Introduction
The transformation of traditional hotels is contributing to the commercialisation of green hotels in the hospitality industry, which is accelerating the green hotel sector’s rise in importance in hospitality and tourism marketing (Al-Gharibah and Mahfod, 2022). Numerous hotel chains have begun to promote green construction practices and green business strategies (Pan et al. 2022). To minimise the negative environmental impact (Wang et al. 2023b), businesses like Hilton Hotels & Resorts, InterContinental Hotels & Resorts, Marriott International, and many other lower-level hotel chains have integrated green management and marketing initiatives in their operations (Fauzi et al. 2022). Sustainability is becoming increasingly important in the hospitality sector (Agag and Colmekcioglu, 2020), as according to academic, governmental, and marketing reports showing that the hotel industry consumes an enormous amount of natural resources, such as excessive water, energy, and lighting consumption (Ansari et al. 2022). Because of this, green hotels have developed several innovative programs that reduce costs while protecting the environment by reducing the production of solid waste, energy, and water (Green Hotel Association, 2024).
Considering consumers have to pay the Pigouvian tax, which is regulated by economists and governments, they have been adjusting their purchasing habits to be more ecologically friendly (Goodwin et al. 2014). Green consumerism is also visible in the service-oriented industries (Wahab and Ismael, 2022), and more consumers have recently started to favour environmentally friendly products and services because environmental issues such as global warming and haze have a substantial negative impact on their daily lives (Wang et al. 2022f). Primarily, tourists choose to stay at green hotels in order to show their appreciation for the environment (Verma et al. 2019). For example, almost 89% of Taiwanese tourists reported that, given the option, they would choose to stay at green hotels when travelling (Pan et al. 2022). However, booking revenue for green hotels has been mostly unchanged despite a rise in consumer awareness of the importance of environmental conservation (Wang et al. 2023a). In other words, although consumers express a willingness to stay at green hotels, there is insufficient evidence to substantiate the notion that their environmental concerns will prompt them to visit green hotels (Wang et al. 2023c).
Previous research has shown that there is an unstable correlation, known as the attitude-behaviour gap, between consumers’ professedly favourable attitudes and their purchasing behaviours when it involves green product/service consumption (Kumar, 2021; Wang, 2022). Studies utilising the theory of reasoned action (TRA) (Al-Gharibah and Mahfod, 2022), theory of planned behaviour (TPB) (Lim et al. 2019), value-attitude-behaviour model (Wang et al. 2022f), moral norm-activation theory of altruism (Rahman and Reynolds, 2019), value-belief-norm theory (Wang et al. 2020a), and goal-framing theory (Wang et al. 2022b) highlighted some of the inadequacy of these studies that embrace a theoretical lens, and as such, the studies’ conclusions are frequently challenged, inconclusive, or even restricted in scope (Wang et al. 2023a; Wang et al. 2022b). As the purpose of visits to green hotels is noticeably underestimated (Nimri et al. 2020a), it is important to comprehend the underlying factors influencing tourists’ intentions to visit green hotels (Ray et al. 2023) for hotels to adapt their operations and satisfy the needs of environmentally conscious visitors (Sultana et al. 2022). Therefore, new theoretical perspectives need to be included in order to better understand visitors’ expectations when they visit green hotels (Wang et al. 2023c).
In contrast to the concept of negative behavioural intention, which has not received much attention (Ulker-Demirel and Ciftci, 2020; Wang et al. 2022d), the majority of literature on hospitality and tourism focused on positive attitudes and intentions by using various antecedents of consumer behaviour (Han et al. 2020a; Joshi et al. 2021; Patharia et al. 2020; Wang, 2022). In particular, most tourists only learnt of the notion of green hotels (Wang et al. 2022e) because it is still relatively new to them (Yeh et al. 2021). Because of this, the majority of tourists are ignorant of the advantages and functions of green practices (Chen et al. 2021). Additionally, they are unsure of what green hotels have to offer (Wang et al. 2022e) or what benefits staying at green hotels may provide (Fauzi et al. 2022). Thus, researchers need to understand the reluctance and identify the barriers that retain travellers from staying at green hotels (Fauzi et al. 2022) in order to investigate the impact of negative attitudes and perspectives on consumer behaviour (Ulker-Demirel and Ciftci, 2020; Wang et al. 2022d).
Indeed, while previous research on green hotels has focused mostly on developed countries (Ansari et al. 2022), this kind of perspective is particularly frequent in emerging countries (Ahn and Kwon, 2020; Sadiq et al. 2022), including China (Wang and Wong, 2021). Because of this, earlier studies were unable to offer a solid theoretical foundation (Wang et al. 2022b) for an approach that would theoretically lead to an investigation of how consumers express unfavourable opinions about their stays at green hotels. Hence, this study will contribute to a better understanding of the factors that affect the decision to stay at green hotels, which include perceived risk, familiarity, novelty, trust, attitude, subjective norm, perceived behavioural control, and intention.
Literature review
Theoretical background
TPB is a development of TRA since it addresses the limitation that people cannot make decisions based only on their own volition (Nimri et al. 2020a). A person’s intention towards a particular actual behaviour depends on several variables: attitude (i.e., beliefs about the perceived outcomes associated with the behaviour), subjective norm (i.e., beliefs about the normative expectations and actions of significant others), and a newly added measurement variable named perceived behavioural control (i.e., beliefs about the existence of the factors that encourage or discourage conducting a specific behaviour) (Wang et al. 2023b). The most direct precursor to identical behaviour is the intention to act, which shows that the person is mentally able to take action (Wang et al. 2020b). In general, when someone has a positive attitude, a strong sense of perceived behavioural control, and a favourable subjective norm, they should be more inclined to engage in the behaviour in question (Liu et al. 2020).
Because of its persistent nature, TPB has emerged as one of the most prominent and important social-psychological models for comprehending human behaviour in recent years (Kumar, 2021; Niloy et al. 2023). In particular, since TPB can be expanded by adding more constructs to improve the explanatory power of the behaviour in varied circumstances (Wahab and Ismael, 2022), many scholars have specifically used TRA and TPB as the underpinning theory in various environmental-related areas of consumer behaviour studies (Bahl and Kumar, 2019; Kumar, 2021), such as green consumer behaviour (Bahl and Kumar, 2019; Kumar, 2021); low carbon emissions (Wahab and Ismael, 2022); responsible environmental behaviour (Casado-Díaz et al. 2020; Lin et al. 2021); organic foods (Cheng et al. 2023; Wibowo et al. 2022); green hotel visitation (Al-Gharibah and Mahfod, 2022; Bashir et al. 2019); and many others.
Attitude
Attitude refers to the degree to which an individual’s overall appraisal of their actions is good or negative (Al-Gharibah and Mahfod, 2022). It is a mental state that influences their reactions to people, objects, and situations (Shah et al. 2023). Consumers’ favourably or unfavourably biased opinions about green products and services reflect how much they value and are willing to purchase them (Wahab and Ismael, 2022). Consumers awareness of environmental risks and their consciousness of the environment have an impact on this decision (Wang et al. 2023a). In general, a greater likelihood of purchasing green products and services will result from a generally positive attitude among consumers (Bryła, 2019). Attitude has been identified as a main predictor of behaviour in psychological theories (e.g., TRA, TPB, value-attitude-behaviour model, value-belief-norm theory), and prior studies have confirmed this predictive capacity in the context of green hotels (Al-Gharibah and Mahfod, 2022; Dwivedi et al. 2022). For instance, Nimri et al. (2020a) found that attitude is a critical predictor for travellers to stay at green hotels, while Wang et al. (2023c) demonstrated that attitude positively influenced consumers’ intentions to select green hotels. Nevertheless, the findings of certain studies showed that attitude does not determine one’s intention to stay at green hotels (Haq et al. 2023). Thus, the following hypothesis is proposed for testing:
H1: Attitude significantly influences intention.
Subjective norm
Subjective norm refers to the social pressure people experience before they are able to establish their own opinions on the stimuli currently present (Niloy et al. 2023). The desire to conform to typical social network individuals’ suggestions on a given relationship influences people’s perceptions of those people’s views (Al-Gharibah and Mahfod, 2022). As a result, people act in their own best interests, and more importantly, they believe that what they are doing is appropriate if others are acting in a similar manner (Yeow and Loo, 2022). A person will typically be appropriately encouraged or discouraged from engaging in a certain behaviour by an external social influence (e.g., close friends, relatives, coworkers, or classmates) (Shah et al. 2023). Recent studies in the field of green marketing have found a positive correlation between subjective norm and intention (Kumar, 2021; Sheraz et al. 2021). For example, subjective norm positively influenced travel operators’ intentions to market low-carbon tours (Sung et al. 2021). Another study also found that the subjective norm positively influenced guests’ intentions to stay at green hotels (Al-Gharibah and Mahfod, 2022; Shah et al. 2023). Thus, the following hypothesis is proposed:
H2: Subjective norm significantly influences intention.
Several academics have recently questioned the significance of the subjective norm in the TPB model because of its complexity and flaws (Wang et al. 2022c; Wang et al. 2022d), especially in societies that value collectivism heavily (Wang and Wong, 2021). On the one hand, previous empirical research showed that, in the context of green marketing, the subjective norm has a limited capacity to predict consumer behaviour (Niloy et al. 2023; Shah et al. 2023; Yeow and Loo, 2022). On the other hand, TPB recognises that attitude, subjective norm, and perceived behavioural control are three independent variables that can influence a person’s behavioural intention (Ajzen, 1991), but little research has examined potential links between these variables because the fact that correlations between them are typically somewhat significant (Quintal et al. 2010). The TPB’s sequence causalities thus, may not be accurate (Wibowo et al. 2022). Consumers’ decision-making processes are unable to exclude the potential of a substantial causal path from the subjective norm to attitude and perceived behavioural control (Wang et al. 2019a; Wang et al. 2023c).
Because people construct their own opinions based on their impressions of others’ expectations and willingness to comply, subjective norm may effect on attitudes (Oliver and Bearden, 1985; Quintal et al. 2010). In fact, because individuals uphold collectivistic principles, those from highly collectivistic regions are identifiable by their interdependence, cooperation, and lack of competitiveness (Wang et al. 2022a). The subjective norm in those societies operates on a large scale as social norms that impact people’s decision-making (Wang et al. 2022d). Consumers are probably more inclined to share their positive and negative experiences with new or unfamiliar products or services with significant others, which may affect their choice (Wang and Wong, 2021). Similarly, because people’s perceived capacity to carry out a certain behaviour is greatly influenced by their agreements with others (Wang et al. 2022d), people’s perceptions of external barriers to action are likely influenced by social pressure to act (Quintal et al. 2010). Certain studies showed that subjective norm had a significant effect on attitude and perceived behavioural control towards green purchase intention (Cheng et al. 2023; Quintal et al. 2010; Wang et al. 2023c). Therefore, the following hypotheses are proposed:
H3: Subjective norm significantly influences attitude.
H4: Subjective norm significantly influences perceived behavioural control.
Perceived behavioural control
An individual’s perceived ability or confidence, the availability of opportunities and resources like time and money, and external factors like facility accessibility can all influence how easy or difficult behaviour is perceived to be (Shah et al. 2023). The drivers of perceived behavioural control are hence direct experience and comparison evaluation of various possibilities (Yeow and Loo, 2022) that are generated by self-evaluation (Niloy et al. 2023). Ajzen (2002) states that the two components that jointly contribute to perceived behavioural control are controllability, or an individual’s perception of whether or not the behaviour is fully within his or her control, and self-efficacy, or an individual’s perceived confidence and ease in performing a particular behaviour (Liu et al. 2020). Thus, people are more likely to engage in certain actions that they believe are easier to complete than in behaviours they believe are more difficult and over which they have less control (Niloy et al. 2023). Possessing complete control over behaviour might help or hinder someone from realising that certain behaviour is possible (Haq et al. 2023). Numerous earlier studies established the connection between perceived behavioural control and intention on green purchasing behaviour (Kumar, 2021; Sung et al. 2021). Nevertheless, some studies have also shown no significant correlation between perceived behavioural control and consumers’ adoption of green hotels (Eid et al. 2021; Han and Yoon, 2015). As such, the following hypothesis is proposed:
H5: Perceived behavioural control significantly influences intention.
Contribution of the current study through extended TPB
The most extensively utilised theory by scholars in the area of green hotels is the TPB (Acampora et al. 2022; Arun et al. 2021). The fundamental reason is that this theory has been extended or modified to include more or new constructs (Ong et al. 2023; Pan et al. 2022) since it enables researchers to incorporate more determinants to raise the amount of variance in intention or behaviour in diverse circumstances (Ajzen, 1991; Eid et al. 2021). However, in order to extend the theory, most earlier research frequently used known conceptual validations that were mostly based on literature review; these validations might not apply to the population of interest (Han et al. 2020b; Nimri et al. 2020a). While some studies used TPB to predict guests’ intentions to stay at green hotels, they failed to extract voluntaristic elements and belief elements such as an individual’s risk beliefs and outcome expectations (Nimri et al. 2020a; Shehawy, 2023), which are critical to enhancing the precision of motivational prediction models like the TPB (Nekmahmud et al. 2022; Shehawy, 2023).
For example, a number of studies on green hotels have observed an increase in greenwashing and green scepticism, whereby these establishments only offer environmentally friendly amenities and services in places where they can sway public opinion without truly advancing the cause of sustainability (Arun et al. 2021; Chen et al. 2019). Therefore, the normative and attitudinal elements influenced by this green trust should be taken into account by researchers. Comparably, the majority of visitors are unaware of the benefits and functions of green hotels since they have only heard of the concept (Choi et al. 2015) and are not familiar with what green hotels have to offer (Wang et al. 2022e). Nevertheless, relatively few studies have examined the degree to which familiarity affects guests’ perceptions and behaviours with regard to green hotels, although familiarity is a crucial precondition that influences guests’ perceptions of green hotels, decision-making processes, and future behaviour (Wang et al. 2022e). Meanwhile, familiarity and novelty are typically seen as being at different extremes of the spectrum in the literature on familiarity in tourism (Casali et al. 2020). The novelty has been a major theme in the literature on tourism, and the majority of earlier research has shown that people are more inclined to visit a destination if they have higher novelty orientations towards it (Assaker and Hallak, 2013; Ponsignon et al. 2020). However, novelty has received very little attention in the literature on green hotels. Further, studies have demonstrated that travellers’ decisions to visit a destination are highly influenced by their perception of risk (Gong et al. 2024; Yi et al. 2020). Because emotional perceptions play a significant role when considering risky accommodations (Alvarez and Campo, 2014), measuring perceived risk in addition to TPB might assist researchers in comprehending the influence of attitude, subjective norm, and perceived behavioural control on the intention to stay at green hotels. This is consistent with the proposal made by Ulker-Demirel and Ciftci (2020) that additional research is necessary to examine the impact of negative attitudes and expressions on an individual’s behaviour.
In conclusion, there is not much research that uses an expanded version of the TPB model that includes perceived risk, trust, familiarity, and novelty into account. These factors have primarily been employed in different investigations. Therefore, there was not plenty of research done in the past to construct a comprehensive model that takes into account the transitory correlations between perceived risk, trust, familiarity, novelty, and TPB. This is also consistent with the critique that other factors are required to explain the diversity in behavioural intention because the TPB is unable to fully explain variation in an individual’s intention and behaviour (Al-Gharibah and Mahfod, 2022; Yadegaridehkordi et al. 2021).
Impact of trust
Trust is a belief (Waris and Hameed, 2020) that pertains to an individual’s relationship with a business and encompasses attributes such as general dependability, credibility, honesty, and kindness (Ganesan, 1994). It may also be defined as one’s level of confidence in another individual (Hart and Saunders, 1997). Trust in green marketing is the desire of a consumer to purchase products or services because of their expectations or perceptions of their reliability and ecological performance (Chen, 2010). Consumers purchase environmentally friendly products and services because they consider these goods to be dependable and capable of protecting the environment (Shah et al. 2023). As a result, trust gauges how confident consumers are with their purchases (Moorman et al. 1993), and they use trust to select products and services that align with their environmental beliefs (Haq et al. 2023). In consumer behaviour and green marketing, the relationship between trust and behavioural intention has become well-established (Han et al. 2019; Shah et al. 2023). Meng and Choi (2016), for instance, showed that individuals are more likely to buy products and services when they think that they are being given more accurate information about them and when they have a higher degree of trust in them. Trust is also seen to be the main element influencing travellers’ intentions to use electric aircraft (Han et al. 2019).
Nevertheless, there is currently little evidence to back up the assertion that consumers’ pro-environmental behaviour is influenced by trust (Shah et al. 2023). Recent studies showed that trust is essential for the development of attitudes and the perception of behavioural control over particular behaviours (Dwivedi et al. 2022; Shah et al. 2023). Businesses with a solid track record of trustworthiness, reputation, and strong perceived brand equity are better able to create distinctive brands than simply rank other products or services according to general objective standards (Salazar-Ordóñez et al. 2018). Therefore, more positive attitudes towards the decision are likely to arise from a higher level of trust regarding the decision’s outcomes (Quintal et al. 2010). Meanwhile, trust can also influence an individual’s perceived ability to manage their behaviour towards environmentally friendly products and services (Sung et al. 2021). For example, people who have less faith in the ecological quality of the products and services associated with green purchases will feel less secure in the perceived behavioural control of their purchases (Koklic et al. 2017). On the other hand, a larger degree of trust in a specific purchasing activity can offset consumers’ inadequacies in control-believing (Koklic et al. 2017). However, other research indicates that the intention to stay at green hotels is not always correlated with green trust (Fauzi et al. 2022). Based on the above arguments, the following hypotheses are proposed:
H6: Trust significantly influences attitude.
H7: Trust significantly influences perceived behavioural control.
H8: Trust significantly influences intention.
Impact of perceived risk
The concept of perceived risk originally came forth by Bauer (1960). It is the conviction that uncertainty may have a detrimental psychological effect on people’s behaviour (Park et al. 2022; Wei and Onder, 2022). People worry about the potential losses they might incur in the future if they make a particular decision (Afshardoost and Eshaghi, 2020). Real risk and perceived risk are two categories of risk (Bauer, 1960). Real risk is the extent to which potential losses are felt subjectively, while perceived risk is the objective assessment of the likelihood of adverse outcomes. Therefore, while evaluating perceived risk, uncertainty and the seriousness of the purchase’s repercussions must be carefully taken into account (Zhang et al. 2021). Most researchers agree that perceived risk is an important theoretical concept that shapes how decision-making processes and consumer behaviour are developed (Aufa and Gunanto, 2023; Stone and Grønhaug, 1993). Particularly because services are intangible and challenging to standardise, decision-making in the sector is heavily influenced by perceived risk (Fuchs and Reichel, 2011). Numerous research studies substantiate the notion that consumers’ perception of risk significantly influences their intentions or reluctance to participate in a specific action (Chang and Tseng, 2013; Mitchell and Greatorex, 1993).
Perceived risk can be measured using a range of indicators and categorised into numerous dimensions based on different cultural backgrounds, research subjects, and situations (Wang et al. 2019b; Zhang and Yu, 2020). Roehl and Fesenmaier (1992) suggested that perceived risk should take satisfaction and environmental risks into account, while Witte et al. (1996) suggested that perceived risk might be assessed based on severity and susceptibility. These risk factors were expanded by Sönmez and Graefe (1998a, 1998b), who included the possibility of terrorism, political instability, and health risks. Information risk, economic risk, time risk, psychological risk, privacy risk, distribution risk, service risk, and operational risk are some of the factors identified by Aufa and Gunanto (2023) as possibly influencing online shoppers. Overall, financial risk, performance risk, psychological risk, physical risk, social risk, and time risk are the most often utilised categories of perceived risks in marketing research (Zhang et al. 2021).
According to literature on hospitality and tourism, when choosing hotels’ products and services, guests may perceive a number of dangers, such as the potential to lose time or endanger their safety (Zhang et al. 2021). Kim et al. (2010) categorised risks into physical, health, social, and economic categories and investigated how tourists’ perceived risk affected their attitudes. Park (2017) classified the perceived risk of passengers reducing their air service into five categories: functional, temporal, physical, psychological, and economic risk. Zhang et al. (2021) drew the conclusion that the most frequently perceived risk aspects associated with tourism-related products and services are financial risk, performance risk, psychological risk, physical risk, and social risk.
This study, therefore, considers the effects of financial risk, performance risk, functional risk, psychological risk, physical risk, time risk, and health risk on consumers’ intentions to visit green hotels. Accordingly, consumers are primarily affected by financial risk, which is the possibility of losing money on investments in products (Aufa and Gunanto, 2023). Performance risk is the possibility that consumers will be dissatisfied with products or services for a variety of reasons, which could be detrimental to the interests and reputations of firms. Failures in delivery, inferior products or benefits, and a lack of post-purchase assistance are the usual causes of this risk. Performance risk is important in influencing travel intention (Khan et al. 2019) and is closely related to the worry that the products or services in question may not function as intended or offer consumers the benefits that have been promised (Küpeli and Özer, 2020). Functional risk is the conviction that inadequate service will be absent, meaning that guests will not get what they expected out of their stay (Fuchs and Reichel, 2011). Psychological risk is a factor that could cause an in-person, psychological, or emotional reaction (Najar and Rather, 2022). It is the likelihood that someone will suffer psychological damage as a result of being in an unfavourable environment (Park et al. 2022). Physical risk is the possibility of a product or service having a negative physical impact on an individual’s health or well-being (Quintal et al. 2010). When tourists purchase products or services at tourist locations, they suffer the perceived risk of health problems (Al-Ansi et al. 2019). It illustrates the possibility that utilising the service might place the consumer’s health at formerly unprecedented risk (Tsaur et al. 1997).
Several studies used TPB in conjunction with perceived risk to determine consumers’ behavioural intention in marketing (Arshad et al. 2021; Sujood et al. 2021). For example, Cuong and Jian (2014) showed that investors’ attitudes are negatively influenced by perceived risk, and tourists’ attitudes about travel are similarly negatively impacted by perceived risk (Wu et al. 2022). Rather (2021) demonstrated that attitudes and consumer brand engagement were significantly influenced by perceived risk. Meanwhile, perceived risks are negative perceptions of employees concerning their environments and generate a social amplification effect (i.e., subjective norm) (Kapuściński and Richards, 2016), continuously producing negative impacts on attitudes, emotions, and behaviours (Xie et al. 2023). When an individual engages in a certain behaviour, the perception of risk interacts with perceived behavioural control, which then engages in some positive or preventive behaviours (Sujood et al. 2021). Park et al. (2022) found a substantial negative association between perceived risk and attitudes, subjective norm, and perceived behavioural control in their study of dangerous airborne pollutants and particulate matter that threaten human health. Based on the above studies that emphasise the importance of risk perception in traveller behaviour, the following hypotheses were developed:
H9: Perceived risk significantly influences attitude.
H10: Perceived risk significantly influences subjective norm.
H11: Perceived risk significantly influences perceived behavioural control.
Consumers’ favourable opinions about products and services or companies that will meet their expectations are referred to as their trust in business (Ye et al. 2019). Because trust can reduce high-risk perceptions and help people move over their worries or uncertainty about their actions and prospective repercussions, perceived risk and trust play a critical role in determining consumer purchase intentions (Ganesan, 1994; McKnight et al. 2002). In other words, it is possible to gain authority over consumers trust by recognising and reducing perceived risks (Han et al. 2019). Al-Ansi and Han (2019) asserted that perceived risk has a direct impact on trust, and Huifeng et al. (2020) indicated the role of trust in moderating the relationship between perceived risk and revisit intention. Ratasuk and Gajesanand (2022) support the significant mediating effects of trust and perceived risk on customers’ purchase intentions. Abror et al. (2022) found that environmental risk and financial risk have a significant negative correlation with trust. Additionally, Alrawad et al. (2023) verified the relationship between trust and perceived risk, and the results showed that perceived risk significantly influenced feature-based trust, process-based trust, and process-based trust. Hence, the following hypothesis is proposed:
H11: Perceived risk significantly influences trust.
Familiarity
Familiarity is the amount of consumer accumulated product-related experiences (Kim et al. 2019), which is regarded as one of the most important marketing variables (Stylidis et al. 2020). In the tourism industry, familiarity is commonly used to contrast the experiences of frequent visits and revisits in order to highlight the differences in destination selection between first-time and repeat tourists (Wang et al. 2022e). Most people associate familiarity with two types of experiences: direct experience (i.e., experience familiarity) and indirect familiarity (i.e., informational familiarity) (Baloglu, 2001). Thus, travellers can become more familiar with products and services directly through purchases and usage (Alcocer and Ruiz, 2020) and through indirect channels contracts with others, advertisements, travel guides, movies, brochures, mass media, and education (Gursoy, 2011; Kim et al. 2018). Familiarity is the basis for comprehending how people perceive a destination (Soliman, 2021) since it can potentially impact travel decisions and visitors feel more secure in familiar surroundings (Chi et al. 2020). Previous research on tourism has shown that visitors with greater familiarity with a destination typically exhibit more positive functional and psychological features (Casali et al. 2020) and a greater intention to visit (Tan and Wu, 2016). Hence, the following hypothesis is proposed:
H13: Familiarity significantly influences attitude.
While searching for new places to visit is one of the main reasons people travel (Chi et al. 2020), the degree of familiarity travellers require from a destination has a significant impact on how they perceive risk (Rather, 2020). Travellers seeking familiarity will likely view strange environments as more risky (Lepp and Gibson, 2003). Information collection to lessen perceived risk is part of assessing a destination’s risk, which has a significant impact on travel intention (Kozak et al. 2007). As opposed to travelling somewhere they are unfamiliar with or not concerned much about in order to reduce risks, visitors are more likely to choose locations they have read about in some ways when choosing a destination (Ju et al. 2021). For example, travellers would particularly seek out information regarding the risk of contracting influenza during a pandemic, as this would greatly influence their travel intentions (Helfenberger et al. 2010). Pandemic anxiety increased the likelihood that travellers would postpone or cancel their journeys (Leggat et al. 2010). Therefore, familiarity reduces risk perceptions and gives travellers greater assurance when choosing a destination (Soliman, 2021), and the following hypothesis is proposed for testing:
H14: Familiarity significantly influences perceived risk.
Novelty
Travel motivation is a specific subset of an individual’s drive that encompasses the network of biological and cultural factors that provide meaning to and impact travel behaviour, choice, and experience (Pearce, 2011). Visitors act for a variety of reasons, including personal experiences, psychological characteristics, and external social and cultural influences (Soliman, 2021). Travellers’ attitudes toward a place are thought to be predicted by their behavioural beliefs about motivational variables (Ulker-Demirel and Ciftci, 2020; Wang et al. 2021). The novelty-seeking theory is one such theory that provides a strong theoretical foundation for comprehending destination choice behaviour (Assaker and Hallak, 2013). Accordingly, novelty is described as consumers’ needs for stimulation being addressed by switching from a previous product or service to a novel one (Kim and Kim, 2015), even when they are satisfied with their initial purchase (Assaker and Hallak, 2013). There are a number of reasons for this, such as the need for change to address the boredom that consumers associate with certain products or services, attribute satisfaction (Kim and Kim, 2015), or the fact that certain experiences like fascinating, fun, enjoyable, emotional, exciting, and multisensorial benefit specific categories of consumers (Ponsignon et al. 2020). In the tourism industry, consumers’ novelty refers to the need for stimulation that drives visitors to select a new location over one they have previously visited (Kim and Kim, 2015). High novelty-seeking travellers also frequently show their desire to explore new locations (Hong et al. 2009), especially those travelling alone who value seeking other cultures over local traditions (Lepp and Gibson, 2003). Thus, the following hypothesis is proposed:
H15: Novelty significantly influences attitude.
Furthermore, previous research on tourism marketing has shown that a visitor’s behavioural paradigm is split between familiarity and novelty (Casali et al. 2020). Individuals who have a strong attraction to novelty tend to choose different destinations and are therefore less likely to go back to the same place (Lepp and Gibson, 2003), and compared to people who desire familiarity, they are more prone to believe that unknown environments are less threatening (Assaker and Hallak, 2013). Under this circumstance, travellers may be drawn to a place by its novelty, while others may be repelled (Elsrud, 2001), because they are driven to vacation and seek a state of disequilibrium (Lepp and Gibson, 2003). Furthermore, because various people have varying needs for novel experiences, tourists range in how much novelty they seek, and this could significantly affect how risky a destination is judged to be (Lepp and Gibson, 2003). Rather (2020) found that novelty considerably reduced tourists’ risk perceptions towards visiting a destination, while other studies suggested that tourists derive greater novelty value from their travel experience, increasing their satisfaction with new destinations (Assaker and Hallak, 2013; Kim and Kim, 2015; Ponsignon et al. 2020) Fig. 1. Based on the preceding discussion, the following hypothesis is proposed:
Fig. 1 [Images not available. See PDF.]
Conceptual research model.
H16: Novelty significantly influences perceived risk.
Research methods
Research design
The positivist philosophy is used in this study because it guarantees that researchers are using mathematical and statistical techniques to observe the external world in an objective and value-free manner, leading to results that can be applied to other contexts (Saunders et al. 2011; Yin, 1994). The type of research is considered an explanatory one since explanatory studies focus on explaining the nature of the causal relationships between dependent and independent variables and can identify which variables are the cause and which are the effect that needs to be predicted (Saunders et al. 2011). A cross-sectional data collection approach was used in this study due to time and cost constraints. As it is difficult to obtain a sampling frame for this type of study, a non-probability sampling technique was used to collect samples (Saunders et al. 2011). When selecting samples, judgemental sampling is employed because it helps researchers choose the most qualified respondents to answer their questions and achieve their research objectives (Saunders et al. 2011).
Previous studies revealed mixed results regarding the willingness of younger and older generations to engage in green consumption, including visiting green hotels (Bhutto et al. 2019; Wang et al. 2020b), but researchers generally agree that the younger generation, particularly students, have significantly lower financial capacity and expenditure than the working population (Wang et al. 2024b), and it may be extremely difficult for researchers across disciplines to replicate student results on non-students (Henry, 2008). However, many recent studies have advocated that green marketing-related studies including green hotels studies needed to select younger generation respondents as their target research sample due to: 1) young adults are better equipped to compare and assess currently operating green hotels (Shah et al. 2023); 2) young adults exhibit greater environmental consciousness than older consumers (Ansari et al. 2022); 3) young adults possess greater education and knowledge regarding green consumption (Wang et al. 2022f); and 4) many businesses target the younger generation as their short-term target market (Pan et al. 2022) because they have the power to shape consumption patterns in the future (Bahl and Kumar, 2019). Hence, the target sample for this study was young adults.
Data collection
An online survey was used for the primary data collection because of its many benefits, including instant access to a large audience, the ability to collect data regardless of respondents’ geographic location, high speed of data collection, low cost, better content display, better access to unique populations, and convenience (Wang et al. 2019a). Self-administered questionnaires were posted and distributed on www.wenjuan.com to gather primary data from online users between 20 December 2023 and 6 January 2024 because the questionnaire enables a greater geographical coverage, less cost, involves less pressure, provides anonymity, allows for quicker collection, and reduces levels of bias compared with the interview technique (Saunders et al. 2011). Besides, wenjuan.com is the largest free online survey platform, which is similar to Google Forms and is well known among individuals, researchers, businesses, and organisations that focus on providing questionnaire creation, distribution, management, collection, and analysis services in China. Wenjuan.com is a member of the CIIA Marketing Research Association and has more than 10 million users in 2020. In addition, 3 Chinese yuan (RMB) per person is offered as a necessary incentive to increase the response rate. A brief explanation of green hotels was provided at the start of the survey, and to be eligible to complete the survey, all respondents needed to: 1) be between 18 and 25 years old, and 2) have stayed at green hotels within the previous twelve months or have the plan to visit green hotels within twelve months after responding to the survey before they can proceed to answer the rest of the questions in the survey. A total of 606 usable responses were collected for analysis, which can be regarded as adequate for structural equation modelling purposes (Hair et al. 2019), as a minimum sample size of 384 is recommended when the target population is unknown or infinite based on Cochran’s Formula (Sarmah et al. 2013).
Questionnaire operationalisation
A closed-ended questionnaire was used in this study, consisting of a set of verified scales (De Vaus, 2013). This study used a seven-point Likert scale from (1) strongly disagree to (7) strongly agree to measure the constructs because a seven-point Likert scale will be more likely to produce slightly higher mean scores within the highest possible attainable score, compared with a ten-point Likert scale (Dawes, 2008). All questionnaire contents were initially translated into Chinese and then back-translated to English by a second team of bilinguals. In addition, the questionnaire was validated by two experts, and the pilot test was conducted with 30 undergraduate students to assess its reliability before data collection. Overall, the endogenous variables in this study are perceived risk, trust, attitude, subjective norm, perceived behavioural control, and intention to visit green hotels. The exogenous variables in the research model are familiarity and novelty. In addition, many previous studies have shown that using demographic variables to predict consumer pro-environmental attitude and behaviour can produce inconsistent and even contradictory results (Caniëls et al. 2021; Wang et al. 2020b). Thus, this study considers demographic variables such as gender, age, educational level, and income as control variables in the research model.
The questionnaire was designed in four sections. Section A includes perceived risk-related items, three items used to measure financial risk were adapted from Abror et al. (2022) and Küpeli and Özer (2020); three items used to measure performance risk were adapted from Khan et al. (2019) and Küpeli and Özer (2020); three items used to measure functional risk were adapted from Han et al. (2019) and Park et al. (2022); three items used to measure psychological risk were adapted from Quintal et al. (2010) and Küpeli and Özer (2020); three items used to measure physical risk were adapted from Lee (2020) and Küpeli and Özer (2020); four items used to measure time risk were adapted from Zhang et al. (2021) and Küpeli and Özer (2020); and three items used to measure health risk were adapted from Shin and Kang (2020). Section B includes familiarity, novelty, and trust-related items; four items used to measure familiarity were adapted from Chi et al. (2020), Soliman (2021), and Wang et al. (2022e); 4 items used to measure novelty were adapted from Kim and Kim (2015) and Thipsingh et al. (2022); and 4 items used to measure trust were adapted from Choi et al. (2015) and Dwivedi et al. (2022). Section C includes TPB’s component-related items; four items used to measure attitude were adapted from Wang et al. (2023c); three items used to measure subjective norm were adapted from Taufique and Vaithianathan (2018); three items used to measure perceived behavioural control were adapted from Wang and Wong (2021); and three items used to measure intention were adapted from Wang et al. (2023a). Section D includes demographic characteristics including gender, age, education level, and income.
Common method bias (CMB)
Considering the influence of CMB, respondents were guaranteed confidentiality and privacy with personal information and answers as all questionnaires were distributed and collected via an online system (i.e., the questionnaire QR code linking within the Chinese electronic survey platform). Most studies still adopt Harman’s single-factor test to examine the potential response bias for marketing studies (Hulland et al. 2018). Results show that the single factor score for data is 40.049%, which is below the threshold of 50%. In addition, the full collinearity of constructs was computed to comment on CMB. The variance inflation factor (VIF) of all constructs was calculated, which was found to be less than 10 (Hair et al. 2010). These results indicate that CMB is not an issue in this study.
Results
For data to be deemed normal, skewness should be between −2 and +2, and kurtosis should be between −7 and +7 (Byrne, 2016). The results show that normality was achieved as the data’s skewness value was between 0.506 and −0.053, and the kurtosis value was between 0.336 and 1.849. The Kaiser-Meyer-Olkin and Bartlett’s test of sphericity indicates sampling adequacy with 0.962 and a p-value below 0.001. The Cronbach’s alpha test results indicate the presence of internal consistent reliability, as all Cronbach’s alpha values were higher than 0.7. Table 1 displays respondents’ demographic characteristics; 23.4% were male and 76.6% were female, and most of the respondents were aged 19 years, with the majority of respondents reporting monthly spending of between 1000 and 2000 Chinese yuan, and approximately 63% of them were freshmen.
Table 1. Demographic characteristics.
Item | Characteristic | Frequency | Percentage (%) |
|---|---|---|---|
Gender | Male | 142 | 23.4 |
Female | 464 | 76.6 | |
Age | 18 | 112 | 18.5 |
19 | 206 | 34 | |
20 | 153 | 25.2 | |
21 | 70 | 11.6 | |
22 | 35 | 5.8 | |
23 | 22 | 3.6 | |
24 | 8 | 1.3 | |
Educational level | Freshman | 382 | 63 |
Sophomore | 34 | 5.6 | |
Junior | 153 | 25.2 | |
Senior | 31 | 5.1 | |
Master | 2 | 0.3 | |
Ph.D. | 4 | 0.7 | |
Monthly spending (RMB) | Below 1000 | 35 | 5.8 |
1000–2000 | 458 | 75.6 | |
2001–3000 | 86 | 14.2 | |
3001–4000 | 12 | 2 | |
Above 4001 | 15 | 2.5 |
Assessment of measurement model
According to Hair et al. (2010), all factor loadings should be greater than 0.5, and ideally higher than 0.7 for the measurement model. For convergent validity, the composite reliability (CR) should be higher than 0.7 and the average variance extracted (AVE) should be greater than 0.5. For discriminate validity, the AVE value should be greater than the maximum shared variance (MSV) and the average shared variance (ASV) (Hair et al. 2010). In addition, all construct-related values should be less than 0.9 (Meyers et al. 2006). After dropping off the low factor loading items (i.e., finance risk1, time risk4, familiarity4, novelty4, trust4), Table 2 and Table 3 display the acceptable convergent and discriminate validity of the measurement model. Next, the model fit indices show that CMIN = 3244.75, DF = 907, CMIN/DF = 3.577, p < 0.001, CFI = 0.929, AGFI = 0.75, PGFI = 0.663, PNFI = 0.793, PCFI = 0.814, NFI = 0.905, RFI = 0.891, IFI = 0.929, TLI = 0.919, SRMR = 0.0635, RMSEA = 0.065.
Table 2. Convergent validity of the measurement model.
Construct (Cronbach’s Alpha) | Items | Factor loadings | CR | AVE | S.D. | VIF |
|---|---|---|---|---|---|---|
Financial risk (α = 0.841) | 1. Rooms in green hotels are very expensive (delete). 2. Green hotels may run the risk of incurring additional charges. 3. I wasted or did not get my money’s worth from services provided by green hotels. | 0.835 0.884 | 0.85 | 0.739 | 1.204 1.247 | 3.46 |
Performance risk (α = 0.96) | 1. I worried whether the green hotels provide services as well as they are supposed to. 2. I worried that green hotels would not provide the level of benefits that I expected. 3. Caused me to concern for how really dependable and reliable the services green hotels would be. | 0.948 0.946 0.948 | 0.963 | 0.897 | 1.188 1.199 1.167 | 5.734 |
Functional risk (α = 0.944) | 1. Green hotels are at risk of problems with the quality of accommodation facilities. 2. Compared with traditional hotels, green hotels have the risk of low quality of function. 3. There is a risk of low service in green hotels. | 0.929 0.932 0.915 | 0.944 | 0.85 | 1.268 1.322 1.333 | 4.312 |
Psychological risk (α = 0.899) | 1. Visting green hotels made me feel psychologically uncomfortable. 2. Visiting green hotels gave me a feeling of unwanted anxiety. 3. Visiting green hotels caused me to experience unnecessary tension. | 0.873 0.861 0.862 | 0.899 | 0.749 | 1.412 1.448 1.377 | 4.742 |
Physical risk (α = 0.887) | 1. Green hotels can be uncomfortable. 2. Green hotels are less clean than traditional hotels due to using certain recyclable materials. 3. Green hotels may be in an unsafe, remote place. | 0.876 0.903 0.772 | 0.888 | 0.726 | 1.303 1.322 1.373 | 3.651 |
Time risk (α = 0.946) | 1. Choosing to stay at a green hotel is not efficient. 2. Choosing a green hotel can take a lot of time. 3. Choosing to stay at a green hotel was a waste of my personal time. 4. Choosing to stay at a green hotel during a trip can put time pressure on me (delete). | 0.917 0.937 0.926 | 0.948 | 0.859 | 1.25 1.203 1.244 | 4.42 |
Health risk (α = 0.95) | 1. Staying at green hotels can lead to health problems. 2. Green hotels are at risk of contracting infectious diseases due to using certain recyclable materials. 3. Staying at a green hotel was a risky decision for my health. | 0.934 0.953 0.918 | 0.954 | 0.874 | 1.281 1.277 1.309 | 4.481 |
Familiarity (α = 0.929) | 1. I obtain information about green hotels from brochures, pamphlets, newspaper, magazines, travel guidebooks and official website. 2. I obtain information about green hotels from friends and relatives’ interactions and travel agency. 3. Compared to my friends and other people, I am very familiar with green hotels. 4. Compared to people who travel a lot, I am very familiar with green hotels (delete). | 0.92 0.896 0.91 | 0.934 | 0.826 | 1.102 1.066 1.136 | 4.176 |
Novelty (α = 0.958) | 1. I want to experience new and different things or environment on vacation (e.g., staying at green hotels). 2. I want there to be a sense of discovery (e.g., visit green hotels) involved as part of my trip. 3. I want to experience green hotels’ cuisine, crafts, handiwork, and new foods. 4. My ideal vacation involves looking at things I have not or rarely seen before (e.g., visit green hotels) (delete). | 0.943 0.951 0.941 | 0.962 | 0.893 | 1.143 1.133 1.147 | 7.56 |
Trust (α = 0.939) | 1. I feel that green hotels’ environmental commitments are generally reliable. 2. I feel that green hotels’ environmental performances are generally dependable. 3. I feel that green hotels environmental arguments are trustworthy. 4. I feel that green hotels keep the environmental promises and commitment they make (delete). | 0.939 0.939 0.896 | 0.947 | 0.855 | 1.276 1.339 1.288 | 2.688 |
Attitude (α = 0.951) | 1. I think it is wise to choose a green hotel. 2. I think it is important to choose a green hotel. 3. I think there is value in choosing a green hotel. 4. I am interested in green hotels. | 0.936 0.923 0.901 0.897 | 0.953 | 0.836 | 1.041 1.076 1.053 1.174 | 6.376 |
Subjective norm (α = 0.945) | 1. People around me think it is a good choice to stay at a green hotel. 2. The people around me were understanding of my choice to stay at a green hotel. 3. The people around me were supportive of my choice to stay at a green hotel. | 0.917 0.937 0.936 | 0.951 | 0.865 | 1.088 1.038 1.082 | 4.219 |
Perceived behavioural control (α = 0.874) | 1. I have unlimited choice of green hotels if I want. 2. I can choose to stay at a green hotel or not. 3. I have the ability to choose green hotels. | 0.774 0.876 0.886 | 0.883 | 0.717 | 1.227 1.198 1.13 | 2.332 |
Intention (α = 0.879) | 1. I will make an effort to stay at a green hotel when travelling. 2. I am likely to stay at a green hotel. 3. I am more likely to stay at a green hotel over a non-green hotel. | 0.922 0.948 0.739 | 0.906 | 0.765 | 1.224 1.23 1.484 | – |
Table 3. Discriminate validity of the measurement model.
Item | CR | AVE | MSV | ASV | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
1. Intention | 0.906 | 0.765 | 0.707 | 0.484 | 0.875 | |||||||
2. Perceived risk | 0.957 | 0.765 | 0.1 | 0.034 | −0.047 | 0.874 | ||||||
3. Familiarity | 0.934 | 0.826 | 0.781 | 0.484 | −0.685 | 0.279 | 0.909 | |||||
4. Novelty | 0.962 | 0.893 | 0.783 | 0.528 | −0.762 | 0.139 | 0.884 | 0.945 | ||||
5. Trust | 0.947 | 0.855 | 0.701 | 0.382 | 0.837 | −0.076 | −0.566 | −0.63 | 0.925 | |||
6. Perceived behavioural control | 0.883 | 0.717 | 0.476 | 0.353 | 0.54 | −0.316 | −0.686 | −0.676 | 0.452 | 0.847 | ||
7. Subjective norm | 0.951 | 0.865 | 0.757 | 0.521 | 0.799 | −0.114 | −0.766 | −0.825 | 0.705 | 0.69 | 0.93 | |
8. Attitude | 0.953 | 0.836 | 0.783 | 0.567 | 0.841 | −0.136 | −0.827 | −0.885 | 0.74 | 0.686 | 0.87 | 0.914 |
Bold values denote the square root of AVE.
Assessment of structural model
The overall goodness-of-fit indices show that CMIN = 5270.696, DF = 925, CMIN/DF = 5.698, p < 0.001, CFI = 0.868, AGFI = 0.677, PGFI = 0.62, PNFI = 0.755, PCFI = 0.776, NFI = 0.845, RFI = 0.827, IFI = 0.869, TLI = 0.853, RMSEA = 0.088. The results of the structural model of the proposed hypotheses are illustrated in Fig. 2 and Table 4.
Fig. 2 [Images not available. See PDF.]
Results of the study.
Table 4. Results of the regression model.
Hypothesis | Parameter | β | C.R. | Sig. | Decision |
|---|---|---|---|---|---|
H1 | Attitude ------------> Intention | 0.386 | 9.505 | *** | Supported |
H2 | Subjective norm --> Intention | 0.221 | 4.875 | *** | Supported |
H3 | Subjective norm --> Attitude | 0.447 | 15.553 | *** | Supported |
H4 | Subjective norm --> Perceived behavioural control | 0.664 | 18.357 | *** | Supported |
H5 | Perceived behavioural control --> Intention | −0.07 | −1.649 | 0.099 | Not supported |
H6 | Trust ---------------> Attitude | 0.315 | 11.519 | *** | Supported |
H7 | Trust ---------------> Perceived behavioural control | −0.013 | −0.397 | 0.692 | Not supported |
H8 | Trust ---------------> Intention | 0.516 | 13.285 | *** | Supported |
H9 | Perceived risk ----> Attitude | 0.028 | 0.777 | 0.437 | Not supported |
H10 | Perceived risk ----> Subjective norm | −0.162 | −3.164 | 0.002 | Supported |
H11 | Perceived risk ----> Perceived behavioural control | −0.265 | −6.627 | *** | Supported |
H12 | Perceived risk ----> Trust | −0.114 | −2.229 | 0.026 | Supported |
H13 | Familiarity --------> Attitude | −0.247 | −5.376 | *** | Supported |
H14 | Familiarity --------> Perceived risk | 0.503 | 9.341 | *** | Supported |
H15 | Novelty ------------> Attitude | −0.59 | −13.383 | *** | Supported |
H16 | Novelty ------------> Perceived risk | −0.288 | −5.571 | *** | Supported |
*** denotes p-value < 0.001.
As shown in Fig. 2 and Table 4, the structural model results show that there is a positive correlation between attitude and intention with β = 0.386, p < 0.05, and C.R. = 9.505, thus, H1 was supported. Subjective norm and intention are positively correlated since β = 0.221, p < 0.05, C.R. = 4.875; hence, H2 was supported. Subjective norm and attitude are positively related due to β = 0.447, p < 0.05, and C.R. = 15.553; hence, H3 was supported. Results show that there is a positive relationship between subjective norm and perceived behavioural control as β = 0.664, p < 0.05, C.R. = 18.357; thus, H4 was supported. However, perceived behavioural control and intention are not significantly correlated with β = −0.07, p > 0.05, and C.R. = −1.649; hence, H5 was rejected. Trust and attitude are considered positively correlated since β = 0.315, p < 0.05, C.R. = 11.519; hence, H6 was supported. Results show a non-significant relationship between trust and perceived behavioural control with β = −0.013, p > 0.05, C.R. = −0.397; hence, H7 was rejected. There is a positive correlation between trust and intention found in this study as β = 0.516, p < 0.05, C.R. = 13.285; hence, H8 was supported. Perceived risk and attitude are statistically non-significantly correlated with β = 0.028, p > 0.05, and C.R. = 0.777; hence, H9 was rejected. Perceived risk negatively influences subjective norm (β = −0.162, p < 0.05, C.R. = −3.164), perceived behavioural control (β = −0.265, p < 0.05, C.R. = −6.627), and trust (β = −0.114, p < 0.05, C.R. = −2.229); thus, H10, H11, and H12 were supported. Familiarity negatively influences attitude with β = −0.247, p < 0.05, C.R. = −5.376 but positively influences perceived risk since β = 0.503, p < 0.05, C.R. = 9.341; thus, H13 and H14 were supported. In addition, results show that novelty negatively correlated with attitude (β = −0.59, p < 0.05, C.R. = −13.383) and perceived risk (β = −0.288, p < 0.05, C.R. = −5.571); hence, H15 and H16 were supported.
Moreover, due to the research model being a second-order one, a regression test was also performed to determine the relationship between first-order variables (i.e., financial risk, performance risk, functional risk, psychological risk, physical risk, time risk, and health risk) and the second-order variable, which is perceived risk. The results showed that all first-order variables are significantly correlated to perceived risk since the critical ratio value exceeded 1.96 and the p-value was below the threshold value of 0.05. Indeed, the standardised coefficients for these relationships are 0.944 (financial risk), 0.889 (performance risk), 0.945 (functional risk), 0.956 (psychological risk), 0.925 (physical risk), 0.743 (time risk), and 0.92 (health risk), and all associations are significant at a p < 0.05 level. About 89.1%, 79%, 89.3%, 91.3%, 85.6%, 55.3%, and 84.6% of the total variance in financial risk, performance risk, functional risk, psychological risk, physical risk, time risk, and health risk are accounted for by the higher structure, respectively.
Discussions and implications of the study
The study posited that consumers’ negative perceptions and barriers to visiting green hotels are largely unexplored. It found that perceived risk encompassing financial, performance, functional, psychological, physical, time, and health risks as its first-order dimensions are direct predictors of trust, subjective norm, and perceived behavioural control towards the intention of visiting green hotels. Familiarity and novelty are immediate antecedents of perceived risk. Trust, novelty, and familiarity are the determinants of attitude towards patronising green hotels. Additionally, the subjective norm is an important predictor of attitude and perceived behavioural control towards green hotel visit intention.
Previous studies on green purchase behaviour verified that attitude is a robust predictor of intention (Niloy et al. 2023; Shah et al. 2023). The results of this study show that attitude has a positive impact on consumers’ intentions to stay at green hotels. This indicates that customers believe staying at green hotels is an intelligent choice and have an overall positive opinion of the attributes of these establishments.
There are controversial results about the effect of subjective norm and perceived behavioural control on intention in green marketing (Shah et al. 2023; Wang and Wong, 2021; Yeow and Loo, 2022). The results of this study show that subjective norm positively influences intention, which means that people (e.g., friends, relatives, and family members) can positively influence consumers to stay at green hotels when they think it is the right choice and when they support their decision to stay at green hotels. Those results are in line with Wang et al. (2024b), who stated that subjective norm plays an important role in determining consumers’ intention to select green hotels. However, the results of this study show that perceived behavioural control does not influence intention. This means that consumers’ perceptions of their ability and available resources, such as money, time, or opportunity, do not always translate into their decision to stay at green hotels. Those results correspond with certain studies that showed that perceived behavioural control cannot be considered a predictor of intention (Patharia et al. 2020; Yeow and Loo, 2022), which is in contrast with some studies showing that perceived behavioural control positively influences intention (Kumar, 2021; Sung et al. 2021).
The subjective norm should be considered as an antecedent of attitude and perceived behavioural control within the TPB model (Wang et al. 2024a), and certain studies confirmed that the subjective norm significantly influences one’s attitude and perceived behavioural control towards intention (Wang et al. 2023c; Wang et al. 2022d). Results of this study show that subjective norm positively influences attitude and perceived behavioural control, respectively. This demonstrates how an individual’s positive assessments of the attributes of green hotels and ability to visit, as well as his or her intention to visit green hotels, are greatly influenced by the opinions of one’s close friends, coworkers, family members, and relatives.
Certain studies demonstrated that trust has an important role in determining consumer attitude, perceived behavioural control, and intention in green marketing (Shah et al. 2023; Sung et al. 2021). Results of this study show that trust positively influences attitude and intention. This implies that consumers will have a more favourable attitude and be more likely to visit green hotels if they believe that green hotels are trustworthy and dependable and that their environmental performances are dependable. However, the results of this study show that trust insignificantly influences perceived behavioural control. This indicates that there is no correlation between consumers’ ability and confidence to visit green hotels and their beliefs about the attributions and environmental performances of green hotels.
Familiarity may enhance an individual’s understanding of perceptions and knowledge of products or destinations through direct or indirect information (Wang et al. 2022e), leading to a higher or lower recognition of products or services’ attributes (Ju et al. 2021), which will influence his intention to purchase products/services or visit a particular destination (Soliman, 2021). Results of this study showed that familiarity positively influences consumers’ perceived risk and negatively influences attitude towards visiting green hotels. This means that consumers who perceive they are knowledgeable about green hotels through information received either from publicity (e.g., newspapers, magazines, green hotels’ websites) or close friends’ and other people’s recommendations, think that green hotels are generally riskier compared to conventional hotels. They also have negative evaluations and assessments of visiting green hotels and the hotels’ attributes.
Furthermore, the results of this study verified novelty negatively influences perceived risk. This finding correlates with previous studies that show consumers who are novelty-oriented reduce their risk perceptions about a particular destination (Ponsignon et al. 2020) and are more willing to visit higher-risk destinations than the destinations they are familiar with (Lepp and Gibson, 2003). Results of this study also showed that novelty negatively influences consumers’ attitudes to visit green hotels. This finding corresponds to some empirical evidence showing that novelty significantly influences consumers’ purchasing attitudes and behaviours (Kim and Kim, 2015; Wang et al. 2022a; Wang et al. 2022b). Those findings mean that although consumers are willing to experience something new like visiting green hotels in this study because of its novel attributions such as curiosity, sense of discovery, and emotional stimulus, they may have negative perceptions and attitudes toward visiting green hotels. Meanwhile, they also have risk concerns about various aspects of the nature of green hotels. They do not want to sacrifice the quality of hotels’ services and products, suffer psychological discomfort, waste time, and take health-related risks compared with traditional hotels.
Previous studies indicated that perceived risk negatively influences consumer’s attitude, subjective norm, perceived behavioural control, and trust (Arshad et al. 2021; Han et al. 2019; Haq et al. 2023; Küpeli and Özer, 2020; Sujood et al. 2021; Teeroovengadum et al. 2021). Results of this study show that perceived risk negatively influences subjective norm, perceived behavioural control, and trust. This means that consumers’ apprehension about patronising green hotels is related to additional charges, low quality of service and facilities, psychological discomfort and anxiety, and health concerns that may result in their low assessments of green hotels. In addition, a lack of reliable and trustworthy recommendations, negative information received from their close-friends, relatives, colleagues, and family members on choosing green hotels, and low confidence to overcome obstacles (e.g., time, opportunity, money) to booking green hotels will result in a higher perception of risk. Those findings correspond to previous studies that showed that financial risk, performance risk, functional risk, psychological risk, physical risk, time risk, and health risk as perceived risk dimensions negatively influence consumers’ subjective norm, perceived behavioural control, and trust (Park, 2017; Park et al. 2022; Sujood et al. 2021; Zhang et al. 2021).
Nevertheless, our findings show that perceived risk does not significantly influence consumers’ attitudes towards visiting green hotels, which is in contrast with previous studies showing that perceived risk negatively influences one’s attitude (Han et al. 2019; Haq et al. 2023). This means that consumers’ positive or negative assessment of green hotels’ attributes when travelling would not pose a threat to their psychological discomfort and well-being, physical safety concerns, quality and function concerns, psychological discomfort, anxiety, and health concerns, as well as additional charge concerns.
In addition, perceived risk should be considered by measuring various indicators based on different research objects and cultural backgrounds for consumers’ behaviours (Aufa and Gunanto, 2023; Küpeli and Özer, 2020; Zhang et al. 2021). The hierarchical dimension’s structure of perceived risk is verified in this study. The empirical assessment of the structural model revealed that the second-order latent variables (i.e., perceived risk) sufficiently acquire the commonality underlying among first-order variables, and they are financial risk, performance risk, functional risk, psychological risk, physical risk, time risk, and health risk.
Theoretical contributions
The majority of earlier research in the fields of hospitality and tourism marketing examines how different variables positively impact consumers’ attitudes and perceptions of intention and behaviour (Patharia et al. 2020). More research should be conducted to gain a better understanding of the negative antecedents of consumers’ attitudes and behaviours (Wang et al. 2022d), as there has not been enough research done on the negative influence on consumers’ behaviour within the behavioural intention framework (Ulker-Demirel and Ciftci, 2020). This study demonstrated that consumers’ perceived risk negatively influences trust, subjective norm, and perceived behavioural control towards visiting green hotels. Meanwhile, even though the influence of perceived risk does not significantly influence attitude, the correlation between perceived risk and attitude is negative. Hence, future research should look at the influence of perceived risk as an antecedent of consumers’ perceptions and behaviours towards visiting green hotels.
Second, previous studies considered perceived risk as a multidimensional concept including various indicators of consumer behaviours based on different research objects and settings, and cultural backgrounds (Zhang and Yu, 2020). The results of this study offer a comprehensive assessment of perceived risk structure to predict consumers’ attitudes, subjective norm, perceived behavioural control, and trust towards visiting green hotels. In other words, the financial risk, performance risk, functional risk, psychological risk, physical risk, time risk, and health risk sufficiently acquire the commonality for perceived risk. The results of this study provide a basic understanding of various indicators of perceived risk that can influence consumers’ perceptions and intentions to patronise green hotels.
Third, the majority of consumers are still unfamiliar with the benefits and functions of green hotels (Wang et al. 2022e) because the concept is still relatively new to them (Wang et al. 2024b). The majority of tourists are unaware of the wide variety of facilities and services offered by green hotels (Choi et al. 2015), having only heard of the concept in recent years (Wang et al. 2022e). This study explored the relationship between familiarity, perceived risk, and attitude towards green hotel selection. Results showed that familiarity positively influences perceived risk and negatively influences attitude. Future studies should consider examining why individuals who are familiar with green hotels but have a more negative overall evaluation of green hotels and a higher-risk perception about visiting green hotels.
Consumers may act irrationally in order to obtain stimulation that elicits strong emotions (Wang et al. 2022a). The concept of novelty views that people concentrate on personal enjoyment and comfort improvement while seeking curiosity and fun, which is influenced by motivational and emotional elements (Wang et al. 2022b). These elements can be included in the TPB to predict an individual’s behaviour (Ulker-Demirel and Ciftci, 2020). This study confirmed that novelty significantly influences perceived risk and attitude towards visiting green hotels, but it does not replace the components of TPB but rather works as an extension of consumption theories.
Consumers make decisions prior to receiving the service and since services are classified as highly unpredictable scenarios (Haq et al. 2023), consumers believe having trust in a business or company can help them feel more confident in their purchase of environmentally friendly products or services (Ponnapureddy et al. 2017). Currently, there is not much empirical evidence regarding how trust influences consumer behaviour in green marketing (Shah et al. 2023) including the selection of green hotels (Dwivedi et al. 2022; Sultana et al. 2022). This study empirically validated the association between trust, attitude, and intention to visit green hotels. Therefore, adding trust to TPB increases its predictive capacity and broadens its application in the selection of green hotels.
TPB and its components are widely applied in green marketing to predict consumer behaviour (Ferreira et al. 2023; Sung et al. 2021). Nevertheless, the interrelationships between attitude, subjective norm, and perceived behavioural control concerning consumers’ intentions to visit green hotels have not been adequately researched (Wang et al. 2024a; Wang et al. 2023c). Results of this study showed that subjective norm positively influences perceived behavioural control, attitude, and intention towards visiting green hotels respectively. Thus, subjective norm should be considered as an antecedent of perceived behavioural control and attitude towards visiting green hotels, although those findings have stood in contrast within the theoretical framework of TPB.
Practical implications
The results of this study can offer important managerial implications since green hotels are looking for factors that would differentiate them from other competing traditional hotels. First, the utilisation of specific facets of the multi-dimensional model of perceived risk is essential. Recognising the importance of perceived risk, green hotel operators need to help their customers understand the financial, performance, functional, psychological, physical, time, and health/safety aspects of green hotels. In fact, green hotels should demonstrate to potential customers that they can provide a higher level of quality services for guests to make them feel they are getting better value compared to traditional hotels. For performance risk issues, green hotels need to assure potential customers that green hotels would be able to provide reliable service and that they carry out environmentally friendly business practices. Since green hotels’ guests are concerned about the risk of low-quality functional benefits when compared with traditional hotels, green hotel operators should provide exceptional green products, facilities, and services to reduce functional concerns. Moreover, green hotels should provide more user-friendly and reliable recyclable materials and products to make consumers feel that staying at green hotels is comfortable and safe. Highlighting the recyclable materials and products’ certificate of origin, production process and certification by accreditation agencies can also reduce consumers’ risk perceptions. Basically, green hotel operators should make consumers feel that their decision to stay at a green hotel is desirable and ethical which can achieve or exceed their (green) expectations.
The results of this study identified the essential role of familiarity in increasing consumers’ perceived risk and decreasing their attitudes towards visiting green hotels. Potential consumers of green hotels may receive more negative than positive information from various channels such as green hotels’ websites, brochures, pamphlets, magazines, and newspapers. Thus, green hotel operators should pay more attention to spreading the right information about the concept of green hotels to the public; for example, that they can provide pro-environmental products and services to the public without sacrificing consumers’ comfort and safety when compared with traditional hotels, and how green hotels’ products (e.g., recyclable materials, organic foods) and resource management system (e.g., solar energy and recycled water system) are superior to traditional hotels. Specifically, they need to highlight why it is necessary to implement green management strategies in the hotel industry to evoke consumers’ environmental consciousness.
The results of this study showed that novelty negatively influences consumers’ perceived risk and attitude towards visiting green hotels. Consumers who are seeking new experiences with green hotels’ facilities, cuisine, amenities, and foods, do not wish to undertake any financial, performance, functional, psychological, physical, time, or health risks. Meanwhile, it seems that consumers do not think that green hotels can provide something different from traditional hotels, resulting in a negative attitude towards visiting green hotels. Hence, the key point green hotels should advertise is they can provide different green products and services compared with traditional hotels by ensuring the quality of such services and products, keeping the products and services performance, and highlighting they provide a better and healthier lifestyle in a timely manner for consumers.
In their promotional messages, green hotel managers and operators should clearly demonstrate the amount of energy and water they can save through their implementation of green business strategies to consumers. For example, they can indicate how the amount of water and electricity that can be saved in a year of operation on their official website. Therefore, consumers will know that green hotels are contributing to the conservation of the environment and natural resources, and feel their green hotel selection decisions are reliable, dependable, and trustworthy.
Subjective norm plays an important role in determining consumer attitude, perceived behavioural control, and intention to visit green hotels. On one hand, green hotel operators should continue to advertise their green hotels’ attributes and functions to the public via traditional channels (e.g., newspaper, radio, magazines) to influence traditional word-of-mouth consumers (e.g., relatives, close-friends, co-workers). On the other hand, electric word-of-mouth communication (e.g., online reviews, social media posts, blogs) should be used as an alternative method to attract and influence potential consumers (e.g., students, netizens). Green hotel operators need to monitor negative feedback and comments on their official websites, booking applications, and online media. They should resolve the issues in a timely manner or leave a reply with a positive message on the relevant online channels, thus, strengthening their reputation and image.
The present study found that attitude positively influences the intention to visit green hotels. Based on this finding, green hotel operators need to make various endeavours to enhance consumers’ level of confidence in green hotels’ attributes. As demonstrated in this study, green hotel operators can reduce consumers’ perceived risk by providing appropriate information to consumers, highlighting their unique green characteristics, increasing their trust in what green hotels can provide, and advertising their green hotels’ attributes to potential consumers via various media channels. This can help green hotel consumers feel that the performance quality of green hotels is reliable and that visiting green hotels would be an excellent choice.
Limitations
This study has certain limitations. First, selecting the young generation as respondents in this study has many advantages, as they are more environmentally conscious than older consumers (Jaiswal and Kant, 2018) and they can shape future consumption patterns (Bahl and Kumar, 2019). However, the financial status and spending power of younger consumers are relatively lower than the working population. Hence, this study’s sample respondents are not representative of the whole population. Second, the concept of green hotels is quite new and investigation on green hotel selection is comparatively lower than in developed countries. Thus, the findings of this study may only apply to similar developing countries, but its reliability and usefulness may need to be re-examined in other settings. Third, the current study investigated the adoption intention of green hotels in a single country context, which presents a generalisability issue. Since consumer behaviour is a function of culture and people of different nationalities show different behavioural tendencies (Arun et al. 2021), further research is required to perform cross-cultural comparisons of consumer behaviour. Moreover, although intention is a robust predictor of consumer actual purchase behaviour, future studies should investigate consumers’ actual green hotel visits since intention does not always equate to actual behaviour. Lastly, the TPB can be expanded to include other psychological or contextual variables to increase its predictive power so that future studies can replicate and expand the current research framework to increase its accuracy and reliability.
Conclusion
The decision-making process of visiting green hotels has not been fully examined empirically in the literature, despite the fact that there are numerous studies on consumers’ intention to visit green hotels based on different consumer antecedents (Nimri et al. 2020b; Ray et al. 2023). Specifically, research on the impact of unfavourable psychological traits on an individual’s intention in green hotel marketing is lacking (Ulker-Demirel and Ciftci, 2020). The current study investigates how trust, perceived risk, familiarity, novelty, along TPB affect consumers’ intentions to visit green hotels. Attitude and subjective norm have been proven to be valuable predictors of intention, and subjective norm can also affect one’s attitude and perceived behavioural control towards visiting green hotels. Nevertheless, perceived behavioural control does not always lead to intention. Trust has a significant influence on attitude and intention but does not affect perceived behavioural control. Familiarity has been proven to be a significant predictor of attitude and perceived risk whereas novelty is also considered to be an important antecedent of one’s attitude and perceived risk. Perceived risk negatively influences consumers’ subjective norm, perceived behavioural control, and trust, but does not correlate with attitude. Moreover, the results of this study showed that perceived risk should be considered as a multi-dimensional concept which can be accessed via financial, performance, functional, psychological, physical, time, and health risks for explaining consumers’ perceived risk towards visiting green hotels. In conclusion, this study can be considered to be one of the pioneer studies that examined the influence of trust, perceived risk, familiarity, novelty, and TPB on consumers’ intention to visit green hotels. The results of this study can provide a more comprehensive understanding of consumers’ green hotel visitation intention based on TPB, which will contribute to the growth of the green hotel marketing literature.
Author contributions
Conceptualization, Methodology: Lei Wang, Qi Zhang, and Yue Gong; Design of the work: Lei Wang and Philip Pong Weng Wong; Literature search, Data interpretation: Qi Zhang, Meng-Jie Ye, and Yue Gong; Data collection: Qi Zhang and Meng-Jie Ye; Formal analysis: Lei Wang, Qi Zhang, and Meng-Jie Ye; Writing – original draft preparation, Writing – review and editing, Critical revision of the article, Visualization, Validation, and Supervision: Lei Wang, Philip Pong Weng Wong, and Yue Gong; Funding acquisition: Lei Wang and Yue Gong.
Data availability
The data obtained and examined in this study are documented in the paper and provided in the supplemental data file.
Competing interests
The authors declare no competing interests.
Ethical approval
The procedures used in this study adhered to the ethical standards set out in the Declaration of Helsinki. As this study was not medical research nor considered human experimentation as stated in the Declaration of Helsinki, and because the questionnaire did not adversely affect the mental health of the respondents, ethical approval was required for this questionnaire-based study according to the regulations of the authors’ institution (Business School Research Ethics Review Committee, Xuzhou University of Technology, (decision of November 2023)). Moreover, by completing the questionnaire, each respondent who was at least 18 years old consented to participate in the research study. The information collected was used exclusively for the study and was treated as strictly confidential and anonymous.
Informed consent
Informed consent was obtained from all participants prior to their participation in the study. The nature and objectives of the study, together with the participants ability to withdraw at any time, were explained to the participants. The informed consent process was conducted from December 2023 to January 2024, concurrently with the questionnaire distribution.
Supplementary information
The online version contains supplementary material available at https://doi.org/10.1057/s41599-024-03935-0.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Abstract
Previous hospitality research typically focuses on consumers’ positive attitudes and intentions toward addressing an existing research gap in the literature on consumer attitude and behaviour in green hotel marketing. Those studies have frequently neglected to investigate the effects of negative attitudes and perspectives. This study aims to explore the relationships between seven indicators of perceived risk, familiarity, novelty, trust, attitude, subjective norm, perceived behavioural control, and intention to visit green hotels. A total of 606 questionnaires were collected and analysed using structural equation modelling. Results showed that perceived risk negatively influences trust, subjective norm, and perceived behavioural control. Familiarity positively influences perceived risk but negatively influences attitude. Novelty negatively influences perceived risk and attitude, while trust positively influences attitude and intention. Subjective norm positively influences attitude, perceived behavioural control, and intention, and attitude influences intention. This study provides an alternative negative factor perspective on green hotel visitation using a theoretically driven approach.
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; Zhang, Qi 2 ; Ye, Meng-Jie 2 ; Wong, Philip Pong Weng 3 ; Gong, Yue 4 1 Xuzhou University of Technology, Faculty of Hospitality and tourism, Xuzhou, China (GRID:grid.464484.e) (ISNI:0000 0001 0077 475X)
2 Xuzhou University of Technology, Business School, Xuzhou, China (GRID:grid.464484.e) (ISNI:0000 0001 0077 475X)
3 Sunway University, School of Hospitality and Service Management, Petaling Jaya, Malaysia (GRID:grid.430718.9) (ISNI:0000 0001 0585 5508)
4 Shanxi Vocational College of Tourism, Faculty of Hospitality and Tourism, Taiyuan, China (GRID:grid.430718.9)