F1000Research F1000Research 2046-1402 F1000 Research Limited London, UK 10.12688/f1000research.163126.1 Brief Report Articles Influence of Anshin-kan and Familiarity on Acceptance of Technologies – A Comparative Study of Brazil, China, and Japan

[version 1; peer review: 1 approved]

 Hugo Costa da SILVA Vithor Data Curation Formal Analysis Investigation Methodology Visualization Writing – Original Draft Preparation Writing – Review & Editing 1 You Zhenwei Data Curation Validation Writing – Review & Editing 2 Koyama Shinichi Conceptualization Funding Acquisition Investigation Methodology Project Administration Resources Supervision Validation Writing – Review & Editing https://orcid.org/0000-0001-6280-5750 a 3 Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Ibaraki Prefecture, 3058574, Japan School of Digital Media and Design Art, Beijing University of Posts and Telecommunications, Beijing, Beijing, 100876, China Institute of Art and Design, University of Tsukuba, Tsukuba, Ibaraki Prefecture, 3058574, Japan skoyama@geijutsu.tsukuba.ac.jp

No competing interests were disclosed.

 22 4 2025 2025 14 449 16 4 2025 Copyright: © 2025 Hugo Costa da SILVA V et al. 2025 This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background

The challenge of understanding the factors that influence the acceptance of new technologies persists worldwide, impacting policymaking for technologies that could benefit society but still lack acceptance. Research has shown that perceived risk and familiarity affect the acceptance of technological risks and that these perceptions vary across cultural contexts. Anshin-kan (a sense of security), a concept linked to safety, fear, and anxiety, may also influence people’s perceptions of technology. However, the correlations among anshin-kan , familiarity, and technology acceptance remain unclear. Additionally, how these relationships vary according to cultural background and technology type remains undefined. This study examines the relationships among anshin-kan , familiarity, and acceptance of 50 scientific technologies across three countries known for their differing levels of technology acceptance and implementation: Brazil, China, and Japan.

 Methods

We surveyed 120 participants (35 Brazilian, 43 Chinese, 28 Japanese, and 14 from other countries) to evaluate 50 scientific technology-related words. The variables evaluated were anshin-kan , familiarity, and acceptance. We investigated the nature of the relationship between variables using a bivariate Kendall’s tau correlation analysis. Differences in the evaluations regarding nationality, gender, age, and specialization field were analysed using a one-way analysis of variance.

 Results

Although anshin-kan and familiarity were positively correlated with technology acceptance, the strength of these relationships differed by country and technology type. The average evaluations did not show significant differences for nationality, age, and specialization field; however, considering gender, men evaluated technologies with higher anshin-kan and familiarity than women.

 Conclusions

This study contributes to the understanding of cross-cultural differences in technology adoption by highlighting the importance of considering anshin-kan in a cross-country analysis. However, the causes of variations in correlations across different technologies and countries remain unclear. In addition, anshin-kan involves multiple factors that should be considered in future studies.

 Technology Acceptance Sense of Security Anshin-kan Familiarity Japan Society for the Promotion of Science 20K21823 This study was supported by a Grant-in-Aid for Scientific Research (Exploratory) (20K21823) awarded to Shinichi Koyama. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Introduction

In 1987, Slovic 1 showed that the acceptance of risks associated with technologies is influenced by the perception of risk (riskiness) and by familiarity. Moreover, this relationship varies depending on the type of technology. Slovic aimed to enhance risk analysis by predicting public reactions to emerging technologies, to thereby, promote effective policy development.

The challenge of understanding the factors that influence the acceptance of new technologies is persistent worldwide. For example, Schoettle & Sivak 2 found that although China exhibits high familiarity with and positive attitudes toward autonomous vehicles, safety concerns remain significant. In contrast, Japan demonstrates low familiarity but lower levels of safety concerns. Brazil has not yet adopted autonomous driving technologies, primarily because of economic and infrastructural barriers, as shown by its lower ranking (30th) on the KPMG Autonomous Vehicles Readiness Index in 2020, compared to Japan (11th) and China (20th). 3

Following Slovic’s research, 1 subsequent studies indicated that reducing anxiety and fear is crucial for fostering the acceptance of new technologies—including autonomous vehicles and human-like robots. 4– 8 Similarly, familiarity has been shown to enhance positive perceptions through repeated exposure, reduce the fear of novelty, and facilitate efficient cognitive processing. 9 Familiarity also serves as a heuristic cue for safety, thereby influencing risk acceptance. 10

Recent studies have emphasised the role of anshin-kan , most frequently translated as a ‘sense of security’. 11 Anshin-kan implies a belief in safety before an event occurs 12 or a state characterised by the absence of negative emotions such as surprise, anxiety, and fear. 13

Building on these findings, we consider the question, ‘How do anshin-kan and familiarity correlate with acceptance of technology?’ Moreover, ‘How do countries and different technologies differ on those correlations?’ We hypothesise that both anshin-kan (H1) and familiarity (H2) are positively correlated with the acceptance of technologies. 1, 4– 10 However, the strength and nature of these relationships may vary, depending on factors such as the type of technology (H3) 1, 4 and the cultural context (H4) in which the technology is introduced. 7, 14– 16

Examining the influence of anshin-kan and familiarity with the acceptance of emerging technologies can provide valuable insights into policy development and risk assessments in the context of future technological advancements in different countries. In addition, anshin-kan is a relatively new concept, and due to its complexity and connection with technology acceptance, further studies are still necessary.

To address the proposed hypotheses, this study investigated the relationships among anshin-kan , familiarity, and acceptance of technologies across three countries: Brazil, China, and Japan.

 Methods Questionnaire

We selected 50 scientific-technology-related topics to be evaluated by the participants in terms of anshin-kan , familiarity, and acceptance. We initially selected scientific technologies based on the work of 17 and terms that represent present and future technologies, some of which are defined in Industry 5.0 and Society 5.0 studies. 18 Anshin-kan was measured by participants’ evaluation of the question, ‘Do you feel safe with the following science and technology?’ This question was answered on a 5-point Likert scale: 1 = ‘Totally disagree’, 2 = ‘Disagree’, 3= ‘I am not sure’, 4 = ‘Agree’, and 5 = ‘Totally agree.’ Acceptance was measured with the question, ‘To what extent are you willing to accept the following science and technology?’ This was also evaluated on a 5-point Likert scale: 1 = ‘Cannot accept’, 2 = ‘Somewhat no acceptable’, 3 = ‘I don’t know’, 4 = ‘Somewhat acceptable’, and 5 = ‘Acceptable’. Familiarity was assessed by, ‘How familiar do you feel you are with the following science and technology?’ Responses were evaluated on a 3-point Likert scale: 1 = ‘I don’t know’, 2 = ‘I know a little about it’, and 3 = ‘I know a lot about it’. In addition, we used attention-confirmation questions. We asked ‘2+2=?’ on which the correct answer to be chosen should be ‘4’, and ‘If you want to delete data, select the topmost or middle option. If you do not want to delete data, select the option at the bottom’.

Participants were also asked about their education level, specialization field at university or college, age, and gender. The questionnaires were presented using the online platform SurveyMonkey in English, Portuguese, Chinese, and Japanese, and native speakers double-checked all translations. The four versions of the questionnaire are available on Zenodo. 19

Participants

After removing completed questionnaires with incomplete answers and participants who did not correctly answer the attention-confirming questions, a total of 120 participants were included in the analysis: 35 Brazilians (15 men, 20 women), 43 Chinese (15 men, 28 women), 28 Japanese (9 men, 19 women), and 14 participants from other countries (3 men, 11 women). The average age of the participants was 29 years (range, 17–68 years). Although Japan is technically ready for technologies such as autonomous cars, 3 these technologies are widespread in China. Conversely, Brazil is not ready to adopt certain technologies and lacks information on how they will be accepted by society in the near future. These aspects, which differentiated the three countries, led us to consider them as the focus of this study.

The dataset of the completed questionnaires of the 120 participants is available on Zenodo. 20

All the participants provided informed consent before participating in the survey. The instructions about the survey were shown to the participant and if they consented, they could answer “I agree” with the consent term before starting. This was approved by the Institutional Review Board (IRB) of the Institute of Art and Design, the University of Tsukuba, according to the principles of the Declaration of Helsinki, on November 8, 2024, with the approval number GEI024-16. To ensure privacy, no identifiable information was collected.

 Descriptive analysis and differences in the evaluations

The participants evaluated 50 technology-related words in terms of anshin-kan , familiarity, and acceptance. The average scores for these evaluations were calculated and analysed using a one-way analysis of variance (ANOVA), with nationality as the independent variable. This analysis allowed us to determine the possible differences in nationality. To provide information on how different technologies are perceived by participants from diverse backgrounds, we calculated the average scores of acceptance, anshin-kan , and familiarity for each technology and each country.

 Correlations of <italic toggle="yes">anshin-kan </italic> and familiarity with acceptance (nationality)

We calculated the average evaluations of anshin-kan , familiarity, and acceptance of all 50 topics. To examine the relationships among these three variables for each country, we used Kendall’s tau Bivariate Correlation, which is suitable for nonparametric data such as Likert scale evaluations. 21 This method allowed us to investigate the strength and direction of the relationships among anshin-kan, familiarity, and acceptance of technologies, and how these relationships vary across countries. The results determined whether our hypotheses (H1, H2, and H4) are supported.

 Correlations of <italic toggle="yes">anshin-kan </italic> and familiarity with acceptance (technology type)

Based on the average evaluations of anshin-kan , familiarity, and acceptance for each of the 50 topics, we calculated the correlations among all three variables for each country, using Kendall’s Tau Bivariate Correlation. This approach enabled us to evaluate the influence of anshin-kan and familiarity on the acceptance of each technology, thereby confirming or rejecting our hypothesis H3.

 Results Descriptive analysis

The participants’ primary fields of specialization were Arts & Design (42.5%), Humanities & Social Sciences (26.7%), Engineering (10.8%), other fields (11.7%), and no specialization (8.3%). A total of 35.8% of participants had an undergraduate degree, while 39.2% held a master’s degree. The remaining participants (21.7%) had completed high school, a Ph.D., or vocational school. Only 3.3% had not attained any of the educational levels mentioned.

The mean values for anshin-kan , familiarity, and acceptance show distinct patterns across the three countries. For anshin-kan , Brazil presented the highest evaluation (M=0.63, SD=0.55), followed by China (M=0.40, SD=0.52), and Japan (M=0.36, SD=0.51). This suggests that anshin-kan is more pronounced in Brazil. Regarding acceptance, China (M=1.07, SD=0.55) had a slightly higher mean than Brazil (M=0.98, SD=0.44) and Japan (M=0.91, SD=0.44). Regarding familiarity, China (M=1.14, SD=0.19) had the highest mean evaluation, followed by Brazil (M=1.05, SD=0.30) and Japan (M=1.01, SD=0.27).

Regarding variability, anshin-kan and acceptance displayed high standard deviations across all three countries, indicating significant variations in responses, whereas the low variability in familiarity suggests a strong consensus on participants’ responses.

The average scores for the evaluation of anshin-kan , familiarity, and acceptance were analysed using a one-way ANOVA, with nationality as the independent variable. Shapiro-Wilk tests indicated that all three variables were normally distributed for each nationality ( p > 0.05). Levene’s test showed homogeneity of variance across countries for anshin-kan (F[3, 116] = 0.03, p = 0.99), familiarity (F[3, 116]) = 1.51, p = 0.22), and acceptance (F[3, 116] = 0.33, p = 0.81).

For anshin-kan (F[3,116] = 1.83 , p = 0.15 , η 2 = 0.05), familiarity (F[3,116] = 1.70 , p = 0.17 , η 2 = 0.04), and acceptance of technologies in general (F[3,116] = 0.77 , p = 0.51 , η 2 = 0.02), no significant difference was found between the three countries. This suggests that the mean scores for these evaluations were similar across all three countries, as shown in Table 1. Table 2 shows the average scores on acceptance, anshin-kan , and familiarity for each country and technology type.

One-way ANOVA comparing <italic toggle="yes">anshin-kan </italic>, acceptance, and familiarity between countries.
Variable Sum of Squares df Mean Square F Sig. η 2
Anshin-kan Between Groups 1.49 3 0.50 1.83 0.15 0.05
Within Groups 31.41 116 0.27
Total 32.90 119
Familiarity Between Groups 0.32 3 0.11 1.70 0.17 0.04
Within Groups 7.27 116 0.06
Total 7.59 119
Acceptance Between Groups 0.43 3 0.15 0.77 0.51 0.02
Within Groups 21.72 116 0.19
Total 22.16 119
Average scores of acceptance, <italic toggle="yes">anshin-kan </italic>, and familiarity for each country and technology type.
Scientific technology-related topics Acceptance Anshin-kan Familiarity
- Brazil China Japan Brazil China Japan Brazil China Japan
Urban beekeeping 0.34 0.88 0.89 -0.11 0.35 0.07 1.20 1.14 1.11
Insect food 1.06 1.70 1.43 0.57 1.05 0.75 1.03 1.26 1.07
Dams 1.29 0.95 1.00 0.77 0.33 0.21 1.00 1.23 0.93
Functional foods 1.63 1.47 1.61 1.17 1.00 0.75 1.20 1.42 1.14
Facial recognition systems (biometric security systems) 0.97 1.63 1.46 0.49 0.98 1.25 1.00 1.16 1.11
Automated delivery robots 0.03 1.26 1.11 -0.17 0.28 0.54 0.83 0.91 0.86
Automated private vehicles 0.34 1.21 1.18 0.23 0.67 0.61 0.89 0.98 1.04
Cultured meat 1.57 1.77 1.64 1.29 1.26 1.39 1.29 1.40 1.21
Artificial sweeteners 0.94 1.54 0.96 0.11 0.58 0.11 1.37 1.58 1.25
Drones 1.03 1.42 0.86 0.57 0.51 0.46 1.34 1.19 0.68
Food additives 1.37 1.67 1.25 0.77 1.12 0.86 1.37 1.51 1.00
AI (Artificial Intelligence) 1.43 1.65 1.64 0.80 1.12 1.07 1.31 1.56 1.07
Social media 1.54 0.51 1.14 0.77 -0.37 0.29 1.49 1.12 1.25
Virtual assistants 1.89 1.81 1.46 1.54 1.16 1.00 1.14 1.44 1.04
Nuclear energy 1.71 1.79 1.79 1.37 0.93 1.25 1.46 1.58 1.57
Corona vaccines 1.29 1.47 1.29 0.63 0.70 0.50 1.29 1.33 1.29
Pet robots 1.80 1.07 0.64 1.60 0.26 0.07 1.34 1.28 1.18
Microwave ovens 1.66 1.14 0.96 0.97 0.54 0.14 0.63 0.70 0.68
X-rays 1.74 0.93 0.96 1.43 0.54 0.57 0.77 0.84 0.93
Cloning technology 0.40 0.74 0.07 0.29 -0.05 -0.61 1.29 1.07 1.07
Facial transplants 0.60 0.56 -0.07 0.34 -0.16 -0.82 0.97 0.98 0.71
Security robots 1.94 1.79 1.43 1.80 1.33 1.14 1.37 1.21 1.29
Artificial cells 1.23 1.05 0.75 0.86 0.56 0.50 0.74 0.86 0.64
5G 0.94 1.42 1.46 0.63 0.98 0.71 0.94 1.16 1.25
Generic medicines 1.66 1.14 1.25 1.26 0.65 1.07 1.06 0.77 0.96
Electronic voting 0.51 0.67 0.54 0.14 0.00 -0.54 0.63 0.98 0.64
Reception robots -0.34 0.12 -0.36 -0.51 -0.44 -0.68 0.69 1.14 0.96
Webcams 0.43 0.86 0.29 0.23 0.21 0.00 0.57 0.93 0.75
Solar power 0.31 1.12 0.71 0.00 0.14 0.00 0.97 1.26 1.00
E-commerce 0.49 0.63 0.93 0.60 0.02 0.46 1.03 1.19 1.07
Pesticides -0.06 1.00 0.93 -0.51 0.07 0.43 1.09 1.28 1.11
Hydrogen engines 1.83 0.91 1.36 1.51 0.30 1.07 1.29 0.95 1.21
Virtual reality goggles 1.00 1.42 1.04 0.63 0.65 0.50 0.80 1.19 1.00
Cancer vaccines 0.63 1.02 1.07 0.26 0.81 0.86 0.40 0.63 0.79
MRI (magnetic resonance imaging) 0.09 0.47 0.32 0.09 0.02 0.11 0.17 0.44 0.39
Wearables (e.g. smart watches, smart rings) 0.51 -0.14 -0.32 0.06 -0.16 -0.39 0.80 0.86 1.00
Automated trains 0.23 -0.09 0.29 0.17 -0.47 0.04 0.60 1.02 0.75
Gene therapy 0.34 0.81 0.75 -0.11 0.23 0.07 1.17 1.14 1.00
Nuclear fusion 0.14 0.86 1.00 0.11 0.35 0.43 0.86 1.16 1.00
Biomass fuels 0.94 0.95 1.11 0.86 0.26 0.57 0.57 0.70 0.68
Electric vehicles (EV) 1.74 1.84 1.79 1.03 1.02 0.89 1.63 1.77 1.64
Plant factories 0.97 1.63 0.96 0.23 0.23 -0.14 1.66 1.70 1.54
Genetically modified crops -1.74 -1.33 -1.18 -1.20 -1.33 -1.46 1.11 0.93 0.79
HIV vaccine 1.69 1.56 0.86 1.26 0.93 0.00 1.34 1.19 0.79
Chemical fertilisers 1.31 1.14 1.25 0.77 0.40 0.61 1.23 1.37 1.29
Proton therapy 1.71 1.19 1.68 1.34 0.05 0.61 1.40 1.12 1.25
Cleaning robots 1.69 1.49 1.00 1.23 0.49 0.57 1.14 1.09 0.86
Cyber terrorism 1.91 1.23 1.39 1.69 0.23 1.00 1.29 1.26 1.39
Smartphones 0.34 0.12 -0.75 0.03 -0.51 -0.96 0.46 0.70 0.18
Blood transfusion 1.80 1.30 0.89 1.57 0.16 0.21 1.34 1.19 0.96
Correlations of anshin-kan and familiarity with acceptance (nationality)

We analysed the correlations among the average evaluations ( N = 50) of anshin-kan , familiarity, and acceptance. The correlation coefficients showed that Brazil relies most heavily on anshin-kan ( τ = 0.84 , p < 0.001), followed by Japan ( τ = 0.75 , p < 0.001) and then China ( τ = 0.69, p < 0.001) in terms of accepting technologies. In contrast, regarding familiarity, China ( τ = 0.48, p < 0.001) placed the greatest emphasis on this parameter, followed by Japan ( τ = 0.46, p < 0.001) and Brazil ( τ = 0.38, p < 0.001).

 Correlations in <italic toggle="yes">anshin-kan </italic>, familiarity, and acceptance (technology type)

Based on the average evaluations of anshin-kan , familiarity, and acceptance for each of the 50 topics, we calculated the correlations among all three variables for each country using Kendall’s Tau Bivariate Correlation. Table 3 shows the correlation coefficients between anshin-kan and acceptance, separated by topic and country and ordered starting from topics with higher coefficients for acceptance- anshin-kan for Brazil. Table 3 presents the correlation coefficients between familiarity and acceptance for the three countries.

Correlation coefficients (τ) between acceptance and <italic toggle="yes">anshin-kan, </italic> and between acceptance and familiarity for Brazil, China, and Japan (** p < 0.001, * p < 0.05).
Scientific technology-related topics Acceptance– Anshin-kan Acceptance–Familiarity
- Brazil China Japan Brazil China Japan
Urban beekeeping 0.81** 0.55** 0.60** 0.26 0.38** 0.43*
Insect food 0.78** 0.46** 0.55** -0.05 0.27 0.18
Dams 0.72** 0.29* 0.27 0.52** 0.22 0.26
Functional foods 0.69** 0.18 0.50** 0.51** 0.40** 0.22
Facial recognition systems (biometric security systems) 0.66** 0.18 0.39* -0.00 0.09 0.16
Automated delivery robots 0.66** 0.17 0.42* 0.08 0.18 0.27
Automated private vehicles 0.65** 0.29* 0.36* 0.17 0.03 0.42*
Cultured meat 0.65** 0.28* 0.74** 0.24 0.27 0.39*
Artificial sweeteners 0.64** 0.43** 0.74** 0.04 0.43** 0.09
Drones 0.64** 0.48** 0.57** 0.38* 0.39** 0.36
Food additives 0.64** 0.39** 0.68** 0.05 0.27 -0.34*
AI (Artificial Intelligence) 0.63** 0.43** 0.42* -0.04 0.17 0.31
Social media 0.62** 0.21 0.13 -0.04 0.26 0.18
Virtual assistants 0.61** 0.16 0.29 0.33* 0.44** 0.56**
Nuclear energy 0.61** 0.23 0.41* 0.20 0.12 -0.12
Corona vaccines 0.60** 0.41** 0.67** -0.10 0.17 -0.14
Pet robots 0.60** 0.38** 0.58** 0.39** 0.15 0.33
Microwave ovens 0.60** 0.13 0.48** 0.19 -0.02 -0.01
X-rays 0.60** 0.34* 0.31 0.22 0.38** 0.36
Cloning technology 0.60** 0.45** 0.62** 0.10 0.06 0.23
Facial transplants 0.57** 0.41** 0.44** 0.45** 0.00 0.14
Security robots 0.55** 0.23 0.42* 0.10 0.12 0.30
Artificial cells 0.55** 0.53** 0.34* 0.01 0.34* 0.23
5G 0.54** -0.01 0.44** 0.25 0.18 0.29
Generic medicines 0.54** 0.59** 0.39* 0.16 0.39** 0.17
Electronic voting 0.53** 0.06 0.53** 0.13 0.02 0.07
Reception robots 0.50** 0.34* 0.37* 0.25 0.24 0.31
Webcams 0.49** 0.33* 0.15 0.04 0.21 0.51**
Solar power 0.49** -0.09 0.47** -0.07 -0.13 -0.22
E-commerce 0.49** -0.02 0.41* 0.43** -0.03 0.66**
Pesticides 0.49** 0.30* 0.59** -0.11 0.35* 0.09
Hydrogen engines 0.47** 0.26 0.44** 0.39* 0.36* 0.27
Virtual reality goggles 0.47** 0.01 0.41* 0.32* 0.29 0.30
Cancer vaccines 0.45** 0.29* 0.46** 0.09 0.22 -0.13
MRI (magnetic resonance imaging) 0.45** 0.22 0.59** 0.54** 0.19 0.62**
Wearables (e.g. smart watches, smart rings) 0.44** 0.34* 0.07 0.23 0.15 0.14
Automated trains 0.44** 0.37** 0.55** 0.28 0.25 0.37*
Gene therapy 0.42** 0.39** 0.49** 0.38* 0.01 0.04
Nuclear fusion 0.42** 0.28* 0.28 0.37* 0.19 0.18
Biomass fuels 0.36* 0.45** 0.45** 0.38* 0.42** 0.33
Electric vehicles (EV) 0.36* 0.49** -0.01 0.22 0.28 -0.10
Plant factories 0.35* 0.23 0.77** 0.66** 0.22 0.48**
Genetically modified crops 0.33* 0.52** 0.65** 0.25 0.21 -0.17
HIV vaccine 0.33* 0.43** 0.46** 0.49** 0.33* -0.08
Chemical fertilisers 0.31* 0.15 0.67** -0.02 0.24 0.33
Proton therapy 0.28 0.35* 0.46** 0.07 0.34* 0.72**
Cleaning robots 0.24 0.19 0.29 0.48** 0.13 0.21
Cyber terrorism 0.14 0.51** 0.34 -0.15 -0.32* -0.30
Smartphones 0.14 0.07 0.16 0.24 0.26 -0.21
Blood transfusion 0.01 0.28* 0.38* 0.27 0.15 0.08
Demographic influences on acceptance of technology (gender, age, and specialization field)

The average scores for the evaluation of anshin-kan , familiarity, and acceptance were analysed using a one-way ANOVA with age, gender, and specialization field as separate independent variables. Anshin-kan ( p = 0.01) and familiarity ( p = 0.01) results showed significant differences between men and women. On average, the men scored higher on anshin-kan and familiarity. Acceptance of technology showed no significant differences in terms of gender ( p = 0.22). We did not find any significant differences in anshin-kan ( p = 0.28), familiarity ( p = 0.63), or acceptance of technology ( p = 0.67) in terms of age. Similarly, we found no significant differences in anshin-kan ( p = 0.53), familiarity ( p = 0.35), or acceptance of technology ( p = 0.49) in terms of specialization field.

 Discussion Descriptive analysis

Despite it was not found significant differences on the average scores of acceptance, anshin-kan , and familiarity for each country, we observed different trends of those scores for each technology type. Those differences motivated us to investigate the relationship of technology acceptance with anshin-kan and familiarity.

 Correlations of anshin-kan and familiarity with acceptance (nationality)

Consistent with H1 and H2, the results indicate that both anshin-kan and familiarity are positively correlated with acceptance of technology. 1, 4– 10 Although the results of our one-way ANOVA revealed no significant differences in the mean evaluations, the strength of the correlations was distinct between countries, suggesting different patterns of association—depending on nationality—as expected from Hypothesis H4. 7, 14– 16

Correlations in <italic toggle="yes">anshin-kan </italic>, familiarity, and acceptance (technology type)

Although past studies found that anshin-kan and familiarity would be positively correlated with the acceptance of new technologies, they did not show differences across technologies and countries. The present study demonstrated that the strength of these relationships also varied by type of technology.

The results found for the correlations in anshin-kan , familiarity, and acceptance by technology type also show that Brazilian participants demonstrate a greater reliance on anshin-kan, as indicated by the strong and moderate correlation coefficients. Brazil had the highest number of scientific technology topics with strong and moderate coefficients (39), followed by Japan (33) and China. 15

This finding is consistent with Hypotheses H1 and H3. 1, 4 Anshin-kan was again positively correlated with technology acceptance, regardless of the nationality of the participants or the type of technology. In addition, different technologies show different relationships between anshin-kan use and acceptance. The same is true for the correlation between familiarity and acceptance.

More than merely determining acceptability, these coefficients highlight the importance of anshin-kan in the acceptance of a specific technology. For example, although the average acceptance score for AI indicated that Chinese participants (M = 1.54) accept it more readily than Brazilians (M = 0.94) and Japanese participants (M = 0.96), the correlation coefficients suggest that Brazilians require a higher level of anshin-kan than the other two groups.

 Demographic influences on acceptance of technology (gender, age, and specialization field)

The differences in the evaluations of anshin-kan and familiarity were found regarding gender. In general, men showed higher anshin-kan and familiarity. However, it is important that for further analysis on demographic influence on the evaluations the sample size should be big enough that we could investigate it for each country separately.

 Conclusion

The results of this study confirm that the acceptance of technologies is positively correlated with anshin-kan and familiarity across different countries, which aligns with previous research findings on the perception of risk and acceptance. 1, 4– 10 However, the strength of these correlations varies among the three countries of Brazil, China, and Japan. Additionally, the relationship between acceptance and anshin-kan depends on the type of scientific technology used. Some technologies showed strong, moderate, or weak correlations, whereas others showed no correlation.

Among the three countries, Brazil exhibited the strongest dependence on anshin-kan and the highest number of technology topics, with strong to moderate correlations between anshin-kan and technology acceptance. The underlying reasons for this are yet to be explored, as they may involve multiple factors, including cultural influences (e.g., cultural dimensions such as Collectivism and Individualism), 16 the political and economic context of the country, 3 perceived benefits, 1 and perceived usefulness. 22

This study contributes to the understanding of cross-cultural differences in technology adoption, highlighting the importance of considering anshin-kan in cross-country analyses for technology decision makers. In addition, the necessity of anshin-kan for technology acceptance underscores the importance of investments in science communication and public demonstrations to assure society of the safety of these technologies. Future research should investigate the causes of variations in correlations across different technologies and countries. Additionally, anshin-kan is a complex concept that cannot simply be reduced to feeling safe or the absence of fear. It may involve multiple factors 23 influencing this perception. Future studies should explore these elements of anshin-kan using technology acceptance research.

 Ethical considerations

This study recruited 120 participants (78 women) aged 17 to 68 years (M = 29, SD = 8) through sharing the online survey designed in SurveyMonkey from November 16, 2024, until January 9, 2025. All the participants provided informed written consent before participating in the survey. The instructions about the survey were shown to the participant and if they consented, they could answer “I agree” with the consent term before starting. The research was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Institutional Review Board (IRB) of the Institute of Art and Design, the University of Tsukuba with the approval number GEI024-16, approved on on November 8, 2024.

 Data availability statement Underlying data

Costa da Silva VH. Influence of Anshin-kan and Familiarity on Acceptance of Technologies. Zenodo; 2025. https://doi.org/10.5281/zenodo.15011517. 19

This project contains the following underlying data:

Influence of Anshin-kan and Familiarity on Acceptance of Technologies Data.xlsx - The dataset contains the 120 participants’ evaluations on anshin-kan , familiarity, and acceptance for 50 technology-related words. In addition, demographic data such as educational level, specialization field, age, gender, and nationality are included.

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

 Extended data

Costa da Silva VH. Questionnaire used in the study on technology acceptance, familiarity and anshin-kan. Zenodo; 2025. https://doi.org/10.5281/zenodo.15023801. 20

This project contains the following underlying data:

Questionnaire used in the study on technology acceptance, familiarity, and anshin-kan. The files available are the questionnaires in each of the languages used in this study, which are Portuguese, Japanese, Chinese, and English. The questionnaire contains questions that evaluate acceptance, familiarity, and anshin-kan of 50 technology-related words, as well as demographic questions related to the participant.

Study on technology acceptance, familiarity and anshin-kan - Portuguese version.pdf

Study on technology acceptance, familiarity and anshin-kan - Japanese version.pdf

Study on technology acceptance, familiarity and anshin-kan - Chinese version.pdf

Study on technology acceptance, familiarity and anshin-kan - English version.pdf

These materials are licensed under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license, permitting unrestricted use, distribution, and reproduction, provided the original authors and the present archived datasets are appropriately cited.

 References Slovic P : Perception of risk. Science. 1987;236(4799):280–285. 10.1126/science.3563507 Schoettle B Sivak M : Public opinion about self-driving vehicles in China, India, Japan, the U.S., the U.K., and Australia. The University of Michigan Transportation Research Institute;2014 [cited October 2014]. Report No.: UMTRI-2014-30. Reference Source KPMG International: Autonomous vehicles readiness index: assessing the preparedness of 30 countries and jurisdictions in the race for autonomous vehicles [pdf]. 2020 [cited July 2020]. Reference Source Tanaka Y : Major factors of deciding public acceptance of a variety of technology. Jpn J Exp Soc Psychol. 1995;35(1):111–117. 10.2130/jjesp.35.111 Suda Y Oguchi T Nakano K : Survey on societal implementation of automated driving systems. Seisan Kenkyu [Prod Res]. 2016;68(2):95–98. 10.11188/seisankenkyu.68.95 Kamide H Arai K : Anshin toward human-like robots. Sci Technol Soc. 2018;16:43–53. 10.24646/jnlsts.16.0_43 Taniguchi A Enoch M Theofilatos A : Understanding acceptance of autonomous vehicles in Japan, UK, and Germany. Urban Plan Transp Res. 2022;10(1):514–535. 10.1080/21650020.2022.2135590 Yao S Xie L Chen Y : Influence of perceived safety in the technology acceptance model. Transp Res F. 2023;99:36–51. 10.1016/j.trf.2023.10.010 Vries M de Holland RW Chenier T : Happiness cools the warm glow of familiarity: psychophysiological evidence that mood modulates the familiarity-affect link. Psychol Sci. 2010;21(3):321–328. 20424063 10.1177/0956797609359878 PMC2948957 Fischhoff B Slovic P Lichtenstein S : How safe is safe enough? A psychometric study of attitudes towards technological risks and benefits. Policy Sci. 1978;9(2):127–152. 10.1007/BF00143739 Shoji Y : How to manage ‘anshin-kan’ from the perspective of kansei-value creation. J Jpn Soc Kansei Eng. 2021;19(4):159–162. 10.5057/kansei.19.4_159 Yanagisawa H : Belief in safety and its mathematical model.In: Safety Engineering Symposium 2021;2021 [cited July 2021]. Reference Source Hashimoto T Yanagisawa H . Modeling feeling of safety of autonomous robot behavior. Proceedings of Conference of Kanto Branch, Japan Society of Mechanical Engineers. 2021. 10.1299/jsmekanto.2021.27.11C11 Haring KS Mougenot C Ono F : Cultural differences in perception and attitude towards robots. Int J Affect Eng. 2014;13(3):149–157. 10.5057/ijae.13.149 Müller JM : Comparing technology acceptance for autonomous vehicles, battery electric vehicles, and car sharing—a study across Europe, China, and North America. Sustainability. 2019;11(16):4333. 10.3390/su11164333 Chang RX Ortiz-Marcos I Rodriguez-Rivero R : A cross-cultural comparison of the impact of individualism–collectivism on risk perception of engineering students from Brazil and Spain. Int J Eng Educ. 2023 [cited March 2025];39(1):108–18. Reference Source Slovic P Fischhoff B Lichtenstein S : Characterizing perceived risk. Kates RW Hohenemser C Kasperson JX , editors. Perilous progress: managing the hazards of technology. Boulder (CO): Westview Press;1985 [cited December 2012]. pp.91–125. Reference Source Maddikunta PKR Pham QV Prabadevi B : Industry 5.0: a survey on enabling technologies and potential applications. J Ind Inf Integr. 2022 [cited March 2022];26:100257. 10.1016/j.jii.2021.100257 Costa da Silva VH : Influence of anshin-kan and familiarity on acceptance of technologies. Zenodo. 2025. 10.5281/zenodo.15011517 Costa da Silva VH : Questionnaire used on the study on technology acceptance, familiarity and anshin-kan. Zenodo. 2025. 10.5281/zenodo.15023801 Keown LL Hakstian AR : Measures of association for the component analysis of likert scale data. J Exp Educ. 1973;41(3):22–27. 10.1080/00220973.1973.11011405 Mhangami W King S Barry D : Accepting technology in aviation safety risk management: an extension of the technology acceptance model. Proc Annu Conf PHM Soc. 2024. 2024 Nov 11;16(1). 10.36001/phmconf.2024.v16i1.3922 Murayama Y Hikage N Fujihara Y : The structure of the sense of security, anshin. Lopez J Hämmerli BM , editors. Critical Information Infrastructures Security. CRITIS 2007. Berlin, Heidelberg: Springer;2008; pp.83–93. 10.1007/978-3-540-89173-4_8 10.5256/f1000research.179426.r389007 Reviewer response for version 1 Zuva Tranos 1 Referee Vaal University of Technology, Vaal, South Africa

Competing interests: No competing interests were disclosed.

 31 7 2025 Copyright: © 2025 Zuva T 2025 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve

A very good paper. The total sample size of 120 is recommendable but the sample of the Japanese participants of 28 and the “other” of 14 are relatively small compared with participants from Brazil and China, which might limit generalizability of the results. This should be acknowledged briefly in the conclusions.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Computer Sciences

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.