Acta Psychologica Sinica ›› 2025, Vol. 57 ›› Issue (4): 671-699.doi: 10.3724/SP.J.1041.2025.0671
• Special Issue on Prosocial Behavior (Part Ⅱ) • Previous Articles Next Articles
XU Liying, ZHANG Yuyan, YU Feng()
Received:
2023-12-02
Published:
2025-04-25
Online:
2025-02-06
Contact:
YU Feng
E-mail:psychpedia@whu.edu.cn
Supported by:
XU Liying, ZHANG Yuyan, YU Feng. (2025). Perceived Robot Threats Reduce Pro-Social Tendencies. Acta Psychologica Sinica, 57(4), 671-699.
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URL: https://journal.psych.ac.cn/acps/EN/10.3724/SP.J.1041.2025.0671
explore | methodologies | Experimental design | Independent variable measurement/manipulation | implicit variable | Key findings | brochure |
---|---|---|---|---|---|---|
1a | Archival database analysis | ? | Perceived Robot Threat Scale (the Eurobarometer 87.1, 2017) | Donation index (the Eurobarometer 87.1, 2017) | Perceived robot threat is negatively correlated with the donation index. | 27901 |
1b | Archival database analysis | ? | Perceived Robot Threat Scale (the Eurobarometer 77.1, 2012) | Support for humanitarian assistance (the Euro barometer 77.1, 2012) | Perceived robotic threat is negatively associated with support for humanitarian assistance. | 26751 |
2 | poll | ? | Perceived Robot Threat Scale (Yogeeswaran et al., | pro-social tendency (Osgood & Muraven, | Perceived robot threat was negatively associated with pro-social tendencies, mediated by collective anxiety. | 148 |
3 | Online Experiment | Robot threat (high vs. low) | video manipulation (Yogeeswaran et al., | pro-social tendency (Osgood & Muraven, | Perceived robot threat reduces pro-social tendencies, mediated by collective anxiety. | 270 |
4 | Online Experiment | Robot threat (high vs. low) | writing manipulation (Xu et al., | pro-social tendency (Touré-Tillery & Light, | Perceived robot threat reduces pro-social tendencies, mediated by collective anxiety. | 270 |
5 | Online Experiment | Robot threat (high vs. low)× Inner and outer groups (inner vs. outer) | News page image manipulation (Jackson et al., | pro-social tendency (Osgood & Muraven, | Perceived robot threat reduces pro-social tendencies in the out-group, but not in the in-group. | 400 |
6 | Online Experiment | Robot threat (high vs. low) | News page image manipulation (Jackson et al., | pro-social tendency (Osgood & Muraven, | Perceived robot threat reduces pro-social tendencies in moral downward comparators, but not in moral upward comparators. | 270 |
7 | behavioral experiment | Robot threat (high vs. low) | writing manipulation (Xu et al., | Write letters of encouragement to people who need help (Gaesser et al., | Perceived robot threat reduces pro-social behavior. | 161 |
Table 1 Summary of studies
explore | methodologies | Experimental design | Independent variable measurement/manipulation | implicit variable | Key findings | brochure |
---|---|---|---|---|---|---|
1a | Archival database analysis | ? | Perceived Robot Threat Scale (the Eurobarometer 87.1, 2017) | Donation index (the Eurobarometer 87.1, 2017) | Perceived robot threat is negatively correlated with the donation index. | 27901 |
1b | Archival database analysis | ? | Perceived Robot Threat Scale (the Eurobarometer 77.1, 2012) | Support for humanitarian assistance (the Euro barometer 77.1, 2012) | Perceived robotic threat is negatively associated with support for humanitarian assistance. | 26751 |
2 | poll | ? | Perceived Robot Threat Scale (Yogeeswaran et al., | pro-social tendency (Osgood & Muraven, | Perceived robot threat was negatively associated with pro-social tendencies, mediated by collective anxiety. | 148 |
3 | Online Experiment | Robot threat (high vs. low) | video manipulation (Yogeeswaran et al., | pro-social tendency (Osgood & Muraven, | Perceived robot threat reduces pro-social tendencies, mediated by collective anxiety. | 270 |
4 | Online Experiment | Robot threat (high vs. low) | writing manipulation (Xu et al., | pro-social tendency (Touré-Tillery & Light, | Perceived robot threat reduces pro-social tendencies, mediated by collective anxiety. | 270 |
5 | Online Experiment | Robot threat (high vs. low)× Inner and outer groups (inner vs. outer) | News page image manipulation (Jackson et al., | pro-social tendency (Osgood & Muraven, | Perceived robot threat reduces pro-social tendencies in the out-group, but not in the in-group. | 400 |
6 | Online Experiment | Robot threat (high vs. low) | News page image manipulation (Jackson et al., | pro-social tendency (Osgood & Muraven, | Perceived robot threat reduces pro-social tendencies in moral downward comparators, but not in moral upward comparators. | 270 |
7 | behavioral experiment | Robot threat (high vs. low) | writing manipulation (Xu et al., | Write letters of encouragement to people who need help (Gaesser et al., | Perceived robot threat reduces pro-social behavior. | 161 |
Nations | Sample size (N) | Sex (% female) | Age (M/SD) | ||||
---|---|---|---|---|---|---|---|
Full Sample | Sample labor force | Full Sample | Sample labor force | Full Sample | Sample labor force | ||
1 | French | 1004 | 412 | 56.37% | 52.40% | 52.13 (19.06) | 42.19 (11.85) |
2 | Belgium | 1023 | 430 | 51.22% | 50.00% | 52.64 (19.01) | 43.81 (11.98) |
3 | the Netherlands | 1015 | 544 | 49.75% | 48.70% | 52.41 (16.39) | 47.36 (12.12) |
4 | German | 1537 | 650 | 49.19% | 50.70% | 54.07 (19.39) | 44.49 (12.22) |
5 | Italy | 1022 | 471 | 54.01% | 54.00% | 49.3 (17.29) | 45.72 (10.50) |
6 | Luxemburg | 510 | 221 | 52.75% | 52.70% | 50.87 (18.53) | 43.13 (11.51) |
7 | Denmark | 1000 | 495 | 50.30% | 49.90% | 55.58 (17.87) | 48.52 (11.94) |
8 | Irish | 1021 | 509 | 51.91% | 47.90% | 48.60 (17.50) | 43.62 (12.01) |
9 | United Kingdom of Great Britain and Northern Ireland | 1346 | 620 | 50.45% | 50.40% | 53.27 (19.63) | 43.40 (13.76) |
10 | Greece | 1010 | 442 | 52.18% | 45.50% | 49.37 (17.97) | 43.24 (12.06) |
11 | Spanish | 1024 | 408 | 51.37% | 45.60% | 49.87 (18.43) | 42.67 (11.49) |
12 | Portugal | 1061 | 600 | 55.80% | 55.20% | 49.64 (18.01) | 43.07 (11.92) |
13 | Suomi | 1012 | 403 | 53.95% | 52.30% | 55.33 (18.89) | 45.38 (12.77) |
14 | Sweden | 1007 | 507 | 42.80% | 40.80% | 58.02 (17.24) | 48.64 (13.52) |
15 | Austrian | 1001 | 588 | 52.85% | 50.90% | 48.80 (16.96) | 42.31 (11.58) |
16 | Cyprus | 501 | 233 | 52.30% | 46.40% | 51.00 (18.16) | 44.16 (11.70) |
17 | Czech Republic | 1058 | 621 | 60.11% | 55.90% | 47.77 (16.64) | 43.55 (11.58) |
18 | Estonia | 1017 | 489 | 64.70% | 60.90% | 56.16 (18.31) | 47.34 (12.27) |
19 | Hungary | 1053 | 525 | 56.98% | 49.70% | 52.76 (17.60) | 43.55 (11.72) |
20 | Latvia | 1004 | 493 | 62.05% | 61.30% | 48.64 (17.35) | 44.67 (13.62) |
21 | the Lithuanian republic, former Baltic Soviet republic | 1001 | 404 | 64.24% | 57.20% | 55.66 (19.10) | 45.00 (12.86) |
22 | Maltese | 500 | 187 | 57.80% | 48.70% | 54.85 (19.00) | 43.72 (14.35) |
23 | Polish | 1008 | 514 | 59.23% | 55.30% | 48.74 (17.30) | 42.08 (11.68) |
24 | Slovakia | 1014 | 481 | 60.45% | 56.80% | 51.21 (17.53) | 43.41 (11.40) |
25 | Slovenia | 1027 | 438 | 59.69% | 55.70% | 52.95 (18.55) | 43.89 (11.59) |
26 | Bulgaria | 1044 | 589 | 53.07% | 52.10% | 48.95 (16.82) | 43.83 (11.14) |
27 | Romania (Tw) | 1033 | 528 | 56.53% | 49.20% | 45.17 (16.58) | 40.14 (11.02) |
28 | Republic of Croatia (1991-) | 1048 | 492 | 60.21% | 58.30% | 44.84 (17.11) | 40.13 (11.05) |
(grand) total | 27, 901 | 13, 294 | 54.97% | 51.50% | 51.38 (18.25) | 44.06 (12.20) |
Table 2 Demographic information at the national level: sex and age
Nations | Sample size (N) | Sex (% female) | Age (M/SD) | ||||
---|---|---|---|---|---|---|---|
Full Sample | Sample labor force | Full Sample | Sample labor force | Full Sample | Sample labor force | ||
1 | French | 1004 | 412 | 56.37% | 52.40% | 52.13 (19.06) | 42.19 (11.85) |
2 | Belgium | 1023 | 430 | 51.22% | 50.00% | 52.64 (19.01) | 43.81 (11.98) |
3 | the Netherlands | 1015 | 544 | 49.75% | 48.70% | 52.41 (16.39) | 47.36 (12.12) |
4 | German | 1537 | 650 | 49.19% | 50.70% | 54.07 (19.39) | 44.49 (12.22) |
5 | Italy | 1022 | 471 | 54.01% | 54.00% | 49.3 (17.29) | 45.72 (10.50) |
6 | Luxemburg | 510 | 221 | 52.75% | 52.70% | 50.87 (18.53) | 43.13 (11.51) |
7 | Denmark | 1000 | 495 | 50.30% | 49.90% | 55.58 (17.87) | 48.52 (11.94) |
8 | Irish | 1021 | 509 | 51.91% | 47.90% | 48.60 (17.50) | 43.62 (12.01) |
9 | United Kingdom of Great Britain and Northern Ireland | 1346 | 620 | 50.45% | 50.40% | 53.27 (19.63) | 43.40 (13.76) |
10 | Greece | 1010 | 442 | 52.18% | 45.50% | 49.37 (17.97) | 43.24 (12.06) |
11 | Spanish | 1024 | 408 | 51.37% | 45.60% | 49.87 (18.43) | 42.67 (11.49) |
12 | Portugal | 1061 | 600 | 55.80% | 55.20% | 49.64 (18.01) | 43.07 (11.92) |
13 | Suomi | 1012 | 403 | 53.95% | 52.30% | 55.33 (18.89) | 45.38 (12.77) |
14 | Sweden | 1007 | 507 | 42.80% | 40.80% | 58.02 (17.24) | 48.64 (13.52) |
15 | Austrian | 1001 | 588 | 52.85% | 50.90% | 48.80 (16.96) | 42.31 (11.58) |
16 | Cyprus | 501 | 233 | 52.30% | 46.40% | 51.00 (18.16) | 44.16 (11.70) |
17 | Czech Republic | 1058 | 621 | 60.11% | 55.90% | 47.77 (16.64) | 43.55 (11.58) |
18 | Estonia | 1017 | 489 | 64.70% | 60.90% | 56.16 (18.31) | 47.34 (12.27) |
19 | Hungary | 1053 | 525 | 56.98% | 49.70% | 52.76 (17.60) | 43.55 (11.72) |
20 | Latvia | 1004 | 493 | 62.05% | 61.30% | 48.64 (17.35) | 44.67 (13.62) |
21 | the Lithuanian republic, former Baltic Soviet republic | 1001 | 404 | 64.24% | 57.20% | 55.66 (19.10) | 45.00 (12.86) |
22 | Maltese | 500 | 187 | 57.80% | 48.70% | 54.85 (19.00) | 43.72 (14.35) |
23 | Polish | 1008 | 514 | 59.23% | 55.30% | 48.74 (17.30) | 42.08 (11.68) |
24 | Slovakia | 1014 | 481 | 60.45% | 56.80% | 51.21 (17.53) | 43.41 (11.40) |
25 | Slovenia | 1027 | 438 | 59.69% | 55.70% | 52.95 (18.55) | 43.89 (11.59) |
26 | Bulgaria | 1044 | 589 | 53.07% | 52.10% | 48.95 (16.82) | 43.83 (11.14) |
27 | Romania (Tw) | 1033 | 528 | 56.53% | 49.20% | 45.17 (16.58) | 40.14 (11.02) |
28 | Republic of Croatia (1991-) | 1048 | 492 | 60.21% | 58.30% | 44.84 (17.11) | 40.13 (11.05) |
(grand) total | 27, 901 | 13, 294 | 54.97% | 51.50% | 51.38 (18.25) | 44.06 (12.20) |
nations | Pro-social behavior index (%) | ln (GDP) | Gini index | ||||
---|---|---|---|---|---|---|---|
Donation index | Helping others | monetary donation | give and take | ||||
1 | French | 33 | 39 | 30 | 31 | 10.52 | 31.90 |
2 | Belgium | 35 | 46 | 34 | 26 | 10.65 | 27.60 |
3 | the Netherlands | 51 | 51 | 64 | 36 | 10.74 | 28.20 |
4 | German | 45 | 58 | 55 | 22 | 10.65 | 31.40 |
5 | Italy | 30 | 44 | 30 | 15 | 10.34 | 35.20 |
6 | Luxemburg | 38 | 37 | 48 | 28 | 11.58 | 31.70 |
7 | Denmark | 44 | 57 | 54 | 21 | 10.91 | 28.20 |
8 | Irish | 53 | 61 | 60 | 39 | 11.05 | 32.80 |
9 | United Kingdom of Great Britain and Northern Ireland | 50 | 58 | 64 | 28 | 10.62 | 33.10 |
10 | Greece | 24 | 50 | 10 | 11 | 9.79 | 35.00 |
11 | Spanish | 33 | 51 | 33 | 14 | 10.19 | 35.80 |
12 | Portugal | 26 | 46 | 14 | 17 | 9.90 | 35.20 |
13 | Suomi | 40 | 55 | 37 | 28 | 10.69 | 27.10 |
14 | Sweden | 41 | 53 | 55 | 14 | 10.86 | 29.60 |
15 | Austrian | 42 | 51 | 48 | 28 | 10.72 | 30.80 |
16 | Cyprus | 38 | 54 | 34 | 24 | 10.12 | 32.90 |
17 | Czech Republic | 18 | 23 | 18 | 14 | 9.83 | 25.40 |
18 | Estonia | 27 | 36 | 22 | 22 | 9.81 | 31.60 |
19 | Hungary | 21 | 36 | 17 | 9 | 9.48 | 30.30 |
20 | Latvia | 18 | 28 | 20 | 5 | 9.57 | 34.30 |
21 | the Lithuanian republic, former Baltic Soviet republic | 16 | 28 | 10 | 10 | 9.62 | 38.40 |
22 | Maltese | 48 | 45 | 73 | 26 | 10.15 | 29.10 |
23 | Polish | 26 | 37 | 27 | 13 | 9.42 | 31.20 |
24 | Slovakia | 26 | 33 | 30 | 16 | 9.71 | 25.20 |
25 | Slovenia | 34 | 40 | 32 | 32 | 9.98 | 24.80 |
26 | Bulgaria | 19 | 34 | 17 | 5 | 8.93 | 40.60 |
27 | Romania (Tw) | 31 | 60 | 24 | 9 | 9.15 | 34.40 |
28 | Republic of Croatia (1991-) | 20 | 21 | 28 | 12 | 9.44 | 30.90 |
Table 3 Indicators of Pro-Social Behavior and Country-Level Control Variables
nations | Pro-social behavior index (%) | ln (GDP) | Gini index | ||||
---|---|---|---|---|---|---|---|
Donation index | Helping others | monetary donation | give and take | ||||
1 | French | 33 | 39 | 30 | 31 | 10.52 | 31.90 |
2 | Belgium | 35 | 46 | 34 | 26 | 10.65 | 27.60 |
3 | the Netherlands | 51 | 51 | 64 | 36 | 10.74 | 28.20 |
4 | German | 45 | 58 | 55 | 22 | 10.65 | 31.40 |
5 | Italy | 30 | 44 | 30 | 15 | 10.34 | 35.20 |
6 | Luxemburg | 38 | 37 | 48 | 28 | 11.58 | 31.70 |
7 | Denmark | 44 | 57 | 54 | 21 | 10.91 | 28.20 |
8 | Irish | 53 | 61 | 60 | 39 | 11.05 | 32.80 |
9 | United Kingdom of Great Britain and Northern Ireland | 50 | 58 | 64 | 28 | 10.62 | 33.10 |
10 | Greece | 24 | 50 | 10 | 11 | 9.79 | 35.00 |
11 | Spanish | 33 | 51 | 33 | 14 | 10.19 | 35.80 |
12 | Portugal | 26 | 46 | 14 | 17 | 9.90 | 35.20 |
13 | Suomi | 40 | 55 | 37 | 28 | 10.69 | 27.10 |
14 | Sweden | 41 | 53 | 55 | 14 | 10.86 | 29.60 |
15 | Austrian | 42 | 51 | 48 | 28 | 10.72 | 30.80 |
16 | Cyprus | 38 | 54 | 34 | 24 | 10.12 | 32.90 |
17 | Czech Republic | 18 | 23 | 18 | 14 | 9.83 | 25.40 |
18 | Estonia | 27 | 36 | 22 | 22 | 9.81 | 31.60 |
19 | Hungary | 21 | 36 | 17 | 9 | 9.48 | 30.30 |
20 | Latvia | 18 | 28 | 20 | 5 | 9.57 | 34.30 |
21 | the Lithuanian republic, former Baltic Soviet republic | 16 | 28 | 10 | 10 | 9.62 | 38.40 |
22 | Maltese | 48 | 45 | 73 | 26 | 10.15 | 29.10 |
23 | Polish | 26 | 37 | 27 | 13 | 9.42 | 31.20 |
24 | Slovakia | 26 | 33 | 30 | 16 | 9.71 | 25.20 |
25 | Slovenia | 34 | 40 | 32 | 32 | 9.98 | 24.80 |
26 | Bulgaria | 19 | 34 | 17 | 5 | 8.93 | 40.60 |
27 | Romania (Tw) | 31 | 60 | 24 | 9 | 9.15 | 34.40 |
28 | Republic of Croatia (1991-) | 20 | 21 | 28 | 12 | 9.44 | 30.90 |
variant | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | distinguishing between the sexes | - | ||||||||||||||||||||||||||
2 | (a person’s) age | -0.012* | - | |||||||||||||||||||||||||
3 | communal | -0.01 | -0.037*** | - | ||||||||||||||||||||||||
4 | teach | 0.01 | -0.103*** | 0.124*** | - | |||||||||||||||||||||||
5 | political orientation | 0.021** | 0.020*** | 0.00 | -0.021** | - | ||||||||||||||||||||||
6 | Technical skills a | 0.056*** | -0.457*** | 0.072*** | 0.308*** | 0.00 | - | |||||||||||||||||||||
7 | Technical skills b | 0.00 | -0.181*** | 0.072*** | 0.277*** | -0.01 | 0.762*** | - | ||||||||||||||||||||
8 | Technical skills c | 0.052*** | -0.324*** | 0.082*** | 0.276*** | -0.01 | 0.738*** | 0.724*** | - | |||||||||||||||||||
9 | Job replacement | 0.059*** | -0.077*** | 0.044*** | -0.051*8* | 0.024* | 0.01 | 0.00 | 0.026** | - | ||||||||||||||||||
10 | Related Understandings | -0.101*** | 0.130*** | -0.062*** | -0.273*** | 0.027*** | -0.334*** | -0.290*** | -0.289*** | -0.030** | - | |||||||||||||||||
11 | Useof robots a | 0.00 | 0.080*** | -0.021*** | -0.080*** | -0.016* | -0.123*** | -0.089*** | -0.097*** | -0.085*** | 0.121*** | - | ||||||||||||||||
12 | Use of robots b | -0.095*** | 0.072*** | -0.013* | -0.057*** | -0.017* | -0.091*** | -0.079*** | -0.082*** | -0.133*** | 0.115*** | 0.129*** | - | |||||||||||||||
13 | Robot use c | -0.030*** | 0.063*** | -0.026*** | -0.031*** | 0.00 | -0.043*** | -0.018* | -0.039*** | -0.061*** | 0.058*** | 0.043*** | 0.058*** | - | ||||||||||||||
14 | social hierarchy | 0.01 | -0.083*** | 0.114*** | 0.311*** | 0.061*** | 0.270*** | 0.240*** | 0.238*** | -0.061*** | -0.225*** | -0.110*** | -0.056*** | -0.035*** | - | |||||||||||||
15 | Gini coefficient | 0.00 | -0.044*** | 0.087*** | -0.136*** | 0.01 | -0.063*** | -0.065*** | -0.060*** | 0.067*** | 0.153*** | 0.062*** | 0.062*** | 0.019** | -0.159*** | - | ||||||||||||
16 | ln GDP | 0.065*** | 0.081*** | -0.033*** | 0.142*** | -0.071*** | 0.154*** | 0.180*** | 0.156*** | -0.101*** | -0.224*** | -0.056*** | -0.056*** | -0.018** | 0.173*** | -0.336*** | - | |||||||||||
17 | Robot threat a | -0.065*** | 0.025*** | -0.048*** | -0.139*** | -0.027*** | -0.116*** | -0.096*** | -0.089*** | 0.036*** | 0.119*** | 0.050*** | 0.054*** | 0.038*** | -0.149*** | 0.134*** | -0.091*** | - | ||||||||||
18 | Robot threat b | -0.053*** | 0.073*** | -0.030*** | -0.127*** | -0.015* | -0.226*** | -0.203*** | -0.204*** | -0.123*** | 0.146*** | 0.111*** | 0.070*** | 0.024*** | -0.092*** | 0.01 | 0.034*** | 0.102*** | - | |||||||||
19 | Robot threat c | 0.01 | 0.030*** | 0.00 | 0.080*** | -0.018** | 0.101*** | 0.132*** | 0.094*** | -0.033*** | -0.155*** | 0.01 | 0.00 | 0.029*** | 0.00 | 0.025*** | 0.075*** | 0.216*** | -0.213*** | - | ||||||||
20 | Robot threat d | -0.051*** | 0.00 | -0.018** | -0.116*** | -0.01 | -0.179*** | -0.192*** | -0.164*** | -0.052*** | 0.184*** | 0.059*** | 0.047*** | 0.00 | -0.052*** | 0.00 | -0.012* | -0.017** | 0.472*** | -0.376*** | - | |||||||
21 | Robot threat e | -0.069*** | 0.050*** | -0.058*** | -0.185*** | -0.01 | -0.162*** | -0.157*** | -0.141*** | 0.045*** | 0.169*** | 0.054*** | 0.070*** | 0.030*** | -0.189*8* | 0.171*** | -0.126*** | 0.638*** | 0.159*** | 0.158*** | 0.044*** | - | ||||||
22 | (a) Robotic threat indicators1 | -0.072*** | 0.041*** | -0.058*** | -0.177*** | -0.020** | -0.152*** | -0.141*** | -0.127*** | 0.046*** | 0.157*** | 0.056*** | 0.067*** | 0.036*** | -0.186*** | 0.167*** | -0.119*** | 0.902*** | 0.145*** | 0.202*** | 0.017** | 0.912*** | - | |||||
23 | Robotic threat indicators2 | -0.084*** | 0.069*** | -0.057*** | -0.188*** | -0.026*** | -0.222*** | -0.203*** | -0.193*** | -0.041*** | 0.182*** | 0.100*** | 0.087*** | 0.045*** | -0.179*** | 0.130*** | -0.053*** | 0.719*** | 0.578*** | 0.238*** | 0.423*** | 0.750*** | 0.807*** | - | ||||
24 | Helping others | 0.076*** | 0.045*** | -0.014* | 0.068*** | -0.067*** | 0.097*** | 0.108*** | 0.082*** | -0.043*** | -0.086*** | 0.00 | -0.027*** | -0.017** | 0.066*** | 0.020** | 0.580*** | -0.050*** | 0.028*** | 0.045*** | 0.017** | -0.071*** | -0.065*** | -0.014* | - | |||
25 | monetary donation | 0.064**8* | 0.063*** | -0.057*** | 0.119*** | -0.073*** | 0.162*** | 0.198*** | 0.164*** | -0.093*** | -0.176*** | -0.025*** | -0.035*** | 0.00 | 0.150*** | -0.320*** | 0.759*** | -0.115*** | -0.01 | 0.051*** | -0.027*** | -0.159*** | -0.151*** | -0.102*** | 0.625*** | - | ||
26 | give and take | 0.031*** | 0.053*** | -0.070*** | 0.050*** | -0.058*** | 0.106*** | 0.136*** | 0.129*** | -0.090*** | -0.162*** | -0.014* | -0.039*** | -0.01 | 0.114*** | -0.445*** | 0.755*** | -0.057*** | 0.062*** | 0.036*** | 0.023*** | -0.094*** | -0.083*** | -0.017** | 0.473*** | 0.681*** | - | |
27 | Donation index | 0.070*** | 0.065*** | -0.053*** | 0.101*** | -0.077*** | 0.150*** | 0.180*** | 0.151*** | -0.089*** | -0.169*** | -0.017** | -0.038*** | -0.01 | 0.134*** | -0.272*** | 0.812*** | -0.094*** | 0.023*** | 0.055*** | 0.00 | -0.134*** | -0.125*** | -0.063*** | 0.817*** | 0.935*** | 0.795*** | - |
M | 51.38 | 1.95 | 19.66 | 5.28 | 2.89 | 3.18 | 2.99 | 1.71 | 0.53 | 0.93 | 0.95 | 0.98 | 2.35 | 31.56 | 10.14 | 3.16 | 2.09 | 3.49 | 1.70 | 3.09 | 3.12 | 2.71 | 33.00 | 44.29 | 34.98 | 19.57 | ||
SD | 18.25 | 0.77 | 5.23 | 2.18 | 1.07 | 0.92 | 0.96 | 0.88 | 0.50 | 0.26 | 0.22 | 0.13 | 0.99 | 3.83 | 0.61 | 0.85 | 0.84 | 0.67 | 0.77 | 0.89 | 0.79 | 0.47 | 11.00 | 11.59 | 17.38 | 9.25 |
Table 4 Full sample: correlation and descriptive data
variant | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | distinguishing between the sexes | - | ||||||||||||||||||||||||||
2 | (a person’s) age | -0.012* | - | |||||||||||||||||||||||||
3 | communal | -0.01 | -0.037*** | - | ||||||||||||||||||||||||
4 | teach | 0.01 | -0.103*** | 0.124*** | - | |||||||||||||||||||||||
5 | political orientation | 0.021** | 0.020*** | 0.00 | -0.021** | - | ||||||||||||||||||||||
6 | Technical skills a | 0.056*** | -0.457*** | 0.072*** | 0.308*** | 0.00 | - | |||||||||||||||||||||
7 | Technical skills b | 0.00 | -0.181*** | 0.072*** | 0.277*** | -0.01 | 0.762*** | - | ||||||||||||||||||||
8 | Technical skills c | 0.052*** | -0.324*** | 0.082*** | 0.276*** | -0.01 | 0.738*** | 0.724*** | - | |||||||||||||||||||
9 | Job replacement | 0.059*** | -0.077*** | 0.044*** | -0.051*8* | 0.024* | 0.01 | 0.00 | 0.026** | - | ||||||||||||||||||
10 | Related Understandings | -0.101*** | 0.130*** | -0.062*** | -0.273*** | 0.027*** | -0.334*** | -0.290*** | -0.289*** | -0.030** | - | |||||||||||||||||
11 | Useof robots a | 0.00 | 0.080*** | -0.021*** | -0.080*** | -0.016* | -0.123*** | -0.089*** | -0.097*** | -0.085*** | 0.121*** | - | ||||||||||||||||
12 | Use of robots b | -0.095*** | 0.072*** | -0.013* | -0.057*** | -0.017* | -0.091*** | -0.079*** | -0.082*** | -0.133*** | 0.115*** | 0.129*** | - | |||||||||||||||
13 | Robot use c | -0.030*** | 0.063*** | -0.026*** | -0.031*** | 0.00 | -0.043*** | -0.018* | -0.039*** | -0.061*** | 0.058*** | 0.043*** | 0.058*** | - | ||||||||||||||
14 | social hierarchy | 0.01 | -0.083*** | 0.114*** | 0.311*** | 0.061*** | 0.270*** | 0.240*** | 0.238*** | -0.061*** | -0.225*** | -0.110*** | -0.056*** | -0.035*** | - | |||||||||||||
15 | Gini coefficient | 0.00 | -0.044*** | 0.087*** | -0.136*** | 0.01 | -0.063*** | -0.065*** | -0.060*** | 0.067*** | 0.153*** | 0.062*** | 0.062*** | 0.019** | -0.159*** | - | ||||||||||||
16 | ln GDP | 0.065*** | 0.081*** | -0.033*** | 0.142*** | -0.071*** | 0.154*** | 0.180*** | 0.156*** | -0.101*** | -0.224*** | -0.056*** | -0.056*** | -0.018** | 0.173*** | -0.336*** | - | |||||||||||
17 | Robot threat a | -0.065*** | 0.025*** | -0.048*** | -0.139*** | -0.027*** | -0.116*** | -0.096*** | -0.089*** | 0.036*** | 0.119*** | 0.050*** | 0.054*** | 0.038*** | -0.149*** | 0.134*** | -0.091*** | - | ||||||||||
18 | Robot threat b | -0.053*** | 0.073*** | -0.030*** | -0.127*** | -0.015* | -0.226*** | -0.203*** | -0.204*** | -0.123*** | 0.146*** | 0.111*** | 0.070*** | 0.024*** | -0.092*** | 0.01 | 0.034*** | 0.102*** | - | |||||||||
19 | Robot threat c | 0.01 | 0.030*** | 0.00 | 0.080*** | -0.018** | 0.101*** | 0.132*** | 0.094*** | -0.033*** | -0.155*** | 0.01 | 0.00 | 0.029*** | 0.00 | 0.025*** | 0.075*** | 0.216*** | -0.213*** | - | ||||||||
20 | Robot threat d | -0.051*** | 0.00 | -0.018** | -0.116*** | -0.01 | -0.179*** | -0.192*** | -0.164*** | -0.052*** | 0.184*** | 0.059*** | 0.047*** | 0.00 | -0.052*** | 0.00 | -0.012* | -0.017** | 0.472*** | -0.376*** | - | |||||||
21 | Robot threat e | -0.069*** | 0.050*** | -0.058*** | -0.185*** | -0.01 | -0.162*** | -0.157*** | -0.141*** | 0.045*** | 0.169*** | 0.054*** | 0.070*** | 0.030*** | -0.189*8* | 0.171*** | -0.126*** | 0.638*** | 0.159*** | 0.158*** | 0.044*** | - | ||||||
22 | (a) Robotic threat indicators1 | -0.072*** | 0.041*** | -0.058*** | -0.177*** | -0.020** | -0.152*** | -0.141*** | -0.127*** | 0.046*** | 0.157*** | 0.056*** | 0.067*** | 0.036*** | -0.186*** | 0.167*** | -0.119*** | 0.902*** | 0.145*** | 0.202*** | 0.017** | 0.912*** | - | |||||
23 | Robotic threat indicators2 | -0.084*** | 0.069*** | -0.057*** | -0.188*** | -0.026*** | -0.222*** | -0.203*** | -0.193*** | -0.041*** | 0.182*** | 0.100*** | 0.087*** | 0.045*** | -0.179*** | 0.130*** | -0.053*** | 0.719*** | 0.578*** | 0.238*** | 0.423*** | 0.750*** | 0.807*** | - | ||||
24 | Helping others | 0.076*** | 0.045*** | -0.014* | 0.068*** | -0.067*** | 0.097*** | 0.108*** | 0.082*** | -0.043*** | -0.086*** | 0.00 | -0.027*** | -0.017** | 0.066*** | 0.020** | 0.580*** | -0.050*** | 0.028*** | 0.045*** | 0.017** | -0.071*** | -0.065*** | -0.014* | - | |||
25 | monetary donation | 0.064**8* | 0.063*** | -0.057*** | 0.119*** | -0.073*** | 0.162*** | 0.198*** | 0.164*** | -0.093*** | -0.176*** | -0.025*** | -0.035*** | 0.00 | 0.150*** | -0.320*** | 0.759*** | -0.115*** | -0.01 | 0.051*** | -0.027*** | -0.159*** | -0.151*** | -0.102*** | 0.625*** | - | ||
26 | give and take | 0.031*** | 0.053*** | -0.070*** | 0.050*** | -0.058*** | 0.106*** | 0.136*** | 0.129*** | -0.090*** | -0.162*** | -0.014* | -0.039*** | -0.01 | 0.114*** | -0.445*** | 0.755*** | -0.057*** | 0.062*** | 0.036*** | 0.023*** | -0.094*** | -0.083*** | -0.017** | 0.473*** | 0.681*** | - | |
27 | Donation index | 0.070*** | 0.065*** | -0.053*** | 0.101*** | -0.077*** | 0.150*** | 0.180*** | 0.151*** | -0.089*** | -0.169*** | -0.017** | -0.038*** | -0.01 | 0.134*** | -0.272*** | 0.812*** | -0.094*** | 0.023*** | 0.055*** | 0.00 | -0.134*** | -0.125*** | -0.063*** | 0.817*** | 0.935*** | 0.795*** | - |
M | 51.38 | 1.95 | 19.66 | 5.28 | 2.89 | 3.18 | 2.99 | 1.71 | 0.53 | 0.93 | 0.95 | 0.98 | 2.35 | 31.56 | 10.14 | 3.16 | 2.09 | 3.49 | 1.70 | 3.09 | 3.12 | 2.71 | 33.00 | 44.29 | 34.98 | 19.57 | ||
SD | 18.25 | 0.77 | 5.23 | 2.18 | 1.07 | 0.92 | 0.96 | 0.88 | 0.50 | 0.26 | 0.22 | 0.13 | 0.99 | 3.83 | 0.61 | 0.85 | 0.84 | 0.67 | 0.77 | 0.89 | 0.79 | 0.47 | 11.00 | 11.59 | 17.38 | 9.25 |
variant | Regression coefficients and significance | variant | Regression coefficients and significance |
---|---|---|---|
(Constant) | ?121.36 (1.111) | (Constant) | ?121.70 (1.119) |
distinguishing between the sexes | 0.38***(0.090) | distinguishing between the sexes | 0.38***(0.090) |
(a person’s) age | 0.01***(0.003) | (a person's) age | 0.01**(0.003) |
communal | ?0.35***(0.058) | communal | ?0.34***(0.058) |
teach | ?0.04***(0.009) | teach | ?0.04***(0.009) |
political orientation | ?0.11***(0.021) | political orientation | ?0.11***(0.021) |
Technical skillsa | 0.43***(0.052) | Technical skillsa | 0.41***(0.052) |
Related Understandings | 0.29**(0.098) | Related Understandings | 0.29**(0.098) |
Robot usea | 1.35***(0.167) | Robot usea | 1.40*** (0.167) |
Robot usec | 0.28 (0.338) | Robot usec | 0.31 (0.338) |
social hierarchy | ?0.01 (0.050) | social hierarchy | 0.00 (0.050) |
Gini coefficient | 0.06***(0.013) | Gini coefficient | 0.06***(0.013) |
ln GDP | 15.03*** (0.081) | ln GDP | 15.07*** (0.080) |
Robotic Threat Indicator 1 | ?0.51***(0.059) | Robotic Threat Indicator 2 | ?0.60*** (0.102) |
Table 5 Robot threat and pro-social behavior in the full sample
variant | Regression coefficients and significance | variant | Regression coefficients and significance |
---|---|---|---|
(Constant) | ?121.36 (1.111) | (Constant) | ?121.70 (1.119) |
distinguishing between the sexes | 0.38***(0.090) | distinguishing between the sexes | 0.38***(0.090) |
(a person’s) age | 0.01***(0.003) | (a person's) age | 0.01**(0.003) |
communal | ?0.35***(0.058) | communal | ?0.34***(0.058) |
teach | ?0.04***(0.009) | teach | ?0.04***(0.009) |
political orientation | ?0.11***(0.021) | political orientation | ?0.11***(0.021) |
Technical skillsa | 0.43***(0.052) | Technical skillsa | 0.41***(0.052) |
Related Understandings | 0.29**(0.098) | Related Understandings | 0.29**(0.098) |
Robot usea | 1.35***(0.167) | Robot usea | 1.40*** (0.167) |
Robot usec | 0.28 (0.338) | Robot usec | 0.31 (0.338) |
social hierarchy | ?0.01 (0.050) | social hierarchy | 0.00 (0.050) |
Gini coefficient | 0.06***(0.013) | Gini coefficient | 0.06***(0.013) |
ln GDP | 15.03*** (0.081) | ln GDP | 15.07*** (0.080) |
Robotic Threat Indicator 1 | ?0.51***(0.059) | Robotic Threat Indicator 2 | ?0.60*** (0.102) |
Variances | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | distinguishing between the sexes | - | ||||||||||||||||||||||||||
2 | (a person’s) age | 0.01 | - | |||||||||||||||||||||||||
3 | communal | 0.00 | -0.024** | - | ||||||||||||||||||||||||
4 | teach | -0.035*** | 0.02 | 0.124*** | - | |||||||||||||||||||||||
5 | political orientation | 0.050*** | 0.01 | 0.01 | -0.037*** | - | ||||||||||||||||||||||
6 | Technical skills a | 0.01 | -0.219*** | 0.062*** | 0.239*** | 0.00 | - | |||||||||||||||||||||
7 | Technical skillsb | 0.00 | -0.181*** | 0.072*** | 0.277*** | -0.01 | 0.762*** | - | ||||||||||||||||||||
8 | Technical skillsc | 0.01 | -0.261*** | 0.083*** | 0.248*** | -0.01 | 0.715*** | 0.724*** | - | |||||||||||||||||||
9 | Job replacement | 0.059*** | -0.077*** | 0.044*** | -0.051*** | 0.024* | 0.01 | 0.00 | 0.026** | - | ||||||||||||||||||
10 | Related Understandings | -0.088*** | 0.01 | -0.055*** | -0.247*** | 0.039*** | -0.276*** | -0.290*** | -0.264*** | -0.030** | - | |||||||||||||||||
11 | Use of robotsa | 0.00 | 0.02 | ?0.01 | -0.077*** | -0.025** | -0.084*** | -0.089*** | -0.088*** | -0.085*** | 0.104*** | - | ||||||||||||||||
12 | Use of robotsb | -0.097*** | 0.032*** | 0.00 | -0.036*** | -0.02 | -0.058*** | -0.079*** | -0.078*** | -0.133*** | 0.118*** | 0.156*** | - | |||||||||||||||
13 | Robot usec | -0.027** | 0.032*** | -0.034*** | -0.022* | 0.01 | -0.01 | -0.018* | -0.025*** | -0.061*** | 0.037*** | 0.043*** | 0.067*** | - | ||||||||||||||
14 | social hierarchy | -0.023** | 0.02 | 0.121*** | 0.315*** | 0.048*** | 0.204*** | 0.240*** | 0.224*** | -0.061*** | -0.188*** | -0.105*** | -0.047*** | -0.036*** | - | |||||||||||||
15 | Gini coefficient | 0.01 | -0.032*** | 0.084*** | -0.125*** | 0.024* | -0.035*** | -0.065*** | -0.040*** | 0.067*** | 0.142*** | 0.074*** | 0.078*** | 0.017* | -0.184*** | - | ||||||||||||
16 | ln GDP | 0.046*** | 0.075*** | -0.048*** | 0.165*** | -0.139*** | 0.161*** | 0.180*** | 0.169*** | -0.101*** | -0.222*** | -0.063*** | -0.068*** | -0.022* | 0.175*** | -0.349*** | - | |||||||||||
17 | Robot threata | -0.057*** | -0.01 | -0.052*** | -0.133*** | -0.020* | -0.074*** | -0.096*** | -0.089*** | 0.036*** | 0.108*** | 0.051*** | 0.056*** | 0.027** | -0.142*** | 0.154*** | -0.098*** | - | ||||||||||
18 | Robot threatb | -0.032*** | 0.01 | -0.028** | -0.101*** | -0.020* | -0.205*** | -0.203*** | -0.195*** | -0.123*** | 0.127*** | 0.116*** | 0.073*** | 0.02 | -0.074*** | -0.018* | 0.052*** | 0.094*** | - | |||||||||
19 | Robot threatc | 0.00 | 0.051*** | 0.00 | 0.072*** | -0.01 | 0.149*** | 0.132*** | 0.101*** | -0.033*** | -0.159*** | 0.01 | 0.01 | 0.036*** | -0.01 | 0.036*** | 0.077*** | 0.199*** | -0.204*** | - | ||||||||
20 | Robot threatd | -0.033*** | -0.034*** | -0.022* | -0.094*** | -0.020* | -0.198*** | -0.192*** | -0.160*** | -0.052*** | 0.169*** | 0.065*** | 0.051*** | -0.01 | -0.038*** | -0.024** | 0.00 | -0.02 | 0.458*** | -0.365*** | - | |||||||
21 | Robot threate | -0.048*** | 0.00 | -0.065*** | -0.191*** | 0.00 | -0.124*** | -0.157*** | -0.136*** | 0.045*** | 0.165*** | 0.049*** | 0.068*** | 0.01 | -0.192*** | 0.187*** | -0.141*** | 0.626*** | 0.140*** | 0.135*** | 0.042*** | - | ||||||
22 | (a) Robotic threat indicators1 | -0.056*** | -0.01 | -0.063*** | -0.178*** | -0.01 | -0.112*** | -0.141*** | -0.125*** | 0.046*** | 0.150*** | 0.053*** | 0.067*** | 0.018* | -0.185*** | 0.189*** | -0.132*** | 0.898*** | 0.129*** | 0.180*** | 0.01 | 0.909*** | - | |||||
23 | Robotic threat indicators2 | -0.064*** | 0.00 | -0.064*** | -0.176*** | -0.023* | -0.181*** | -0.203*** | -0.187*** | -0.041*** | 0.166*** | 0.106*** | 0.094*** | 0.027** | -0.174*** | 0.132*** | -0.049*** | 0.723*** | 0.562*** | 0.231*** | 0.415*** | 0.748*** | 0.810*** | - | ||||
24 | Helping others | 0.076*** | 0.032*** | -0.025** | 0.106*** | -0.119*** | 0.099*** | 0.108*** | 0.118*** | -0.043*** | -0.090*** | 0.00 | -0.023** | -0.020* | 0.049*** | 0.041*** | 0.580*** | -0.049*** | 0.036*** | 0.060*** | 0.019* | -0.078*** | -0.069*** | -0.01 | - | |||
25 | monetary donation | 0.048*** | 0.055*** | -0.050*** | 0.149*** | -0.122*** | 0.166*** | 0.198*** | 0.192*** | -0.093*** | -0.177*** | -0.026** | -0.041*** | 0.00 | 0.157*** | -0.315*** | 0.775*** | -0.126*** | 0.019* | 0.061*** | 0.00 | -0.185*** | -0.172*** | -0.096*** | 0.632*** | - | ||
26 | give and take | 0.019* | 0.033*** | -0.079*** | 0.066*** | -0.115*** | 0.112*** | 0.136*** | 0.135*** | -0.090*** | -0.150*** | -0.022** | -0.047*** | -0.01 | 0.112*** | -0.429*** | 0.766*** | -0.066*** | 0.081*** | 0.036*** | 0.038*** | -0.109*** | -0.098*** | -0.02 | 0.486*** | 0.698*** | - | |
27 | Donation index | 0.057*** | 0.051*** | -0.056*** | 0.134*** | -0.135*** | 0.154*** | 0.180*** | 0.179*** | -0.089*** | -0.167*** | -0.019* | -0.041*** | -0.01 | 0.130*** | -0.256*** | 0.818*** | -0.101*** | 0.043*** | 0.066*** | 0.01 | -0.154*** | -0.141*** | -0.057*** | 0.820*** | 0.936*** | 0.805*** | - |
M | 44.06 | 1.98 | 20.55 | 5.38 | 3.23 | 3.18 | 3.03 | 1.71 | 0.47 | 0.91 | 0.92 | 0.98 | 2.47 | 31.57 | 10.13 | 3.11 | 2.04 | 3.50 | 1.67 | 3.02 | 3.06 | 2.67 | 44.10 | 34.73 | 19.37 | 32.79 | ||
SD | 12.20 | 0.79 | 4.75 | 2.15 | 0.85 | 0.92 | 0.93 | 0.88 | 0.50 | 0.29 | 0.27 | 0.14 | 0.97 | 3.86 | 0.62 | 0.85 | 0.81 | 0.65 | 0.75 | 0.89 | 0.79 | 0.45 | 11.71 | 17.42 | 9.35 | 11.13 |
Table 6 Sample labor force: correlation and descriptive data
Variances | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | distinguishing between the sexes | - | ||||||||||||||||||||||||||
2 | (a person’s) age | 0.01 | - | |||||||||||||||||||||||||
3 | communal | 0.00 | -0.024** | - | ||||||||||||||||||||||||
4 | teach | -0.035*** | 0.02 | 0.124*** | - | |||||||||||||||||||||||
5 | political orientation | 0.050*** | 0.01 | 0.01 | -0.037*** | - | ||||||||||||||||||||||
6 | Technical skills a | 0.01 | -0.219*** | 0.062*** | 0.239*** | 0.00 | - | |||||||||||||||||||||
7 | Technical skillsb | 0.00 | -0.181*** | 0.072*** | 0.277*** | -0.01 | 0.762*** | - | ||||||||||||||||||||
8 | Technical skillsc | 0.01 | -0.261*** | 0.083*** | 0.248*** | -0.01 | 0.715*** | 0.724*** | - | |||||||||||||||||||
9 | Job replacement | 0.059*** | -0.077*** | 0.044*** | -0.051*** | 0.024* | 0.01 | 0.00 | 0.026** | - | ||||||||||||||||||
10 | Related Understandings | -0.088*** | 0.01 | -0.055*** | -0.247*** | 0.039*** | -0.276*** | -0.290*** | -0.264*** | -0.030** | - | |||||||||||||||||
11 | Use of robotsa | 0.00 | 0.02 | ?0.01 | -0.077*** | -0.025** | -0.084*** | -0.089*** | -0.088*** | -0.085*** | 0.104*** | - | ||||||||||||||||
12 | Use of robotsb | -0.097*** | 0.032*** | 0.00 | -0.036*** | -0.02 | -0.058*** | -0.079*** | -0.078*** | -0.133*** | 0.118*** | 0.156*** | - | |||||||||||||||
13 | Robot usec | -0.027** | 0.032*** | -0.034*** | -0.022* | 0.01 | -0.01 | -0.018* | -0.025*** | -0.061*** | 0.037*** | 0.043*** | 0.067*** | - | ||||||||||||||
14 | social hierarchy | -0.023** | 0.02 | 0.121*** | 0.315*** | 0.048*** | 0.204*** | 0.240*** | 0.224*** | -0.061*** | -0.188*** | -0.105*** | -0.047*** | -0.036*** | - | |||||||||||||
15 | Gini coefficient | 0.01 | -0.032*** | 0.084*** | -0.125*** | 0.024* | -0.035*** | -0.065*** | -0.040*** | 0.067*** | 0.142*** | 0.074*** | 0.078*** | 0.017* | -0.184*** | - | ||||||||||||
16 | ln GDP | 0.046*** | 0.075*** | -0.048*** | 0.165*** | -0.139*** | 0.161*** | 0.180*** | 0.169*** | -0.101*** | -0.222*** | -0.063*** | -0.068*** | -0.022* | 0.175*** | -0.349*** | - | |||||||||||
17 | Robot threata | -0.057*** | -0.01 | -0.052*** | -0.133*** | -0.020* | -0.074*** | -0.096*** | -0.089*** | 0.036*** | 0.108*** | 0.051*** | 0.056*** | 0.027** | -0.142*** | 0.154*** | -0.098*** | - | ||||||||||
18 | Robot threatb | -0.032*** | 0.01 | -0.028** | -0.101*** | -0.020* | -0.205*** | -0.203*** | -0.195*** | -0.123*** | 0.127*** | 0.116*** | 0.073*** | 0.02 | -0.074*** | -0.018* | 0.052*** | 0.094*** | - | |||||||||
19 | Robot threatc | 0.00 | 0.051*** | 0.00 | 0.072*** | -0.01 | 0.149*** | 0.132*** | 0.101*** | -0.033*** | -0.159*** | 0.01 | 0.01 | 0.036*** | -0.01 | 0.036*** | 0.077*** | 0.199*** | -0.204*** | - | ||||||||
20 | Robot threatd | -0.033*** | -0.034*** | -0.022* | -0.094*** | -0.020* | -0.198*** | -0.192*** | -0.160*** | -0.052*** | 0.169*** | 0.065*** | 0.051*** | -0.01 | -0.038*** | -0.024** | 0.00 | -0.02 | 0.458*** | -0.365*** | - | |||||||
21 | Robot threate | -0.048*** | 0.00 | -0.065*** | -0.191*** | 0.00 | -0.124*** | -0.157*** | -0.136*** | 0.045*** | 0.165*** | 0.049*** | 0.068*** | 0.01 | -0.192*** | 0.187*** | -0.141*** | 0.626*** | 0.140*** | 0.135*** | 0.042*** | - | ||||||
22 | (a) Robotic threat indicators1 | -0.056*** | -0.01 | -0.063*** | -0.178*** | -0.01 | -0.112*** | -0.141*** | -0.125*** | 0.046*** | 0.150*** | 0.053*** | 0.067*** | 0.018* | -0.185*** | 0.189*** | -0.132*** | 0.898*** | 0.129*** | 0.180*** | 0.01 | 0.909*** | - | |||||
23 | Robotic threat indicators2 | -0.064*** | 0.00 | -0.064*** | -0.176*** | -0.023* | -0.181*** | -0.203*** | -0.187*** | -0.041*** | 0.166*** | 0.106*** | 0.094*** | 0.027** | -0.174*** | 0.132*** | -0.049*** | 0.723*** | 0.562*** | 0.231*** | 0.415*** | 0.748*** | 0.810*** | - | ||||
24 | Helping others | 0.076*** | 0.032*** | -0.025** | 0.106*** | -0.119*** | 0.099*** | 0.108*** | 0.118*** | -0.043*** | -0.090*** | 0.00 | -0.023** | -0.020* | 0.049*** | 0.041*** | 0.580*** | -0.049*** | 0.036*** | 0.060*** | 0.019* | -0.078*** | -0.069*** | -0.01 | - | |||
25 | monetary donation | 0.048*** | 0.055*** | -0.050*** | 0.149*** | -0.122*** | 0.166*** | 0.198*** | 0.192*** | -0.093*** | -0.177*** | -0.026** | -0.041*** | 0.00 | 0.157*** | -0.315*** | 0.775*** | -0.126*** | 0.019* | 0.061*** | 0.00 | -0.185*** | -0.172*** | -0.096*** | 0.632*** | - | ||
26 | give and take | 0.019* | 0.033*** | -0.079*** | 0.066*** | -0.115*** | 0.112*** | 0.136*** | 0.135*** | -0.090*** | -0.150*** | -0.022** | -0.047*** | -0.01 | 0.112*** | -0.429*** | 0.766*** | -0.066*** | 0.081*** | 0.036*** | 0.038*** | -0.109*** | -0.098*** | -0.02 | 0.486*** | 0.698*** | - | |
27 | Donation index | 0.057*** | 0.051*** | -0.056*** | 0.134*** | -0.135*** | 0.154*** | 0.180*** | 0.179*** | -0.089*** | -0.167*** | -0.019* | -0.041*** | -0.01 | 0.130*** | -0.256*** | 0.818*** | -0.101*** | 0.043*** | 0.066*** | 0.01 | -0.154*** | -0.141*** | -0.057*** | 0.820*** | 0.936*** | 0.805*** | - |
M | 44.06 | 1.98 | 20.55 | 5.38 | 3.23 | 3.18 | 3.03 | 1.71 | 0.47 | 0.91 | 0.92 | 0.98 | 2.47 | 31.57 | 10.13 | 3.11 | 2.04 | 3.50 | 1.67 | 3.02 | 3.06 | 2.67 | 44.10 | 34.73 | 19.37 | 32.79 | ||
SD | 12.20 | 0.79 | 4.75 | 2.15 | 0.85 | 0.92 | 0.93 | 0.88 | 0.50 | 0.29 | 0.27 | 0.14 | 0.97 | 3.86 | 0.62 | 0.85 | 0.81 | 0.65 | 0.75 | 0.89 | 0.79 | 0.45 | 11.71 | 17.42 | 9.35 | 11.13 |
variant | Regression coefficients and significance | variant | Regression coefficients and significance |
---|---|---|---|
(Constant) | ?125.47 (1.636) | (Constant) | ?125.90 (1.654) |
distinguishing between the sexes | 0.38**(0.130) | distinguishing between the sexes | 0.39**(0.130) |
(a person's) age | 0.01 (0.006) | (a person's) age | 0.01 (0.006) |
communal | ?0.28**(0.083) | communal | ?0.27**(0.083) |
teach | ?0.02 (0.014) | teach | ?0.01 (0.014) |
political orientation | ?0.15***(0.030) | political orientation | ?0.15***(0.030) |
Technical skillsa | ?0.40**(0.127) | Technical skillsa | ?0.42**(0.127) |
Technical skillsb | 0.54 *** (0.120) | Technical skillsb | 0.54*** (0.120) |
Technical skillsc | 0.58 *** (0.108) | Technical skillsc | 0.58*** (0.108) |
Job replacement | ?0.13 (0.075) | Job replacement | ?0.17*(0.075) |
Related Understandings | 0.31*(0.141) | Related Understandings | 0.29*(0.141) |
Robot usea | 1.03***(0.223) | Robot usea | 1.08*** (0.223) |
Robot useb | 0.48*(0.232) | Robot useb | 0.46*(0.232) |
Robot usec | 0.26 (0.456) | Robot usec | 0.27 (0.457) |
social hierarchy | ?0.22**(0.073) | social hierarchy | ?0.19**(0.073) |
Gini coefficient | 0.13***(0.019) | Gini coefficient | 0.13***(0.018) |
ln GDP | 15.19 *** (0.116) | ln GDP | 15.23*** (0.116) |
Robotic Threat Indicator 1 | ?0.60***(0.085) | Robotic Threat Indicator 2 | ?0.63*** (0.152) |
Table 7 Robot Threat and Pro-Social Behavior in the Labor Force Sample
variant | Regression coefficients and significance | variant | Regression coefficients and significance |
---|---|---|---|
(Constant) | ?125.47 (1.636) | (Constant) | ?125.90 (1.654) |
distinguishing between the sexes | 0.38**(0.130) | distinguishing between the sexes | 0.39**(0.130) |
(a person's) age | 0.01 (0.006) | (a person's) age | 0.01 (0.006) |
communal | ?0.28**(0.083) | communal | ?0.27**(0.083) |
teach | ?0.02 (0.014) | teach | ?0.01 (0.014) |
political orientation | ?0.15***(0.030) | political orientation | ?0.15***(0.030) |
Technical skillsa | ?0.40**(0.127) | Technical skillsa | ?0.42**(0.127) |
Technical skillsb | 0.54 *** (0.120) | Technical skillsb | 0.54*** (0.120) |
Technical skillsc | 0.58 *** (0.108) | Technical skillsc | 0.58*** (0.108) |
Job replacement | ?0.13 (0.075) | Job replacement | ?0.17*(0.075) |
Related Understandings | 0.31*(0.141) | Related Understandings | 0.29*(0.141) |
Robot usea | 1.03***(0.223) | Robot usea | 1.08*** (0.223) |
Robot useb | 0.48*(0.232) | Robot useb | 0.46*(0.232) |
Robot usec | 0.26 (0.456) | Robot usec | 0.27 (0.457) |
social hierarchy | ?0.22**(0.073) | social hierarchy | ?0.19**(0.073) |
Gini coefficient | 0.13***(0.019) | Gini coefficient | 0.13***(0.018) |
ln GDP | 15.19 *** (0.116) | ln GDP | 15.23*** (0.116) |
Robotic Threat Indicator 1 | ?0.60***(0.085) | Robotic Threat Indicator 2 | ?0.63*** (0.152) |
nations | Sample size (N) | Sex (% female) | Age (M/SD) | ln (GDP) (billions) | |
---|---|---|---|---|---|
1 | French | 1059 | 55.15% | 49.97 (19.19) | 3.46 |
2 | Belgium | 1051 | 53.00% | 49.81 (17.80) | 2.72 |
3 | the Netherlands | 1014 | 52.86% | 50.68 (18.61) | 2.96 |
4 | German | 1552 | 49.74% | 52.44 (18.22) | 3.57 |
5 | Italy | 1036 | 56.56% | 47.44 (16.82) | 3.36 |
6 | Luxemburg | 501 | 54.69% | 47.81 (17.87) | 1.79 |
7 | Denmark | 1019 | 50.34% | 50.84 (18.26) | 2.54 |
8 | Irish | 1008 | 54.27% | 45.64 (17.14) | 2.38 |
9 | United Kingdom of Great Britain and Northern Ireland | 1331 | 53.57% | 50.74 (20.21) | 3.42 |
10 | Greece | 999 | 53.25% | 46.08 (17.36) | 2.45 |
11 | Spanish | 1004 | 53.09% | 47.18 (18.87) | 3.17 |
12 | Portugal | 1009 | 54.51% | 48.90 (18.74) | 2.39 |
13 | Suomi | 1003 | 53.64% | 55.58 (18.60) | 2.44 |
14 | Sweden | 1016 | 48.33% | 57.51 (15.78) | 2.76 |
15 | Austrian | 1031 | 56.06% | 46.01 (15.94) | 2.64 |
16 | Cyprus | 506 | 50.99% | 43.38 (17.29) | 1.44 |
17 | Czech Republic | 1003 | 56.73% | 46.12 (16.40) | 2.36 |
18 | Estonia | 1000 | 62.60% | 49.84 (19.26) | 1.37 |
19 | Hungary | 1021 | 56.90% | 47.83 (17.37) | 2.15 |
20 | Latvia | 1024 | 53.52% | 42.76 (16.86) | 1.44 |
21 | the Lithuanian republic, former Baltic Soviet republic | 1021 | 54.95% | 46.32 (18.12) | 1.64 |
22 | Maltese | 500 | 62.00% | 51.63 (17.88) | 0.98 |
23 | Polish | 1000 | 58.90% | 49.26 (18.85) | 2.72 |
24 | Slovakia | 1000 | 58.20% | 45.96 (15.32) | 2.00 |
25 | Slovenia | 1017 | 52.02% | 48.33 (17.90) | 1.71 |
26 | Bulgaria | 1006 | 51.99% | 48.16 (16.82) | 1.76 |
27 | Romania (Tw) | 1020 | 47.65% | 44.98 (16.61) | 2.28 |
(grand) total | 26, 751 | 54.08% | 48.72 (18.08) |
Table 8 Demographic information and economic level of countries
nations | Sample size (N) | Sex (% female) | Age (M/SD) | ln (GDP) (billions) | |
---|---|---|---|---|---|
1 | French | 1059 | 55.15% | 49.97 (19.19) | 3.46 |
2 | Belgium | 1051 | 53.00% | 49.81 (17.80) | 2.72 |
3 | the Netherlands | 1014 | 52.86% | 50.68 (18.61) | 2.96 |
4 | German | 1552 | 49.74% | 52.44 (18.22) | 3.57 |
5 | Italy | 1036 | 56.56% | 47.44 (16.82) | 3.36 |
6 | Luxemburg | 501 | 54.69% | 47.81 (17.87) | 1.79 |
7 | Denmark | 1019 | 50.34% | 50.84 (18.26) | 2.54 |
8 | Irish | 1008 | 54.27% | 45.64 (17.14) | 2.38 |
9 | United Kingdom of Great Britain and Northern Ireland | 1331 | 53.57% | 50.74 (20.21) | 3.42 |
10 | Greece | 999 | 53.25% | 46.08 (17.36) | 2.45 |
11 | Spanish | 1004 | 53.09% | 47.18 (18.87) | 3.17 |
12 | Portugal | 1009 | 54.51% | 48.90 (18.74) | 2.39 |
13 | Suomi | 1003 | 53.64% | 55.58 (18.60) | 2.44 |
14 | Sweden | 1016 | 48.33% | 57.51 (15.78) | 2.76 |
15 | Austrian | 1031 | 56.06% | 46.01 (15.94) | 2.64 |
16 | Cyprus | 506 | 50.99% | 43.38 (17.29) | 1.44 |
17 | Czech Republic | 1003 | 56.73% | 46.12 (16.40) | 2.36 |
18 | Estonia | 1000 | 62.60% | 49.84 (19.26) | 1.37 |
19 | Hungary | 1021 | 56.90% | 47.83 (17.37) | 2.15 |
20 | Latvia | 1024 | 53.52% | 42.76 (16.86) | 1.44 |
21 | the Lithuanian republic, former Baltic Soviet republic | 1021 | 54.95% | 46.32 (18.12) | 1.64 |
22 | Maltese | 500 | 62.00% | 51.63 (17.88) | 0.98 |
23 | Polish | 1000 | 58.90% | 49.26 (18.85) | 2.72 |
24 | Slovakia | 1000 | 58.20% | 45.96 (15.32) | 2.00 |
25 | Slovenia | 1017 | 52.02% | 48.33 (17.90) | 1.71 |
26 | Bulgaria | 1006 | 51.99% | 48.16 (16.82) | 1.76 |
27 | Romania (Tw) | 1020 | 47.65% | 44.98 (16.61) | 2.28 |
(grand) total | 26, 751 | 54.08% | 48.72 (18.08) |
variant | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | distinguishing between the sexes | - | ||||||||||||
2 | (a person's) age | 0.02 *** | - | |||||||||||
3 | communal | 0.01 | -0.05*** | - | ||||||||||
4 | teach | -0.01* | -0.13 *** | 0.13 *** | - | |||||||||
5 | social hierarchy | -0.02 *** | -0.07 *** | 0.04 *** | 0.23 *** | - | ||||||||
6 | scientific interest | -0.02 *** | -0.15 *** | 0.08 *** | 0.27 *** | 0.19 *** | - | |||||||
7 | Use of robots a | 0.01 | -0.05 *** | 0.01 | 0.03 *** | 0.04 *** | 0.06 *** | - | ||||||
8 | Use of robots b | -0.12 (*** | -0.06 *** | -0.02** | 0.06 *** | 0.04 *** | 0.12 *** | 0.09 *** | - | |||||
9 | Robot use c | -0.02 *** | -0.04 *** | -0.02** | 0.01* | 0.02** | 0.04 *** | 0.02 *** | 0.00 | - | ||||
10 | Robot general attitude | -0.13 *** | -0.12 *** | 0.05 *** | 0.2 *** | 0.13 *** | 0.40 *** | 0.12 *** | 0.13 *** | 0.05 **)(** | - | |||
11 | ln GDP | -0.02 *** | 0.00 | 0.01 | -0.01* | 0.10 **(*)* | 0.07 *** | 0.02 *** | 0.04 *** | -0.00 | -0.01 | - | ||
12 | robotics threat | -0.10** | 0.06 *** | -0.04 *** | -0.20 *** | -0.14 (*** | -0.26 *** | -0.08 *** | -0.10 *** | -0.04 *** | -0.55 *** | 0.01 | - | |
13 | pro-social tendency | -0.00 | -0.05 *** | 0.06 *** | 0.15*** | -0.11 *** | 0.22 (*** | 0.03*** | 0.02 ** | 0.00 | -0.2 *** | -0.02** | -0.15 *** | - |
M | 48.72 | 1.92 | 19.02 | 5.54 | 2.01 | 0.05 | 0.06 | 0.01 | 2.87 | 2.46 | 2.52 | 2.99 | ||
SD | 18.08 | 0.80 | 4.74 | 1.63 | 0.68 | 0.21 | 0.23 | 0.11 | 0.74 | 0.68 | 0.48 | 0.51 |
Table 9 Correlation and Descriptive Data
variant | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | distinguishing between the sexes | - | ||||||||||||
2 | (a person's) age | 0.02 *** | - | |||||||||||
3 | communal | 0.01 | -0.05*** | - | ||||||||||
4 | teach | -0.01* | -0.13 *** | 0.13 *** | - | |||||||||
5 | social hierarchy | -0.02 *** | -0.07 *** | 0.04 *** | 0.23 *** | - | ||||||||
6 | scientific interest | -0.02 *** | -0.15 *** | 0.08 *** | 0.27 *** | 0.19 *** | - | |||||||
7 | Use of robots a | 0.01 | -0.05 *** | 0.01 | 0.03 *** | 0.04 *** | 0.06 *** | - | ||||||
8 | Use of robots b | -0.12 (*** | -0.06 *** | -0.02** | 0.06 *** | 0.04 *** | 0.12 *** | 0.09 *** | - | |||||
9 | Robot use c | -0.02 *** | -0.04 *** | -0.02** | 0.01* | 0.02** | 0.04 *** | 0.02 *** | 0.00 | - | ||||
10 | Robot general attitude | -0.13 *** | -0.12 *** | 0.05 *** | 0.2 *** | 0.13 *** | 0.40 *** | 0.12 *** | 0.13 *** | 0.05 **)(** | - | |||
11 | ln GDP | -0.02 *** | 0.00 | 0.01 | -0.01* | 0.10 **(*)* | 0.07 *** | 0.02 *** | 0.04 *** | -0.00 | -0.01 | - | ||
12 | robotics threat | -0.10** | 0.06 *** | -0.04 *** | -0.20 *** | -0.14 (*** | -0.26 *** | -0.08 *** | -0.10 *** | -0.04 *** | -0.55 *** | 0.01 | - | |
13 | pro-social tendency | -0.00 | -0.05 *** | 0.06 *** | 0.15*** | -0.11 *** | 0.22 (*** | 0.03*** | 0.02 ** | 0.00 | -0.2 *** | -0.02** | -0.15 *** | - |
M | 48.72 | 1.92 | 19.02 | 5.54 | 2.01 | 0.05 | 0.06 | 0.01 | 2.87 | 2.46 | 2.52 | 2.99 | ||
SD | 18.08 | 0.80 | 4.74 | 1.63 | 0.68 | 0.21 | 0.23 | 0.11 | 0.74 | 0.68 | 0.48 | 0.51 |
variant | pro-social tendency |
---|---|
Regression coefficients and significance | |
Sensing the Robot Threat | -0.077*** (0.007) |
General attitude towards robots | 0.069*** (0.005) |
Robot use: at home | 0.024 (0.014) |
Robot use: on the job | -0.026 (0.013) |
Robot use: elsewhere | -0.024 (0.027) |
scientific interest | containment |
social hierarchy | containment |
teach | 0.002*** (0.0003) |
communal | containment |
(a person’s) age | -0.000** (0.0002) |
distinguishing between the sexes | 0.052*** (0.006) |
constant | 2.614*** (0.043) |
Country fixed effects | containment |
observed value | 25769 |
Adjustment R2 | 0.113 |
F | 99.74 *** |
Table 10 Perceived robot threat and pro-social tendencies
variant | pro-social tendency |
---|---|
Regression coefficients and significance | |
Sensing the Robot Threat | -0.077*** (0.007) |
General attitude towards robots | 0.069*** (0.005) |
Robot use: at home | 0.024 (0.014) |
Robot use: on the job | -0.026 (0.013) |
Robot use: elsewhere | -0.024 (0.027) |
scientific interest | containment |
social hierarchy | containment |
teach | 0.002*** (0.0003) |
communal | containment |
(a person’s) age | -0.000** (0.0002) |
distinguishing between the sexes | 0.052*** (0.006) |
constant | 2.614*** (0.043) |
Country fixed effects | containment |
observed value | 25769 |
Adjustment R2 | 0.113 |
F | 99.74 *** |
Figure 2. Pro-social tendency scores of different perceived robotic threat groups for internal and external group members. Note. * p < 0.05, ** p < 0.01.
[1] |
Adler, N. E., Epel, E. S., Castellazzo, G., & Ickovics, J. R. (2000). Relationship of subjective and objective social status with psychological and physiological functioning: Preliminary data in healthy white women. Health Psychology, 19(6), 586-592.
doi: 10.1037//0278-6133.19.6.586 pmid: 11129362 |
[2] | Agarwat, R., & Karahanna, E. (2000). Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665-694. |
[3] | Agrawal, A., Gans, J., & Goldfarb, A. (2019). The economics of artificial intelligence: An agenda. Chicago and London: The University of Chicago Press. |
[4] | Andrighetto, L., Vezzali, L., Bergamini, G., Nadi, C., & Giovannini, D. (2016). Inside the earthquake: Perceived disaster exposure and helping intentions among Italian and immigrant victims of the 2012 Italian earthquakes. Group Processes and Intergroup Relations, 19(6), 753-768. |
[5] | Aron, A., Aron, E. N., & Smollan, D. (1992). Inclusion of Other in the Self Scale and the structure of interpersonal closeness. Journal of Personality and Social Psychology, 63(4), 596-612. |
[6] | Asimov, I. (1950). I, Robot. New York: Gnome Press. |
[7] | Bai, M., Zhang, H., Zhang, J., Jiang, Y., & Xu, J. (in press). Challenging or threatening? The double-edged sword effect of intelligent technology awareness on accountants’ unethical decision-making. Journal of Business Ethics. |
[8] | Barnes, C. M., Dang, C. T., Leavitt, K., Guarana, C. L., & Uhlmann, E. L. (2018). Archival data in micro- organizational research: A toolkit for moving to a broader set of topics. Journal of Management, 44(4), 1453-1478. |
[9] | Batson, C. D., & Powell, A. A. (2003). Altruism and prosocial behavior. In T. Millon & M. J. Lerner (Eds.), Handbook of psychology: Personality and social psychology (Vol. 5, pp. 463-484). Hoboken, NJ: Wiley. |
[10] |
Bavel, J. J. V., Baicker, K., Boggio, P. S., Capraro, V., Cichocka, A., Cikara, M.,... Willer, R. (2020). Using social and behavioural science to support COVID-19 pandemic response. Nature Human Behaviour, 4(5), 460-471.
doi: 10.1038/s41562-020-0884-z pmid: 32355299 |
[11] | Bobo, L. (1983). Whites' opposition to busing: Symbolic racism or realistic group conflict? Journal of Personality and Social Psychology, 45(6), 1196-1210. |
[12] | Bordot, F. (2022). Artificial intelligence, robots and unemployment: Evidence from OECD countries. Journal of Innovation Economics and Management, 37(1), 117-138. |
[13] | Bostrom, N. (2002). Existential risks: Analyzing human extinction scenarios and related hazards. Journal of Evolution and Technology, 9, 1-30. |
[14] | Brewer, M. B. (1999). The psychology of prejudice: Ingroup love or outgroup hate?. Journal of Social Issues, 55(3), 429-444. |
[15] | Brewer, M. B. (2001). The many faces of social identity: Implications for political psychology. Political Psychology, 22(1), 115-125. |
[16] | Butz, D. A., & Yogeeswaran, K. (2011). A new threat in the air: Macroeconomic threat increases prejudice against Asian Americans. Journal of Experimental Social Psychology, 47(1), 22-27. |
[17] | Carradore, M. (2022). People’s attitudes towards the use of robots in the social services: A multilevel analysis using eurobarometer data. International Journal of Social Robotics, 14, 845-858. |
[18] |
Cortland, C. I., Craig, M. A., Shapiro, J. R., Richeson, J. A., Neel, R., & Goldstein, N. J. (2017). Solidarity through shared disadvantage: Highlighting shared experiences of discrimination improves relations between stigmatized groups. Journal of Personality and Social Psychology, 113(4), 547-567.
doi: 10.1037/pspi0000100 pmid: 28581301 |
[19] |
Diel, K., Grelle, S., & Hofmann, W. (2021). A motivational framework of social comparison. Journal of Personality and Social Psychology, 120(6), 1415-1430.
doi: 10.1037/pspa0000204 pmid: 33507785 |
[20] | Diel, K., & Hofmann, W. (2019). Inspired to perspire: The interplay of social comparison direction and standard extremity in the context of challenging exercising goals. Social Cognition, 37(3), 247-265 |
[21] |
Dovidio, J. F., ten Vergert, M., Stewart, T. L., Gaertner, S. L., Johnson, J. D., Esses, V. M., Riek, B. M., & Pearson, A. R. (2004). Perspective and prejudice: Antecedents and mediating mechanisms. Personality and Social Psychology Bulletin, 30(12), 1537-1549.
doi: 10.1177/0146167204271177 pmid: 15536238 |
[22] | Drury, J. (2018). The role of social identity processes in mass emergency behaviour: An integrative review. European Review of Social Psychology, 29(1), 38-81. |
[23] |
Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175-191.
doi: 10.3758/bf03193146 pmid: 17695343 |
[24] |
Fleischmann, A., Lammers, J., Diel, K., Hofmann, W., & Galinsky, A. D. (2021). More threatening and more diagnostic: How moral comparisons differ from social comparisons. Journal of Personality and Social Psychology, 121(5), 1057-1078.
doi: 10.1037/pspi0000361 pmid: 33646800 |
[25] | Fleming, D. A., Chong, A., & Bejarano, H. D. (2014). Trust and reciprocity in the aftermath of natural disasters. The Journal of Development Studies, 50(11), 1482-1493. |
[26] | Ford, M. R. (2009). The lights in the tunnel: Automation, accelerating technology and the economy of the future. Wayne, PA: Acculant Publishing. |
[27] |
Frankenberg, E., Nobles, J., & Sumantri, C. (2012). Community destruction and traumatic stress in post- tsunami Indonesia. Journal of Health and Social Behavior, 53(4), 498-514.
doi: 10.1177/0022146512456207 pmid: 22940603 |
[28] | Frey, C. B., Berger, T., & Chen, C. (2018). Political machinery: Did robots swing the 2016 US presidential election? Oxford Review of Economic Policy, 34(3), 418-442. |
[29] | Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation?. Technological Forecasting and Social Change, 114, 254-280. |
[30] |
Fuesting, M. A., Diekman, A. B., Boucher, K. L., Murphy, M. C., Manson, D. L., & Safer, B. L. (2019). Growing STEM: Perceived faculty mindset as an indicator of communal affordances in STEM. Journal of Personality and Social Psychology, 117(2), 260-281.
doi: 10.1037/pspa0000154 pmid: 30869983 |
[31] |
Fulford, D., Johnson, S. L., Llabre, M. M., & Carver, C. S. (2010). Pushing and coasting in dynamic goal pursuit: Coasting is attenuated in bipolar disorder. Psychological Science, 21(7), 1021-1027.
doi: 10.1177/0956797610373372 pmid: 20519486 |
[32] |
Gaesser, B., Shimura, Y., & Cikara, M. (2020). Episodic simulation reduces intergroup bias in prosocial intentions and behavior. Journal of Personality and Social Psychology, 118(4), 683-705.
doi: 10.1037/pspi0000194 pmid: 31157527 |
[33] |
Gamez-Djokic, M., & Waytz, A. (2020). Concerns about automation and negative sentiment toward immigration. Psychological Science, 31(8), 987-1000.
doi: 10.1177/0956797620929977 pmid: 32697627 |
[34] | Gordils, J., Elliot, A. J., Toprakkiran, S., & Jamieson, J. P. (2021). The effects of COVID-19 on perceived intergroup competition and negative intergroup outcomes. The Journal of Social Psychology, 161(4), 419-434. |
[35] | Goyal, A., & Aneja, R. (2020). Artificial intelligence and income inequality: Do technological changes and worker's position matter?. Journal of Public Affairs, 20(4), e2326. |
[36] | Gray, H. M., Gray, K., & Wegner, D. M. (2007). Dimensions of mind perception. Science, 315(5812), 619. |
[37] | Gray, K., Yam, K. C., Zhen’An, A. E., Wilbanks, D., & Waytz, A. (2023). The psychology of robots and artificial intelligence. In D. Gilbert et al (Eds.), Handbook of social psychology (pp. 1-83). Situational Press. |
[38] | Harari, Y. N. (2017). Reboot for the AI revolution. Nature, 550(7676), 324-327. |
[39] | Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York: Guilford Press. |
[40] | Hirschberger, G., & Pyszczynski, T. (2011). Killing with a clean conscience:Existential angst and the paradox of morality. In M. Mikulincer & P. R. Shaver (Eds.), Social psychology of morality: Exploring the causes of good and evil (pp. 331-347). Washington, DC: American Psychological Association. |
[41] |
Howard, A., & Borenstein, J. (2018). The ugly truth about ourselves and our robot creations: The problem of bias and social inequity. Science and Engineering Ethics, 24(5), 1521-1536.
doi: 10.1007/s11948-017-9975-2 pmid: 28936795 |
[42] | Huang, H. L., Cheng, L. K., Sun, P. C., & Chou, S. J. (2021). The effects of perceived identity threat and realistic threat on the negative attitudes and usage intentions toward hotel service robots: The moderating effect of the robot’s anthropomorphism. International Journal of Social Robotics, 13, 1599-1611. |
[43] | Im, Z. J., Mayer, N., Palier, B., & Rovny, J. (2019). The “losers of automation”: A reservoir of votes for the radical right? Research & Politics, 6(1), 1-7. |
[44] | Jackson, J. C., Castelo, N., & Gray, K. (2020). Could a rising robot workforce make humans less prejudiced? American Psychologist, 75(7), 969-982. |
[45] | Jackson, J. W. (1993). Realistic group conflict theory: A review and evaluation of the theoretical and empirical literature. Psychological Record, 43(3), 395-413. |
[46] | Jetten, J., Mols, F., Healy, N., & Spears, R. (2017). “Fear of falling”: Economic instability enhances collective angst among societies’ wealthy class. Journal of Social Issues, 73(1), 61-79. |
[47] | Jetten, J., Mols, F., & Steffens, N. K. (2021). Prosperous but fearful of falling: The wealth paradox, collective angst, and opposition to immigration. Personality and Social Psychology Bulletin, 47(5), 766-780. |
[48] | Jetten, J., & Wohl, M. J. A. (2012). The past as a determinant of the present: Historical continuity, collective angst, and opposition to immigration. European Journal of Social Psychology, 42(4), 442-450. |
[49] | Kahn, B. (2023). Elon Musk and Apple cofounder Steve Wozniak among over 1, 100 who sign open letter calling for 6-month ban on creating powerful A.I. Retrieved August 17, 2024, |
[50] | Kahn, D. T., Björklund, F., & Hirschberger, G. (2022). The intent and extent of collective threats: A data-driven conceptualization of collective threats and their relation to political preferences. Journal of Experimental Psychology: General, 151(5), 1178-1198. |
[51] | Khasawneh, O. Y. (2018). Technophobia: Examining its hidden factors and defining it. Technology in Society, 54, 93-100. |
[52] | Kung, F. Y., Chao, M. M., Yao, D. J., Adair, W. L., Fu, J. H., & Tasa, K. (2018). Bridging racial divides: Social constructionist (vs. essentialist) beliefs facilitate trust in intergroup contexts. Journal of Experimental Social Psychology, 74, 121-134. |
[53] | Lalonde, R. N. (2002). Testing the social identity-intergroup differentiation hypothesis: We’re not American eh! British Journal of Social Psychology, 41(4), 611-630. |
[54] | Lasarov, W., & Hoffmann, S. (2020). Social moral licensing. Journal of Business Ethics, 165, 45-66. |
[55] | Leo, X., & Huh, Y. E. (2020). Who gets the blame for service failures? Attribution of responsibility toward robot versus human service providers and service firms. Computers in Human Behavior, 113, 106520. |
[56] | Li, J. J., Bonn, M. A., & Ye, B. H. (2019). Hotel employee's artificial intelligence and robotics awareness and its impact on turnover intention: The moderating roles of perceived organizational support and competitive psychological climate. Tourism Management, 73, 172-181. |
[57] | Li, J., & Huang, J. S. (2020). Dimensions of artificial intelligence anxiety based on the integrated fear acquisition theory. Technology in Society, 63, 101410. |
[58] | Lin, J., Panganiban, A. R., Matthews, G., Gibbins, K., Ankeney, E., See, C.,... Long, M. (2022). Trust in the danger zone: Individual differences in confidence in robot threat assessments. Frontiers in Psychology, 13, 601523. |
[59] | Lucas, T., Rudolph, C., Zhdanova, L., Barkho, E., & Weidner, N. (2014). Distributive justice for others, collective angst, and support for exclusion of immigrants. Political Psychology, 35(6), 775-793. |
[60] | Mann, F. D., Krueger, R. F., & Vohs, K. D. (2020). Personal economic anxiety in response to COVID-19. Personality and Individual Differences, 167, 110233. |
[61] | Mathias, P. (2013). The first industrial nation: The economic history of Britain 1700-1914. London: Routledge. |
[62] | McClure, P. K. (2018). “You’re fired,” says the robot: The rise of automation in the workplace, technophobes, and fears of unemployment. Social Science Computer Review, 36(2), 139-156. |
[63] |
McFarland, S., Webb, M., & Brown, D. (2012). All humanity is my ingroup: A measure and studies of identification with all humanity. Journal of Personality and Social Psychology, 103(5), 830-853.
doi: 10.1037/a0028724 pmid: 22708625 |
[64] | Merritt, A. C., Effron, D. A., & Monin, B. (2010). Moral self-licensing: When being good frees us to be bad. Social and Personality Psychology Compass, 4(5), 344-357. |
[65] | Miao, X., Liu, L., Dang, J., Wei, C., Huang, L., & Liu, Z. (2023). Unity or estrangement under crises? Perceived resource scarcity moderates the effect of a common threat on intergroup cooperation. Social Psychological and Personality Science, 15(5), 659-669. |
[66] |
Miloyan, B., Bienvenu, O. J., Brilot, B., & Eaton, W. W. (2018). Adverse life events and the onset of anxiety disorders. Psychiatry Research, 259, 488-492.
doi: S0165-1781(17)30520-6 pmid: 29154170 |
[67] | Neufeind, M., O’Reilly, J., & Ranft, F. (2018). Work in the digital age: Challenges of the fourth industrial revolution. London: Rowman and Littlefield. |
[68] | Osgood, J. M., & Muraven, M. (2015). Self-control depletion does not diminish attitudes about being prosocial but does diminish prosocial behaviors. Basic and Applied Social Psychology, 37(1), 68-80. |
[69] | Peng, K., Nisbett, R. E., & Wong, N. Y. C. (1997). Validity problems comparing values across cultures and possible solutions. Psychological Methods, 2(4), 329-344. |
[70] |
Penner, L. A., Dovidio, J. F., Piliavin, J. A., & Schroeder, D. A. (2005). Prosocial behavior: Multilevel perspectives. Annual Review of Psychology, 56, 365-392.
pmid: 15709940 |
[71] |
Riek, B. M., Mania, E. W., & Gaertner, S. L. (2006). Intergroup threat and outgroup attitudes: A meta-analytic review. Personality and Social Psychology Review, 10(4), 336-353.
pmid: 17201592 |
[72] | Roccas, S., & Amit, A. (2011). Group heterogeneity and tolerance: The moderating role of conservation values. Journal of Experimental Social Psychology, 47(5), 898-907. |
[73] | Römpke, A. K., Fritsche, I., & Reese, G. (2019). Get together, feel together, act together: International personal contact increases identification with humanity and global collective action. Journal of Theoretical Social Psychology, 3(1), 35-48. |
[74] | Rughiniş, C., Zamfirescu, R., Neagoe, A., & Rughiniş, R. (2018, April). Visions of robots, networks and artificial intelligence: Europeans’ attitudes towards digitisation and automation in daily life. In The international scientific conference eLearning and software for education (Vol. 2, pp. 114-119), Bucharest, Romania. |
[75] | Sherif, M. (1966). In common predicament: Social psychology of intergroup conflict and cooperation. New York: Houghton Mifflin. |
[76] | Shoss, M. K., & Ciarlante, K. (2022). Are robots/AI viewed as more of a workforce threat in unequal societies? Evidence from the eurobarometer survey. Technology, Mind, and Behavior, 3(2), 1-13. |
[77] | Sidanius, J. (1993). The psychology of group conflict and the dynamics of oppression:A social dominance perspective. In S. Iyengar & W. McGuire (Eds.), Explorations in political psychology (pp. 183-219). Durham, NC: Duke University Press. |
[78] | Simpson, B., & Willer, R. (2015). Beyond altruism: Sociological foundations of cooperation and prosocial behavior. Annual Review of Sociology, 41(1), 43-63. |
[79] |
Spencer, S. J., Zanna, M. P., & Fong, G. T. (2005). Establishing a causal chain: Why experiments are often more effective than mediational analyses in examining psychological processes. Journal of Personality and Social Psychology, 89(6), 845-851.
pmid: 16393019 |
[80] | Stephan, W. G., Ybarra, O., & Morrison, K. R. (2009). Intergroup threat theory. In T. D. Nelson (Ed.), Handbook of prejudice (pp. 43-59). Mahwah, NJ: Lawrence Erlbaum. |
[81] | Sumner, W. G. (1906). Folkways: A study of mores, manners, customs and morals. Mineola, NY: Dover Publications. |
[82] | Susskind, R. E., & Susskind, D. (2015). The future of the professions: How technology will transform the work of human experts. New York: Oxford University Press. |
[83] | Tabri, N., Wohl, M. J. A., & Caouette, J. (2018). Will we be harmed, will it be severe, can we protect ourselves? Threat appraisals predict collective angst (and its consequences). European Journal of Social Psychology, 48(1), 72-85. |
[84] | Tajfel, H., & Turner, J. C. (1986). The social identity theory of intergroup behavior. In S. Worchel & W. G. Austin (Eds.), Psychology of Intergroup Relations (pp. 7-24). Chicago, IL: Nelson-Hall. |
[85] | Touré-Tillery, M., & Light, A. E. (2018). No self to spare: How the cognitive structure of the self influences moral behavior. Organizational Behavior and Human Decision Processes, 147, 48-64. |
[86] | Uenal, F., Sidanius, J., Roozenbeek, J., & Linden, S. (2021). Climate change threats increase modern racism as a function of social dominance orientation and ingroup identification. Journal of Experimental Social Psychology, 97, 104228. |
[87] | Vezzali, L., Andrighetto, L., Drury, J., Di Bernardo, G. A., & Cadamuro, A. (2017). In the aftermath of natural disasters:Fostering helping towards outgroup victims. In H. Zagefka & E. Van Leeuwen (Eds.), Intergroup helping: The positive side of intergroup behaviour (pp. 305-330). New York, NY: Springer. |
[88] | Vu, H. T., & Lim, J. (2021). Effects of country and individual factors on public acceptance of artificial intelligence and robotics technologies: A multilevel SEM analysis of 28-country survey data. Behaviour and Information Technology, 41(7), 1515-1528. |
[89] | Wohl, M. J. A., & Branscombe, N. R. (2008). Collective angst:How threats to the future vitality of the ingroup shape intergroup emotion. In H. A. Wayment & J. J. Bauer (Eds.), Transcending self-interest: psychological explorations of the quiet ego (pp. 171-181). Washington, DC: American Psychological Association |
[90] | Wohl, M. J. A., Branscombe, N. R., & Reysen, S. (2010). Perceiving your group’s future to be in jeopardy: Extinction threat induces collective angst and the desire to strengthen the ingroup. Personality and Social Psychology Bulletin, 36(7), 898-910. |
[91] | Wohl, M. J. A., Giguère, B., Branscombe, N. R., & McVicar, D. N. (2011). One day we might be no more: Collective angst and protective action from potential distinctiveness loss. European Journal of Social Psychology, 41(3), 289-300. |
[92] | Wohl, M. J. A., Squires, E. C., & Caouette, J. (2012). We were, we are, will we be? The social psychology of collective angst. Social and Personality Psychology Compass, 6(5), 379-391. |
[93] | World, Bank. (2011). World Bank national accounts data, and OECD National Accounts data files. Retrieved August 27, 2024, from https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?end=2011&start=2011 |
[94] | World, Bank. (2016). World Bank national accounts data, and OECD National Accounts data files. Retrieved August 27, 2024, from https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?end=2016&start=2016 |
[95] | Wu, N. (2022a). Misattributed blame? Attitudes toward globalization in the age of automation. Political Science Research and Methods, 10(3), 470-487. |
[96] | Wu, N. (2022b). “Restrict foreigners, not robots”: Partisan responses to automation threat. Economics & Politics, 35(2), 1-24. |
[97] | Wu, T. J., Liang, Y., & Wang, Y. (in press). The Buffering Role of Workplace Mindfulness: How Job Insecurity of Human- Artificial Intelligence Collaboration Impacts Employees’ Work-Life-Related Outcomes. Journal of Business and Psychology. |
[98] |
Xu, L., Wang, X., Yu, F., & Peng, K. (2024). The influence of perceived robot threat on workplace objectification. Acta Psychologica Sinica, 56(2), 210-225.
doi: 10.3724/SP.J.1041.2024.00210 |
[99] | Yang, Y., Sedikides, C., Wang, Y., & Cai, H. (2024). Nature nurtures authenticity: Mechanisms and consequences. Journal of Personality and Social Psychology, 126(1), 79-104. |
[100] | Yam, K. C., Tang, P. M., Jackson, J. C., Su, R., & Gray, K. (2023). The rise of robots increases job insecurity and maladaptive workplace behaviors: Multimethod evidence. Journal of Applied Psychology, 108(5), 850-870. |
[101] | Yogeeswaran, K., Złotowski, J., Livingstone, M., Bartneck, C., Sumioka, H., & Ishiguro, H. (2016). The interactive effects of robot anthropomorphism and robot ability on perceived threat and support for robotics research. Journal of Human-Robot Interaction, 5(2), 29-47. |
[102] | Yudkowsky, E. (2008). Artificial intelligence as a positive and negative factor in global risk. In N. Bostrom, & M. M. Ćirković (Eds.), Global catastrophic risks (Vol. 1, p. 184). New York: Oxford University Press. |
[103] | Złotowski, J., Yogeeswaran, K., & Bartneck, C. (2017). Can we control it? Autonomous robots threaten human identity, uniqueness, safety, and resources. International Journal of Human-Computer Studies, 100, 48-54. |
[1] | XU Liying, WANG Xuehui, YU Feng, PENG Kaiping. The influence of perceived robot threat on workplace objectification [J]. Acta Psychologica Sinica, 2024, 56(2): 210-225. |
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