Single-sample analyses
GoF and quality indices of the structural equation models are presented in Table 2. The models that excluded (Model 1) and included (Model 2) past behavior exhibited adequate fit and quality indices in both the Australian and U.S. samples. Standardized parameter estimates for the proposed direct effects for each model in the Australian and U.S. samples are presented in Fig. 1. Full parameter estimates for models in both samples are presented in Supplementary Appendix E. Parameter estimates, CIs, and effect sizes for the indirect effects of the models in both samples are summarized in Table 3.
Table 2.  Model quality and GoF statistics for the structural equation models of the integrated model in the Australian and U.S. samples and multigroup model
Sample  Model  APC  AR2  AVIF  AFVIF  GoF  SPR  R2CR  SSR  NLBCDR
Australia  1  .104**  .177***  1.177  1.561  .391  .841  .977  .889  .873
2  .116***  .338***  1.222  1.904  .543  .819  .991  .931  .785
USA  1  .098**  .192***  1.187  1.823  .410  .889  .995  .825  .754
2  .116***  .338**  1.222  1.904  .543  .819  .991  .931  .785
MS  1  .100***  .182***  1.159  1.704  .398  .905  .995  .794  .817
2  .113***  .300***  1.186  1.760  .511  .931  .997  .917  .840
Model 1 = model excluding past behavior; Model 2 = model including past behavior.
AFVIF average full collinearity variance inflation factor; APC average path coefficient; AR2 average R2; AVIF average block variance inflation factor; GoF Tenenhaus’s goodness-of-fit index; MS multiple sample analysis; NLBCDR nonlinear bivariate causality direction ratio; R2CR R2 contribution ratio; SPR Sympson’s paradox ratio; SSR statistical suppression ratio.
*p < .05, **p < .01, ***p < .001
Table 3.  Standardized parameter estimates for indirect effects for the structural equation model of the integrated model in the Australian and U.S. samples
Effect  Model excluding past behavior  Model including past behavior
β   p  95% CI  ES   Β   p  95% CI  ES
LB  UB     LB  UB
Australian sample
 Indirect effects
  Att.→Int.→Beh.  .011  .359  −.052  .074  .003  .004  .444  −.059  .067  .001
  SN→Int.→Beh.  .042  .094  −.021  .105  .016  .016  .312  −.047  .079  .006
  MN→Int.→Beh.  .068  .016  .005  .131  .024  .028  .192  −.035  .091  .010
  AR→Int.→Beh.  .011  .356  −.052  .074  .003  .003  .457  −.060  .066  .001
  PBC→Int.→Beh.  .040  .101  −.023  .103  .011  .016  .307  −.047  .079  .005
  Int.→AP→Beh.  .040  .106  −.023  .103  .014  .011  .365  −.052  .074  .004
  Hab. (T1).→Hab. (T2)→Beh.  .102  <.001  .041  .163  .016  .078  .007  .017  .139  .013
  PB→Hab.→Beh.  –  –  –  –  –  .021  .214  −.030  .072  .011
  PB→Beh.a  –  –  –  –  –  .081  .034  −.007  .169  .042
 Total effectsb
  Int.→Beh.  .220  <.001  .134  .306  .081  .090  .022  .004  .176  .033
  PBC→Beh.  .126  <.001  .040  .212  .036  .055  .110  −.033  .143  .016
  Hab. (T1)→Beh.  .096  .016  .010  .182  .015  .076  .044  −.012  .164  .012
  PB→Beh.  –  –  –  –  –  .494  <.001  .412  .576  .258
 U.S. sample
 Indirect effects
  Att.→Int.→Beh.  <.001  .495  −.052  .054  <.001  .004  .443  −.049  .057  .001
  SN→Int.→Beh.  .072  .003  .019  .125  .029  .023  .190  −.030  .076  .009
  MN→Int.→Beh.  .102  <.001  .051  .153  .044  .040  .067  −.013  .093  .017
  AR→Int.→Beh.  .023  .192  −.030  .076  .011  .001  .478  −.052  .054  .001
  PBC→Int.→Beh.  .088  <.001  .037  .139  .025  .038  .079  −.015  .091  .011
  Int.→AP→Beh.  .061  .011  .008  .114  .029  .004  .441  −.049  .057  .002
  Hab. (T1).→Hab. (T2)→Beh.  .212  <.001  .161  .263  .075  .166  <.001  .115  .217  .059
  PB→Hab.→Beh.  –  –  –  –  –  .068  <.001  .025  .111  .043
  PB→Beh.a  –  –  –  –  –  .178  <.001  .105  .251  .112
 Total effectsb
  Int.→Beh.  .377  <.001  .306  .448  .177  .142  <.001  .069  .215  .066
  PBC→Beh.  .146  <.001  .073  .219  .042  .074  .024  .001  .147  .021
  Hab. (T1)→Beh.  .242  <.001  .169  .315  .086  .171  <.001  .098  .244  .061
  PB→Beh.  –  –  −.052  .074  –  .673  <.001  .604  .742  .423
aSum of indirect effects of past behavior on behavior through all model constructs.
bTotal effect comprising sums of all indirect effects through model constructs plus the direct effect.
β standardized parameter estimate; 95% CI 95% confidence interval of standardized parameter estimate; AP action planning; AR anticipated regret; Att. attitude; Beh. behavior; ES effect size of the standardized parameter estimate; Hab. (T1) self-reported habit measured at baseline (T1); Hab. (T2) self-reported habit measured at follow-up (T2); Int. intention; LB lower bound of 95% CI; MN moral norm; PB past behavior; PBC perceived behavioral control; SN subjective norm; UB upper bound of 95% CI. Focusing on the model excluding past behavior (Model 1), intention, action planning, and habit at follow-up were statistically significant direct predictors of social distancing behavior, with effect size for intention and habit generally larger in the U.S. sample. PBC directly predicted behavior in the Australian sample only, also with a small effect size. Intention predicted action planning in both samples with large effect sizes. Subjective norm, moral norm, and PBC predicted intention in both samples, with small-to-medium effect sizes, but effects of attitude were not significant. There was a small effect of anticipated regret on intention in the U.S. sample only. Habit at baseline predicted habit at follow-up in both samples, with large effect sizes. There was also a small-sized effect of habit at baseline on intention in the U.S. sample only. Overall, the model accounted for significant variance in social distancing behavior (Australian sample, R2 = .198; U.S. sample, R2 = .361), intentions (Australian sample, R2 = .571; U.S. sample, R2 = .623), and habit at follow-up (Australian sample, R2 = .416; U.S. sample, R2 = .486). Intentions (Australian sample, R2 = .066; U.S. sample, R2 = .148), action planning (Australian sample, R2 = .029; U.S. sample, R2 = .044), and habit at follow-up (Australian sample R2 = .041; U.S. sample, R2 = .129) each accounted for substantive variance in behavior. Action planning significantly moderated the intention–behavior relationship in the Australian sample only. While the effect was not in the predicted direction, probing the interaction revealed that the intention–behavior relationship increased as the level of planning increased, consistent with theory. However, the intention–behavior relationship is more likely to be smaller at lower levels of planning, and it seems that planning makes less of a difference when the intention–behavior relationship is large. A plot of the interaction effect is presented in Supplementary Appendix F.
Turning to the indirect effects, there were significant indirect effects of subjective norm, moral norm, and PBC on social distancing behavior mediated by intention in the U.S. sample. By contrast, only the indirect effect of moral norm on behavior through intention was significant in the Australian sample. The smaller indirect effects in the Australian sample is principally due to the significantly smaller effect size for the intention–behavior relationship in this sample compared to the U.S. sample. Habit at baseline predicted behavior through habit at follow-up in both samples. Effect sizes in all cases were small. There were significant total effects of intention, PBC, and habit at baseline on behavior, with effect sizes larger in the U.S. sample than in the Australian sample.
For the model including past behavior, significant effects of past behavior on all model constructs were observed in both samples with effect sizes ranging from small to large. The effects of past behavior on social distancing behavior were particularly large. Inclusion of past behavior led to an attenuation of model effects in both samples. Specifically, the effects of intention and habit at follow-up on behavior were reduced but remained statistically significant in both samples with small effect sizes. In addition, effects of subjective norm, moral norm, and PBC on intention, and the effect of intention on action planning, remained statistically significant in both samples, with small-to-medium effect sizes. The effect of habit at baseline on habit at follow-up was statistically significant in both samples, with large effect sizes. Variance explained in social distancing behavior increased substantially with the inclusion of past behavior, with only modest changes in explained variance in intentions (Australian sample R2 = .598; U.S. sample, R2 = .702) and habit at follow-up (Australian sample R2 = .416; U.S. sample, R2 = .486). Specifically, intentions (Australian sample, R2 = .029; U.S. sample, R2 = .065), past behavior (Australian sample, R2 = .216; U.S. sample, R2 = .311), and habit at follow-up (Australian sample R2 = .031; U.S. sample, R2 = .101) each accounted for substantive variance in behavior.
Turning to indirect effects, we found significant indirect effects of habit at baseline on behavior mediated by habit at follow-up in both samples with small effect sizes. There were also significant total effects of intention and habit at baseline on behavior in both samples, and of PBC on behavior for the U.S. sample, with small effect sizes. There were significant total indirect and total effects of past behavior on behavior in both samples, with large effect sizes. There was a small-sized indirect effect of past behavior on behavior mediated by habit at both time points in the U.S. sample, but the effect was not significant in the Australian sample.