Nudged by a robot: Responses to agency and feedback
Introduction
With the growing force of travel and tourism worldwide comes significant challenges to reduce the environmental burdens of tourism activities and make tourism a catalyst for positive change toward sustainability (UNWTO, 2018). One of key priorities in tourism development is ensuring significant environmental benefits of the sector, focusing on ways to reduce water consumption, energy use, and carbon emission, as well as addressing the need to preserve biodiversity and heritage (UNWTO, 2011, UNWTO, 2012). To address these, a concerted effort from all stakeholders in the travel and tourism ecosystem, where each endeavors to take part through enactment of environmentally beneficial policy, investment, and other initiatives, is needed. In hotels, there have been tremendous efforts toward developing technological solutions for green infrastructure and innovative operations, as well as other green practices to improve resource efficiency (Kim, Hlee, & Joun, 2016). One of these is promoting positive behavior change to hotel guests through interventions and nudges targeting such behaviors as energy conservation and towel reuse (e.g., Chang, Huh, & Lee, 2015; Reese, Loew, & Steffgen, 2014). Previous studies have suggested that the accommodation sectors account for the most significant consumption of resources and, as such, generate a substantial share of the environmental impacts of tourism (Bohdanowicz & Martinac, 2007; Gössling, 2002). As the main actors driving resource consumption in tourism destinations, tourists thus have a fundamental role in the effort to promote sustainability (Nisa, Varum, & Botelho, 2017).
According to Schultz (2014), most environmental problems have their origins in human behavior. Behavior change is thus required for any solutions to these problems. With technological advances in ambient intelligence, sensors, and internet-of-things, an emerging approach is to use intelligent solutions for behavior change interventions (He, Greenberg, & Huang, 2009). Furthermore, the integration of artificial intelligence (AI) and robotics into voice-activated virtual assistant or in-room companion robot is one of the latest developments in intelligent hotel rooms that presents a new opportunity for interventions. Not only that these agents can be programmed to provide feedback, which has been proven effective in influencing positive behavior change (Karlin, Zinger, & Ford, 2015), but also that the presence of a social agent in the room, also known as automated social presence (van Doorn et al., 2017), can induce feeling of being watched (cues of surveillance), which could trigger more socially desirable behaviors (Bateson, Callow, Holmes, Redmond Roche, & Nettle, 2013). Since this intervention approach has not been implemented to date, it is of theoretical and practical importance to assess its potential effectiveness.
Previous studies have identified intervention approaches that are consistently effective at inducing behavior change: social norms and nudges (Nisa et al., 2017; Ölander & Thøgersen, 2014), feedback (Karlin et al., 2015), and cues of agency or surveillance from intentional agents (i.e., intelligent machines designed for social interactions) (Krátský, McGraw, Xygalatas, Mitkidis, & Reddish, 2016). Taking these into consideration, this study aims to investigate the effect of interventions involving intentional agents in hotel rooms that provide feedback on pro-environmental behavior. Specifically, this study examines the effectiveness of using different intentional agents (virtual assistant vs. robot) and of providing social feedback (presence vs. absence of social feedback) at inducing pro-environmental behavioral intention in hotel guests. This research is designed to compare people's reaction to feedback from a robot (an embodied agent) and those from a virtual assistant (a disembodied/pervasive agent). This carries a theoretical significance in terms of how effective the two types of intentional agents are in influencing human behavior. The findings indicate no direct significant effects of types of intelligent agents and presence or absence of social feedback on guests' pro-environmental behavior intention. However, positive interaction effect between social feedback and virtual assistant was found. The results inform hotels how to design intelligent agents to influence guests' behavior.
Section snippets
Pro-environmental behavior interventions
The effectiveness of interventions in enticing intention and actual behavior associated with resource conservation amongst hotel guests has been evidenced in various studies (Nisa et al., 2017; Scheibehenne, Jamil, & Wagenmakers, 2016). Chang et al. (2015) found that behavioral intentions toward electricity conservation were more pronounced when a nudge was provided in hotel rooms. Baca-Motes, Brown, Gneezy, Keenan, and Nelson (2013) revealed that a small, carefully planned intervention, such
The watching-eyes effect
The watching-eyes effect refers to the phenomenon that people tend to behave more pro-socially when artificial surveillance cues, such as an image of watching eyes, are present in their environment (Haley & Fessler, 2005; Pfattheicher & Keller, 2015). Theoretically speaking, the watching-eyes effect can be attributed to reputation-based partner choice models of the evolution in social cooperation (Roberts, 1998). These models suggest that people act pro-socially as an investment in their social
Machines, agency, and feedback
Akin to cues of surveillance, people respond socially to cues of agency. It has been suggested that people tend to anthropomorphize things, attributing human characteristics on non-human objects (Epley, Waytz, Akalis, & Cacioppo, 2008). Further, researchers have also introduced the concept of automated social presence (ASP), which refers to the extent to which people perceive the presence of another social entity (van Doorn et al., 2017), and how it plays a role in human experience. Indeed,
Factors contributing to pro-environmental behavior intention
While this research focuses mainly on testing the effects of cues of intentional agents/surveillance and feedback on behavior change, it is important to recognize the effects relative to those of other factors.
Thus, this study also considers attitudes toward interventions and personal factors associated with pro-environmental behavior to control for the intervention effects. It is noteworthy that the goal was not to be inclusive of all possible factors affecting pro-environmental behavior and
Stimuli
In order to achieve the objectives, this research employed a 2 × 2 factorial between-subjects experimental design (virtual assistant vs. robot and absence vs. presence of social feedback), providing four experimental conditions in the form of systematically varied scenarios, also known as vignettes (see Table S1). Along with the scenario, an image of a companion robot called Churi-Chan and that of a tablet, both placed on a nightstand, was presented to respondents in virtual assistant and robot
Results
The results from CFA indicated that the goodness of fit of the measurement model as well as the validity and reliability of the latent constructs were supported. All factor loadings are above 0.6 (Table 1). Furthermore, average variance extracted (AVE) values of all latent constructs are well above the cut-off value of 0.5 (Hair et al., 2010), thus convergent validity was supported. Construct reliability (CR) and Cronbach's Alpha values of all latent constructs are above 0.7 (Hair et al., 2010
Discussion
This study did not find a direct significant effect of different types of intelligent agents on pro-environmental behavior intention amongst hotel guests. No one type of agent is more effective in inducing behavior change than the other. Considering that the aggregate mean values of pro-environmental behavior intention in both conditions are higher than the neutral value (virtual assistant: Mean = 3.78, St. Dev. = 1.10; robot: Mean = 3.76, St. Dev. = 1.07), it can be suggested that both types
Conclusion and implications
The main objective of this research was to assess the effect of interventions involving intelligent agents in hotel rooms on pro-environmental behavior intention. A scenario-based experimental approach (vignette experiments) was employed by manipulating the types of agent (virtual assistant vs. robot) and social feedback (presence vs. absence of social feedback) to isolate these effects on behavior intention. This approach was considered important to confirm the novelty and relevance of
Acknowledgements
This work was supported by the University of Surrey's Faculty of Arts and Social Sciences Pump Priming Fund 2018.
Iis Tussyadiah is Reader in Hospitality and Digital Experience and Head of Department of Hospitality in the School of Hospitality and Tourism Management at University of Surrey. Her research interest lies in the intersection of digital technologies and tourism experiences.
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Iis Tussyadiah is Reader in Hospitality and Digital Experience and Head of Department of Hospitality in the School of Hospitality and Tourism Management at University of Surrey. Her research interest lies in the intersection of digital technologies and tourism experiences.
Graham Miller is Professor of Sustainability in Business and Executive Dean of the Faculty of Arts and Social Sciences at University of Surrey, where he conducts research into business ethics, sustainable tourism and corporate social responsibility. He acts in an editorial capacity for several prestigious tourism journals and contributes his expertise to government bodies, trade associations and the UN.