High-Tech or High-Touch? Effects of Tourism Service Mode on Tourists’ Pro-Environmental Behavior

Abstract

Tourists’ Pro-Environmental Behavior (PEB) is critical for sustainable tourism. While service robots are being increasingly adopted in tourism, their impact on PEB, compared with that of human services, remains unclear. Drawing on Social Exchange Theory and Norm Activation Theory, this study examines how service modes (robots vs. humans) influence PEB through dual mediating mechanisms of social distance and personal norms, moderated by interaction type (task-oriented vs. relationship-oriented type) and destination support (instrumental support vs. emotional support). Four experimental studies reveal that compared with robot services, human services enhance PEB more effectively by reducing perceived social distance and strengthening personal norms. Relationship-oriented interaction amplifies the disparity in social distance between human and robot services, whereas emotional support widens the gap in personal norms between human and robot services. These findings advance the theoretical understanding of service mode effects on PEB and offer practical insights for designing tourism services that promote sustainability.

Keywords

robot service human service social distance personal norms interaction type destination support pro-environmental behaviors

Highlights

This study compares the effects of high-tech versus high-touch service on tourists’ pro-environmental behavior.

We identify social distance and personal norms as key psychological mechanisms.

Our findings show that interaction type moderates the effect of service mode on social distance.

We offer actionable insights for designing targeted sustainability interventions.

Introduction

The application of service robots in the tourism industry has become increasingly widespread in recent years (Fang et al., 2022). From artificially intelligent concierges in luxury hotels to robot tour guides at heritage sites, the tourism industry has witnessed the rapid adoption of service robots. This technological shift has significant implications for sustainable tourism development. Tourists, as key stakeholders in destination ecosystems, can profoundly impact environmental conservation through their pro-environmental behaviors (PEBs; Bernard et al., 2024; Wu et al., 2020). Social Exchange Theory suggests that service quality directly influences such behaviors: When tourists receive satisfactory services, they often reciprocate by adopting environmentally responsible actions (Bi et al., 2024).

The application of service robots enhances service quality and optimizes service delivery, fostering users’ altruistic behavior, such as PEB (Tian & Liu, 2024). However, critics argue that service robots struggle to recognize emotional needs and adapt to subtle situational nuances (X. Liu et al., 2022). Thus, whether they can fully replace human employees, refine physical and social elements in tourism services, and effectively encourage tourists’ PEB remains an open question. The distinct characteristics of service robots and human employees shape different physical and social elements in service encounters (Fang et al., 2022), influencing relationship intimacy, perceived social distance, and, ultimately, tourists’ PEB (Zhao et al., 2023). While service robots enhance efficiency, reduce costs, and streamline service delivery (Manthiou et al., 2020), they often lack the emotional intelligence to address nuanced needs, resulting in limited flexibility, personalization, and emotional connection (X. Liu et al., 2022). Consequently, some tourists perceive greater social distance from robots and may even reject robot services in favor of human interaction (Tussyadiah, 2020). By contrast, human service providers can forge deeper emotional bonds with visitors through empathy, adaptability, and genuine care. They are often better equipped to convey a sense of responsibility and concern, thereby potentially guiding visitors towards more environmentally conscious behavior in a more positive manner (Buzova et al., 2022; G. Chen & Peng, 2023).

Prior research in hospitality and tourism highlights how external service factors—such as tour guide humor, activity design, and social distance—shape tourists’ psychological responses and PEB (Su et al., 2025; Zhao et al., 2023). Social Exchange Theory suggests that human social behavior can be viewed as a process of resource exchange based on the principle of reciprocity, which involves not only the exchange of material resources but also that of nonmaterial resources, such as emotions and services (Rasoolimanesh et al., 2015). Social distance refers to the degree of proximity between tourists and others as perceived in the interaction process (Nyaupane et al., 2015). Tourists evaluate the rewards and costs of the relationship in the process of interaction with service employees (Fang et al., 2022). If tourists perceive that the social distance between them and service employees is small, then, on the basis of the principle of reciprocity, they are more likely to reciprocate PEB. In contrast, if the social distance is seen as being too great, then it causes reciprocal relationship imbalance and reduces the likelihood of engaging in PEB (Wu et al., 2020). Despite comparative studies on robot versus human services, few studies have examined social distance as a mediator in the relationship between service mode and PEB.

In addition, the individual’s view of their responsibilities and obligations—namely, personal norms—plays an important role in driving his or her behavior, including PEB (Confente & Scarpi, 2020). Norm Activation Theory explains the process of personal norm formation and activation. Individuals form constraints and guidelines for self-behavior in social life by perceiving social norms and internalizing them as personal norms (Q. Li et al., 2025). When an individual realizes that PEB is consistent with their personal norms, they are more likely to develop the will and action to implement that behavior (Pearce et al., 2022). S. Lee et al. (2021) reported that personal norms significantly influence tourists’ environmental behavior. Despite its importance, such an influencing mechanism among service mode, social distance, personal norms, and PEB has yet to be investigated.

By shaping their interactions, service providers significantly influence visitors’ personal emotional experiences. Beyond providing fundamental services, the flexibility with which service providers engage with visitors and fulfill their emotional needs plays a pivotal role in the cultivation of visitors’ personal norms (G. Chen & Peng, 2023). Although interactions with service robots and human staff each possess distinct advantages and disadvantages, existing research focuses predominantly on the role of humans in fostering PEB, while the impact of service robots remains underexplored. Moreover, destination support is a vital factor influencing visitors’ personal norms (G. Chen & Peng, 2023). As the functional and emotional support provisions of service robots and human staff differ, their respective effects on the formation of visitors’ personal norms and PEB warrant further investigation. The tourism industry advances towards intelligent development, so it is imperative to investigate on the mechanisms by which different service models (robot vs. human) influence visitors’ eco-friendly behavior. Particular emphasis should be placed on re-examining and reaffirming, through comparative analysis, the value of human service in fostering emotional connections and ethical guidance in the digital era. This holds urgent theoretical and practical necessity for constructing a tourism service ecosystem that balances efficiency with warmth, thereby achieving a sustainable development.

Guided by Social Exchange Theory and Norm Activation Theory, this study employs four experiments to examine how service modes (robot vs. human) affect PEB. Study 1 tests the main effect of service mode and the mediating role of social distance. Study 2 validates the mediating role of personal norms, and Studies 3 and 4 validate the moderating role of interaction and destination support, respectively, in the model. This study deepens the understanding of how robot and human services influence tourists’ PEB, thereby expanding the scope of the application of Social Exchange Theory and Norm Activation Theory in the field of tourism environmental protection. Practically, the results provide management strategies that can facilitate the selection of tourism service modes, promote the protection of the ecological environment, and facilitate the sustainable development of tourism destinations.

Theoretical Basis and Hypothesis Development

Theoretical Basis

Social Exchange Theory has been extensively applied in tourism research to explain interactions among stakeholders, particularly the mechanisms underpinning the formation of attitudes and behaviors between residents and visitors (Chang, 2021). The theory’s core assumption posits that individuals weigh the benefits and costs of actions to determine whether to engage in exchange behaviors (Rasoolimanesh et al., 2015). Within tourism contexts, residents’ level of support for tourism development hinges upon their perceptions of its economic, sociocultural, and environmental impacts (Wang et al., 2023). For instance, residents are more likely to endorse tourism development if they perceive its economic benefits (such as increased employment and income) to outweigh its social costs (such as congestion and pollution; Özel & Kozak, 2016). Existing research frequently combines Social Exchange Theory with Emotional Solidarity to explore how residents’ emotional responses towards tourists influence their supportive behavior (Erul et al., 2020). Furthermore, Social Exchange Theory has been applied to analyze tourist-destination interactions, such as how tourists’ perceptions of destination social responsibility influence their behavioral intentions (S. Lee et al., 2021). However, current research predominantly focuses on the resident perspective, with relatively limited exploration of social exchange mechanisms from the tourist perspective (Wang et al., 2023).

PEB refers to actions taken by tourists during their travels that actively benefit the environment, such as reducing waste and using eco-friendly transport (Esfandiar et al., 2022). Research on PEB predominantly employs dual-pathway frameworks integrating cognitive and affective dimensions, with Norm Activation Theory and Value-Belief-Norm Theory serving as prevalent theoretical foundations (Confente & Scarpi, 2020). Research indicates that tourists’ PEB is driven by multiple factors, including environmental values, moral norms, and affective factors (Q. Li & Wu, 2020; Pearce et al., 2022). Furthermore, external factors such as destination management strategies and social influences significantly impact PEB (Su et al., 2025). Recent research has begun examining subcategories of PEB, such as differing behavioral manifestations in nature reserves versus urban tourism (Eusébio et al., 2023), while emphasizing the role of emotional solidarity in fostering PEB (S. Li et al., 2021). Nevertheless, most research remains concentrated on individual psychological mechanisms, with insufficient investigation into PEB driven by social interactions and external environmental factors (Gao et al., 2023).

The relationship established through social exchange is characterized by a certain degree of instability and is easily affected by external factors. In recent years, some services that were originally provided by human beings have gradually come to be provided by robots, a shift that has greatly changed the service mode employed by tourism destinations. However, few scholars have explored the impacts of the use of robots in tourism services on tourists’ perceived social distance and PEB in light of Social Exchange Theory. Comparative studies on the impacts of robot service and human service on tourists’ PEB remain lacking. Therefore, this study discusses how tourists can obtain material or nonmaterial support through social exchange, both in situations involving robot service and those involving human service, following which it explores the corresponding differences in the effects of these two types of service on tourists’ perceived social distance and PEB.

To address the gaps in existing research, this study integrates Social Exchange Theory and Norm Activation Theory to construct a dual-path theoretical framework. Social Exchange Theory explains the external interaction mechanism-human services reduce social distance (perceived similarity) through emotional reciprocity, thereby enhancing PEB (Wu et al., 2020). Norm Activation Theory complements this by explaining the internal psychological mechanism-services that evoke emotional connections strengthen personal norms (internalized sense of responsibility), which directly motivate PEB (Han, 2014). This integrated framework effectively links service modes (robot vs. human) to PEB through the parallel mediating roles of social distance and personal norms. This overcomes the limitations from single-theory approaches and provides a holistic explanation for how service characteristics influence tourists’ environmental behaviors.

Tourism Services and PEB

Tourism services encompass a wide range of interactions, including human and robotic services, which significantly influence tourist experiences and behaviors (Filieri et al., 2022; Tussyadiah, 2020). Current research has focused on how service elements such as emotional solidarity, social exchange, and service scenarios affect tourist satisfaction and loyalty (He et al., 2018; S. Li et al., 2021). For example, studies have explored the role of host sincerity and emotional connections in enhancing tourist experiences (Buzova et al., 2022; S. Li et al., 2021). However, a significant gap exists in understanding the comparative effectiveness of human versus robot services, particularly in terms of their impact on PEB (X. Liu et al., 2022; Manthiou et al., 2020). Additionally, while some research has examined how servicescapes and social interactions influence general tourist behaviors, less attention has been paid to how these services specifically drive PEB in tourism contexts (S. Chen et al., 2023; Yin et al., 2023).

Tourism services are closely linked to tourists’ PEB through mechanisms such as norm activation, emotional solidarity, and social influence (Confente & Scarpi, 2020; Esfandiar et al., 2019). Human services, with their capacity for emotional depth and interpersonal rapport, often involve personalized interactions that foster emotional connections and reinforce social norms, which can motivate PEB by enhancing feelings of responsibility and community (Han, 2014; S. Li et al., 2021). For instance, emotional solidarity with hosts and perceived social norms have been shown to positively correlate with PEB (S. Li et al., 2021; Pearce et al., 2022). In contrast, robot services, while efficient and technologically advanced, may primarily rely on transactional or standardized interactions, which could limit their effectiveness in transmitting social norms or evoking deep emotional engagement (Guo et al., 2024; X. Liu et al., 2022). Although robot services can encourage pro-environmental actions through informational clarity or convenience , the perceived impersonality or lack of genuine affective resonance in these interactions may constrain their impact on sustained PEB, which often requires internalization of norms and emotional commitment. Therefore, the following hypothesis is proposed:

H1: Compared with robot services, human services are more likely to enhance tourists’ PEB.

The Mediating Effect of Social Distance

Social distance, defined as the perceived similarity or dissimilarity between individuals or groups, significantly influences interpersonal perceptions and behaviors in tourism contexts (Liviatan et al., 2008). Existing research has explored its role in shaping residents’ attitudes toward tourists (D. Joo et al., 2018) and tourists’ behaviors in religious destinations (Nyaupane et al., 2015). However, a notable gap remains in understanding how social distance mediates the relationship between service modes (human vs. robot) and tourists’ PEB, particularly in the context of evolving service technologies like robotics in tourism (Tussyadiah, 2020).

Service modes, such as human-operated versus robot-provided services, can alter tourists’ perceptions of social distance. For instance, human services often foster a sense of emotional solidarity and reduce social distance through interpersonal interactions (D. Joo et al., 2018), whereas robot services may heighten perceptions of dissimilarity due to the lack of human warmth and emotional reciprocity (X. Liu et al., 2022). Studies indicate that human-like robot appearances can mitigate social distance by eliciting perceptions of warmth or competence, but they still fall short of replicating the reduced social distance achieved through human interactions (Garcia et al., 2024; Tian & Liu, 2024). This suggests that human services are more effective in minimizing social distance compared to robot services.

Social distance directly impacts tourists’ PEB by influencing their emotional and normative responses. Reduced social distance enhances emotional solidarity and personal norms, which are critical drivers of PEB (Confente & Scarpi, 2020; Q. Li & Wu, 2020). For example, tourists who perceive lower social distance with local hosts or service providers are more likely to adopt environmentally responsible behaviors due to heightened empathy and a sense of shared responsibility (M. Li et al., 2023; Nyaupane et al., 2015). Conversely, greater social distance can diminish these affective connections, thereby weakening PEB intentions (Hughes et al., 2021). Therefore, the following hypotheses are proposed:

H2: Tourists perceive less social distance in situations featuring human services than in those featuring robot services.

H3: Social distance mediates the relationship between tourism services and tourists’ PEB.

The Mediating Effect of Personal Norms

Schwartz (1977) proposed Norm Activation Theory, which identifies the three factors of outcome awareness, responsibility attribution, and personal norms as the primary influences on people’s willingness to behave altruistically or pro-socially. Personal norms refer to the social norms, morals, and sense of responsibility that are internalized by individuals and reflect their self-expectations concerning their behavior (Bertoldo & Castro, 2016). Norm Activation Theory posits that individuals engage in specific behaviors in light of the impact of the outcomes of those behaviors for others and the environment and that individuals’ behaviors are influenced by their sense of morality and responsibility; that is, the personal norms that people develop are important drivers of the behaviors in which they engage (Han, 2014; Kim & Li, 2020). Therefore, people’s awareness of the consequences of their actions stimulates individuals’ sense of responsibility with respect to environmental protection and elicits stronger individual norms, thus leading to PEB (Esfandiar et al., 2019).

In addition, the servicescape represents the physical and social environments in which tourists engage in tourism activities, and these environmental factors affect the strength of individual norms and tourists’ PEB (K. Joo et al., 2024). In terms of social relationships, the evaluation of others and social pressure affect the strength of people’s personal norms, thereby influencing their PEB (Niu et al., 2023). Tourists engage in closer social relationships with human staff than they do with service robots, which do not have vital signs. Service modes involving human interaction may foster emotional solidarity and social exchange, thereby strengthening personal norms (S. Li et al., 2021), whereas robot services might lack such interpersonal connections. Strong personal norms subsequently promote PEB (Confente & Scarpi, 2020; Niu et al., 2023). Therefore, the following hypotheses are proposed:

H4: Compared with robot services, human services lead tourists to develop stronger personal norms.

H5: Personal norms mediate the relationship between tourism service mode and tourists’ PEB.

The Moderating Effect of Interaction

Since the advent of robot services, new forms of service interactions have emerged, including both human–human and human–machine interactions (Ma et al., 2025). These can be divided into task-oriented and relationship-oriented interactions (Pasquinelli et al., 2023). Task-oriented interactions focus on completing specific tasks for tourists, addressing functional needs; relationship-oriented interactions aim to build and maintain close relationships, addressing emotional needs (Pasquinelli et al., 2023).

Different interaction types play key moderating roles between service modes and social distance, with their influence pathways shaped by their inherent nature (Ku & Chen, 2015). In tourism, task-oriented and relationship-oriented interactions are often complementary. Service robots, using advanced technology, often show higher efficiency and professionalism, excelling in task-oriented interactions. However, unlike human staff, robots lack genuine emotional understanding and empathy. Research suggests that in relationship-oriented interactions, moderately human-like robots may increase familiarity, reducing psychological distance (Garcia et al., 2024). In such cases, visitors may feel closer social distance with robotic services. That is, the more human-like the robot in behavior or appearance, the more likely it evokes closeness (Ma et al., 2025). However, human staff naturally have greater affinity in appearance, emotional response, and interaction flexibility, especially in adapting service strategies and addressing emotional needs contextually (Filieri et al., 2022; Hughes et al., 2021). Thus, human staff typically excel in relationship-oriented interactions. Based on this, the following hypothesis is proposed:

H6: Interaction influences tourism service models and perceived social distance. When tourists engage in relationship-oriented interactions, they perceive closer social distance when receiving human service compared to robotic service.

The Moderating Effect of Destination Support

Tourism destination support refers to the perceived or obtained support from a tourism destination that can trigger environmentally friendly behavior among visitors. This encompasses instrumental support (material resources, personnel, information, etc.) and emotional support (respect, understanding, recognition, etc.; Tosun et al., 2020). Such support plays a significant motivational role in shaping individual visitor behavior norms (G. Chen & Peng, 2023; J. Liu et al., 2024; Xu et al., 2022). Instrumental support primarily enhances tourists’ cognitive understanding of the destination, helping them overcome psychological barriers arising from unfamiliar environments, thereby promoting eco-friendly behavior (G. Chen & Peng, 2023). Emotional support focuses on eliciting emotional resonance and identification among tourists, influencing behavioral intentions by establishing positive emotional connections (S. Li et al., 2021; Skarin et al., 2019). According to Social Exchange Theory, tourists perceive destination support as a social investment, with subsequent eco-friendly behavior often serving as reciprocation for this support (C.-K. Lee et al., 2021). Within this process, the establishment of personal norms acts as a bridging mechanism, proving particularly crucial when translating emotional support into action.

Specifically, instrumental support primarily functions through cognitive pathways. Compared to instrumental support, emotional support delivers more direct and profound emotional value, resonating more deeply with visitors (Buzova et al., 2022). It helps destinations swiftly gain visitor trust and goodwill, establish positive social norms (S. Lee et al., 2023), and motivate visitors to engage in more altruistic eco-friendly behaviors to maintain positive relationships and seek emotional rewards (Erul et al., 2020). Based on the foregoing analysis, the following hypothesis is proposed:

H7: Tourism destination support moderates the effect of tourism service models on personal norms. When receiving emotional support, tourists develop more positive personal norms when they receive human service than when they receive robot service.

Overall, in this study, a theoretical model of the influence of different modes of tourism services on tourists’ PEB is constructed. The proposed research model with all the hypotheses is presented in Figure 1.

Research Design Overview

This study employed multiple methods, diverse data sources, and text-picture stimulus materials to mitigate the limitations of relying on a single research approach. Given the complexity of the experimental manipulation, the study also incorporated progressive questions, repeated testing, and cross-testing across different measures and samples to enhance variable control, validate hypothesized effects, and strengthen the robustness and reliability of the findings. Additionally, the text-picture research design allowed for targeted hypothesis testing in varied contexts, improved control over potential confounds, and increased the validity of the results.

This research examined the proposed hypotheses through four studies. Specifically, Study 1 verified the main effect of service modes on tourists’ PEB through an experimental approach, specifying the different effects of robot and human services on tourists’ PEB. Study 2 verified the mediating role of personal norms through the questionnaire method. Study 3 further applied experiments to validate the moderating effect of interaction on the relationship between service modes and social distance, and explored the differences between task-oriented and relationship-oriented interactions in moderation. Similarly, Study 4 explored the moderating effect of destination support on the relationship between service mode and PEB, and differentiated between instrumental and emotional support in the model. Ethical procedures were followed in all the studies, including informing participants of the purpose of the study, its expected duration and procedures, their right to refuse or withdraw, and the data and personal privacy security.

To ensure that participants were actual or potential tourists, we implemented a pre-screening question at the beginning of the survey: “Have you had any travel experiences in the past year?” Only those who answered “Yes” were allowed to proceed to the main experimental scenario. This criterion was used to enhance the ecological validity of our findings by focusing on individuals with recent travel experiences.

Study 1

Design and Pretest

Study 1 examined the effect of the tourism service mode (i.e., robot service vs. human service) on tourists’ PEB (H1). We employed a combination of text and pictures to conduct a contextual experiment while distinguishing between these two service modes graphically. First, participants were asked to read the experimental materials. To avoid interference from participants’ preconceived ideas, we assumed that they traveled to tourist attraction A, and that after their arrival at A, they needed to know information regarding the natural landscape and history and culture of A and the rules pertaining to tourism at A; accordingly, they were informed that a specialized service mode would provide them with this information. The specific design of these two experimental groups is illustrated in Figure 2. The two experimental groups differed only in the design used for service mode, and participants were required to complete the questionnaire after they read all the materials. Moreover, to improve the robustness and consistency of the experiments, we use the same experimental images (see Appendix Table A3 in the online supplemental material), but the language descriptions vary according to experimental needs.

PEB was measured via five items drawn from He et al. (2018). The measurement (see Appendix Table A1) was scored on a 7-point Likert scale. In addition, gender, age, education, income and occupation were included as control variables. Various attention and concentration items were also included, with the goal of identifying and excluding invalid responses.

A total of 36 participants were recruited via “wjx.cn” (Wenjuanxing platform) and randomly divided into two groups. Participants were then asked to read the experimental materials illustrated in Figure 2 and answer the following two questions: (Q1) To what extent do you agree that the service is provided by a robot? (Q2) To what extent do you agree that the service is provided by a human? The results of the t test revealed a significant difference between the robot service group and the human service group (Q1: M _robot = 5.61, M _human = 3.05, t = 6.505, p <0.001; Q2: M _robot = 2.56, M _human = 5.11, t = -7.875, p <0.001). Thus, participants who were assigned to the robot service group (vs. the human service group) perceived themselves as receiving robot service (vs. human service), thus indicating that the experimental materials met the requirements for manipulation.

Formal Experiments

In the formal experiment, the same manipulation procedure as that used in the pre-experiment was adopted. From August 11 to 15, 2025, 220 participants were recruited via social media platforms and randomly assigned to one of two groups (robots and humans). Each group received a questionnaire corresponding to their assigned scenario. Participants were then asked to report their perceptions of the service model and their willingness to purchase (PEB), followed by the completion of demographic information. After excluding invalid questionnaires, 200 valid samples were obtained (93 males and 107 females), yielding a sample validity rate of 90.90%. Sample power was analyzed using the F test in G*Power 3.1. With two groups, an overall effect size (F) of 0.25, and a significance level of 0.05, the power value for a sample size of 200 was 0.94 (> 0.80), indicating good statistical power for the questionnaire. With respect to reliability, the PEB scale demonstrated good reliability (α = 0.916).

Results Analysis

This study further analyzed the collected data through SPSS (Version 26). The results of a t test revealed a significant difference between the robot service group and the human service group (Q1: M _robot = 5.38, M _human = 3.41, t = 8.360, p < .001; Q2: M _robot = 3.07, M _human = 5.12, t = -8.769, p < .001), thus indicating that participants could correctly identify the service mode to which they had been assigned and that the independent variable manipulation was successful.

Finally, a one-way ANOVA was conducted to verify the main effects and examine the differential impact of service models on tourists’ PEB. The dependent variable was PEB, the fixed factor was the service model type, and the covariates were the participants’ demographic variables. The results indicated that among the covariates, only occupation significantly influenced PEB. When occupations were controlled for (see online Appendix Table A4), the two types of information exerted significantly different effects on measured PEB: F_(1,199) = 39.552, p < .001, η² = 0.170. Moreover, η² = 0.170 exceeds 0.14, indicating a large effect size. This signifies that service mode accounts for 17% of the variance in tourists’ PEB. Specifically, compared with machine-provided services, human-provided services elicited greater PEB: M _robot = 4.50 versus M _human = 5.06), thus supporting H1.

Study 2

Design and Pretest

Study 2 focused on the effects of the tourism service mode (robot vs. human) on tourists’ PEB as well as the mediating role of social distance and personal norms (H1–H5). The experimental material used was the same as that used in Study 1. The measurements of personal norms were based on three items drawn from Han (2014). The social distance scale was drawn from D. Joo et al. (2018) and included four items; higher scores represented less social distance, whereas lower scores indicated greater social distance.

In the pre-experiment, 50 participants (22 males and 28 females) were equally divided into two groups (robot and human) to test the manipulation of service modes, following the same experimental procedure as that used in Study 1. The results revealed a significant difference between the robot service group and the human service group (Q1: M _robot = 6.28, M _human = 3.44, t = 7.627, p < .001; Q2: M _robot = 3.52, M _human = 5.92, t = -5.365, p < .001), thus suggesting that participants could correctly identify the service mode to which they had been assigned. Accordingly, the manipulation was successful.

Formal Experiments

The formal experimental procedure for Study 2 was identical to that of Study 1. Between August 20–30, 2025, the research team recruited 300 participants via social media platforms, yielding an effective sample size of 251 (N _robot = 128, N _human = 123). The sample comprised 127 males and 124 females. The F test analysis in G*Power (Version 3.1) indicated a power value of 0.96 (> 0.80) for the sample size, confirming the strong statistical power of the questionnaire. In terms of reliability, the social distance scale (α = 0.821), the personal norms scale (α = 0.835), and the PEB scale (α = 0.797) demonstrated excellent reliability.

The results revealed a significant difference between the robot service group and the human service group (Q1: M _robot = 4.81, M _human = 2.97, t = 8.314, p < .001; Q2: M _robot = 2.66, M _human = 4.55, t = -8.697, p < .001), thus suggesting that participants could correctly identify the service mode to which they had been assigned. Accordingly, the manipulation was successful.

Results Analysis

With respect to the main effect tests, the results of the one-way ANOVA revealed that among the covariates, only occupation significantly influenced PEB. Controlling for education level, occupation, and monthly income, significant differences emerged in PEB among groups receiving different service modes: F_(1,250) = 17.564, p < .001, η² = 0.596). Moreover, η² = 0.596 exceeds 0.14, indicating a large effect size. This signifies that service mode accounts for 59.6% of the variance in tourists’ PEB. Furthermore, compared with robot-provided services, human-provided services exerted a stronger positive effect on visitors’ PEB (M _robot = 3.31 vs. M _human = 4.34), thereby validating H1.

Next, a two-factor ANOVA was conducted to test H2 and H4. The results indicated that among the covariates, only occupation significantly influenced social distance and personal norms. When these variables were controlled for (see online Appendix Table A5), different service models exerted distinct effects on social distance, F_(1,250) = 145.596; p < .001; η² = 0.374, and personal norms, F_(1,250) = 65.169; p < .001; η2 = 0.211). Moreover, η² values of 0.374 and 0.211 respectively exceeded 0.14, both qualifying as large effect sizes. This indicates that service mode accounts for 37.4% of variance in social distance and 21.1% of variance in personal norms. Compared with robotic service, human service exerted stronger positive effects on social distance (M _robot = 3.36 vs. M _human = 4.50) and personal norms (M _robot = 3.78 vs. M _human = 4.56), thus validating H2 and H4, respectively.

Finally, Model 4 of Process 3.3 was used to verify the mediating effects of social distance and personal norms. First, the independent variable (service model), dependent variable (PEB), mediating variables (social distance and personal norms), and covariates (demographic information) were included in the model. The results (see the Table) revealed that the mediating path “service model → social distance/personal norms → PEB” was significant (the 95% confidence interval did not include 0), validating H3 and H5. Specifically, the total effect and direct effect were positive, indicating that the independent variable (service model) has an overall positive effect on the dependent variable (PEB). This positive influence persists even after controlling for the mediating variables (social distance and personal norms). Both the indirect effects of social distance and personal norms were positive, accounting for 11.96% and 26.67% of the total effect, respectively.

Table.

Mediating Effect Test.

Dependent Variable	Mediating Variable		Effect Value	Boot SE	LLCI	ULCI	Relative Effect Value
Service Model		Total effect	1.020	0.076	0.869	1.170	-
		Direct effect	0.626	0.072	0.485	0.767	61.37%
	Social distance	Intermediary effect	0.122	0.055	0.016	0.230	11.96%
	Personal norms	Intermediary effect	0.272	0.053	0.173	0.380	26.67%

Study 3

Design and Pretest

Study 3 featured a two-factor between-group experimental design—service mode (robot vs. human) × interaction (task-oriented vs. relationship-oriented). Participants were randomly divided into four groups: the robot-task-oriented interaction group, the robot-relationship-oriented interaction group, the human-task-oriented group, and the human-relationship-oriented group. As with Study 1, the experimental design is presented in Appendix Table A3 (Experimental design of the task-oriented and relationship-oriented), and participants were required to complete the questionnaire after they read all the experimental materials. To measure interaction, the scale developed by Williams and Spiro (1985) was used after appropriate adjustments were made to ensure that the scale was suitable for the current experimental situation; the other variables were consistent with those used in Study 2.

For the pretest, 100 participants were recruited and randomly divided into four groups. The results of the independent sample t test revealed significant differences between the robot service group and the human service group (Q1: M _robot = 4.78 M _human = 3.08, t = 5.474, p < .001; Q2: M _robot = 3.30, M _human = 4.90, t = -4.883, p < .001) as well as between the task-oriented interaction group and the relationship-oriented interaction group (the task-oriented: M _{task-oriented} = 4.70 vs. M _{relationship-oriented} = 3.26, p < .001; the relationship-oriented: M _{task-oriented} = 3.73 vs. M _{relationship-oriented} = 4.67, p < .001). Accordingly, the manipulation was successful.

Formal Experiments

Study 3 used the same manipulation process as that employed in the pretest. A total of 300 participants were recruited and randomly assigned to one of the four experimental groups. An effective sample size of 234 (111 males and 123 females) was obtained after the exclusion of invalid questionnaires. As in Studies 1 and 2, we conducted a power analysis of the sample size, which revealed that the power value was 0.92 (> 0.80) for a sample size of 234. In terms of reliability, the scales for task-oriented interaction (α = 0.928), relationship-oriented interaction (α = 0.958), social distance (α = 0.725) and PEB (α = 0.941) all demonstrated good reliability.

The results of the independent sample t test revealed significant differences between the robot service group and the human service group (Q1: M _robot = 5.13 M _human = 2.68, t = 14.800, p < .001; Q2: M _robot = 3.10, M _human = 5.17, t = -12.720, p < .001) as well as between the task-oriented interaction group and the relationship-oriented interaction group (task-oriented: M _{task-oriented} = 4.79 vs. M _{relationship-oriented} = 3.34, p < .001; relationship-oriented: M _{task-oriented} = 3.80 vs. M _{relationship-oriented} = 4.70, p < .001). Accordingly, the manipulation was successful.

Results Analysis

Using Model 7 of Process 3.3 to examine the moderating effect of interaction, we sequentially incorporated the dependent variable (PEB), independent variable (service model), mediating variable (social distance), moderating variable (interaction), and covariate (demographic information) into the model. The results indicate that the interaction term between the service mode and interaction significantly influences social distance (β = 0.628, Boot SE = 0.161, p < .001, 95% CI [0.311, 0.946], excluding 0). Furthermore, in task-oriented interactions, social distance did not significantly mediate the relationship between service mode and PEB (β = -0.037, Boot SE = 0.055, 95% CI [-0.048, 0.175], including 0). However, in relationship-oriented interactions, social distance significantly mediated the relationship between service mode and PEB (β = 0.551, Boot SE = 0.215, 95% CI [0.219, 1.036], excluding 0). Thus, Hypothesis H3 was validated.

Study 4

Design and Pretest

Study 4 employed a 2 (tourism service: robot vs. human) × 2 (destination support: instrumental support vs. emotional support) between-group experimental design to test the moderating role of destination support. The experimental design is presented in Appendix Table A3 (Experimental design of the instrumental support and emotional support).

For the pretest, 100 participants were recruited. The scales used to measure destination instrumental support and emotional support were drawn from Tosun et al. (2020), while the other variables were the same as those used in Study 2. The results of an ANOVA revealed differences between the robot service group and the human service group as well as between the instrumental support group and the emotional support group; therefore, the manipulations of human service and destination support were successful. Accordingly, a formal experimental study could be conducted.

Formal Experiments and Results

The formal experiment conducted for Study 4 employed the same manipulation process as that used in Study 3. A total of 320 participants were recruited, all of whom were randomly assigned to one of the four experimental groups. The valid sample size was 294 (144 males and 150 females). We conducted a power analysis of the sample size, which revealed that the power value was 0.95 (> 0.80) for a sample size of 294. In terms of reliability, the scales for instrumental support (α = 0.864), emotional support (α = 0.879), personal norms (α = 0.703) and PEB (α = 0.933) all demonstrated good reliability.

The results of the independent sample t test revealed significant differences between the robot service group and the human service group (Q1: M _robot = 5.57 M _human = 2.91, t = 17.449, p < .001; Q2: M _robot = 3.06, M _human = 5.71, t = -17.487, p < .001) as well as between the instrumental support group and the emotional support group (instrumental support: M _instrumental = 4.77 vs. M _emotional = 3.56, p < .001; emotional support: M _instrumental = 3.87 vs. M _emotional = 4.45, p < .001). Accordingly, the manipulation was successful.

Results Analysis

Using Model 7 of Process 3.3 to examine the moderating effect of interaction, we sequentially incorporated the dependent variable (PEB), independent variable (service model), mediating variable (personal norms), moderating variable (destination support), and covariate (demographic information) into the model. The results indicate that the interaction term between service mode and destination support significantly influences personal norms (β = 0.529, Boot SE = 0.163, p < .001, 95% CI [0.208, 0.850], excluding 0). Furthermore, with respect to instrumental support, personal norms did not significantly mediate the relationship between service mode and PEB (β = -0.016, Boot SE = 0.034, 95% CI [-0.038, 0.103], including 0). However, with respect to emotional support, personal norms significantly mediated the relationship between service mode and PEB (β = 0.290, Boot SE = 0.122, 95% CI [0.097, 0.569], excluding 0). Thus, Hypothesis H5 was validated.

Discussion and Implications

Discussion

This research comprehensively explores the effects of different tourism service modes (robot vs. human service) on tourists’ PEB. By conducting four studies, this research revealed the following five major findings.

First, the effects of robot services and human services on tourists’ PEB differ. Although the application of robot services improves the efficiency and quality of services, it also causes tourists to feel emotionally deficient in comparison with human services (Fang et al., 2022; Guo et al., 2024); accordingly, this approach psychologically increases social distance, and this discrepancy further affects tourists’ behaviors (X. Liu et al., 2022). These findings support the conclusions reported by Tussyadiah (2020); that is, tourism service modes involving robots differ from service modes involving humans, thus leading to differences in tourists’ cognition and emotions, which in turn lead to different behaviors.

Second, the results of Study 1 indicate that compared with robot services, human services are more likely to cause people to feel a stronger sense of closeness. According to Social Exchange Theory, when tourists perceive less social distance and good social interaction, as important elements of emotional support, they can motivate their PEB (M. Li et al., 2023). Human employees and tourists are more similar to tourists than are robots; thus, human services cause tourists to perceive less social distance (Tung & Law, 2017). As an important indicator of people’s perceptions of social interactions as well as an important criterion for measuring the degree of closeness of social relationships, social distance affects tourists’ altruistic behaviors (Hughes et al., 2021), and their perceived proximity encourages them to engage in PEB to different degrees; therefore, the mediating effect of social distance is verified.

Third, interaction moderates the relationship between tourism service mode and social distance. In situations involving human services, both task-oriented and relationship-oriented interactions entail high levels of social distance between services and tourists, and no significant differences are observed. We argue that human employees and tourists exhibit certain common characteristics in terms of appearance and emotions, a situation that makes it easier for them to establish closer social relationships because the sense of strangeness and distance between these two parties is relatively weak (X. Liu et al., 2022). When a service robot engages in relationship-oriented interactions with tourists, it compensates for the shortcomings of robot services, thus decreasing the perceived social distance between the robot and tourists (Manthiou et al., 2020; Tung & Law, 2017). These findings support the conclusions of Oono et al. (2024), who reported that adopting relationship-oriented interactions in the robot service process can enhance a robot’s ability to provide emotional value to tourists and reduce their sense of unfamiliarity and social distance.

Fourth, the results of this study confirm that personal norms play a partial mediating role in the relationship between tourism service mode and tourists’ PEB. In addition, the strength of tourists’ personal norms is significantly greater for human services than for robot services. This finding may be attributed to the fact that human employees, who also exhibit life and social characteristics, have more in common with tourists than do robots, and the former are more likely to exhibit social needs and establish emotional connections (Filieri et al., 2022). Therefore, owing to the need for social interaction and the maintenance of relationships, a stronger sense of environmental responsibility emerges (Niu et al., 2023).

Fifth, the study reveals that the relationship between the service mode and personal norms is moderated by destination support. Specifically, destination support motivates visitors to establish personal norms, and emotional support (relative to instrumental support) has a stronger influence on the differentiated impact of service mode on visitors’ personal norms (G. Chen & Peng, 2023; Zhang et al., 2022). Emotional support influences tourists’ affective and psychological experiences more directly than does instrumental support (Tosun et al., 2020), consequently facilitating the rapid establishment of favorable personal norms and generating positive emotional experiences.

Theoretical Contributions

This study makes the following theoretical contributions by systematically examining the differential effects of robotic and human services on visitors’ perceived environmental benefits (PEB). First, it deepens the application of Social Exchange Theory within tourism service contexts. Findings reveal that human services significantly reduce perceived social distance by enhancing emotional connection and empathy, thereby stimulating stronger environmental responsibility and eco-friendly behavior (Fang et al., 2022; Guo et al., 2024; X. Liu et al., 2022). This corroborates Tussyadiah’s (2020) conclusion that tourism service models involving robots differ from those involving humans, leading to distinct perceptions and emotions among tourists and consequently triggering divergent behaviors. This finding deepens the application of Social Exchange Theory in tourism contexts, elucidating the central role of emotional interaction in shaping environmental behavior.

Second, this study extends the application of Norm Activation Theory to tourism environmental behavior research. Findings indicate that the type of service provider influences the strength of tourists’ internalized norms: human service providers more readily evoke tourists’ ethical awareness and moral identification, thereby promoting the internalization of environmental responsibility (Niu et al., 2023). This finding further clarifies the mediating role of personal norms in tourism services’ influence on visitor behavior, highlighting the significance of emotional arousal in the norm internalization process. Results confirm that human staff, possessing life characteristics and social attributes, generate stronger empathy than robots, thereby more readily demonstrating social demands and establishing emotional connections (Filieri et al., 2022).

Last, by incorporating tourism destination support as a moderating variable into the analytical framework, this study constructs a multi-level, contextualized theoretical model that effectively bridges the theoretical gap between micro-level service interactions and macro-level contextual factors. Findings indicate that emotional support, rather than instrumental support, more effectively amplifies the positive influence of human service on personal norms. This provides a nuanced theoretical explanation for understanding the contextual boundaries within which different tourism service models exert their effects. Specifically, destination support prompts tourists to establish personal norms, with emotional support (relative to instrumental support) exerting a more pronounced influence on how service mode differences affect tourists’ personal norms (G. Chen & Peng, 2023; Zhang et al., 2022). Emotional support more directly impacts tourists’ emotional and psychological experiences than instrumental support (Tosun et al., 2020), thereby accelerating the formation of positive personal norms and fostering favorable emotional experiences.

Practical Implications

Our findings offer rich implications with respect to environmental protection and sustainable development in tourism destinations.

First, it is recommended that robot service design incorporate refined emotional interaction strategies. Findings indicate that in relationship-oriented interactions, the sense of social distance generated by robotic services diminishes. Consequently, destination managers should not merely aim to make robots appear “friendly,” but should prioritize investments in enhancing their relationship-building capabilities. Specifically, service robots should incorporate relationship-oriented interaction scripts. For instance, dialogue programs should proactively inquire about visitor experiences (e.g., “How do you find the natural scenery here?”) and express empathy (e.g., “I hear you care about the environment—that’s truly commendable”), rather than merely delivering task-based information. Furthermore, leveraging big data analytics to understand visitor preferences enables robots to proactively offer personalized emotional interactions (e.g., greeting repeat visitors with “It’s lovely to see you again”). This maximizes the simulation of authentic social connections within the limitations of human-like emotional depth, thereby narrowing social distance.

Second, for human-based services, given their distinct advantages in reducing social distance and shaping personal norms, destinations should provide specialized training and management processes, aiming to strengthening emotional connections and fostering a sense of responsibility. Given human staff’s irreplaceable role in stimulating PEB, destinations must ensure services authentically convey sincerity and professionalism. Managers should develop standardized processes integrating environmental awareness into service delivery. For instance, training staff to consciously highlight the destination’s conservation efforts and visitor responsibilities during interactions (e.g., “Together we strive to protect this natural beauty”), thereby directly activating visitors’ personal norms. Concurrently, incentive mechanisms should encourage staff to forge deep emotional bonds with visitors through personalized, authentic interactions, transforming service encounters into potent platforms for environmental education and social norm dissemination.

Last, leveraging the moderating effect of destination support, construct a multi-tiered incentive system for visitor eco-behavior centered on emotional support with supplementary instrumental support. Findings indicate that emotional support significantly amplifies the positive impact of human services on personal norms. Destination management should therefore systematically cultivate an emotional atmosphere that acknowledges, respects, and encourages visitors’ eco-friendly actions. This includes: establishing instant positive feedback mechanisms, such as sending thank-you messages via apps or awarding virtual badges after visitors complete waste sorting; and publicly recognizing eco-conscious visitors by showcasing their contributions on scenic area noticeboards or social media. Concurrently, as instrumental support (e.g., clear recycling facility signage) remains indispensable as foundational infrastructure, using emotive language on signage such as “Thank you for taking that extra step to protect our environment” achieves synergy between practical tools and emotional engagement.

Limitations and Future Research

This study has several limitations that suggest directions for future research. First, this research focused only on service modes, leaving other aspects of the tourism servicescape—such as spatial layout, ambient conditions, and the presence of other tourists—unexplored. Although the experimental scenarios were carefully designed, they may not fully reflect real-world complexity. Future studies could use field experiments or observational methods, like tracking waste disposal behavior after service interactions, to improve ecological validity. Second, while our study examined task-oriented and relationship-oriented interaction modes, it did not consider how tourist motivations might influence mode preference. Future work could investigate whether tourists driven by hedonic motives (e.g., pleasure) prefer different interaction styles to those with utilitarian motives (e.g., efficiency). Adding motivational variables would provide a fuller understanding of the psychological drivers of PEB. Third, this study used a stepwise experimental approach to test main, mediating, and moderating effects separately. Although this clarified individual hypotheses, it prevented holistic testing of the full model within the same sample. Future research could apply structural equation modeling to analyze the complete model with more representative samples, revealing net effects and assessing overall model fit.

In conclusion, despite its limitations, this study contributes significantly by using an integrated Social Exchange – Norm Activation Theory framework to compare robotic and human services in promoting PEB. It finds that human services enhance PEB more effectively by reducing social distance and strengthening personal norms, with relationship-oriented interactions and emotional support as key factors. These insights advance theory on PEB in tourism and offer practical guidance for designing sustainable services. By outlining its constraints and future research paths, this work lays a foundation for further exploration of service technology, tourist psychology, and environmental sustainability.

Supplemental Material

sj-docx-1-jht-10.1177_10963480261463297 – Supplemental material for High-Tech or High-Touch? Effects of Tourism Service Mode on Tourists’ Pro-Environmental Behavior

Supplemental material, sj-docx-1-jht-10.1177_10963480261463297 for High-Tech or High-Touch? Effects of Tourism Service Mode on Tourists’ Pro-Environmental Behavior by Huimin Song, Ning Zhong, Daisy X. F. Fan and Mengjie Wu in Journal of Hospitality & Tourism Research

Footnotes

Acknowledgements

We thank the students of the Department of Exhibition Economics and Management, Huaqiao University for providing data research support.

ORCID iDs

Huimin Song

Ning Zhong

Daisy X. F. Fan

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by Research supported by Huaqiao University’s Academic Project Supported by the Fundamental Research Funds for the Central Universities (Grant No. 25SKGC-QT05).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Huimin Song, PhD, is an associate professor in the College of Tourism at Huaqiao University, China. Dr. Song focuses on tourism research in particular on the analysis of mediating and moderating effects. She also has several years of teaching experience in the tourism education as well as managerial experience in the tourism industry.

Ning Zhong is a graduate student in the College of Tourism at Huaqiao University. Her research interests include heritage tourism, and ecotourism.

Daisy X. F. Fan is an associate professor at School of Hotel and Tourism Management, The Hong Kong Polytechnic University, interested in social contact, active ageing and social sustainability.

Mengjie Wu is a graduate student in the College of Tourism at Huaqiao University. Her research interests include heritage tourism, and ecotourism.

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