Sage Journals: Discover world-class research

Abstract

Recent developments foretell that social robots will soon become an integral part of everyday life, offering companionship and intimate closeness of different kinds. While research thus far is limited in scope and data, the current research offers two studies into how and why gender affects social robots’ acceptance among European and American participants. Study 1 (N = 26,344) is used to identify overall patterns, while Study 2 (N = 426), divided into quantitative and qualitative analyses, is used to investigate specific differences in accepting four types of robots: helpers, companions, lovers, and sex partners. Results show that women have significantly less positive attitudes toward social robots as lovers and sex partners than men. The qualitative analyses of Study 2 show that this is due to women seeing such robots more negatively in terms of social norms, psychological health, morality, and functionality. The study further offers an axis system, on which attitudes toward robots can be theorized and examined.

Keywords

social robots intimacy companionship norms human–machine communication

Introduction

Recent years have seen drastic leaps in robotics and related technologies. A growing number of robots are being programmed to function in social interactions with humans (Fortunati et al., 2022; Sundar, 2020; Yao & Ling, 2020). Different models can hold a conversation, provide companionship, and engage in physical and sexual contact. These models, generally known as social robots, are anticipated to become ubiquitous (Henkel et al., 2020; Kislev, 2022). In fact, the International Federation of Robotics reports that the annual growth rate of worldwide sales of robots stands at around 30% each year at the end of the 2010s and going into the 2020s (IFR, 2020). According to some estimates, 3.13 million social and entertainment robots were sold worldwide in 2020 (Boch et al., 2021). If Amazon’s and Tesla’s new focus on social robotics, revealed in late 2021, and the need for robots brought about by the COVID-19 pandemic are indicative, these numbers are just early signs for the mainstreaming of the field.

As with many pioneering innovations, the main question researchers and policymakers face now is how this development will be received (Hancock et al., 2020; Liu, 2021). In particular, this question pertains to gender differences, as they proved significant in past adoption of new technologies. For example, men were initially the primary market of the telephone as it served professional usages then dominated by males. Later, it turned to serve social purposes and was widely adopted by women (Fischer, 1988). Yet, very little research has covered gender differences in social robotics (Nomura, 2017; Tannenbaum et al., 2019).

In considering gender differences when predicting new technologies’ adoption, sociological and psychological studies have suggested some systematic gender differences. Unfortunately, early literature considered only men and women (Hyde et al., 2019). It was found, for example, that women score higher than men on social traits such as nurturance, trust, and extraversion (Feingold, 1994). Women also report having stronger and more rewarding friendships (Wright & Scanlon, 1991). Likewise, women tend to look for emotional support as a way of coping with life difficulties and emotional distress (Nolen-Hoeksema, 2012; Tamres et al., 2002) and disclose more personal information than men, especially when talking to a familiar person (Dindia & Allen, 1992). Men, in contrast, hold an avoidant attachment style more frequently, especially in romantic situations (Del Giudice, 2011). In business settings, women have a more cooperative style of negotiating (Walters et al., 1998), a more democratic and deliberative style of leading (Eagly & Johnson, 1990), provide mental support as mentors (O’brien et al., 2010), and prefer compromise over conflict in resolutions (Holt & DeVore, 2005).

Together, this evidence leads to a gender division that some define as a difference between people-orientation and thing-orientation, statistically speaking (Lippa, 1998, 2010; Zheng et al., 2012). No wonder that when it comes to robotics, ample research has already shown gendered differences in attitudes toward social robots, in what defined here as gender identity or self-perception of gender (Lindqvist et al., 2021). The overall results and trends seem to suggest that the attitude toward social robots among women is less positive than men. A recent systematic review of 97 studies with over 13,000 participants (Naneva et al., 2020) found that men overall reported higher trust levels in social robots. Yet, the review also found that “for most outcomes, the number of studies was quite small, and it was difficult to draw clear conclusions regarding the effect of gender” (Naneva et al., 2020 p. 1195). Similarly, another literature review of 44 studies (Whelan et al., 2018) also revealed that data is limited (many had sample sizes as small as 10) and may carry potential selection biases.

Indeed, in a review of robot–human interactions, Broadbent (2017) concluded that the majority of research focused on robotics from a technical viewpoint, and that there needs to be more research on the way humans work and interact with robots. In addition, the authors noted that studying robotics from the human perspective is beneficial to developers and researchers, as well-developed social robots can teach us about human behavior, emotions, and cognition. Others stated that “overall, the use of robot acceptability measures is still relatively new, and further psychometric work is necessary to provide confidence in the validity and reliability of these scales” (Krägeloh et al., 2019 p. 1).

The current research, therefore, asks two questions. First, are there gender differences in attitudes toward social robots? And second, and perhaps more importantly, why do we see gender differences in attitudes to social robots? If we understand the reasons behind gender differences, we will be able to interpret, classify, and theorize previous studies’ results.

The first question will be addressed by quantitative data analyses, while the second is addressed by a systematic text analysis from open-ended questions. Indeed, the two studies cover only European and American men and women. Yet, while most studies on attitudes toward social robotics have been quantitative and/or descriptive, the use of qualitative data here as part of a mixed-methods approach can help identify the mechanisms behind correlative relationships at least in these countries.

Gendered Attitudes to Social Robots

Many different types of social robots have been developed for human use. While they are all designed to be used in human–robot interactions (HRIs), their intended use varies widely and can be roughly divided into four categories: social robots made for platonic or friendly interactions; those intended for more in-depth or psychologically intimate emotional connections; those intended to provide physical and sexual companionship; and those made for physical assistance. Attitudes are hypothesized to differ between men and women for each of these categories.

An example of the first category of social robots for platonic and friendly interactions is a recent evaluation of a social robot named Alpha, a 40-centimeter-tall humanoid robot that gesticulates and speaks in response to humans. Under laboratory conditions, Xu (2019) evaluated how 110 US college students (55 males and 55 females) aged 18–34 reacted to vocal and kinetic cues from Alpha. While the analyses show that male respondents exhibited a higher interest in the future use of Alpha, the remainder of the findings are mixed: the vocal and kinetic cues appear to, for the most part, affect male and female participants’ opinions of Alpha similarly. The exception is that males were more likely to respond positively if Alpha gesticulated while introducing themselves, as indicated by more positive opinions and higher interest in further interaction with Alpha.

These findings, however, are somewhat in contrast with other studies that show differences in attitudes to friendly social robots in HRI. In one study, for instance, Lee (2008) found that women respond more positively to social robots’ flattery compared with men. Other review studies indicate that when it comes to platonic interaction with social robots, attitudes depend on the intended use: men are more interested in social robots that present themselves as assistive and helpful, while women feel more comfortable with social robots that engage in small-talk or general social interaction (de Graaf & Ben Allouch, 2013). Studies also show that gender matching of the robot and the user affects the results in well-studied social robots such as NAO and Pepper (Jackson et al., 2020; Vega et al., 2019).

Gender differences have also been recorded regarding psychological intimacy with social robots (Samani, 2016). One study suggested that male participants responded more acutely to psychological intimacy with a social robot named Robovie (Kahn et al., 2015). In a twenty-minute experiment, Robovie introduced itself as a tour guide and showed the participant around the lab. Robovie asked each participant to keep a little secret. While male and female respondents were equally likely to honor Robovie’s privacy, subsequent analyses found that Robovie’s request for secrecy led to increased attribution of high mental and emotional qualities, but for males only. In other words, this study suggests that male participants responded more acutely to psychological intimacy with a social robot.

Another study showed that men and women have very similar attitudes toward platonic love robots, while men show more positive attitudes toward sex robots (Nordmo et al., 2020a). Thus, while studies found that women are generally less likely to trust and accept social robots in psychologically intimate situations (Deniztoker, 2019), the results are far from being uniform or universally conclusive and depend on the context and the exact definition.

When it comes to sexual or more physically intimate relationships with social robots, the literature is in greater agreement. Studies generally find men to view physically intimate interactions with social robots more favorably (Appel et al., 2019; Nordmo et al., 2020a). These findings have been backed by later findings that women, particularly politically conservative women, feel threatened by the presence of sex robots in society (Oleksy & Wnuk, 2021).

While these empirical studies are of value, they do not explicitly identify the mechanisms explaining gender differences in attitudes toward social robots (where they exist) and sometimes contrast each other. In fact, it is highly plausible that the disagreements in the results are due to underlying mechanisms and definitions that cause men and women to react differently to social robots. Moreover, it is unclear how socio-demographic factors (such as age, education, religiosity, well-being, relationship status, and others) affect men and women. Therefore, the current study seeks to understand the mechanisms behind these possible differences.

Furthermore, while some scholars identified that attitudes toward social robots differ among men and women in the empirical studies described above, exclusively quantitative studies are mostly limited in their ability to offer correlational information only. These studies can at best speculate why there are gender differences. Qualitative studies on the subject, on the other hand, are frequently small-scaled and highly product-specific. Therefore, this study delves into a qualitative study on top of quantitative results in order to present reliable results, further establishing a conceptualization of this emerging field and theory.

Method

Samples

This research first used the second version of the Eurobarometer database published in December 2021 (Commission, 2021). This survey was funded by the European commission and conducted by TNS in 2017 as part of the standard Eurobarometer (number 87.3). The module used in this Eurobarometer survey is that on attitudes toward the impact of digitization and automation on daily life. This module contains questions on the integration of robots in daily life, being assisted by such technology, awareness of robots, and more.

The survey includes 27,901 respondents aged 15 years and over from each of the 28 Member States as well as from the 5 candidate countries (Turkey, North Macedonia, Montenegro, Serbia, and Albania) and the Turkish Cypriot Community. All interviews were conducted face-to-face in people’s homes and in the appropriate national language. To more closely match Study 2’s sample, only respondents aged 22 and over were included in the analyses. This also helped with having education included in the multivariate analyses since interviewees were only asked in the survey about the age in which they fully completed their education (in any event, sensitivity analyses, available upon request, yielded similar results to those presented below). Thus, the overall sample included 26,344 respondents before excluding cases with missing values in the multivariate regressions (see below).

To further develop and understand the results of the Eurobarometer research and previous studies, Study 2 is a survey conducted for this current study throughout 2020–2021, before and after the breakout of the COVID-19 pandemic (N = 426). The survey included demographic and socioeconomic questions, imagined scenarios of various types of friendships and relationships with robots, rated on a scale of 0–10, and open-ended questions about interaction and romantic relationships with human-like robots. Participants were recruited via the Amazon Mechanical Turk platform. All study procedures have gone through institutional IRB approval procedures successfully. The STATA software, version 15.1, was used for the statistical analysis. Finally and noteworthy, the two studies are separated and have no aim to be compared as such.

Study 2’s survey sample is relatively representative of the U.S. population in terms of age, income, and gender (as detailed below) and includes people from different locales, socioeconomic statuses, and demographics. 54% of the sample are men and 46% are women. The age range of the respondents is 22–77, with an average age of 42 and a median of 39. The self-reported average participants’ income level on a scale of 0–10 is 4.7 and a median of 5, normally distributed. 46% of respondents were married at the time of the survey, 7% were divorced, 6% cohabited, 40% single, and 1% were widowed.

Quantitative Analysis Procedure of Study 1

In the framework of Study 1, the two most relevant questions found in the database were examined. The first is a broad one: “Generally speaking, do you have a very positive, fairly positive, fairly negative or very negative view of robots and artificial intelligence?” Answers to this question were rated on a 1–4 scale and were reversed for convenience. 2335 missing values were recorded to this question. The second question is: “Here is a list of things that could be done by or with robots. For each of them, please tell me, using a scale from 1 to 10, how you would personally feel about it… Having a robot to provide you services and companionship when infirm or elderly.” 958 missing values were recorded to this question.

The two questions were first analyzed pair-wise for men and women. In the second stage, the questions were analyzed while accounting for secondary variables: age; social status (a replacement for the income variable, which is missing in this survey) rated by five brackets; the age when education was completed, ranging from less than 15 years up to 22 years and older in 9 brackets; having children; and marital status. The country-variable was estimated as a second level in hierarchical models. In the third stage, gender was estimated in interaction with social status and education.

Quantitative Analysis Procedure of Study 2

The closed questions of Study 2 were first analyzed with 95% confidence intervals, comparing men and women for relevant questions. Four independent variables were at the focus of the quantitative part. These variables were based on the degree to which respondents agree with the following statements on a 0–10 scale: “Some robots can provide physical assistance when you are old. Would you like to have one?”; “New robots can provide companionship. Would you like to have one?”; “Do you see yourself forming a romantic relationship with such a friend?”; and “If possible, do you see yourself having sex with such a friend?”

Multivariate regression analyses were used to examine the associations between gender and social robots’ acceptance after accounting for several possible intervening mechanisms. These mechanisms include age; years of schooling; relative income measured by the question: “relative to the average income in your country, how would you rate your income over the last 12 months?” rated on a scale of 0–10; degree of religiosity on a scale of 0–10; life satisfaction on a scale of 0–10; satisfaction with current relationship status rated on a scale of 0–10, whether in a relationship; and friends’ importance measured by the question: “please rate the extent to which you agree with the following statements—having friends is important” rated on a scale of 0–10. Effect sizes were measured for all models.

In addition to the main quantitative analysis, four appendix tables are attached to show the associations found between social, economic, and demographic variables and the four attitudes toward robots examined here. In addition, the appendix tables show the associations between relationship variables together with the overall life satisfaction and the four measures of attitudes toward robots tested here. Seven more sensitivity analyses were conducted in order to test interaction terms of gender with the following variables: education, relative income, religiosity, relationships status, life satisfaction, relationship satisfaction, and friendships importance. These estimations did not yield additional insights and were not added here due to their length (available upon request).

Qualitative Analysis Procedure of Study 2

The qualitative part was coded by three separate, trained researchers who classified and coded all responses to the open-ended questions about interaction with social robots and, separately, about romantic relationships with social robots. Respondents were asked to provide at least 40 words for each question. The first question was: “What do you think about people interacting with robots, V.R., and A.I. personal assistants in the future?” The second question was more specific regarding romantic relationships: “What do you think about people having romantic relationships with robots, V.R., and A.I. personal assistants in the future?” The average number of words for women is 49.77, while for men, this is 56.29.

The coding system was developed based on the grounded theory methodology while recursively examining the data (see Denzin & Lincoln, 2005; Hsieh & Shannon, 2005; Weston et al., 2001). Central themes were codified while examining the agreement between the coders regarding how the data should be coded, with inter-rater reliability standing at 82%.

Thereafter, another coder, a third one, has taken the task of re-reading all answers and rank the degree to which each response matches a specific theme and emphasizes that theme, rated on a scale of 0 (no match) to 5 (very high match) for each of the two questions cited above. The third coder had no knowledge regarding the results of the quantitative analyses presented here, nor did they take into account the respondent’s gender or any other characteristics. In this way, the rating was only based on content analysis (and see Hsieh & Shannon, 2005). Next, the ratings were added (to make a 0–10 scale) and analyzed statistically according to respondents’ gender in a pair-wise analysis (separating the analyses of the two questions yielded similar results and these analyses are available upon request).

Results

Study 1: Comparative Statistics

Table 1 presents comparative results of men and women across 30 European countries surveyed in the Eurobarometer. While the average age and level of education were similar among men and women, a statistically significant difference was registered in terms of income, having children, and marital status distribution. These variables were incorporated in the multilevel analyses below.

Table 1.

Gender Differences: Study 1.

Factor	Man	Woman	p-value
N	N = 11,758	N = 14,586
Age	53.4 (16.9)	53.3 (16.9)	.65
Relative social status (1–5)	2.3 (1.0)	2.3 (1.0)	.44
Education (1–9)	5.8 (2.7)	5.6 (2.7)	.002
Marital Status			<.001
Single	1835 (15.7%)	1760 (12.1%)
Married/cohabitating	8332 (71.3%)	9154 (63.1%)
Divorced or separated	876 (7.5%)	1343 (9.3%)
Widowed	647 (5.5%)	2253 (15.5%)
Having children	0.3 (0.5)	0.4 (0.5)	<.001
Robots—general appraisal (1–4)	2.8 (0.8)	2.6 (0.8)	<.001
Robots feel about: infirm/elderly services (1–10)	4.6 (3.1)	4.0 (3.1)	<.001

Data presented as mean (S.D.) for continuous measures, and n (%) for categorical measures.

Regarding attitudes toward robots among men and women, the first variable tested is that of general appraisal of robots and the second, more closely related to the focus of this paper, is whether the respondents feel robots can help them with services and companionship when they are old or infirm. These two variables registered a statistically significant difference between men and women, with women being less accepting of robots.

Study 1: Cross-Country Multilevel Regression Analyses

To further examine the results presented above while accounting for intervening factors, Table 2 presents estimations of men’s and women’s attitudes toward robots in cross-country multilevel analyses. All models show that older people were less accepting of robots, while acceptance of robots rose with income and education.

Table 2.

Attitudes Toward Robots, With and Without Gender Interaction: Study 1.

	General appraisal	Help with elders/infirm	General appraisal	Help with elders/infirm
Women	−0.15^*** (0.01)	−0.59^*** (0.04)	−0.13^*** (0.03)	−0.25^* (0.11)
Age	−0.00^*** (0.00)	−0.02^*** (0.00)	−0.00^*** (0.00)	−0.02^*** (0.00)
Relative social status (1–5)	0.07^*** (0.01)	0.25^*** (0.02)	0.07^*** (0.01)	0.26^*** (0.03)
Education (1–9 scale)	0.05^*** (0.00)	0.08^*** (0.01)	0.05^*** (0.00)	0.11^*** (0.01)
Having children	−0.02 (0.01)	−0.06 (0.04)	−0.02 (0.01)	0.06 (0.04)
Marital Status^a
Single	0.04^* (0.02)	−0.08 (0.06)	0.04^* (0.02)	−0.08 (0.06)
Divorced or separated	0.04 (0.02)	−0.08 (0.08)	0.04 (0.02)	−0.08 (0.08)
Widowed	−0.03 (0.02)	−0.26^** (0.09)	−0.03 (0.02)	−0.28^** (0.09)
Interaction terms
Gender*income			−0.00 (0.01)	−0.02 (0.04)
Gender*education			−0.00 (0.00)	−0.05^** (0.01)
Constant	2.55^*** (0.04)	4.57^*** (0.16)	2.53^*** (0.04)	4.40^*** (0.17)
Variance components
Country variance	−1.97^*** (0.13)	−0.41^** (0.13)	−1.97^*** (0.13)	−0.40^** (0.13)
Individual variance	−0.32^*** (0.00)	1.05^*** (0.00)	−0.32^*** (0.00)	1.05^*** (0.00)
N countries	30	30	30	30
N	22,864	24,101	22,864	24,101

^aMarried people omitted as a reference group.

Standard errors in parentheses.

^* p < .05, ^** p < .01, ^*** p < .001.

In addition, the first model shows that after accounting for demographic and socioeconomic variables, women’s general appraisal of robots was statistically significantly lower than that of men. Similarly, women’s acceptance of social robots that can help them in old age or in times they are infirm was also statistically significantly lower than that of men.

Furthermore, the third model tested whether the relative social status or level of education interacts with women differently than they interact with men in the association with robots’ appraisal. No difference was registered in this sense. However, the fourth model shows that when it comes to a more specific question about robots’ potential to assist respondents, results show that acceptance rises in a lower slope when women are more educated than men. In other words, while more educated men and women are more accepting of robots, women are less affected by education, and even the more educated remain more aversive. While these results are based on a high number of cases, they require further investigations into why and how men and women differ in their attitudes toward social robots.

Study 2: Descriptive and Comparative Statistics

Since the Eurobarometer database is quite general, like many other surveys in the field, Study 2 delved into the questions of how and why gender differences appear toward social robots. The quantitative part of Study 2 indicated that most people rejected the idea that robots might become their companions, with no significant difference between women and men. On a scale of 0–10, men and women scored 3.39 and 2.84, respectively. People turn more open only when asked about the plain physical assistance a robot might provide, on which they scored 5.86 and 5.7, respectively, statistically significantly different from both group’s ratings of companionship’s acceptance.

Social acceptance even worsened, and gender disparities appeared when probed further into the possibility of having romantic and intimate relationships with robots. Looking at those questions, men were indeed more supportive, or at least less likely to reject the idea of the use of robots in physical intimacy or emotional rapprochement. On a scale of 0–10, men scored 2.15 and 2 on willing to have sex and romantic companionship, respectively. Women scored 0.87 and 1.24 on the same measures.

Table 3 presents comparative statistics on the variables examined in Study 2, by men and women. It can be seen that age, religiosity, and current relationship status differed between the groups and therefore should be accounted for in further multivariate regression analyses below. Women in the sample were overall older, more religious, and more out of a relationship at the time of the survey.

Table 3.

Gender Differences: Study 2.

Factor	Man	Woman	p-value
N	228	198
Age	39.7 (11.0)	43.6 (11.9)	<.001
Years of schooling	14.5 (2.1)	14.3 (2.2)	.53
Relative average income	4.8 (2.3)	4.6 (2.4)	.45
Religiosity	2.3 (3.1)	3.8 (3.6)	<.001
Life satisfaction	6.2 (2.6)	6.2 (2.9)	.94
Happy with current relationship status	6.4 (3.0)	6.8 (3.0)	.14
In a relationship	105 (46.1%)	44 (22.2%)	<.001
Having friends is important	7.4 (2.3)	7.2 (2.6)	.34
Robots’ physical assistance	5.9 (3.1)	5.7 (3.3)	.62
Robots’ companionship	3.4 (3.1)	2.8 (3.3)	.078
Robots’ romantic relationship	2.0 (2.6)	1.2 (2.3)	.002
Having sex with robots	2.2 (2.9)	0.9 (2.0)	<.001

Data presented as mean (S.D.) for continuous measures, and n (%) for categorical measures.

In addition, and before accounting for demographic and socioeconomic differences between the groups, women showed statistically significantly less acceptance toward robots as romantic and sex partners, while a marginal statistical significance was registered in terms of men being more accepting toward robots as companions.

Study 2: Multivariate Regression Analyses

In light of the differences found above, multivariate regression analyses were merited to examine the associations between gender and social robots’ acceptance after accounting for other variables. Indeed, Table 4 shows that gender was a significant predictor of romantic and sexual relationships with robots, with women relating to the idea more negatively. Accounting for other factors, self-reported relative income was positively associated with robots’ acceptance as companions and romantic or sexual partners, similarly to the results of Study 1. Satisfaction with current relationships was negatively related to accepting robots as companions or sexual partners.

Table 4.

Attitudes Toward a Relationship With Robots: Study 2.

	Physical assistance	Companionship	Romantic relationships	Sexual intimacy
Women	−0.109 (0.334)	−0.475 (0.328)	−0.764^** (0.252)	−1.294^*** (0.260)
Age	0.013 (0.014)	0.002 (0.014)	−0.014 (0.011)	0.010 (0.011)
Years of schooling	0.040 (0.082)	−0.024 (0.080)	0.015 (0.062)	−0.022 (0.064)
Relative income	0.090 (0.087)	0.267^** (0.086)	0.187^** (0.066)	0.158^* (0.068)
Religiosity	0.023 (0.050)	0.026 (0.049)	0.051 (0.037)	0.049 (0.039)
Life satisfaction	0.079 (0.085)	0.075 (0.084)	0.026 (0.065)	−0.037 (0.066)
Happy with relationship status	−0.113 (0.073)	−0.215^** (0.072)	−0.107 (0.055)	−0.138^* (0.057)
In a relationship	0.014 (0.384)	0.063 (0.376)	0.220 (0.290)	−0.036 (0.298)
Friendship importance	−0.039 (0.071)	−0.126 (0.070)	−0.137^* (0.054)	−0.084 (0.056)
Constant	4.809^*** (1.319)	4.118^** (1.293)	2.725^** (0.996)	2.956^** (1.026)
N	421	421	421	421
R ²	0.01	0.07	0.1	0.11
Eta-squared	0.01	0.07	0.1	0.1

Standard errors in parentheses.

^* p < .05, ^** p < .01, ^*** p < .001.

Study 2: Qualitative Analyses

While the statistical analyses indicate that women in the sample had more negative impressions of social robots than men when it came to romantic relationships or physical intimacy, they do not explain why this might be the case. A thematic analysis of the qualitative data from the study is needed here to show the mechanisms at play.

Following the procedure described above (and see Hsieh & Shannon, 2005), reasons for resistance, opposition, or discomfort to the use of social robots for these purposes were classified into four main themes: social normativity, psychological/mental health, ideology/morality, and functionality. These four themes were emerged following the two coders’ classification and coding of all text responses, comparing the codes, and matching those with the most agreement. Instances for these themes can be obtain upon request and were cut from this paper due to limited space.

The first theme, social normativity, relates to all social circumstances that make relationships with robots seem deviant, peculiar, and socially awkward. The mental health theme refers to statements that mention how such relationships can be emotionally harmful or maintain psychological disorders. Ideology/morality relates to all statements rejecting this idea based on ethics, values, and religious concepts. Functionality emerged from comments focusing on ease of use, robots’ uncanny behavior, and unhuman touch, speak, or feel. Overall, one can see a quadrable model here, in which attitudes are divided into social, psychological, cultural, and functional dimensions. These four dimensions can be conceptualized in the following axis system: Figure 1

Thereafter, another coder, a third one, has taken the task of re-reading all answers and ranked the degree to which the text of each respondent matches a specific theme and emphasizes that theme. This third coder was blinded to the hypotheses and background of this study in order to maintain objectivity as much as possible. The overall result of this analysis on a scale of 0–10 was as follows: 1. Social normativity (3.3), 2. Psychological/mental health (1.6), 3. Ideology/morality (2.2), and 4. Functionality (1.3). Table 5 presents the results divided by gender and examined statistically.

Table 5.

Text Analysis’s Themes of Reasons to Reject a Relationship With Robots, by Gender: Study 2.

Theme	Men	Women	p-value
Social normativity	2.5 (2.3)	4.1 (2.7)	<.001
Psychological/mental health	1.4 (1.9)	1.9 (2.1)	.007
Ideology/morality	1.7 (2.1)	2.8 (2.4)	<.001
Functionality	1.1 (1.8)	1.5 (2.0)	.023

Indeed, all dimensions showed statistically significantly differences between men and women. Figure 2 further visualizes these results.

Figure 1.

Axis system conceptualization: Social attitudes toward social robots.

Discussion

Overall, comparative statistics and multivariate regression analyses indicate that women have significantly less favorable attitudes toward social robots than men. The results of Study 1 confirm previous studies and show how this pattern holds true after analyzing a large database. Still, further investigations are required to understand how and why women reject social robots more than men. Indeed, Study 2 further shows that the difference in rejection appears in two particular realms: romantic and sexual relationships with robots. In contrast, men and women approach platonic relationships and physical assistance more equally.

In this way, this research first advances our understanding of the robot-gender divide in offering a quadruple model that distinguishes between four realms of interactions and relationships with human-like robots. This conceptualization of four realms helps explain some of the unclear and sometimes contradictory results of previous studies and meta-analyses that did not distinguish the context of attitudes toward robots (see in Naneva et al., 2020; Whelan et al., 2018).

While the findings of Study 1 and Study 2 confirm the results of some previous studies showing that males feel more positively toward the advent of robots than females (Nordmo et al., 2020b; Scheutz & Arnold, 2016), Study 2 shows that attitudes toward robots are context-dependent. It follows that previous studies that showed that men have more favorable attitudes toward social robots should be confined to more intimate contexts and be understood as such. For example, Appel et al. (2019) found men to view physically intimate interactions with social robots more favorably, and Nordmo et al. (2020a) found men to show more positive attitudes toward sex robots. In contrast, women were found to be less trusting and accepting of robots in psychologically intimate situations (Deniztoker, 2019). Earlier results are thus easier to understand in light of this current study that conceptualizes human–robot relationships and divides them into four realms.

The results of the text analysis of Study 2 further show that the differences between men and women in sexual and romantic contexts are explained by a quadruple model of four dimensions: social norms, psychological/mental health, ideology/morality, and functionality, as presented in Figure 1. To further theorize and explain these results, previous studies should be considered. First, women in the context of this study may be more strongly and negatively impacted by the potential stigma of deviating from the norm (Major & O’Brien, 2005). Indeed, previous studies on deviant behavior indicate that gender can impact negative social consequences of social deviation, with women being more at risk of being ostracized for deviant or non-normative acts (Heimer, 1996; Schur, 1984). Thus, it could be that women expressed negative views more strongly in an attempt to reduce the negative social risks of being in a relationship with a social robot.

Figure 2.

The quadruple model of attitudes toward robots: Visual presentation of text analysis results of causes for rejecting social robots by gender.

Second, women might tend to reject relationships with robots more forcefully on psychological terms, matching studies that showed women’s tendency to be more friendly, nurturing, and caring than men (Feingold, 1994; Wright & Scanlon, 1991). In addition, studies have shown that emotional support is more essential to women, as they look for such support to cope with difficulties and emotional distress (Nolen-Hoeksema, 2012; Tamres et al., 2002). This also matches studies that have shown that women have a more cooperative and deliberative style in conducting and leading businesses (Eagly & Johnson, 1990; Holt & DeVore, 2005; Walters et al., 1998). Men, in contrast, hold an avoidant attachment style more frequently, especially in romantic situations (Del Giudice, 2011).

The third dimension, that of ideology and morality, also shows a stark difference between men and women. Morality, in this sense, proved to be closely related to religiosity, in which women in this study have presented higher levels, as Table 3 shows. This matches a recent study by Oleksy and Wnuk (2021) that showed that conservative women, in particular, feel threatened by the presence of sex robots in society.

Finally, the functionality dimension is mainly connected to reciprocity, which was essential to women more than men in Study 2. Women focused on the emotional functions of robots and their ability to form human-like connections in such relationships more than men. While men did not like the uncanniness of robots (Mori et al., 2012; Schneider et al., 2007), they took it as only one dimension while the sexual dimension or “one-time” aspect could be satisfactory in themselves.

This plays into the evidence suggesting a gender division that some define as a difference between people-orientation and thing-orientation, statistically speaking (Lippa, 1998, 2010; Zheng et al., 2012). This is not to say that such differences are part of any inherent characteristics of men and women or that gender fluidity cannot disrupt this dichotomous division, influenced by too-rigid cultural and sociological circumstances. But the conceptual question seems to be whether robots are a “people-issue” or a “thing-issue” (Lippa, 1998, 2010; Zheng et al., 2012). In many samples, men and women show a difference in their tendencies regarding the divide between being people-oriented and thing-oriented. People-oriented individuals tend to be interested in professions and activities that involve dealing with and thinking about people (e.g., hospitality, counseling, and human management). In contrast, thing-oriented people focus on occupations and activities involving mechanical processes and systems (e.g., computer programming, engineering, and construction). Although this plays into many stereotypes and is probably influenced by socialization processes, studies found time and again that men tend to be more thing-oriented than women, while women tend to be more people-oriented than men (Lippa, 1998, 2010; Zheng et al., 2012).

In turn, at the current state of the robotic field, women have been shown in this study to be influenced by their people-orientation sides more than men. This might mean that more human-like robots may be perceived better by women, while machine-like robots and those currently on the market are perceived better by men. If this conclusion and conceptualization prove valid in future studies, this may lead to a broader issue in dealing with robots, relating to what robots may provide men and women.

Finally, this study is not without limitations. First, although the research method used has strengths regarding the number of respondents and the representation of the population in the United States in terms of age, income, and gender, the questions are not encompassing as in a full interview. The questions are also theoretical in nature rather than in an experiment where the respondents are exposed to the technology at hand. The reaction to actual robots may be different, as previous studies showed that exposure to technology affects people’s attitudes (Broekens et al., 2009; Lu et al., 2021). Despite the mixed-methods approach used here, experimental designs may be needed in future investigations to explore the effect of exposure on men and women in the four realms presented here and examine them by the four dimensions developed in this study.

Another limitation is that not enough cases were recorded in the datasets analyzed here regarding gender fluid people, members from the LGBTQ + community, and people of color. This is important as research on gender should be multidimensional and involves intersectionality, which may yield significant results in future studies (Dudek & Young, 2022; Lindqvist et al., 2021; Wingfield & Wingfield, 2014).

Moreover, the American and European contexts might be different from other contexts, such as the Japanese, where society at large is more accepting toward robots (Robertson, 2018) and thus might influence women’s attitudes, at least in the dimensions of social normativity and morality. Larger, more cross-cultural databases should be developed to address these questions (Korn et al., 2021).

Conclusion

In sum, researchers and policymakers should prepare society for the coming spread of social robots, with particular attention paid to the differences between men and women. This should be done systematically in at least four realms of interaction: physical, platonic, romantic, and sexual. In addition, each examination should take into account at least four dimensions of social attitudes toward such robots: social, psychological, moral, and functional.

Amazon’s and Tesla’s new focus on social robotics, revealed in late 2021, should not be taken lightly. Innovative powerhouses such as that of these two giants can swiftly change societal norms, and society must be ready. Social acceptance of personal robotics is still low, as this research and other studies have shown. But if the fast spread of mobile phones and electric vehicles is indicative of other emerging technologies, social robotics certainly merits our attention and should not be neglected or discussed only in specialized forums. Gender division, particularly in this field, proves to be acute and thus requires special consideration. This research, therefore, paves the way for further research in the field, regardless of any ethical stance researchers and policymakers might hold.

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Elyakim Kislev

Appendix

Appendix Table A1. Attitudes Toward Robots’ Physical Assistance, by Gender: Study 2.

	Women	Men
Years of schooling	−0.003 (0.022)	0.014 (0.020)
Age	−0.026 (0.125)	0.088 (0.108)
Relative income	0.211 (0.128)	−0.045 (0.122)
Religiosity	−0.006 (0.068)	0.027 (0.072)
Life satisfaction	−0.062 (0.133)	0.179 (0.112)
Happy with relationship status	−0.040 (0.117)	−0.171 (0.093)
In a relationship	−1.545^* (0.604)	1.073^* (0.514)
Friendship importance	−0.003 (0.108)	−0.071 (0.098)
Constant	7.188^*** (1.900)	4.093^* (1.905)
N	194	227
R ²	0.05	0.05

Standard errors in parentheses.

^* p < .05, ^** p < .01, ^*** p < .001.

Appendix Table A2. Attitudes Toward Robots’ Companionship, by Gender: Study 2.

	Women	Men
Years of schooling	−0.009 (0.021)	0.001 (0.020)
Age	−0.128 (0.123)	0.051 (0.107)
Relative income	0.478^*** (0.125)	0.053 (0.120)
Religiosity	0.076 (0.067)	−0.068 (0.071)
Life satisfaction	−0.133 (0.130)	0.229^* (0.110)
Happy with relationship status	−0.092 (0.115)	−0.294^** (0.091)
In a relationship	−0.476 (0.593)	0.748 (0.506)
Friendship importance	−0.010 (0.106)	−0.220^* (0.096)
Constant	4.512^* (1.866)	4.187^* (1.873)
N	194	227
R ²	0.12	0.07

Standard errors in parentheses.

* p < .05, ^** p < .01, ^*** p < .001.

Appendix Table A3. Attitudes Toward Romantic Relationships With Robots, by Gender: Study 2.

	Women	Men
Years of schooling	−0.047^*** (0.014)	0.018 (0.017)
Age	0.026 (0.082)	0.027 (0.092)
Relative income	0.207^* (0.083)	0.174 (0.103)
Religiosity	0.029 (0.044)	0.066 (0.061)
Life satisfaction	−0.004 (0.087)	0.067 (0.094)
Happy with relationship status	−0.137 (0.076)	−0.087 (0.078)
In a relationship	−0.177 (0.395)	0.195 (0.434)
Friendship importance	−0.108 (0.070)	−0.103 (0.083)
Constant	3.725^** (1.242)	0.701 (1.609)
N	194	227
R ²	0.19	0.05

Standard errors in parentheses.

^* p < .05, ^** p < .01, ^*** p < .001.

Appendix Table A4. Attitudes Toward Sexual Intimacy With Robots, by Gender: Study 2.

	Women	Men
Years of schooling	−0.033^** (0.012)	0.055^** (0.019)
Age	−0.012 (0.070)	0.009 (0.101)
Relative income	0.205^** (0.071)	0.126 (0.114)
Religiosity	0.063 (0.038)	0.020 (0.068)
Life satisfaction	−0.092 (0.074)	0.023 (0.104)
Happy with relationship status	−0.094 (0.065)	−0.161 (0.087)
In a relationship	−0.208 (0.336)	−0.179 (0.481)
Friendship importance	−0.089 (0.060)	0.000 (0.091)
Constant	3.306^** (1.058)	0.176 (1.782)
N	194	227
R ²	0.21	0.05

Standard errors in parentheses.

^* p < .05, ^** p < .01, ^*** p < .001.

Author Biography

Elyakim Kislev research articles are on the subjects of technology, singles studies, population studies, and relationships published in various academic journals. Kislev first book is Happy Singlehood: The Rising Acceptance and Celebration of Solo Living, published by the University of California Press and translated into eight languages. His second book is Relationships 5.0: How AI, VR, and Robots will Reshape our Emotional Lives, published by Oxford University Press, translated into five languages.

References

Appel

Marker

Mara

(2019). Otakuism and the appeal of sex robots [original research]. Frontiers in Psychology, 10(569), Article 569. https://doi.org/10.3389/fpsyg.2019.00569

Boch

Lucaj

Corrigan

(2021). A robotic new hope: Opportunities, challenges, and ethical considerations of social robots. Springer.

Broadbent

(2017). Interactions with robots: The truths we reveal about ourselves. Annual review of psychology, 68(1), 627–652. https://doi.org/10.1146/annurev-psych-010416-043958

Broekens

Heerink

Rosendal

(2009). Assistive social robots in elderly care: A review. Gerontechnology, 8(2), 94–103. https://doi.org/10.4017/gt.2009.08.02.002.00

Commission

(2021). Eurobarometer 87.1 (2017). GESIS datenarchiv, köln. ZA6861 datenfile Version 2.0.0. https://doi.org/10.4232/1.13738

de Graaf

M. M. A.

Ben Allouch

(2013). Exploring influencing variables for the acceptance of social robots. Robotics and Autonomous Systems, 61(12), 1476–1486. https://doi.org/10.1016/j.robot.2013.07.007

Del Giudice

(2011). Sex differences in romantic attachment: A meta-analysis. Personality and Social Psychology Bulletin, 37(2), 193–214. https://doi.org/10.1177/0146167210392789

Deniztoker

(2019). Lovotics and the big-five: An exploration of the psychology of human-robot intimacy. In 7th International Student Research Conference-ISRC.

Denzin

Lincoln

(2005). The Sage handbook of qualitative research (3 ed.). Sage Publications.

10.

Dindia

Allen

(1992). Sex differences in self-disclosure: A meta-analysis. Psychological Bulletin, 112(1), 106–124. https://doi.org/10.1037/0033-2909.112.1.106

11.

Dudek

S. Y.

Young

J. E.

(2022). Fluid sex robots: Looking to the 2LGBTQIA+ community to shape the future of sex robots. HRI, late breaking report.

12.

Eagly

A. H.

Johnson

B. T.

(1990). Gender and leadership style: A meta-analysis. Psychological Bulletin, 108(2), 233–256. https://doi.org/10.1037/0033-2909.108.2.233

13.

Feingold

(1994). Gender differences in personality: A meta-analysis. Psychological Bulletin, 116(3), 429–456. https://doi.org/10.1037/0033-2909.116.3.429

14.

Fischer

C. S.

(1988). Gender and the residential telephone, 1890–1940: Technologies of sociability. Sociological Forum, 3(2), 211–233. https://doi.org/10.1007/bf01115291

15.

Fortunati

Manganelli

A. M.

Höflich

Ferrin

(2022) How the social robot Sophia is mediated by a YouTube video. New Media & Society, Advanced online publication, 14614448221103176.

16.

Hancock

J. T.

Naaman

Levy

(2020). AI-mediated communication: Definition, research agenda, and ethical considerations. Journal of Computer-Mediated Communication, 25(1), 89–100. https://doi.org/10.1093/jcmc/zmz022

17.

Heimer

(1996). Gender, interaction, and delinquency: Testing a theory of differential social control. Social Psychology Quarterly, 59(1), 39–61. https://doi.org/10.2307/2787118

18.

Henkel

A. P.

Čaić

Blaurock

Okan

(2020). Robotic transformative service research: Deploying social robots for consumer well-being during Covid-19 and beyond. Journal of Service Management, 31(6), 1131–1148. https://doi.org/10.1108/josm-05-2020-0145

19.

Holt

J. L.

DeVore

C. J.

(2005). Culture, gender, organizational role, and styles of conflict resolution: A meta-analysis. International Journal of Intercultural Relations, 29(2), 165–196. https://doi.org/10.1016/j.ijintrel.2005.06.002

20.

Hsieh

H.-F.

Shannon

S. E.

(2005). Three approaches to qualitative content analysis. Qualitative health research, 15(9), 1277–1288. https://doi.org/10.1177/1049732305276687

21.

Hyde

J. S.

Bigler

R. S.

Joel

Tate

C. C.

van Anders

S. M.

(2019). The future of sex and gender in psychology: Five challenges to the gender binary. American Psychologist, 74(2), 171–193. https://doi.org/10.1037/amp0000307

22.

IFR (2020). Service robots record: Sales worldwide up 32%. https://ifr.org/ifr-press-releases/news/service-robots-record-sales-worldwide-up-32

23.

Jackson

R. B.

Williams

Smith

(2020). Exploring the role of gender in perceptions of robotic noncompliance. In Proceedings of the 2020 ACM/IEEE International Conference On Human-Robot Interaction, Sapporo, Japan, 7–10 March 2022.

24.

Kahn

P. H.

Kanda

Ishiguro

Gill

B. T.

Shen

Gary

H. E.

Ruckert

J. H.

(2015). Will people Keep the Secret of a humanoid robot? Psychological Intimacy in HRI. In Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction, Portland, OR, 02–05 March 2015.

25.

Kislev

(2022). Relationships 5.0: How, A. I., V. R., and robots will reshape our emotional lives. Oxford University Press.

26.

Korn

Akalin

Gouveia

(2021). Understanding cultural preferences for social robots: A study in German and Arab Communities. ACM Transactions on Human-Robot Interaction (THRI), 10(2), 1–19. https://doi.org/10.1145/3439717

27.

Krägeloh

C. U.

Bharatharaj

Sasthan Kutty

S. K.

Nirmala

P. R.

Huang

(2019). Questionnaires to measure acceptability of social robots: A critical review. Robotics, 8(4), 88. https://doi.org/10.3390/robotics8040088

28.

Lee

E.-J.

(2008). Flattery may get computers somewhere, sometimes: The moderating role of output modality, computer gender, and user gender. International Journal of Human-Computer Studies, 66(11), 789–800. https://doi.org/10.1016/j.ijhcs.2008.07.009

29.

Lindqvist

Sendén

M. G.

Renström

E. A.

(2021). What is gender, anyway: A review of the options for operationalising gender. Psychology & Sexuality, 12(4), 332–344. https://doi.org/10.1080/19419899.2020.1729844

30.

Lippa

(1998). Gender-related individual differences and the structure of vocational interests: The importance of the people–things dimension. Journal of Personality and Social Psychology, 74(4), 996–1009. https://doi.org/10.1037/0022-3514.74.4.996

31.

Lippa

R. A.

(2010). Sex differences in personality traits and gender-related occupational preferences across 53 nations: Testing evolutionary and social-environmental theories. Archives of Sexual Behavior, 39(3), 619–636. https://doi.org/10.1007/s10508-008-9380-7

32.

Liu

(2021). A.I. We trust? Effects of agency locus and transparency on uncertainty reduction in human–A.I. Interaction. Journal of computer-mediated communication, 26(6), 384–402. https://doi.org/10.1093/jcmc/zmab013

33.

L.-C.

Lan

S.-H.

Hsieh

Y.-P.

Lin

L.-Y.

Lan

S.-J.

Chen

J.-C.

(2021). Effectiveness of companion robot care for dementia: A systematic review and meta-analysis. Innovation in aging, 5(2), Article igab013. https://doi.org/10.1093/geroni/igab013

34.

Major

O’Brien

L. T.

(2005). The social psychology of stigma. Annual review of psychology, 56(1), 393–421. https://doi.org/10.1146/annurev.psych.56.091103.070137

35.

Mori

MacDorman

K. F.

Kageki

(2012). The uncanny valley. IEEE Robotics & Automation Magazine, 19(2), 98–100. https://doi.org/10.1109/mra.2012.2192811

36.

Naneva

Sarda Gou

Webb

T. L.

Prescott

T. J.

(2020). A systematic review of attitudes, Anxiety, acceptance, and trust towards social robots. International Journal of Social Robotics, 12(6), 1179–1201. https://doi.org/10.1007/s12369-020-00659-4

37.

Nolen-Hoeksema

(2012). Emotion regulation and psychopathology: The role of gender. Annual Review of Clinical Psychology, 8(1), 161–187. https://doi.org/10.1146/annurev-clinpsy-032511-143109

38.

Nomura

(2017). Robots and gender. Gender and the Genome, 1(1), 18–26. https://doi.org/10.1089/gg.2016.29002.nom

39.

Nordmo

Næss

J. Ø.

Husøy

M. F.

Arnestad

M. N.

(2020a). Friends, lovers or nothing: Men and women differ in their perceptions of sex robots and platonic love robots [original research]. Frontiers in Psychology, 11(355), Article 355. https://doi.org/10.3389/fpsyg.2020.00355

40.

Nordmo

Næss

J. Ø.

Husøy

M. F.

Arnestad

M. N.

(2020b). Friends, lovers or nothing: Men and women differ in their perceptions of sex robots and platonic love robots. Frontiers in Psychology, 11, 355. https://doi.org/10.3389/fpsyg.2020.00355.

41.

O’brien

K. E.

Biga

Kessler

S. R.

Allen

T. D.

(2010). A meta-analytic investigation of gender differences in mentoring. Journal of Management, 36(2), 537–554. https://doi.org/10.1177/0149206308318619

42.

Oleksy

Wnuk

(2021). Do women perceive sex robots as threatening? The role of political views and presenting the robot as a female-vs male-friendly product. Computers in Human Behavior, 117, Article 106664. https://doi.org/10.1016/j.chb.2020.106664

43.

Robertson

(2018). Robo sapiens japanicus: robots, gender, family, and the Japanese nation. University of California Press.

44.

Samani

(2016). The evaluation of affection in human-robot interaction. Kybernetes, 45(8), 1257–1272. https://doi.org/10.1108/k-09-2015-0232

45.

Scheutz

Arnold

(2016). Are we ready for sex robots? 2016 11th ACM. In IEEE International Conference on Human-Robot Interaction (HRI), Christchurch, New Zealand, 07–10 March 2016.

46.

Schneider

Wang

Yang

(2007). Exploring the uncanny valley with Japanese video game Characters. DiGRA Conference.

47.

Schur

E. M.

(1984). Labeling women deviant: Gender, stigma, and social control. Random House New York.

48.

Sundar

S. S.

(2020). Rise of machine agency: A framework for studying the psychology of human–A.I. Interaction (HAII). Journal of computer-mediated communication, 25(1), 74–88. https://doi.org/10.1093/jcmc/zmz026

49.

Tamres

L. K.

Janicki

Helgeson

V. S.

(2002). Sex differences in coping behavior: A meta-analytic review and an examination of relative coping. Personality and Social Psychology Review, 6(1), 2–30. https://doi.org/10.1207/s15327957pspr0601_1

50.

Tannenbaum

Ellis

R. P.

Eyssel

Zou

Schiebinger

(2019). Sex and gender analysis improves science and engineering. Nature, 575(7781), 137–146. https://doi.org/10.1038/s41586-019-1657-6

51.

Vega

Ramírez-Benavides

Guerrero

L. A.

López

(2019). Evaluating the NAO robot in the role of personal assistant: The effect of gender in robot performance evaluation. Multidisciplinary Digital Publishing Institute Proceedings, 31(1), 20. https://doi.org/10.3390/proceedings2019031020.

52.

Walters

A. E.

Stuhlmacher

A. F.

Meyer

L. L.

(1998). Gender and negotiator competitiveness: A meta-analysis. Organizational Behavior and Human Decision Processes, 76(1), 1–29. https://doi.org/10.1006/obhd.1998.2797

53.

Weston

Gandell

Beauchamp

McAlpine

Wiseman

Beauchamp

(2001). Analyzing interview data: The development and evolution of a coding system. Qualitative Sociology, 24(3), 381–400. https://doi.org/10.1023/a:1010690908200

54.

Whelan

Murphy

Barrett

Krusche

Santorelli

Casey

(2018). Factors affecting the acceptability of social robots by older adults including people with dementia or cognitive impairment: A literature review. International Journal of Social Robotics, 10(5), 643–668. https://doi.org/10.1007/s12369-018-0471-x

55.

Wingfield

A. H.

Wingfield

J. H.

(2014). When visibility hurts and helps: How intersections of race and gender shape Black professional men’s experiences with tokenization. Cultural Diversity and Ethnic Minority Psychology, 20(4), 483–490. https://doi.org/10.1037/a0035761

56.

Wright

P. H.

Scanlon

M. B.

(1991). Gender role orientations and friendship: Some attenuation, but gender differences abound. Sex Roles, 24(9), 551–566. https://doi.org/10.1007/bf00288413

57.

(2019). First encounter with robot Alpha: How individual differences interact with vocal and kinetic cues in users’ social responses. New Media & Society, 21(11–12), 2522–2547. https://doi.org/10.1177/1461444819851479

58.

Yao

M. Z.

Ling

(2020). What is computer-mediated communication?”—an introduction to the special issue. Journal of Computer-Mediated Communication, 25(1), 4–8. https://doi.org/10.1093/jcmc/zmz027

59.

Zheng

Hart

T. A.

Zheng

(2012). The relationship between intercourse preference positions and personality traits among gay men in China. Archives of Sexual Behavior, 41(3), 683–689. https://doi.org/10.1007/s10508-011-9819-0

The Robot-Gender Divide: How and Why Men and Women Differ in Their Attitudes Toward Social Robots

Abstract

Keywords

Introduction

Gendered Attitudes to Social Robots

Method

Samples

Quantitative Analysis Procedure of Study 1

Quantitative Analysis Procedure of Study 2

Qualitative Analysis Procedure of Study 2

Results

Study 1: Comparative Statistics

Study 1: Cross-Country Multilevel Regression Analyses

Study 2: Descriptive and Comparative Statistics

Study 2: Multivariate Regression Analyses

Study 2: Qualitative Analyses

Discussion

Conclusion

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iD

Appendix

Author Biography

References