Content area
Purpose
This study aims to predict customers’ intention (INT) to visit green hotels through an extended norm activation model (NAM) framework. The extended NAM includes environmental concern (EC), price consciousness (PC) and aesthetic values (AEV).
Design/methodology/approach
A quantitative survey design was used to collect 340 valid responses from customers in Pakistan. Partial least square - structural equation modeling was used to assess the hypothesized relationships.
Findings
The results show that EC and awareness of consequences have a significant ascription of responsibility (AR) and personal norms (PN) towards INT. The results confirmed the moderating effect of PC on the relationship between PN and INT and the moderating effect of AEV on relationship between PN and INT and AR and INT. The findings indicate that PN does not mediate the relationship between AR and INT. These findings indicate that customers are aware of the consequences of resource depletion and environmental pollution that result from the unsustainable hotel practices. Therefore, customers tend to visit aesthetically green hotels at affordable prices.
Practical implications
The study proposes that green hotels should design marketing strategies in a way that increases customers’ awareness and concern towards environmental issues. Marketing campaigns focusing on environmental aspects of green hotels enhance its green reputation and motivate customers to visit green hotels. In addition, managers must consider enhancing the beauty of hotels at a competitive price.
Originality/value
This is the first study to use an extended NAM by integrating EC, average variance extracted and PC to predict customers’ INT towards green hotels. The extended NAM framework provides comprehensive understanding of the relationships between EC, AR and PN regarding visiting green hotels. In addition, the study emphasizes on AEV’s crucial role in influencing customers’ INT to visit green hotels.
Norm activation model
Intention
Awareness of consequences
Ascription of responsibility
Personal norms
Environmental concern
Price consciousness
Aesthetic values
Introduction
Tourism is considered one of the largest and fastest-growing industries worldwide (Patwary et al., 2023). The significant expansion of tourism in recent decades has made it one of the most lucrative sectors in the world economy (Zulfiqar et al., 2024; Osorio-Molina et al., 2023). Lower airfares, improved transport networks, technological advances and innovative business approaches have contributed to the continued growth of the tourism industry. Before the outbreak of the pandemic, hospitality industry accounted for 10.4% of the world’s $9.2tn gross domestic product (GDP) and was responsible for creating one in four jobs globally, totalling up to 334 million jobs (WTTC, 2020). Many nations consider tourism to be a catalyst for growth, both for present and future generations, thanks to its socio-economic contributions (Mahadevan et al., 2017). However, economic activities on such a scale have colossal adverse impact on environment due to increasing CO2 gas emissions (Pablo-Romero et al., 2023). For example, a well-documented issue is the link between increased tourism activities and its impact on global warming as measured by greenhouse gas (GHG) emissions (Mishra et al., 2022). Climate change is the greatest ecological challenge, affecting not only humans but also all living organism on the Earth. Moreover, it has the potential to cause significant environmental changes in the places where tourism thrives (Patwary et al., 2023).
In order to main temperature rise below 2°C, the Paris Agreement on climate change has instructed hotels to minimize CO2 release to 90% by 2050 (Patwary et al., 2023). It is important to achieve the targets set by the Paris Agreement to reduce the adverse impact of global warming which can severely affect world’s natural beauty. Recent studies show that tourism industry is highly contributing to environmental degradation (Dube and Nhamo, 2021). In this regard, Lenzen et al. (2018) argued that tourism sector is responsible for the emission of 8% of the global GHG. There are many types of services that hotels provide such as room heaters, heated pools, air condition and impressive lighting, which have substantial energy consumption. In addition, hotels consume significant amount of water for swimming pools, water parks and laundry services (Antonova et al., 2023). Finally, environmental sustainability is affected by the solid waste generation from the non-recyclable products in the hotels (Adhikari et al., 2024).
Pakistan is blessed with natural beauty which includes wonderful landscapes, diverse cultural heritage, warm hospitality and rich traditions (Arshad et al., 2018). The country is a hub for several ancient religions, such as Buddhism, Hinduism and Sikhism, with many religious sites that attract believers from worldwide (Adnan Hye and Ali Khan, 2013). Research indicates that the country’s tourism sector recorded revenue of Pakistani rupees (PKR) 1,445.9 billion in 2018, and it is expected to grow at a rate of 5.9% that accounts PKR 2,564.3 billion value in the year 2028. This shows that tourism has significant potential to contribute to the country’s economy (Liu et al., 2019). However, the increase in the number of tourists and their energy consumptions significantly increase CO2 emissions in the country, which further degrades environmental quality (Waris et al., 2024; Anjum et al., 2021). Additionally, tourism activities have promoted the use of non-recyclable materials and exhausted the natural resources (Raza et al., 2024; Raza and Farrukh, 2023). In addition, customers’ lack of awareness and unsustainable behaviour which include littering and inadequate waste disposal have contributed to environmental pollution (Mousazadeh et al., 2023). Therefore, it is pertinent to understand customers’ visiting intention (INT) to green hotels to design and promote eco-friendly practices in the tourism industry.
There are many past studies that assessed customers’ INT to visit green hotels, which failed to provide a comprehensive framework in understanding customers’ INT to visit green hotels. For example, past studies have used an extended value belief norm (VBN) (Waris et al., 2024), personal values (Raza and Farrukh, 2023), goal-framing theory (Wang et al., 2022), theory of planned behaviour (TPB) (Nimri et al., 2020) and integrated VBN and TPB (Fauzi et al., 2022). However, the application of norm activation model (NAM) has been limited in the context of visiting green hotels. Therefore, this study adds environmental concern (EC), price consciousness (PC) and aesthetic beauty (aesthetic values [AEV]) in the NAM framework to predict green hotels visiting INT. Research indicates that EC is an essential construct which significantly predicts pro-environmental behaviour; especially, it exerts a significant impact towards visiting green hotels (Dong et al., 2024; Verma et al., 2019). In the context of developing market, PC effects customers’ decision-making because they compare the costs and the benefits they gain (Choi and Kim, 2007). In the context of green hotels, AEV has a substantial impact as it refers to the beauty, ambiance and visual attractiveness elements that enhance customers’ experiences (Le et al., 2021; Gupta et al., 2019; Tribot et al., 2018). Researchers argued that AEV plays a crucial role in predicting customers’ INT to visit green hotels (Xie et al., 2023). Integrating EC, PC and AEV into NAM framework will help to comprehensively understand customers’ environmental consciousness, economic concern and hotels’ aesthetics, which are crucial aspects in the context of tourism industry. These factors provide a deeper understanding of motivational factors and help green hotel managers to implement strategies that appeal to green customers.
To our best knowledge, past studies have not paid attention to PC and AEV in green hotels’ perspectives. In addition, integration of EC as a NAM antecedent will present a novel aspect and fills the gap in existing literature. Based on an extended NAM framework, the current study contributes to the literature in the following ways: First, this study evaluates an extended NAM framework that examines the influence of EC, awareness of consequences (AC), ascription of responsibility (AR) and personal norms (PN) on customers’ INT to visit green hotels in Pakistan, one of the fastest growing tourism industries. Second, the study added EC as additional constructs in NAM and will its relationship between AR and PN. Third, the study will establish a connection between AC and PN. Fourth, the study will assess the mediating influence of PN between AR and INT. Fifth, based on the theoretical conceptualization, this study included the moderating effect of PC and AEV on INT to visit green hotels. In addition, the study will provide practical implications that will assist green hotels’ managers in designing strategies that attract customers to visit green hotels and contribute for a sustainable future.
Literature review and theoretical background
An extended norm activation model (NAM)
Schwartz (1974) presented the NAM which argued that the individual pro-social INT is influenced by the moral norm, AC and AR (Schwartz, 1974; Zhang et al., 2013). Therefore, the original NAM contains three main variables, namely problem awareness, AR and moral norm. Individual moral obligation to perform and avoid specific actions represents moral norm (Schwartz and Howard, 1981). Moral norm is also called PN which is the most widely used construct in the NAM framework to predict INT (Han et al., 2015). Problem awareness refers to an individual’s awareness of the problems associated with his/her particular actions (De Groot and Steg, 2009). Problem awareness is also known as AC. AR is an individual’s evaluation of others actions that have an impact on the environment. AR is essential for fair evaluation of an individual’s responsibility (Schwartz, 1974). Although NAM has been used across various fields, the relationships between its variables remain unclear (Han et al., 2015). In fact, the connection among the variables has been explained in different ways (Steg and De Groot, 2010). For example, some studies have considered NAM as a moderator model (Han and Hwang, 2016; Schultz and Zelezny, 1998; Hopper and Nielsen, 1991). They posited that AC and AR exert significant influence on the relationship between norms and pro-social behaviour. However, other studies have contradicted and claim the usefulness of NAM’s sequential model in predicting INT (Savari et al., 2023; Stern, 2000). Stern (2000) used mediator interpretation of NAM to extend the theoretical framework. In predicting environmental friendly behaviour in the hospitality industry, previous researchers used the NAM mediating model (Van Riper and Kyle, 2014; Klöckner and Matthies, 2004). Consistently and Han (2015) emphasized that the NAM mediating model has higher predictive power than the moderating model in the context of eco-tourism.
Past researchers have reached a consensus that using AC and AR as predictors of pro-environmental behaviour in the NAM model is more accurate than using them as moderators, particularly in the perspective of eco-tourism (Han et al., 2023; Han, 2021). This study proposes that EC and AC influence AR and PN, which impacts PN towards green hotels visiting INT. In addition, this study extended NAM and evaluated the moderating effects of PC and AEV on INT to visit green hotels. This study presents a comprehensive NAM framework that considers EC, AC, AR and PN as environmental factors, PC as an economic factor and AEV as a sustainable décor of green hotels that influences customers’ INT to visit green hotels. Figure 1 shows an extended NAM framework.
Environmental concern (EC)
EC is an individual awareness that human unsustainable activities have an adverse impact on environmental sustainability (Dong et al., 2024; Wu et al., 2022). EC was found an important predictor of pro-environmental behaviour (Kim et al., 2016; Chen and Hung, 2016). In the context of visiting green hotels, Verma et al. (2019) found a positive influence of EC on AR. Similarly, other researchers have found that EC motivates individuals to visit in green hotels (Dong et al., 2024; Chen and Tung, 2014). Dong et al.’s (2024) study confirmed the positive influence of EC on AR towards visiting green hotels. Past studies also revealed that EC plays a significant impact on PN (Dong et al., 2024; Wu et al., 2022). Dong et al. (2024) evaluated an extended VBN and assessed INT to visit green hotels. They found a significant impact of EC on PN. Wu et al. (2022) conducted a study on students’ waste management behaviour which revealed that EC significantly influences PN. Therefore, this study assumes that EC will significantly influence AC and PN towards visiting green hotels. Hence, it is hypothesized that
Awareness of consequences (AC)
AC refers to an individual’s awareness of the adverse consequences to environment that may arise due to their actions (Dalvi-Esfahani et al., 2017). Research shows that people behave pro-environmentally to ensure the safety of environment from the potential impacts of their actions (Ly, 2024; Yang et al., 2024). Han (2020) found that AC is positively associated with AR in the purchase of environmentally friendly catering products. Gao et al. (2017) found a positive correlation between customers' perceptions of the adverse impacts of tourism and their AR. Furthermore, Esfandiar et al. (2020) conducted a study to assess tourist binning behaviour in parks which found that AC significantly influences PN. Therefore, this study assumes that customers’ AC positively influences customers’ AR and PN towards visiting green hotels. Hence, it is hypothesized that
Ascription of responsibility (AR)
AR refers to a social and psychological perspective in which an individual recognizes their responsibility for the problems that result from their actions (De Groot and Steg, 2009). Stern et al. (1999) consider AR as an individual’s sense of responsibility in understanding the environmental issues caused by human unsustainable activities. Nyborg et al. (2006) highlight that people ascribed the responsibility of environmental issues and engage in pro-environmental behaviour. AR was found to significantly influence PN and INT (Van Riper and Kyle, 2014). Previous studies that assessed green hotels visiting INT found significant influence of AR on INT (Dong et al., 2024; Kim et al., 2019). This study assumed that customers ascribed the responsibility to safeguard the environment by visiting green hotels. Hence, it is hypothesized that
Personal norm (PN) as a mediator
PN refers to an individual’s personal and moral assessment of right and wrong, which motivates them to engage in pro-environmental behaviour (Lindenberg and Steg, 2007). Researchers found that PN is a crucial element that drives people to engage in pro-environmental behaviour (Khan and Abbas, 2022; Ashraf et al., 2020; Stern and Dietz, 1994). PN helps to transform an individual’s belief and sense of obligation which effect their values and actions towards the nature (Choi et al., 2015). Verma et al. (2019) argued that individual values and sense of AR are essential factors which motivate them to visit green hotels. Waris et al. (2024) found that an individual’s pro-environmental belief shapes PN which eventually effects INT to visit green hotels. Han et al. (2015) posit that PN is the immediate predictor of guests’ INT to visit green hotels. The study conducted by Megeirhi et al. (2020) revealed a positive link between PN and customers’ INT to visit green hotels. In addition, research revealed an indirect significant influence of PN on customers’ INT to visit green hotels (Dong et al., 2024; Wang et al., 2023). Hence, it is hypothesized that
Price consciousness (PC) as a moderator
The extent to which customers pay to products’ and services during the purchase refers to PC (Saleki et al., 2019). The increase in product and service prices causes customers to switch to alternatives (Balabanis and Stathopoulou, 2021). The study conducted by Saleki et al. (2019) found that PC significantly moderates the relationship between INT and behaviour. Roy (2015) conducted a study and found that customers are price sensitive which hinders them to pay more for environmentally friendly restaurants. Mahasuweerachai and Suttikun (2023) found a significant moderating effect of PC on customers’ willingness to pay higher prices at green restaurants. Hence, it is hypothesized that
Moderating effects of aesthetic values (AEV)
Green hotels include sustainable amenities that provide unique ambiance and attract eco-friendly customers (Chang et al., 2024). The natural and environmentally friendly ambiance increases eco-conscious customers’ experience and satisfaction. In addition, green hotels’ aesthetics include green technologies and green theme decor that differentiate it from the traditional hotels and depict hotels’ commitment to sustainability (Singh et al., 2024). These aesthetic experiences appeal eco-conscious customers and increases INT to stay green hotels. Researchers indicate that AEV influences customers’ destination INT (Wang et al., 2008), especially when customers have had negative aesthetic experiences that shape their perception of the trip and destination through a pessimistic lens (Le et al., 2021). The aesthetic appeal of a destination can strongly influence customers' overall satisfaction, which in turn influences the likelihood of a repeat visit (Andarabi and Hassan, 2018). Researcher found that aesthetic experience of tourists significantly moderates the relationships between perception of authenticity and satisfaction (Genc and Gulertekin Genc, 2023). Although previous studies have paid attention to the aesthetic aspects of tourist places, there is still a lack of research to investigate the moderating effect of AEV. Hence, it is hypothesized that
Methodology
Instrument
This study used a quantitative approach and a questionnaire for data collection. AEV was measured using a four-item scale adapted from the study by Le et al. (2021). EC was measured using a five-item scale adapted from the study by Kilbourne and Pickett (2008). AC was measured using a four-item scale adapted from the study by Han (2015). Li et al. (2018) six-item scale was used to measure AR. PN and PC were measured using a three- and four-item scale, respectively, adapted from the Mahasuweerachai and Suttikun (2023) study. INT was measured using a five-item scale adapted from the study by Kokkhangplu et al. (2023). The instrument comprised five sections. The first section introduces the purpose of the study and participants’ informed consent. The second section details about the independent variable. The third section is about mediating variables. The fourth section is about the dependent variable. Finally, the fifth part of the questionnaire comprised demographic information about the participants. The questionnaire was designed and submitted to three academic experts from Pakistan to review its contents. They modified the survey instrument for a better understanding of the participants. In addition, the structure of the questionnaire was modified by two marketing assistant professors to ensure content and face validity. To ensure the reliability and validity of the scales measuring the constructs, we conducted a pilot test with 45 hotel customers. The reliability analysis showed that all constructs had Cronbach values of over 0.70. Finally, we asked customers for feedback on the wording and layout of the questionnaire to further improve it. Based on customers’ responses, we made further revisions and finalized the questionnaire.
Data collection
This study employed a self-administered questionnaire to collect data from the participants in Pakistan. The population of the study included individuals who visited a hotel at least once in a lifetime. The participants were approached in major cities and tourist destinations in Pakistan, including Islamabad, Karachi, Lahore, Peshawar, Swat, Hunza, Chitral, Murree and Quetta. The participants in this study were purposefully selected. The use of a purposive sample allows the researchers to control the representativeness of their sample. In addition, the aim was to collect data from hotel customers to fully understand the influence of factors that affect their visiting INT. The data collection took place over two months, from October 2 to December 24, 2023. The survey was conducted with the help of four Ph.D. students who were trained to collect the data. During the survey period, the questionnaire was distributed to 410 customers. After discarding the responses with missing values and removing the outliers, we conducted an analysis of 340 customers. The demographic data of the participants can be found in Table 1.
Results
Measurement model
The measurement model assessment includes the evaluation of internal consistency using Cronbach’s alpha (α), the composite reliability values (CR), the confirmation of convergent validity through the evaluation of indicator reliability and the average variance extracted (AVE), as well as the confirmation of discriminant validity. First, factor loadings of the constructs were assessed to ensure that each item must be greater than 0.708. AC1, AR2 and AR4 items were deleted from the measurement due to low factor loading values. The factor loading values of the remaining items are greater than 0.708. CR and α are used to measure data internal consistency. Hair et al. (2019) suggested minimum threshold value of 0.70 for data reliability. The measurement model as shown in Table 2 shows that α and CR have a value of 0.70. Third, the study examined the convergent validity by evaluating the AVE values. Table 2 shows that the values of AVE are greater than 0.50, indicating the presence of convergence among constructs (Hair et al., 2019).
Discriminant validity refers to the degree of differentiation among the measures (Henseler et al., 2015). Heterotrait-monotrait ratio (HTMT) criteria were used to assess discriminant validity. The constructs HTMT values are less than 0.90 (Table 3), which confirms that constructs used in this study are unrelated (Sarstedt et al., 2017).
Structural model
After confirming the reliability and validity of the measurement model, the study assessed the coefficients of determination (R-square value) of the hypothesized model. R-square (R2) quantifies the proportion of variance in the dependent variables explained by the independent variables. Figure 2 shows that 74.4% of the variation in customers’ INT is attributed to the independent variables. To assess the predictive accuracy of the model, the study utilized Stone-Geisser’s (Q2) blindfolding method. A Q2 value greater than zero indicates the predictive accuracy of the model. The Q2 for the endogenous constructs AR, PN and INT were 15%, 14.6% and 43.6%, respectively, indicating high predictive accuracy of the structural model.
The statistical significance of the proposed hypothesized model was assessed by employing a bootstrapping technique involving 5,000 resamples. The decision for the acceptance and rejection of the hypothesized model was based on p-values and t-values. Table 4 shows the results of the direct, mediating and moderating effect. The findings revealed that EC has a positive significant impact on AR (β = 0.197; t = 3.067; p > 0.05) and PN (β = 0.263; t = 4.425; p > 0.05). Hence, H1 and H2 were supported. AC has a positive significant impact on AR (β = 0.365; t = 5.692; p > 0.05) and PN (β = 0.127; t = 1.994; p > 0.05), supporting H3 and H4. The positive impact of AR on PN (β = 0.125; t = 2.010; p < 0.05) and INT were significant (β = 0.137; t = 3.828; p < 0.05), and thus, H5 and H6 were supported. PN has a positive significant impact on INT (β = 0.263; t = 5.629; p > 0.05), supporting H7. Figure 2 shows the structural model.
Mediation analysis
The study employed bootstrapping method for the analysis of mediation. The research utilized partial least square - structural equation modeling (PLS-SEM) to assess the indirect impact, following the recommendation of Hayes and Preacher (2010), and employed confidence intervals to confirm the significance. The results indicate that PN does not mediate the relationship between AR and INT (β = 0.033; t = 1.096; p > 0.05), and thus, H8 rejected. The insignificant indirect effect via PN indicates that only a direct relationship exists between AR and INT.
Moderation analysis
This study assessed the moderating effect via product indicator approach (Sarstedt et al., 2017). This approach included generating the interaction term. Table 4 shows the results indicating that PC does not moderate the relationship between AR and INT (β = −0.010; t = 0.257; p > 0.05), and H9 is rejected. However, PC moderates the relationship between PN and INT (β = −0.106; t = 2.651; p < 0.05), supporting H10. Furthermore, the results indicate that AEV moderates the relationships between AR and INT (β = 0.098; t = 2.546; p < 0.05) and PN and INT (β = 0.134; t = 2.882; p < 0.05), supporting H11 and 12. The slope analyses of the significant moderating effects are shown in Figure 3–5.
Multi-group analysis (MGA)
The study conducted categories of groups: gender and age. Multi-group analysis (MGA) offers in-depth understanding of differences between the groups (Bou and Satorra, 2010). Gender category was divided between male and female. Gender category shows a difference in results. In the male group, AC and PC were insignificant predictors. Also, AEV does not moderate the relationship between PN and INT. In the female group, the results indicate insignificant impact of EC on AR, AC on PN and AR on PN. Also, the results show insignificant mediation and moderation, except the positive moderating effect of AEV on the relationship between PN and INT. The details are summarized in Table 5. Then, the age category was divided into two age groups: 18–33 years and 34 years and above. The results of age group 18–33 years match with the findings of the complete sample. However, age group 34 years and above reports inconsistent findings with respect to the complete sample. The relationship between EC to AR and AC to PN was insignificant. Also, results indicate insignificant mediating and moderating effects, except the positive moderating effect of AEV on the relationship between PN and INT. Finally, MGA comparison shows no significant difference among gender and age groups. The results of multi-group comparison show insignificant differences in both group participants (age and gender), and only the significant differences were found between AC and PN and EC and PN in the age group. The findings are summarized in Table 6.
Discussion
The results of the study indicate that EC has a significant impact on AR and PN. The results are consistent with past studies (Dong et al., 2024; Verma et al., 2019), which shows that customers consider that conventional hotels’ unsustainable practices are serious threats to environmental sustainability, and they intent to make efforts to improve environmental quality. The results also confirmed the significant positive impact of AC on AR and PN. Han (2020) and Gao et al. (2017) also reported the similar findings which indicate that customers are aware of the consequences of resource depletion and environmental pollution that results from the unsustainable hotel practices. The findings indicate the positive impact of AR on PN and INT, which is consistent with past studies (Dong et al., 2024; Van Riper and Kyle, 2014). The results of the study indicate that PN does not mediate the relationship between AR and INT, which contradicts with the findings of Dong et al. (2024), who revealed that PN mediates the positive relationship between AR and INT. The results confirmed the moderating effect of PC on the relationship between PN and INT, which is consistent with the findings of Mahasuweerachai and Suttikun (2023). The results suggest that customers who are less price-conscious are more inclined towards green hotels. However, if green hotels increase the prices, then the customers will switch to traditional hotels. In addition, AEV moderates the relationship between AR and INT and PN and INT which matches with previous studies (Le et al., 2021; Andarabi and Hassan, 2018). The moderating effect of AEV shows green hotels’ commitment towards investing in the beauty of hotel by adopting green practices which enhances its appeal and help to attract customers. These initiatives have a positive impact on customers’ sense of responsibility and PN to act responsibly by visiting green hotels.
Conclusion
The current study aimed to analyse customers’ INT to visit green hotels through the lens of an extended NAM. The study included EC and AEV as additional constructs to NAM. The data were gathered from respondents living in different cities and tourists places in Pakistan. Pakistan is included among the most polluted countries in the world, which adversely impacts the tourism industry. Researchers argued that individuals may be inclined to make short-term sacrifices in favour of long-term benefits (Raza and Farrukh, 2023; Waris et al., 2024). Therefore, the current study used an extended NAM to assess individuals’ INT to visit green hotels for a sustainable future. A total 340 respondents have participated in the study. The data have been analysed using PLS-SEM. The results of the study indicate that EC has a significant impact on AR and PN, which shows that customers consider making efforts by visiting green hotels. The positive effect of AC on AR and PN was also significant, which depicts customers’ sense of responsibility towards environmental sustainability. The findings also indicate direct positive effects of AR and PN on INT. Additionally, the findings confirmed the moderating effects of PC and AEV on the relationship between PN and INT, which shows AEV attract customers but high prices for accommodations are major concern of the customers in developing country. Based on MGA findings, male participants and the younger age group (18 years–33 years) have more EC and AC than female and older age group (34 years and more). These findings indicate that young male customers consider participating in sustainable activities, which eventually improves environmental health. Additionally, significant differences between young and older customers were reported regarding the moderating effect of PC and AEV on INT to visit green hotels. Young customers were found to be more attracted to the aesthetics of green hotels, but they are conscious of the high prices of green hotels.
Theoretical implication
Theoretically, this study extends NAM by incorporating EC, AEV and PC and enriches the literature of green hotels. In past, studies have ignored the potential impact of EC, AEV and PC on INT to visit green hotels. The study findings indicate that EC and AVE have a crucial impact on customers’ INT in the context of visiting green hotels because the extended framework explained 74.4% variation on INT to visit green hotels. The findings of the study highlight a significant link between human actions and environmental sustainability which transforms customers’ behaviour towards green hotels. The significant moderating effect of PC on the relationship between PN and INT shows that hotels must implement fair pricing strategies to attract price-sensitive customers. Additionally, the study confirms the moderating impact of AEV in the context of visiting green hotels which shows that green practices enhance the beauty of green hotels and increases customers’ green hotels visiting INT.
Practical implications
The current study has several implications for green hotel managers and policymakers. First, the study highlights the significance of impact of EC and AC on AR and PN towards visiting green hotels. Therefore, managers should design effective marketing messages that align with green customers’ ethical and moral values. In addition, they can introduce reward points for customers who prioritize saving water and energy during their stay and choose environmentally friendly modes of transport. The reward points display the symbol of green and loyal customers and at the same time help to attract price-sensitive customers. Second, the marketing strategy should demonstrate the benefits of eco-friendly hotels to ecologically conscious customers by emphasizing on nature beauty, hotel ambience and social well-being. Hotels' green practices should influence customers and encourage them to participate in programmes that promote environmental sustainability. Past studies show that people remember information and experiences that are consistent with their values (Ballantyne et al., 2018; Ye et al., 2018). Therefore, it is suggested that hotel management must pay attention to environmental social responsibility which fosters customers’ participation in green management events because these initiatives have long-lasting experiences, which ultimately promote green behaviour. The green management events can also promote the use of wooden cutlery, less plastic or efforts to reduce water and electricity consumption. In addition, information about the misuse of natural resources such as water and energy can be disseminated via the hotels' official booking websites. Furthermore, research findings highlight the importance of AEV in green hotels as it significantly enhanced the positive relationships between AR and INT and PN and INT. Green hotels’ AEV serve to enhance visual appeal and attract environmentally conscious customers. Green hotels’ aesthetics also serve to enhance environmentally conscious customers’ commitment to sustainability and motivate them to reconsider green hotels in future. The findings highlight the significance of green attributes in hotels, which increases visual appeal of hotels; therefore, managers can introduce green exterior and interior designs by improving sustainable materials in buildings, increasing menu for organic foods and installing light-emitting diode (LED) lighting in hotels to strengthen the connection between sense of responsibility and INT to visit green hotels.
Limitations and future research directions
Although the present study has extended NAM and offers several practical implications, it has some limitations that call for further research. First, this study is conducted in a developing market context which limits its application to developed countries. Researchers can use the same framework and compare the findings from developed and developing markets, which offers better insights and help marketers to design relevant strategies for specific markets. Second, this study used NAM, an environmental model, to predict INT. Future studies can employ stimulus organism and response model and other behavioural models to identify the factors affecting customers’ INT to visit green hotels. Third, the current study controlled for the influence of demographic variables on customers’ INT to visit green hotels. Previous studies show that factors such as age, gender and income level have a significant influence on individual pro-environmental behaviour (Barber et al., 2010). Therefore, future researchers should examine the influence of demographic factors on INT to visit green hotels.
Conflict of interest: The authors declare no conflict of interest.
Figure 1
An extended norm activation model
[Figure omitted. See PDF]
Figure 2
Structural model
[Figure omitted. See PDF]
Figure 3
Moderating effect of PC on PN and INT
[Figure omitted. See PDF]
Figure 4
Moderating effect of AEV on AR and INT
[Figure omitted. See PDF]
Figure 5
Moderating effect of AEV on PN and INT
[Figure omitted. See PDF]
Table 1
Demographic profile
| Demographic variables | Classification | Frequency | Percentage (%) |
|---|---|---|---|
| Gender | Male | 205 | 60.3 |
| Female | 135 | 39.7 | |
| Age | 18–25 years | 73 | 39.4 |
| 26–33 years | 122 | 29.3 | |
| 34–41 years | 74 | 26.2 | |
| 42–49 years | 42 | 3.1 | |
| Above 49 years | 29 | 2 | |
| Qualification | Intermediate/Diploma | 15 | 11.3 |
| Bachelor | 132 | 42.3 | |
| Master | 144 | 43.7 | |
| MPhil/Ph.D. | 49 | 2.8 | |
| Family income | Below 50,000 PKR | 69 | 20.3 |
| 50,001 PKR to 75,000 PKR | 133 | 39.1 | |
| 75,001 PKR to 100,000 PKR | 77 | 22.6 | |
| 100,001 PKR to 125,000 PKR | 19 | 5.6 | |
| 125,001 PKR to 150,000 PKR | 23 | 6.8 | |
| More than 150,000 PKR | 19 | 5.6 |
Note(s): Pakistani rupees = PKR
Source(s): Authors’ own creation
Table 2
Measurement model
| Constructs | Items | Loading | α | CR | AVE |
|---|---|---|---|---|---|
| Awareness of consequences | AC2 | 0.778 | 0.803 | 0.884 | 0.718 |
| AC3 | 0.885 | ||||
| AC4 | 0.875 | ||||
| Aesthetic value | AEV1 | 0.807 | 0.906 | 0.934 | 0.780 |
| AEV2 | 0.845 | ||||
| AEV3 | 0.872 | ||||
| AEV4 | 0.907 | ||||
| Ascription of responsibility | AR1 | 0.703 | 0.796 | 0.868 | 0.623 |
| AR3 | 0.777 | ||||
| AR5 | 0.803 | ||||
| AR6 | 0.864 | ||||
| Environmental concern | EC1 | 0.828 | 0.880 | 0.913 | 0.677 |
| EC2 | 0.837 | ||||
| EC3 | 0.860 | ||||
| EC4 | 0.740 | ||||
| EC5 | 0.841 | ||||
| Intention | INT1 | 0.809 | 0.835 | 0.884 | 0.603 |
| INT2 | 0.783 | ||||
| INT3 | 0.767 | ||||
| INT4 | 0.750 | ||||
| INT5 | 0.773 | ||||
| Price consciousness | PC1 | 0.750 | 0.848 | 0.894 | 0.679 |
| PC2 | 0.865 | ||||
| PC3 | 0.802 | ||||
| PC4 | 0.872 | ||||
| Personal norm | PN1 | 0.920 | 0.903 | 0.939 | 0.837 |
| PN2 | 0.925 | ||||
| PN3 | 0.900 |
Note(s): CR = composite reliability; AVE = average variance extracted
Source(s): Authors’ own creation
Table 3
Heterotrait-monotrait ratio (HTMT) results
| Latent variables | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| Awareness of consequences | |||||||
| Aesthetic value | 0.273 | ||||||
| Ascription of responsibility | 0.583 | 0.199 | |||||
| Environmental concern | 0.671 | 0.572 | 0.481 | ||||
| Intention | 0.297 | 0.871 | 0.399 | 0.580 | |||
| Price consciousness | 0.450 | 0.680 | 0.322 | 0.593 | 0.776 | ||
| Personal norm | 0.386 | 0.576 | 0.346 | 0.432 | 0.759 | 0.608 |
Source(s): Authors’ own creation
Table 4
Hypothesis testing
| Hypotheses | Path coefficients | t-values | p-values | C.I. | Decision |
|---|---|---|---|---|---|
| H1: EC → AR | 0.197 | 3.067 | 0.002 | Supported | |
| H2: EC → PN | 0.263 | 4.425 | 0.000 | Supported | |
| H3: AC → AR | 0.365 | 5.692 | 0.000 | Supported | |
| H4: AC → PN | 0.127 | 1.994 | 0.046 | Supported | |
| H5: AR → PN | 0.125 | 2.010 | 0.045 | Supported | |
| H6: AR → INT | 0.137 | 3.828 | 0.000 | Supported | |
| H7: PN → INT | 0.263 | 5.629 | 0.000 | Supported | |
| Mediating effect | |||||
| H8: AR → PN → INT | 0.033 | 1.906 | 0.057 | 0.003, 0.071 | Rejected |
| Moderating effects | |||||
| H9: PC × AR → INT | −0.010 | 0.257 | 0.797 | Rejected | |
| H10: PC × PN → INT | −0.106 | 2.651 | 0.008 | Supported | |
| H11: AEV × AR → INT | 0.098 | 2.546 | 0.011 | Supported | |
| H12: AEV × PN → INT | 0.134 | 2.882 | 0.004 | Supported | |
Note(s): C.I. = confidence interval, significant at (p < 0.05). Italic values indicate insignificant relationships
Source(s): Authors’ own creation
Table 5
Hypothesis testing (gender sample)
| Male | Female | |||
|---|---|---|---|---|
| Hypotheses | Path coefficients | p-values | Path coefficients | p-values |
| H1: EC → AR | 0.243 | 0.005 | 0.143 | 0.154 |
| H2: EC → PN | 0.172 | 0.028 | 0.393 | 0.000 |
| H3: AC → AR | 0.343 | 0.353 | 0.394 | 0.001 |
| H4: AC → PN | 0.113 | 0.118 | 0.146 | 0.138 |
| H5: AR → PN | 0.219 | 0.012 | 0.011 | 0.904 |
| H6: AR → INT | 0.157 | 0.000 | 0.140 | 0.035 |
| H7: PN → INT | 0.234 | 0.000 | 0.296 | 0.000 |
| Mediating effect | ||||
| H8: AR → PN → INT | 0.051 | 0.023 | 0.003 | 0.906 |
| Moderating effects | ||||
| H9: PC × AR → INT | −0.002 | 0.976 | 0.009 | 0.913 |
| H10: PC × PN → INT | −0.104 | 0.076 | −0.122 | 0.071 |
| H11: AEV × AR → INT | 0.101 | 0.048 | 0.081 | 0.281 |
| H12: AEV × PN → INT | 0.109 | 0.089 | 0.158 | 0.037 |
Note(s): Path coefficients are significant at 0.05 levels. Italic values indicate insignificant p-value
Source(s): Authors’ own creation
Table 6
Hypothesis testing (age sample)
| Age category = 18–33 years | Age category = 34 and above | |||
|---|---|---|---|---|
| Hypotheses | Path coefficients | p-values | Path coefficients | p-values |
| H1: EC → AR | 0.234 | 0.017 | 0.136 | 0.124 |
| H2: EC → PN | 0.164 | 0.038 | 0.412 | 0.000 |
| H3: AC → AR | 0.282 | 0.003 | 0.491 | 0.000 |
| H4: AC → PN | 0.289 | 0.000 | −0.134 | 0.198 |
| H5: AR → PN | 0.170 | 0.044 | 0.109 | 0.212 |
| H6: AR → INT | 0.183 | 0.001 | 0.125 | 0.021 |
| H7: PN → INT | 0.182 | 0.001 | 0.342 | 0.000 |
| Mediating effect | ||||
| H8: AR → PN → INT | 0.031 | 0.094 | 0.037 | 0.204 |
| Moderating effects | ||||
| H9: PC × AR → INT | −0.010 | 0.858 | 0.046 | 0.606 |
| H10: PC × PN → INT | −0.132 | 0.023 | −0.103 | 0.136 |
| H11: AEV × AR → INT | 0.132 | 0.018 | 0.050 | 0.488 |
| H12: AEV × PN → INT | 0.140 | 0.032 | 0.144 | 0.017 |
Note(s): Path coefficients are significant at 0.05 levels. Italic values indicate insignificant p-value
Source(s): Authors’ own creation
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