Satisfaction with Performance

a) SONA study

In the SONA study in stage 1 (see table F-5A), positive initial valence has a positive significant effect on performance satisfaction such that it makes task performance satisfaction higher than average satisfaction with performance when valence is neutral; this effect is equal to a B coefficient of 1.00 (beta=0.28, t (282) =4.50, p-value= 0.00). Negative initial valence also has a negative significant effect beyond the reference category of neutral initial valence, i.e. it reduced average performance satisfaction when valence is neutral by 0.75 (beta= -0.22, t (282) =-3.49, p-value=0.00). The R square or variance explained by initial valence is approximately 0.19 or 19%. (F (2, 282) = 32.03, p-value of 0.00). So in general getting positive feedback in the first stage of doing a task increases satisfaction with performance while negative feedback reduces it.

In stage 5 (see table F-5B), three variables have an effect: positive and negative trend valence and positive initial/ mean valence. With respect to positive trend valence, it increases or adds to the average of performance satisfaction (the average being the value when trend is neutral or flat – the reference category- regardless of inconsistency and when initial valence is neutral or negative since initial/mean negative feedback valence is insignificant) by the B coefficient of 0.92 (or by standardized value or beta of 0.24, t (278) of 4, p-value <0.01) while negative initial/mean valence reduces average performance satisfaction (over when trend is flat and also when initial valence is neutral or negative) by 0.79 ( or by standardized value of -0.20, t (278) =-3.41, p-value<0.01). Positive initial valence also increases average performance

satisfaction (over its level when trend is flat or neutral and initial valence is neutral or even negative) by 1.03 (beta=0.26, t (278) =4.37, p-value <0.01). The R square or variance explained in performance satisfaction at stage 5 is 25% (F (5, 278) = 18.74, p-value<0.01). If initial or mean valence is not included in the model, positive feedback trend has a slightly higher beta of 0.24 (and B=0.92) at the same significance and the same applies to negative trend which will have a beta of -0.20 (B=-0.79, again at same p-value). The difference in R square between when

only trend and inconsistency are considered and when initial valence is also added is 0.10 (F (2, 278) for R square change=19.40, p-value<0.01).

In terms of control variables in stage 1, controlling for gender, race, experience, age and GPA reduces slightly the effect of positive valence (B=,0.97 beta=0.27, p-value<0.01) and negative valence (B=-0.74, beta=-0.22, p-value<0.01) but none of the variables has a significant effect on performance satisfaction. The R square here changed from an insignificant 0.02 (p- value>0.10) to a significant 0.19 (F (2,252) =26.03, p-value<0.01). The big five personality characteristics also has no effect on performance satisfaction as predictors and including them only slightly changed the effect of positive valence to (B=) 1.01 (beta=0.28, t (279) =, p- value<0.01) and negative valence to (B=)-0.76 (beta=-0.22, p-value<0.01). The change in R square here is 0.19 from an insignificant 0.01 (p-value>0.10) to a significant 0.20 (F (2,277) for R square change =32.35, p-value<0.01).

As for general affect and implicit theory, only general PA has an effect on performance satisfaction of 0.21 (beta=0.11, t (279) =1.93, p-value<0.10); the R square change from the model with only control variables to model that included initial valence variables is 0.18 and this is the difference between R square of 0.02 (which is itself not significant at 0.10 significance level) and a significant R square of 0.20 (F (2, 279) for R square change = 31.09, p-value<0.01). Including general affect and implicit theory in the model however changed the effect of positive valence to (B=) 0.96 (beta=0.27, p-value<0.01) and negative valence to (B=)-0.76 (beta=-0.22, p-value<0.01).

With respect to control variables and their effect is stage 5, in the case of PA, NA and implicit theory, including them reduces the effect of positive trend to a B of 0.88 (beta=0.23, t (275) =3.86, p-value<0.01) but keeps the beta of negative trend the same -0.20 (B =-0.78, t (275) =-3.41, p-value<0.01), reduces the effect of positive initial/mean valence (B= 0.96, beta=0.24,t (275) =4.11, p-value<0.01) and makes negative valence significant (B=-0.39 , beta=-0.10 , t (275) =-1.75 , p-value<0.10). In terms of the effect of the control variables as predictors, both implicit theory (B= 0.24, beta= 0.13, t (275) =2.45, p-value<0.05) and general PA (but not general NA; general PA B=0.27, beta=0.12, t (275) =2.36, p-value<0.05) have an effect on performance satisfaction with a R square change from a model that only included control variables to a model that included all feedback valence variables of a significant (p-value<0.01) 0.24.

In the case of the big five personality variables, none have a significant effect on

performance satisfaction in stage 5, but controlling for them increases the effect of positive trend slightly to B=1.00 (beta=0.26, t (273) =4.23, p-value<0.01) and also the effect of positive

valence (B=0.98, beta=0.25, t (273) =4.15, p-value<0.01) but maintains the effect of negative trend (beta of -0.20 with a B of -0.75, t (273) =-3.24, p-value<0.01) and keeps negative initial/mean valence insignificant in shaping performance satisfaction with a R square change from a model that only included control variables to a model that included all feedback valence variables of a significant (p-value<0.01) 0.26.

Finally, when testing for the effects of the final group of control variables, being of the black race (B=0.43, beta=0.11, t (248) =1.70, p-value<0.10), having longer job experience (B=0.06, beta=0.17, t (248) =1.97, p-value<0.10), and being younger (age B=-0.06, beta=-0.22, t (248) =-2.57, p-value<0.05) all increase performance satisfaction. Including these variables in the model changes results to positive trend effect of 1.00 (beta= 0.25, t (248) =4.14, p-

value<0.01), negative trend effect of -0.86(beta=-0.22, t (248) =-3.49, p-value<0.01) , positive initial/mean valence effect of 0.88 (beta=0.22, t (248) = 3.52, p-value<0.01) and negative initial/mean valence effect of -0.52 (beta= -0.14, t (248) = -2.15, p-value<0.05) with a R square change from a model that only included control variables to a model that included all feedback valence variables of a significant (p-value<0.01) 0.25.

Thus, to conclude, the results of the SONA study with respect to the outcome of performance satisfaction fully support hypotheses 3b (even more than in any of the outcome above because both positive and negative valences have significant effects and differ in the expected ways form neutral valence), i.e. positive initial valence increases performance satisfaction while negative initial valence reduces it as compared to neutral valence. Also, hypothesis 12c is supported because positive trend increases performance satisfaction while negative trend reduces it (and there is also evidence that neutral trend when tested also reduces performance satisfaction; for example, by comparing average satisfaction after including trend and inconsistency with average before any predictors are included in the model shows a

reduction: 3.58-3.41=0.17 units and also when tested not as a reference category but with positive trend as reference category, neutral trend reduces average but to a lesser extent, i.e. smaller B, than negative trend). However, feedback inconsistency has no effect on performance satisfaction as compared to feedback consistency and so hypothesis 18b (that inconsistency

reduces satisfaction with task performance) is not supported. In general, control variables do not affect the insights about the relationships for task performance satisfaction in either the SONA study or the BUSA study as discussed next.

b) BUSA study

In the BUSA study, in the case of performance satisfaction after five rounds (see table F- 5C for the variable run alone in HLM), both mean performance and trend have negative

significant effects (and this is because performance satisfaction here and as explained earlier is reverse-scored) while standard deviation or inconsistency in feedback does not have any significant effect. The coefficient of mean performance is -0.02 (SE=0.00, t(77)=-6.76, p-

value=0.00) and the coefficient of trend is -0.06 (SE=0.01, t(77)=-9.31, p-value=0.00) while the coefficient of standard deviation is 0.00 (p>0.10). The same coefficients and significance for all the predictor variables are repeated with group-centered data except that the coefficient for mean becomes -0.03 instead of -0.02.

Based on the results, some of the negative effects of trend on performance dissatisfaction (or positive effect on performance satisfaction) are explained by mean performance and the negative effects of both mean and trend performance offset the positive effects of standard deviation (which when run alone using grand-centered data has a negative effect since here performance satisfaction is reverse-scored. i.e. it has a coefficient of 0.04, SE=0.01, t (79) =4.70, p-value=0.00, R square=0.07) on performance satisfaction (see table F-5D) with almost the same results (when approximated to two decimal places) in group-centered data.

However, when year 5 performance score/feedback (or current feedback) is included in the picture (again this is only tested using grand-centered variables and data for overall patterns; see tables F-5D and F-5E in appendix F), the results point that it overshadows all the rest of the variables in positively affecting performance satisfaction after five rounds (coefficient= -0.03, SE= 0.003, t(77)= -9.294 , p-value= 0.000 while the rest of the variables have insignificant effects even at a significance level of 0.10, e.g. trend coefficient= -0.01, SE= 0.004, t(77)= - 1.462 , p-value= 0.148 and standard deviation (St.Dev.) or inconsistency coefficient= 0.004, SE= 0.01, t(77)=0.074 , p-value= 0.94, pseudo R square=0.35 while variance explained only at level 1 and 2 or individuals and teams =0.41). But unlike the case of task satisfaction after five rounds, if mean performance for round 2 to 5 is controlled for, initial feedback does seem to have an effect on performance dissatisfaction (albeit a positive one: coefficient= 0.01, SE=0.003, t (78) = 2.724,

p-value= 0.008; see table F-5E); also, if mean performance for round 2 to 5, trend and inconsistency are controlled for, year 1 or initial feedback shows a significant effect on performance satisfaction after five rounds (see F-5D).

Looking at results over the ten periods of the game as a whole (see performance satisfaction after ten rounds run alone in HLM in table F-5F), results change a little: trend and mean performance over the ten periods are significant in effecting performance dissatisfaction by reducing it (so they have a negative effect or alternatively a positive effect on performance satisfaction; mean B coefficient = -0.04, SE=0.00, t(77)=-10.01, p-value=0.00 while trend B coefficient= -0.09, SE=0.02, t(77)=-3.57, p-value=0.00) but standard deviation also has an effect and the effect on performance dissatisfaction is also negative and significant (i.e. standard deviation or inconsistency in performance throughout the game also increase performance satisfaction; coefficient= -0.02, SE=0.01, t(77)=-4.50, p-value=0.00; see table F-5G).

However, standard deviation in this case only has an effect on performance satisfaction if mean performance and trend are controlled for, otherwise, standard deviation has no significant effect (i.e. when run alone: coefficient= 0.02, SE= 0.01, t (79) =1.611, p-value= 0.11, R

square=0.00). Again when year or round ten performance feedback enters the picture (see both tables F-5G and F-5H), it overshadows all other effects with a coefficient of -0.04 (SE= -0.04, SE=0.003, t(77)=-10.54, p-value= 0.000 and an insignificant almost zero coefficient for both trend and standard deviation); the results of all the analysis where current valence is included with mean, trend and standard deviation in the model need to be considered very carefully because of the high correlation between mean, trend and current valence even after year 5 or 10 (as current valence) is centered by group (section) or even standardized (the z scores are used).

High collinearity was diagnosed by looking at correlations (all 0.5 approximately or higher- up to 0.9), VIFs and tolerance values when running these variables as predictors with outcomes at the same level like sixth year performance and also by exploring the stability of betas, values of SEs and R square changes when different variables are combined together in one model versus alone. As in the case of task satisfaction after ten rounds, in the case of

performance satisfaction, current feedback (10th year) has an effect on performance satisfaction but unlike the case of task satisfaction after ten rounds, initial feedback does not (see table F- 5H). See table F-5J for analyses with control variables.

In terms of support for the hypotheses, the results of the BUSA study with respect to performance satisfaction whether after five rounds or ten support hypotheses 3b and 12c but not 18b because current feedback has a positive effect on performance satisfaction and so does feedback trend (i.e. as feedback trend increases so does performance satisfaction) but feedback inconsistency or standard deviation as it increases either leads to no significant effect on the grand mean of performance satisfaction (as is the case after five rounds) or actually has the opposite effect to what was predicted, i.e. it increased performance satisfaction (after ten rounds). Also, when the effect of fifth round valence rather than feedback value is tested for an effect on average performance satisfaction after controlling for mean for performance feedback from round 1 to 4, the results support the other findings and so support hypothesis 3b with respect to current rather than initial valence. So, in the case of valence based on group-centered values, the B coefficient for positive fifth valence is -1.04 with SE=0.11, t (78) =-9.770 and p- value=0.00 (the coefficient is negative but denotes a positive effect because performance satisfaction is reverse-scored) while negative valence B coefficient is =1.04 and so positive

denoting a negative effect when group-centered mean for rounds 1 to 4 is controlled for.

At the grand-centered level, positive current feedback valence B coefficient is -0.93 with SE=0.07, t (78) =-13.44 and p-value=0.00 meaning that positive valence increases average performance satisfaction by 0.93 over when valence is negative when grand-centered mean for rounds 1 to 4 is controlled for. Another interesting finding here that is not hypothesis-related but that answers a question about initial valence paused in the dissertation is that as opposed to the case of task satisfaction, initial valence seems to have an effect on performance satisfaction midway through the game but this effect does not linger to the end or when performance in the game as a whole could be reviewed or revisited. This finding is also supported by the results of the SONA study discussed above where positive initial valence was found to have a significant effect even at stage five- however, it is important to note that due to the design of both studies, in the case of the SONA study as well as in the BUSA study, initial valence also affects general overall valence but this is stronger in the SONA study than in the BUSA study where correlation between mean is higher with year or round 5 performance feedback than with year 1

performance feedback (value).

With regards to the effects of trend valence (based on both grand-centering and group- centering) and the extent to which inconsistency is higher or lower than grand/group average in

stage 2 of the BUSA study, positive trend valence has a negative effect on performance dissatisfaction (B coef= -0.67, SE= 0.072, t (78) =-9.190, p-value=0.00) with group-centered mean performance values for rounds 1 to 4 controlled (B coef= -0.027, SE= 0.002, t(78)= -14.50, p-value=0.00; INTERCEPT=2.72) while negative trend consequently having an equal positive effect on performance dissatisfaction (compared to positive trend valence). However, level of inconsistency (higher or lower than group average) has no significant effects. As for grand- centered valences, very similar results occur for positive trend valence (but -0.61instead of -0.67 with p-value=0.00) and grand-centered mean vale coef of -0.02 (p-value=0.00) while again

inconsistency has no significant effects. This provides further evidence that hypothesis 12c is

supported but not 18b.