart tourist destinations (Mandić and Garbin Praničević, 2019). Besides, people of today’s world are accustomed to using smart technologies (i.e. smart phones) and social media (García-Haro et al., 2024). Consequently, approximately 192.26 million people use mobile phones in Bangladesh in 2024 (Chowdhury et al., 2022). Out of that amount, about 63.3% use smartphones in Bangladesh (Xpress, 2023, December 28).

Therefore, the smartphone users who visit tourist destinations prefer smart tourist destinations. As a result, Bangladesh has become one of the important tourist zones because it is a historic country with over 50 attractive tourist spots with both esthetic beauty and economic value (Hasan, 2023), and thus, approximately 650 thousand foreign visitors in 2023 and 20,000 thousand domestic visitors in 2022 travel to Bangladesh (Siddiqui, 2023; The Bangladesh Monitor, 2023). Furthermore, one-fourth (approximately 45.9 million) of the country’s young population (e.g. age of 15–29 years) is more accustomed to using smart technologies (i.e. smartphone) than the other generations (Islam et al., 2022) This young generation is called Generation-Z who are the main customers of the tourism industry in Bangladesh. Hence, the smart tourism has become pivotal for the growth of the tourism industry in Bangladesh, and thus, studies focused on exploring factors influencing STD visiting behaviors have gained priority among researchers.

2.1 Difference between tourists and visitors

Tourists and visitors are seemingly same; however, they are distinct from each other. According to the World Tourism Organization, tourists are people who travel to or stay outside their usual environment less than one consecutive year for leisure or other purposes (Camilleri, 2018), while visitors are people who visit a destination outside their usual environment for less than a year for any purpose such as holidays, recreation, education, business, etc. (McCreary et al., 2024). Besides, tourists are a specific type of visitors who travel to a specific destination primarily for leisure, recreation and cultural experiences, while visitors refer to people who travel to a specific place for leisure, business or personal reasons (Camilleri, 2018). Furthermore, tourists are often engaged in activities such as sightseeing, local attractions explorations, etc., while visitors engage themselves in visiting friends and relatives, attending events or performing business activities in a particular location (McCreary et al., 2024).

2.2 Theory of e-consumption behavior (TeCB)

In social psychology, theory of reasoned action (TRA) and TPB are widely used for predicting human behaviors. TRA was developed by Ajzen and Fishbein (1975), which explains that individuals’ intentions are described by their attitude and subjective norms. Later, Ajzen (1991) extended TRA by adding new predictive variable-perceived behavioral control, because human behavioral intentions are not only determined by their attitude and subjective norms but also by their capacity and resources, and thus, TPB was developed. TPB describes that human behaviors are explained by behavioral intention, which is influenced by individuals’ attitude, subjective norms and perceived behavioral control (Ajzen, 1991). In the same fashion, this study extends TPB because tourists’ e-consumption behavior (i.e. STD visiting behaviors) may not be explained solely by their attitude, subjective norms and perceived behavioral control. E-consumption behavior may be predicted by how individuals can react towards a particular product/service through the electronic means/media. Thus, this study extends TPB, incorporating e-attitude, resource efficiency norms and perceived environmental sustainability, and develops TeCB to measure STD visiting behaviors among domestic and international tourists in Bangladesh.

2.3 Smart tourism destination visiting behaviors

Behavior refers to actions measured by commonly accepted standards (Godey et al., 2016). Sun et al. (2020) and Hasan and Aziz (2024) demonstrate behavior as a function of intention determined by individuals’ perceived behavioral control, subjective norms and attitudes. Ghosh and Mukherjee (2023) argue that tourists’ behavior is the process or way in which they choose tourist destinations, travel there and evaluate about that destination. Furthermore, Torabi et al. (2022) contend that STD visiting behaviors are likely to be influenced by tourists’ attitudes and subjective norms. Based on these discussions, it can be argued that STD visiting behavior could be the function of STD visit intentions, which may be determined by e-attitude, resource efficiency norms and perceived environmental sustainability.

2.4 e-Attitude and smart tourism destination visit intentions

Empirical studies have established the relationship between e-attitude and intentions, where e-attitude is defined as an individual's psychological disposition of liking or disliking the use of ICT for storing, retrieving and transmitting information electronically in a digital form (Hasan, 2022b). Furthermore, Berhe et al. (2020) explain e-attitude as the degree to which an individual evaluates electronic means favorably or unfavorably. Conclusively, e-attitude towards STDs can be described as the degree to which a tourist favorably or unfavorably evaluates particular STDs before performing a specific behavior.

In a previous study, the importance of attitude has been discussed in several contexts, where Novianti et al. (2022) explained that tourists’ attitude plays a vital role in explaining their intentions towards STDs. Furthermore, Chung et al. (2018) demonstrated that attitude significantly influences tourists’ behavioral intention towards heritage tourism in Korea. Therefore, it can be assumed that investigating the causal effect of e-attitude on behavioral intentions towards STDs is pertinent, and thus, the study proposes the following hypothesis.

H1.

e-Attitude significantly influences STD visit intentions.

2.5 Resource efficiency norms and smart tourism destinations visit intentions

Earlier empirical studies have established a strong relationship between norms and intentions in different contexts, where a norm is defined as something which is expected or standard widely accepted in society (Farrow et al., 2017). Basically, norms are two types: injunctive and descriptive (Linek and Ostermaier-Grabow, 2018). Descriptive norms involve individuals’ perceptions that are typically expected or performed (Conner, 2020). Hence, resource efficiency norms are one kind of descriptive norms which can be typically expected to be performed by citizens in society.

In previous studies, a norm is explained as an important predictor of individual’s behavioral intentions. As such, Novianti et al. (2022) explain that tourist beliefs and subjective norms are significantly related to traveling intentions towards STDs. García-Milon et al. (2020) mention that social influence (subjective norms) significantly affects tourists’ STD visit intentions. Therefore, it is pertinent to examine the causal relationship between resource efficiency norms and STD visit intentions, and thus, the current study proposes the following hypothesis.

H2.

Resource efficiency norms significantly influence STD visit intentions.

2.6 Perceived environmental sustainability and smart tourism destinations visit intentions

Although earlier studies have established a strong tie between perception and intentions, the relationship between perceived environmental sustainability and intentions is less studied. McNeely (2021) demonstrates environmental sustainability as the condition, where resources like wildlife, water and air are preserved for future generations. Besides, Baloch et al. (2023) explain environmental sustainability as an ecological condition, where natural biodiversity exists, reducing environmental pollution and carbon emissions and saving natural resources. Conclusively, perceived environmental sustainability can be explained as the degree to which an individual evaluates or treats environmental issues that help generate economic benefits and preserve natural resources. The concept of environmental sustainability consists of three dimensions, such as social, economic and environmental issues.

In the smart tourism context, economic and environmental issues are highlighted, and it is argued that if travelers are concerned about environmental issues, then they prefer some systems or mechanisms that maximize economic growth, preserving the natural environment (Wu and Madni, 2021). Thus, it can be assumed that examining the causal effect of perceived environmental sustainability on STD visit intentions is crucial. Thus, the study proposes the following hypothesis.

H3.

Perceived environmental sustainability significantly influences STD visit intentions.

2.7 Smart tourism destination visit intentions and smart tourism destination visiting behaviors

Before defining STD visit intentions, behavioral intention is explained as individuals’ tendency to perform specific future behaviors (Hasan, 2023a). Behavioral intentions refer to planning commitment that is predicted by pre-intention variables such as affective and conative aspects of smart tourism (Hasan, 2022a). Hasan and Rahman (2023) explain behavioral intentions as the likelihood of engaging in a particular behavior. Furthermore, Liu et al. (2020) argue that behavioral intention is formed by motivational components that are highly correlated with the behavior itself. In this regard, STD visit intentions can be described as the degree to which a tourist formulates a conscious plan to visit STDs or not in future. Thus, STD visit intentions may be the function of e-attitude, resource efficiency norms and perceived environmental sustainability.

In the early literature, Jeong and Shin (2020) explain that behavioral intention leads to the actual behavior towards STDs. Besides, Pai et al. (2020) mention that tourists’ revisit intention is significantly related to tourists’ happiness towards STDs. Based on these discussions, it can be inferred that STD visit intentions can significantly explain tourists’ visiting behaviors towards STDs, and thus, the study proposes the following hypothesis.

H4.

STD visit intentions significantly influence STD visiting behaviors.

3. Methodology

Bangladesh is one of the most potential countries in the world for tourism industry and has many tourist attractions such as historical mosques, mountains, archaeological sites, longest natural beach, hill forests, picturesque landscape, etc. Bangladesh receives 650 thousand (6.50 lakh) foreign visitors in 2023, contributing 4.7% to the total national GDP (Siddiqui, 2023; Hafsa, 2020) and 2 crore domestic tourists in 2022 (The Bangladesh Monitor, 2023). Thus, the study considers both domestic and foreign tourists from different tourist destinations in Bangladesh.

3.1 Measures

Due to the scarcity of existing validated measurement items of resource efficiency norms and perceived environmental sustainability, the study attempts to develop necessary measurement items of each construct. Thus, a review of literature and focus group discussions (FGDs) have been administered step by step. First, construct domains and items are identified by a review of literature on smart tourism. Second, FGDs have been administered, which consist of 8 members, including industry operators, academicians and both domestic and foreign tourists. Social science researchers extensively use FGDs because of its insightful data and low cost (Woodyatt et al., 2016). The members of FGDs are selected conveniently, and participants are provided the meeting time and date via a smartphone, using WhatsApp mobile applications. At the scheduled date and time, FGD sessions start and the floor is open for all the members to freely discuss the resource efficiency norms and perceived environmental sustainability.

Initially, 9 items of resource efficiency norms and 11 items of perceived environmental sustainability have been generated in a Likert-scale-type format. Third, in-depth interviews among 10 experts in the tourism industry and STDs were administered to verify the content validity of the developed measurement items. Fourth, a pre-test was conducted among the 40 tourists who have visited STDs in Bangladesh as well as the world to check the content conciseness and clarity. Fifth, reliability and validity of the newly developed measurement items are checked, and thus, the measurement items’ reliability is checked by Cronbach's alpha and composite reliability (CR) values; and the items’ validity is checked by factor loadings and average variance extracted (AVE). Finally, 3 items of resource efficiency norms and 4 items of perceived environmental sustainability have been retained for the main survey to test the proposed hypotheses in the context of STD visiting behaviors in Bangladesh.

On the other hand, the measurement items of e-attitude, STD visit intentions and STD visit intentions are adopted and modified to fit into the research context. Three measurement items of e-attitude are adapted from Hasan (2022a, b). Three items suggested by Hasan (2023b, c) are used for measuring STD visit intentions. Furthermore, three items for measuring STD visiting behaviors are adopted from Hasan (2023a). Importantly, the respondents were asked to score all the items that are presented in a five-point scale, where “1” denotes strongly disagree and “5” denotes strongly agree.

3.2 Questionnaire design

The final survey questionnaires were first developed in English and translated into Bangla by proficient individuals and then again translated into English by other native language specialists to detect confusing words and remove them. After careful translations, the final questionnaires were prepared in a structured format with three sections. First, the concept and purposes of the study are briefly described. Second, the demographic variables such as gender, age, occupation and annual income are briefly illustrated. In the last section, the study constructs and all measurement items of each construct are presented in an assertive sentence format for quick and easy understanding.

3.3 Study population, sampling procedures and data collection

The population of this study is both domestic and foreign tourists who visit STDs in the world at least once in their life, currently living in Bangladesh, aged between 18 and 70 years. A convenience sampling technique was applied to collect data from both domestic and international tourists from different selected tourist destinations in Bangladesh. Purposively, Cox’s Bazar Beach, Kuakata Beach, Sundarbans mangrove forest, Ratargul Swamp Forest and Bisnakandi were selected as study locations. Importantly, the study follows the same sampling techniques opted by Gani et al. (2023) and Poon and Tung (2024) in their studies.

To carry out this study, 5 well-trained enumerators were appointed and briefed about the concept and purpose of this study. In the subsequent efforts, the enumerators purposively invited 1,000 participants and asked to fill up the survey questionnaires. Importantly, the respondents were asked to participate in the study process voluntarily. A total of 452 (with a response rate of 45.20%) respondents agreed to take part in the study process and provided the requested information. After data screening and evaluations, 39 problematic data with extreme values and missing information were removed, and the subsequent 413 usable (with a valid response rate of 41.30%) responses were used for further analysis. It is important to note that among the 413 valid responses, 56 were collected from foreign tourists and the remaining 357 were collected from domestic tourists from July 2023 to September 2023. Proportionately, respondents as foreign tourists are far fewer than domestic tourists due to the difficulty of approaching them and communicating with them in Bangladesh.

3.4 Data analysis

Following Anderson and Gerbing's (1988) two-step approach, the study administers the research through partial least squares structural equation modeling (PLS-SEM), using SmartPLS 3.3.3. Since data are collected from specific categorical respondents and specific time periods, the question of common method bias (CMB) may arise. To solve this issue, the study employed several techniques. For instance, order bias is controlled by mixing up the measurement items of all variables. Besides, participants were asked to complete extra questions unrelated to the study within the questionnaire to avoid response format bias. Furthermore, respondents are informed that there are no right and wrong answers to reduce method bias. Furthermore, variance inflation factors (VIFs) are obtained by a full collinearity test, and according to Table 3, the VIF values are less than 3.3, which indicates that the model is free from CMB (Kock, 2015).

Importantly, although covariance based structural equation modeling (CB-SEM) is widely used to test the existing theories in social psychological research, PLS-SEM is appropriate and required in the explanatory stage for building a theory (Usakli and Rasoolimanesh, 2023). Furthermore, PLS-SEM requires the least assumptions about properties of normal distribution of data, while CB-SEM requires multivariate normal data. In addition, PLS-SEM is used to explain the complex model, where all constructs are reflective in nature (Hair et al., 2021). Importantly, the present study has developed a new theory, extending the TPB. Thus, due to the characteristics of PLS-SEM, this study has found PLS-SEM more appropriate than CB-SEM in the research context.

4. Results

Using SPSS 25.0, respondents’ descriptive statistics are prepared and presented in Table 1. The frequency analysis indicates that 60.77% (n = 251) of participants are male and the rest 39.23% (n = 162) are female. In the age group, 35.35% (n = 146) of respondents belong to the 18–30 age group, 37.05% (n = 153) of participants belong to the 31–45 age group and the rest 27.60% (n = 114) belong to the 46–70 age group, with the mean age of 38.57 years. In case of occupation, 26.87% (n = 111) of respondents are students, 45.28% (n = 187) are office workers and the rest 27.85% (n = 115) are involved in other professions. In the level of participants’ annual income, income of 31.71% (n = 131) of respondents is below US$4000, that of 43.83% (n = 181) of respondent ranges from US$4000 to US$10000, and that of the rest 24.46% (n = 101) of respondents is above US$10000.

4.1 Evaluation of the measurement model

To evaluate the measurement model, PLS-SEM is used following the steps suggested by Hair et al. (2013). The study performs confirmatory factor analysis to identify that each item loads to a specific construct and meets the criterion of all indicators’ reliability. The reliability is measured by Cronbach's alpha and CR values. Table 2 and Figure 2 represent the α values and CR values that are greater than the recommended threshold of 0.70 (Hair et al., 2013). Furthermore, the measurement model’s adequacy is checked by convergent validity and discriminant validity, where convergent validity is checked based on three criteria. First, each item’s factor loadings should be greater than 0.70; second, α and CR for each construct must be greater than 0.70 and third, each constructs’ AVE should exceed 0.50 (Ab Hamid et al., 2017). Table 2 represents that the factor loadings range from 0.794 to 0.938, α values are between 0.834 and 0.898, CR values range from 0.901 to 0.929 and AVE values range from 0.751 to 0.812, which have met the aforementioned criteria. Hence, the constructs’ convergent validity has been confirmed.

Similarly, the measurement model’s discriminant validity is measured by two criteria. First, item loadings on any other construct are smaller than the item loadings to construct correlations; and second, the square root of the AVE of each construct is larger than the AVE of each construct (Fornell and Larcker, 1981). Table 3 shows that all AVE values of each construct are smaller than the square root of the AVE of each construct. Thus, discriminant validity has been established.

4.2 Evaluation of the structural model

4.2.1 Evaluation of model fitness

The fit of the research model is measured by calculating goodness-of-fit indices. Due to this fact, the current study analyzes cross-validated redundancy (Q²) and effect size (f²) for measuring the model’s predictive relevance. Q² is measured when the goodness-of-fit index is not appropriate for model validation because the characteristics of a valid model and invalid model seem to be similar (Henseler and Sarstedt, 2013). The Q² values of STD visit intentions and STD visiting behaviors are presented in Table 4 as Q² = 0.275 and Q² = 0.265 that are greater than zero, respectively, indicating that the proposed research model is relevant for this study. Furthermore, effect size (f²) is calculated to quantify the effects of exogenous variables on endogenous variables compared with the changes in R-square. f² is calculated by the following formula. Effect size f2=R2 Inclusive−R2 Exclusive1−R2 Inclusive

According to this study, Table 5 represents the effect size (f²) of e-Attitude, resource efficiency norms, perceived environmental sustainability and STD visit intentions, which are 0.119, 0.085, 0.051, and 0.558, respectively, reflecting small, small, small and large effect size since Cohen (1988) notes that f² value of 0.02, 0.15 and 0.35 or above, respectively, represents small, medium and large effect size.

4.2.2 Hypothesis testing

The present study evaluates the SEM to calculate the effects of exogenous variables on endogenous variables in terms of path coefficients (β), T-statistics and p-value. Table 6 and Figure 3 reveal that all hypothesized relationships of the proposed model are accepted, where H1 demonstrates that the strength of relationship between e-attitude and STD visit intentions is found to be positively significant (β = 0.292, t = 5.712, p = 0.000), supporting H1, and thus, this result implies that tourists who are positive about the applications and usage of information technologies in tourist destinations and tourism services have strong positive intentions towards visiting smart tourist destinations. Consistent with the findings of this study, Hasan (2022a, b) explains that e-attitude significantly affects behavioral intentions among ridesharing customers in Bangladesh. However, Trivedi et al. (2018) note that attitude towards environment-friendly packaging is insignificantly related to consumers’ green purchase intentions.

According to H2, resource efficiency norms are significantly related to STD visit intentions (β = 0.285, t = 5.300, p = 0.000), supporting H2, and thus, the study findings indicate that tourists who are involved in conserving natural resources show positive intentions towards smart tourist destinations. Consistently, Wang et al. (2018) mention that personal norms are significantly related to STD intentions. Furthermore, this study’s result demonstrates that perceived environmental sustainability is significantly related to smart tourist destination visit intentions (H3; β = 0.217, t = 4.131, p = 0.000), which supports H3, and thus, this result indicates that tourists who believe that STDs conserve natural ecosystems and use biodegradable and low-impact materials are likely to show positive intentions towards STDs. Aligned with this result, Pandža Bajs (2015) have found that tourists’ perceived value has a significant influence on their behavioral intentions.

Furthermore, H4 states that STD visit intentions significantly influence STD visiting behavior (β = 0.598, t = 15.888, p = 0.000), which supports H4, and thus, this result indicates that tourists having positive strong intentions are more likely to visit STDs. Aligned with this result, Hasan (2023a) has found that agro-tourist destination visit intention has a significant influence on agro-tourist destination visiting behaviors.

5. Conclusion and implications

Destinations’ facilities, landscapes, access to available information and infrastructure are considered to be the most critical factors that influence tourists’ STD visiting behaviors (Tavitiyaman et al., 2021). In the past studies, mainly tourists’ experience, attitude, emotional attachment and tourists’ prior knowledge are identified as key factors leading to STD visit intentions (Pai et al., 2020). However, so far scholars have not paid adequate attention to exploring factors related to technology perception, resource conservation and environmental sustainability, which can motivate tourists’ STD visiting behaviors in the era of smart technologies.

Thus, the current study uniquely develops TeCB to measure the impacts of e-attitude, resource efficiency norms and perceived environmental sustainability on STD visit intentions and the subsequent effect of intentions on smart tourist destination visiting behaviors among international and domestic tourists in Bangladesh. The study results indicate that all hypothesized relationships are accepted, which will uniquely contribute to tourism literature including natural, heritage and STDs in Bangladesh as well as the world.

5.1 Theoretical implications

The current study has left several unique theoretical implications for researchers and academicians because it has developed TeCB by extending TPB by incorporating e-attitude, resource efficiency norms and perceived environmental sustainability as predictive variables of intentions. Thus, the study has identified several unique factors that significantly influence tourists’ visiting behaviors towards STDs. First, the findings reveal that STD visit intentions are significantly related to STD visiting behaviors, which is consistent with Hasan (2024) who explains that travelers’ renewable energy consumption intention is significantly related to renewable energy consumption behavior among eco-tourists in Bangladesh. Thus, this study will uniquely contribute to tourism literature by explaining the significant impact of intentions on behaviors among both domestic and foreign travelers.

Second, Hasan (2022a, b) explains that e-attitude has a significant impact on behavioral intentions among ridesharing customers in Bangladesh, which is measured by three questions described by Hasan (2022a, b). Similarly, this study finding has revealed that e-attitude is significantly related to STD visit intentions, which will uniquely contribute to tourism literature. Third, most of the studies have demonstrated that norms such as subjective norms, personal norms and social norms significantly influence travelers’ visit intentions (Park et al., 2017). Consistently, the current study has uniquely identified that resource efficiency norms significantly affect STD visit intentions among domestic and foreign tourists in Bangladesh, which will uniquely contribute to tourism literature and smart tourism literature as well.

Fourth, existing studies have focused on perceived behavioral control that is significantly related to tourists’ visit intentions (Verma and Chandra, 2018). Consistently, this study has revealed that perceived environmental sustainability is significantly related to tourists’ smart tourist destination visit intentions, which will uniquely contribute to tourism literature. In this way, the current study expands tourists’ behavioral studies by empirically testing the proposed model’s hypotheses by collecting data from both domestic and international tourists, broadening the range of studies and generalizing study findings.

5.2 Practical implications

The study has offered several practical implications for STD marketers and policymakers. First, since tourists’ visiting behaviors are impacted by their intentions, industry operators should add new technologies (e.g. mobile application–based room services, IT-enabled situational information provider installation, etc.). Second, industry operators should develop such an environment that provides technology-enabled descriptions of tourist destinations’ history, routes, weather and itineraries. Third, industry operators should develop electronic flowcharts of places which are visible and easy to find. Fourth, since today’s tourists are positive towards saving resources (Wu et al., 2017), industry operators should use products made of low-impact materials and low-energy-consuming machinery.

Fifth, most tourists have become concerned about environmental sustainability since the last 2 decades (Sharpley, 2020); thus, industry operators must adopt green technologies and use biodegradable materials in tourist destinations. Finally, destination marketers should display destinations’ history and culture in the forms of games and stories, which will increase visitors’ traveling interest.

5.3 Limitations and future research

Despite offering several useful findings regarding STD visiting behaviors, the study is still constrained by several limitations. First, the current study extends TPB and develops TeCB, while future studies may redesign the model again, including knowledge, experiences and motivation variables. Second, the study is conducted using a convenience sampling technique, which may generate bias, while future studies may use a simple random sampling technique to generalize the findings more accurately. Third, the study collects data simultaneously from both domestic and international tourists and analyzes the data as a single dataset.

However, further studies may collect data separately and classify them into two groups and compare them with each other. Finally, the study is conducted for a specific period, which may include attendant limitations of non-representativeness. However, future studies may collect data using a longitudinal study approach that is more comprehensive in nature and builds up more realizable and accurate key events in the study participants’ lives (Hollstein, 2019) and can generalize the results to other STDs in other countries.

Figures

Figure 1

Proposed research framework

Figure 2

Measurement model

Figure 3

Structural model

Table 1

Demographic profile of respondents (n = 413)

Variables	n	Percentage (%)
Gender
Male	251	60.77
Female	162	39.23
Age (years)
18 to 30	146	35.35
31 to 45	153	37.05
46 to 70	114	27.60
Mean age is 38.57 years
Occupation
Students	111	26.87
Office workers	187	45.28
Others	115	27.85
Annual income (US$)
Below US$4000	131	31.71
US$4000–US$10000	181	43.83
Above US$10000	101	24.46

Source(s): Created by authors

Table 2

Measurement model results

Constructs/Items	Outer loadings	Cronbach's alpha (α)	Composite reliability (CR)	Average variance extracted (AVE)
e-Attitude
Information and communication technologies used in smart tourism destinations are a good idea	0.899	0.884	0.928	0.812
Information and communication technologies used in smart tourism destinations are a wise decision	0.938
Information and communication technologies used in smart tourism destinations are beneficial	0.864
Resource Efficiency Norms
I think that smart tourism destinations install low-flow shower head	0.885	0.855	0.912	0.775
If light bulbs are needed, the smart tourism destinations install LED or CFL bulbs	0.919
I think that smart tourism destinations use insulating in building to use less heating and cooling energy	0.835
Perceived Environmental Sustainability
I believe that smart tourism destinations conserve ecosystems using low-impact materials	0.794	0.898	0.929	0.768
I believe that smart tourism destinations implement green supply chain strategies	0.891
I believe that smart tourism destinations recycle waste and recover energy	0.913
I believe that smart tourism destinations use bio-degradable materials	0.901
Smart Tourism Destinations Visit Intentions
I will visit smart tourism destinations	0.895	0.839	0.903	0.757
I’m willing to visit smart tourism destinations	0.879
I will make an effort to visit smart tourism destinations	0.834
Smart Tourism Destinations Visiting Behaviors
I frequently visit smart tourism destinations	0.844	0.834	0.901	0.751
When visiting tourism destinations, I often visit smart tourism destinations	0.901
I frequently go for visiting tourism destinations that have the characteristics of smart tourism destinations	0.854

Source(s): Created by authors

Table 3

Results of discriminant validity and collinearity

	eATT	REN	PES	STDVI	STDVB
eATT	0.901
REN	0.329	0.880
PES	0.270	0.554	0.876
STDVI	0.444	0.501	0.454	0.870
STDVB	0.460	0.546	0.488	0.598	0.867
VIFs	1.136	1.519	1.462	1.000	1.253

Note(s): eATT = e-attitude; REN = resource efficiency norms; PES = perceived environmental sustainability; STDVI = smart tourism destination visit intentions; STDVB = smart tourism destination visiting behaviors; VIFs = variance inflation factors

Source(s): Created by authors

Table 4

Blindfolding results

Total	SSO	SSE	Q2 (=1-SSE/SSO)
STDVI	1,239	898.775	0.275
STDVB	1,239	910.793	0.265

Note(s): STDVI = smart tourism destination visit intentions; STDVB = smart tourism destination visiting behaviors; SSO = sum of squares of observations; SSE = sum of squares of prediction errors

Source(s): Created by authors

Table 5

Effect size of latent variables on behavioral intentions

Latent variables	f2	Decision
eATT	0.119	Small
REN	0.085	Small
PES	0.051	Small
STDVI	0.558	Large

Note(s): eATT = e-attitude; REN = resource efficiency norms; PES = perceived environmental sustainability; STDVI = smart tourism destination visit intentions

Source(s): Created by authors

Table 6

Path coefficients and hypothesis testing

Hypothesis	Relationships	Beta	T-statistics	p-values	Decisions
H1	e-Attitude -> smart tourism destination visit intentions	0.292	5.712	0.000	Supported
H2	Resource efficiency norms -> smart tourism destination visit intentions	0.285	5.300	0.000	Supported
H3	Perceived environmental sustainability -> smart tourism destination visit intentions	0.217	4.131	0.000	Supported
H4	Smart tourism destination visit intentions -> smart tourism destination visiting behaviors	0.598	15.888	0.000	Supported

Source(s): Created by authors