Media effects on the perceptions of robots

Human Behavior and Emerging TechnologiesVolume 3, Issue 5 p. 989-1003 EMPIRICAL ARTICLEOpen Access Media effects on the perceptions of robots Nina Savela, Corresponding Author Nina Savela nina.savela@tuni.fi orcid.org/0000-0002-7042-6889 Faculty of Social Sciences, Tampere University, Tampere, Finland Correspondence Nina Savela, Faculty of Social Sciences, Tampere University, Kalevantie 5, 33100 Tampere, Finland. Email: nina.savela@tuni.fiSearch for more papers by this authorTuuli Turja, Tuuli Turja orcid.org/0000-0001-7815-9511 Faculty of Social Sciences, Tampere University, Tampere, FinlandSearch for more papers by this authorRita Latikka, Rita Latikka orcid.org/0000-0003-3798-0017 Faculty of Social Sciences, Tampere University, Tampere, FinlandSearch for more papers by this authorAtte Oksanen, Atte Oksanen orcid.org/0000-0003-4143-5580 Faculty of Social Sciences, Tampere University, Tampere, FinlandSearch for more papers by this author Nina Savela, Corresponding Author Nina Savela nina.savela@tuni.fi orcid.org/0000-0002-7042-6889 Faculty of Social Sciences, Tampere University, Tampere, Finland Correspondence Nina Savela, Faculty of Social Sciences, Tampere University, Kalevantie 5, 33100 Tampere, Finland. Email: nina.savela@tuni.fiSearch for more papers by this authorTuuli Turja, Tuuli Turja orcid.org/0000-0001-7815-9511 Faculty of Social Sciences, Tampere University, Tampere, FinlandSearch for more papers by this authorRita Latikka, Rita Latikka orcid.org/0000-0003-3798-0017 Faculty of Social Sciences, Tampere University, Tampere, FinlandSearch for more papers by this authorAtte Oksanen, Atte Oksanen orcid.org/0000-0003-4143-5580 Faculty of Social Sciences, Tampere University, Tampere, FinlandSearch for more papers by this author First published: 25 October 2021 https://doi.org/10.1002/hbe2.296 Funding information: Academy of Finland, Grant/Award Numbers: 292980, 314180; Koneen Säätiö, Grant/Award Number: 202011325; Finnish Cultural Foundation (Robots and US Project, PIs Jari Hietanen, Atte Oksanen, and Veikko Sariola), Grant/Award Number: 2018-2020 AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract This study utilizes the media persuasion perspective to investigate the connection of media exposure of robots on the general attitude and trust toward robots of people from different cultural backgrounds. Study 1 utilized 2016 survey data from Finnish care workers (N = 3800). Study 2 used two 2019 datasets from the adult population of the United States (N = 1003; N = 969), and Study 3 involved a trust game experiment on robots (n = 331). Results showed that the overall media exposure of robots is linked to a positive general attitude toward robots, but only fact-based media channel exposure had a positive impact on trust in robots. Results suggest that the experience of robots gained through media exposure is a relevant factor affecting people's perceptions of robots. The findings contribute to the discussions on media effects and have practical implications for gaining evidence about the precursors of accepting robots. 1 INTRODUCTION Over the years, science fiction entertainment has produced a vast number of images and narratives about robots influencing the representations of robots available to people. Even the concept of a robot originates from an entertainment production: a Czech play R.U.R. by Karel Čapek from 1920 (Stone, 2004). Several stories and visual imagery using the concept of robots have since increased in the entertainment products of various genres, some of which have become familiar cultural products for people from different cultural backgrounds. Similar to the first fictional representation of robots, dystopian elements depicting robots as a threat to humankind have been a part of classic entertainment media products such as Fritz Lang's Metropolis (1927), Ridley Scott's Bladerunner (1982, based on Philip K. Dick's Do Androids Dream of Electric Sheep [1968]), and Terminator (1984) and its sequels (1991–2019). Hollywood also has history in presenting more utopistic visions of robots that not only aid humans but also provide companionship similar to other humans, such as in the case of droids in the Star Wars franchise, which has had a massive cultural significance since the first Star Wars movie in 1977. Therefore, it is not surprising that C-3PO and R2-D2 from the Star Wars universe are one of the first representations of robots that people recall (Broadbent et al., 2010; Khan, 1998; Kriz et al., 2010). The newer robot representations in entertainment media include both animated robots, such as the science fiction animation WALL-E (2008), and more realistic robot designs positioned in the emerging fields of robotics, such as the science fiction indie drama Robot and Frank (2012). In addition to popular media, robots have also been part of the contemporary discourses through somewhat more reality-based channels, such as documentaries, exhibitions, and news, which also provide information about existing robot products. Media is a significant part of people's daily lives influencing the conversation topics and thinking processes going through our minds (King et al., 2017). Previous research has shown that people are affected by the news (Berkowitz, 1991; Johnston & Davey, 1997). Thus, journalistic content, such as news and documentaries on robots, is also likely to influence people's perceptions about robots. Receiving robot information through various media channels might evoke certain representations of robots in individuals and even cultivate the previously held conceptions toward new adjusted ones. Perceptions are also prone to be affected when consuming fictive stories and imagery (Green et al., 2008; Sargent et al., 2002; van Laer et al., 2014). Researchers have suspected that science fiction narratives and images have a significant influence on the field of robotics and people's perceptions of robots (Bartneck et al., 2007; Bruckenberger et al., 2013; Ray et al., 2008). However, currently, there is a lack of stronger empirical evidence about the impact of both fiction- and fact-based robot information on people's perceptions of robots. This article aims to fulfill the research gap by examining the topic of robot perceptions with the focus on the persuasive nature of robot information mediated through fiction- and fact-based sources. Our research question was: Are positive attitude and trust toward robots stronger among people with more extensive media exposure to robots? With this approach, our goals were to gain evidence about and contribute to the on-going discussion on attitudes and trust toward technology, as well as the conditions of technology usage in general (Hancock et al., 2020; Oksanen et al., 2020; Shin, 2021b, 2021c, 2021d, 2021f). In this article, our theoretical framework combines the media persuasion perspective, utilizing the following theories: cultivation theory (Gerbner et al., 2002), familiarity principle (Reis et al., 2011), mere-exposure effect (Zajonc, 1968), multiple source effect (Harkins & Petty, 1981), elaboration likelihood model (ELM; Petty & Cacioppo, 1986) and heuristic-systematic model of information processing (HSM; Chaiken, 1980), narrative transportation theory (Gerrig, 1993), cognitive dissonance theory (Festinger, 1957), and the idea of cognition-based trust in trust theory (McAllister, 1995). Although ELM and HSM have been deployed to comprehend how users perceive artificial intelligence (AI) in the context of algorithmic journalism (Shin, 2021b, 2021e), the theoretical framework of media persuasion has not previously been utilized in the analysis of trust and general attitude toward robots. 2 MEDIA PERSUASION EFFECTS ON ATTITUDES AND TRUST Attitudes refer to subjective assessments on a certain person or object and are often categorized as positive, negative, or neutral but also overlapping, ambivalent, and varying in strength (Bohner & Dickel, 2011). When asked about their attitudinal stance, it is suggested that people first activate an unconscious appraisal from memory intuitively, deliberately search information and previous assessments, and, finally, summarize the information from previous phases into a response (Krosnick et al., 2005). Attitude assessment therefore relies on the mental and socially shared representations of the object based on previously received information and experiences that are recalled from a memory when requested (Moscovici, 1990; Smith, 1998). Specific concepts and discourse might influence what memories and imagery are triggered, and in which the evaluations will ultimately be based on (de Groot, 1989; Wagner et al., 1996, 1999). Trust toward a person or object can be seen as a concept close to attitude or even as a hyponym to it, as trust has also been defined as an affective attitude (Jones, 1996). However, trust theories have conceptualized both an affective and cognitive basis for trust (McAllister, 1995). This multidimensional view of trust is similar to some theoretical views on attitudes and persuasion (Petty & Cacioppo, 1986), and it has been utilized in research regarding trust in information technology (Li et al., 2008). Both attitude and trust concepts have been included when measuring the acceptance of robots and other technology (Shin, 2021d; Venkatesh et al., 2003). Trust in AI has been found to be associated with both emotional factors, such as uneasiness and comfortableness (Nomura et al., 2006; Shin, 2021d), and cognitive factors, such as explainability and accountability (Shin, 2020a, 2020b, 2021c, 2021f). This highlights the similar components in both attitude and trust constructs. Differences in attitude and trust can also be found depending on the specific type of attitude and trust being measured. Attitude measurement can be a useful tool to capture anticipated attitudes in general toward a technology before the adoption (Perugini & Bagozzi, 2001). A complementing trust measurement could then be targeted at measuring trust in specific user situations or actual trusting behavior. Because self-reported survey questions about general trust in the target object are not necessarily the best predictors of trusting behavior (Glaeser et al., 2000), one reliable option would be to measure trust in a precise trust factor, such as trust in the technology's safety, or indirectly through a behavioral experiment. A trust game is an experimental method that originates from investment games in the field of economics (Berg et al., 1995; Camerer, 2003) and has been used to measure trust toward various opponents in different contexts (Johnson & Mislin, 2011; Samson & Kostyszyn, 2015; Xin et al., 2016). Assessing trust regarding specific user situations and through trusting behavior in a monetary game could steer one's focus more on fact-based information and responding with cognitive-based trust (McAllister, 1995; Petty & Cacioppo, 1986). Thus, when looking at the attitude and trust formation and experiences that are recalled when evaluating one's perceptions, research performed under the theoretical framework of persuasion is critical for examining how people's beliefs, motivations, and behavior are influenced (Wood, 2000). The persuasiveness of the information concerns the information content (Wood et al., 1985) and source credibility (Heesacker et al., 1983). The two-route persuasion mechanism is manifested in ELM (Petty & Cacioppo, 1986) and HSM (Chaiken, 1980) research, which both aim to explain the cognitive process of persuasion with two information processing paths, a central or systematic route and a peripheral or heuristic route, the former one used when strongly motivated and latter one used to minimize cognitive effort. The dual-process framework of ELM and HSM have been utilized to study trust and attitudes toward AI in the context of algorithmic journalism (Shin, 2021b, 2021e). When people hold incoherent attitudes or contradictory knowledge about something, this cognitive dissonance is said to drive people to re-evaluate their beliefs to reduce the dissonance (Festinger, 1957). Although the cognitive dissonance theory offers explanation on how fact-based information is processed, it does not explain how fiction influences people's thinking. Narrative transportation theory (Gerrig, 1993) is one attempt to unravel the mechanism in which fictional stories impact perceptions. Stories stored in memory have argued to have long-lasting effects (Hamby et al., 2020), even though the information content and source are known to be fiction-based and created to attract the receiver by other means (van Laer et al., 2014). In the context of entertainment media effects, consuming media content is likely to influence people's thinking gradually, as demonstrated in the cultivation theory (Potter, 2014). Finally, one branch of persuasion theory and research on the mere-exposure effect (Zajonc, 1968) and familiarity principle (Reis et al., 2011) also argue that, regardless of the content and source, the mere frequency of exposure is inclined to positively affect one's attitude due to familiarity attraction. The multiple source effect argues that being exposed to the information through multiple channels instead of just one increases its trustworthiness because people consider it more credible if the same information is provided in multiple sources (Harkins & Petty, 1981). In the field of marketing, researchers have suggested that extensive coverage increases the cognitive processing of the information, although the effect on attitude is not necessarily positive (Chang & Thorson, 2004). 3 MEDIA PERSUASION AND PERCEPTIONS OF ROBOTS There is some existing research literature on media persuasion and perceptions of robots indicating that media persuasion affects the perceptions of robots as well. One field study found that participants watching a fictional film featuring a humanoid robot gained better understanding of humanoid robots, which predicted willingness to interact with them (Appel et al., 2016). Based on another field experiment, participants who read a fictive story of robot protagonist perceived humanoid robots less eerie compared to the ones who read nonnarrative information or received no prior information about robots (Mara & Appel, 2015). One survey study found that people who recalled a higher number of media portrayals of robots had lower level of anxiety toward robots (Sundar et al., 2016). Eurobarometer (2017) asked Europeans about their general attitude toward and opinions about AI and robots as well as whether people had seen, heard, or read about AI in the last 12 months. People exposed to information about AI had more positive general attitude toward robots and AI compared to those not exposed (75 vs. 49%; Eurobarometer, 2017). The average attitudes toward robots vary among different countries, and researchers have hypothesized that this is partly caused by different emphasis in the media depending on the culture (Li et al., 2010; Turja & Oksanen, 2019). De Graaf et al. (2016) suggested that preadoption perceptions are based on affect or cognition and can be gained through indirect experiences with technology, such as media exposure. The potential of media influence was apparent in the interview results because participants recalled movie and documentary representations when asked what comes to mind when thinking about robots (de Graaf et al., 2016). Previous researchers have also found that user characteristics such as sociodemographic factors are associated with the perceptions of robots (de Graaf & Allouch, 2013; Flandorfer, 2012; Gnambs & Appel, 2019). The influence of age and gender has varied depending on the specific dependent variable being explained, but older people's and women's perceptions and anticipated interaction with robots seem to be more negative than what younger people and men have (de Graaf & Allouch, 2013). Researchers have also suggested that technological education influences human–robot interaction (Nomura & Takagi, 2011). However, the relationship between sociodemographic factors and the acceptance of robots seems to be fairly complex, and Flandorfer (2012) has proposed that previous experiences of technology are likely to mitigate such connections. People seem to be more willing to accept robots when they have firsthand experiences of them (Bartneck et al., 2007; Heerink et al., 2010; Nomura et al., 2006), which is in line with the research on technology adoption in general (Jeyaraj et al., 2006). Without firsthand experiences people are expected to use, for example, fictional representations of robots to reduce the uncertainty (Spatola & Urbanska, 2020). Similar notions are found in research on trust in technology. Trust in the automation literature shows that trust is established by gaining knowledge and experience through interactions (Hancock et al., 2011, 2020; Lee & See, 2004; Schaefer et al., 2016). However, lack of prior interaction experience with and direct information about a technology has been stated to direct people to depend on indirect sources such as second-hand knowledge, the degree of cognitive familiarity determining their initial trusting beliefs toward it (Li et al., 2008). 4 RESEARCH OVERVIEW We examined in three survey studies whether previous media exposure to robots is associated with general attitude and trust toward robots. Our studies are based on persuasion theories and the literature on the acceptance of robots. In the light of cultivation theory, we were interested in media effects cultivating over time, rather than short-term reactions to specific messages (Potter, 2014). In Study 1, we investigated the connection of prior robot media exposure to the positive attitude and trust toward robots with a Finnish sample of care work professionals. In Study 2, we examined whether the association of media exposure and positive attitude toward robots was found in the general adult population of the United States. Finally, Study 3 focused on media exposure and trust toward robots using a trust game experiment. Previous literature has suggested that the overall exposure to an attitude object increases the positivity toward it due to the familiarity (Reis et al., 2011; Zajonc, 1968). In addition, the multiple source effect (Harkins & Petty, 1981) states that the effect of media exposure on perceptions increases as the variety of media exposure sources increases. Based on these theoretical arguments and on previous findings regarding exposure to fictive robots (Appel et al., 2016; de Graaf et al., 2016; Mara & Appel, 2015; Sundar et al., 2016), we expected that more extensive media exposure to robots would be connected to positive general attitude toward robots (H1). We also divided the media exposure in fiction and fact-based media outlets and investigated the difference of fiction- and fact-based media persuasion on attitudes and trust. Based on the cognitive and calculative bases of trust in two-dimensional trust theory (McAllister, 1995) and the previous literature on the cognitive factors behind trusting robots and other technology (Hancock et al., 2011, 2020; Lee & See, 2004; Li et al., 2008; Schaefer et al., 2016), we expected the more extensive fact-based media exposure to robots to be connected to higher trust toward robots (H2). We treated age, gender, technological education, and firsthand experience with robots as control variables in all analyzed models. H1.Positive attitude toward robots increases as the variety of sources in prior media exposure to robots increases. H2.Trust toward robots increases as the variety of sources in prior fact-based media exposure to robots increases. 5 STUDY 1 A fairly new domain for robotics is welfare services, to which robots are currently being designed to aid social and health care staff and take over some of the tasks previously done by care workers (Maalouf et al., 2018; Savela et al., 2018). As care workers are one of the main end users in this field, their attitudes and trust toward robots are key issues in the overall acceptance and successful deployment of new technology. In Study 1, we examined Finnish care workers' prior media experiences with robots and their perceptions of robots. Thus, Study 1 investigated the connection of prior robot media exposure to attitude and trust toward robots (H1 and H2) in a specific end user group. 5.1 Method 5.1.1 Participants In Study 1, we utilized random sample of Finnish care workers (N = 3800, 95% female, Mage = 46.5 years, SDage = 11.3 years, range 17–70 years) in December 2016. The sample was collected in co-operation with two Finnish trade unions. Drawing the sample from the trade union member registers was rationalized by the fact that a high proportion (90%) of Finnish nurses are unionized. The division between practical nurses (64.9%) and registered (35.1%) nurses in the sample was similar to the division between practical (64.7%) and registered (35.3%) nurses in the Finnish care worker population (Turja et al., 2019). 5.1.2 Measures The first dependent variable was the participants' perceived general attitude toward robots, which was measured by asking the participants to rate their response on a scale from 1 to 4: "Generally speaking, how positive or negative is your view on robots?" We grouped together the values 1–2 and 3–4 for a dummy variable as the first dependent variable in the analysis, value 1 indicating positive and value 0 negative. To analyze the second dependent variable, trust in the safety of care robots, we drew a subsample (n = 544) from the care worker sample. This subsample included those care workers who had some firsthand experience with care robots. Each of the four robot types reported to have been used opened up seven additional questions from the Almere model (Heerink et al., 2010)—for example, the intention to use the robot and trust in the safety of the robot. The answers were given on a 5-point Likert scale from totally disagree to totally agree, and after standardizing the scales, a higher rating indicated more trust toward the robot (M = 3.26, SD = 1.08). Main independent variable, previous media exposure to robots, was measured by asking the participants if they had heard, read, or seen anything about robots in any of the following eight outlets: books and other stories; movies or TV shows; games; documentary movies or TV shows; news websites; newspapers; or educational or promotional event. We considered the last option to be interactive media in the sense that it is a channel mediating fact-based information. We defined the first three as fiction-based and the four latter ones as fact-based outlets. For analyses, we created three summative sum variables: prior media exposure (scale 0–7), prior fiction-based media exposure (scale 0–3), and prior fact-based media exposure (scale 0–4). Control variables included age, gender, a technology degree, and firsthand experience with robots. We used age and firsthand robot experience as continuous variables and gender and a technology degree (4% of the respondents) as dummy variables. The firsthand robot experience measured any robot use experience prior to the study at home, work, or elsewhere (e.g., 88% no experience, 10.9% experienced in one of the contexts, 1.1% experienced in two or three contexts). All Study 1 variables are presented in Table 1. TABLE 1. Summary of descriptive statistics of the Study 1 variables (N = 3800) Measure n % M SD Range General attitude toward robots 2771 2.58 0.71 1–4 1 = Positive 2771 58.4 0 = Negative 2771 41.6 Trust in the safety of care robots 456 3.24 1.08 1–5 Age 3700 46.50 11.30 17–70 Gender 3685 0 = Female 95 1 = Male 5 A degree from technology 3670 1 = Yes 4 0 = No 96 Firsthand experience with robots 3736 1 = Yes 21 0 = No/Maybe 79 Prior media exposure to robots 3396 Fiction-based 3396 0.62 0.92 0–3 Fact-based 3996 1.94 1.02 0–4 5.1.3 Analysis We used the binary logistic regression method for analyzing general attitude and ordinary least squares (OLS) regression with trust analysis. We also report Pearson product–moment correlation r and p values for the main variables. For logistic regression, we report odds ratios (ORs), confidence intervals for ORs (95% CI), p values for the different measures, Nagelkerke pseudo R2, and model test χ2 statistics. For the linear regression model, we report unstandardized regression coefficients (B) and their standard errors (SE B), standardized beta coefficients (β), and p values for the different measures, in addition to model goodness-of-fit measure (R2), model test (F), and the p value of the model. We did not detect problematic multicollinearity or heteroscedasticity in the regression models. 5.2 Results Based on the Pearson correlation results in Study 1, general attitude toward robots correlated with overall media exposure (r = .25, p < .001), fiction-based media exposure (r = .16, p < .001), and fact-based media exposure (r = .24, p < .001) to robots. Table 2 shows the regression analysis results for general attitude. While controlling for confounding effects of the background variables, more extensive overall exposure to robots in various media outlets was associated with positive general attitude toward robots in Model 1 (OR = 1.40, p < .001). The association was similar separately for fiction- (OR = 1.52, p < .001) and fact-based (OR = 1.26, p < .001) media exposure to robots in Model 2. TABLE 2. Summary of regression analyses for Study 1 variables, general attitude toward robots as a dependent variable: Finnish care workers (N = 3800) Measure Model 1 Model 2 OR 95% CI p OR 95% CI p Age 1.02 1.01–1.02 <.001 1.02 1.01–1.02 <.001 Female gender .68 .46–1.01 .056 .67 0.45–0.99 .044 Technology degree 1.26 .83–1.93 .276 1.27 0.84–1.94 .262 Firsthand robot experience .58 .46–.72 <.001 .59 0.47–0.74 <.001 Prior media exposure to robots 1.40 1.32–1.48 <.001 Prior fiction-based media exposure to robots 1.26 1.14–1.39 <.001 Prior fact-based media exposure to robots 1.52 1.39–1.65 <.001 Model Nagelkerke pseudo-R2 .10 .10 Model X2 206.35 212.78 Model p <.001 <.001 Note: Dependent variable: General attitude toward robots. Trust toward the safety of care robots correlated with fact- (r = .11, p = .019) but not fiction-based media exposure. Similarly, regression analysis results for trust reported in Table 3 show that trust toward the safety of care robots was associated only with fact-based media exposure (β = .13, p = .014). TABLE 3. Summary of regression analyses for Study 1 variables, Trust in the Safety of care robots as a dependent variable: Finnish care workers (n = 544) Measure Model 1 Model 2 B SE B β B SE B β Age .01 .01 .01 .00 .01 −.01 Female gender −.23 .21 −.06 −.21 .21 −.05 Technology degree .00 .24 .00 .01 .24 .00 Overall firsthand experience with robots .12 .09 .07 .11 .09 .06 Prior media exposure to robots .05 .03 .09 Prior fiction-based media exposure to robots −.03 .06 −.03 Prior fact-based media exposure to robots .12 .05 .13** p < .05 Model R2 .02 .02 Model F 1.30 1.60 Model p .26 .15 Notes. Dependent variable: Trust in the safety of care robots. * p < .05 6 STUDY 2 In Study 2, we aimed to replicate the result in Study 1 regarding attitudes and the first hypothesis (H1) in the general adult population of the United States. Again, we measured the media exposure as an aggregate factor and divided it further into fiction- and fact-based media outlets. Differently from the previous study, in Study 2, we measured general attitude in a scale of 1–7, treated it as a continuous variable, and, for the linear correlations, utilized an OLS regression instead of a nonparametric method. We considered age, gender, a degree in technology, and firsthand experience with robots as control variables, as in Study 1. 6.1 Method 6.1.1 Participants We collected survey data in 2019 in January (N = 1003, 48.89% male, Mage = 37.36 years, SDage = 11.80 years, range 19–78 years) and April (N = 969, 48.85% male, Mage = 37.15 years, SDage = 11.35 years, range 15–94 years). We used Amazon's Mechanical Turk for recruiting respondents as a reliable method to reach various demographical groups of the US adult population so as to carry out behavioral research that includes survey experiments (Huff & Tingley, 2015; Paolacci & Chandler, 2014; Yeager et al., 2019). To guarantee the validity of the data and to avoid any problems caused by nonnaive respondents, the second sample included only unique participants who did not take part in the previous sample (Chandler et al., 2014, 2015). We also excluded non-US participants from the data (Kennedy et al., 2020) and checked the data for odd response behavior, such as too quick responses or identical answers throughout the survey. Participants were informed that by completing the survey they give their consent for the data to be used for re