Learning Behavior and Intentions

a) SONA study

In the SONA study, as opposed to the BUSA to be discussed next (and as already mentioned above), the interest is in the valence of the feedback and its patterns rather than the numerical values for these variables. Also, learning behavior was mot measured but was

substituted by specific (as opposed to generic) task-related learning and development intentions. A shown in table F-1A in appendix F (section F-1 which is all on learning intentions), learning intentions in stage 1 were not affected by either positive or negative (as compared to neutral valence or to each other) and the R square for the model was almost zero (p-value>0.10). However, the average value of the learning intentions in stage 5 was affected by both positive trend and inconsistency as shown in table F-1B. According to the results displayed in this table, if the feedback given to respondents was consistent across the five stages, positive trend

increased the average learning intentions level (B=2.89) when trend is flat or negative by (B=) 0.37 units (beta or b= 0.13, t (281) =1.90, p-value<0.10) while if inconsistency is also present then this average (when trend is negative or flat) increases even more by an additional (B=) 0.28 units (beta=0.10, t (281) = 1.69, p<0.10). The error variance in learning intentions explained (R square) by this model is 0.03 or 3% (adjusted=2%; p<0.10). Adding initial feedback valence to

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the model does not change results and both positive and negative initial valence are insignificant

with p-values >0.10. Thus, in general and before adding control variables to the models, it seems

that hypothesis 2 with regards to learning behavior (nor effect for initial valence) as well as hypothesis 17a (since inconsistency increases rather than decreases learning intentions) are not supported in this study but hypothesis 12a is supported (trend when positive increases learning intentions).

With regards to control variables (control variables only tested with initial feedback valence, and later only trend and inconsistency in the model), none had an effect at stage 1. However, when the control variables included in the model are general PA, general NA and implicit theory of intelligence, NA has the only significant effect (B=0.17, beta=0.15, t (279) =2.50, p-value<0.05) while when the big five are the control variables tested, neuroticism is the only personality variable that has an effect (B=0.18, beta= 0.12 t (277) = 1.96, p-value<0.10). On the other hand, when controlling for age, gender, experience, race (white race and black race are the two dummy variables used with neither a reference category) and GPA, three variables have a significant effect on learning intentions such that average learning intentions are lower for respondents who are white (white race B= -0.46, beta=-0.22 , t (252) =-3.05 , p-value<0.01; black race has no significant effect beyond that of the reference category of other races), have longer job experiences (experience B= -0.04, beta=-2.22 , t (252) = -2.36 , p-value<0.05) and are younger in age (age B= 0.03, beta=0.23 , t (252) =2.51 , p-value<0.05).

In stage 5, controlling for the big five personality characteristics (with possibly the personality variable of conscientiousness being mainly the one to pinpoint as the source of the effect based on further exploration and analyses) makes both feedback inconsistency and positive trend valence insignificant in effecting learning intentions (p-values of more than 0.10) (while positive and negative initial valence remained insignificant if added) while both

conscientiousness and neuroticism are significant in their direct effects on learning intentions: for conscientiousness, it increases average learning intentions by a B of 0.47 or a beta of 0.24 (t (276) =3.60, p-value<0.01) and for neuroticism its effect is less significant but also positive (B=0.21, beta=0.11, t (276) =1.71, p<0.10)- these results are for the model run without initial valence variables. The R-square change from a model with only the five personality control variables to a model with feedback trend and inconsistency included is 0.02 (from 0.06 to 0.08; F (3, 276) for R square change= 1.66, p-value>0.10).

As for the another set of control variables (general PA, general NA and implicit theory of intelligence: considered the third set of control variables throughout this study), they make the relationship between positive trend and learning intentions insignificant by rendering the effect of positive trend insignificant (B=0.29, beta=0.10, t (278) = 1.58, p-value>0.10) while general PA has a significant direct effect on learning intentions (B= 0.38, beta=0.24, t (278) =4.10, p- value<0.01) on learning intentions (feedback inconsistency’s effect remains unchanged) and so does general NA (B=0.18, beta=0.12, t (278) =2.02, p-value<0.05). Implicit theory of

intelligence does not have any significant effects on learning intentions. R square change in this case is 0.02 (from 0.07 to 0.10; F (3, 278) for R square change= 2.28, p-value<0.10).

Controlling for age, race, gender, GPA and experience together while excluding both positive and negative initial valence which were insignificant from the model led to the opposite: an increase in the effects of both positive trend (to a B of 0.39, beta=0.14, t (251) =2.04, p-value <0.05) and feedback inconsistency (to B=0.38, beta=0.14, t (251) =2.25, p-value<0.05). The R square change from the model when only the control variables are included to the when where feedback trend and inconsistency are also included is 0.03 so from 0.09 to 0.12 (F (3,251) for R square change= 3.27, p-value<0.05).On the other hand, all the control variables with the

exception of GPA and being white race had a direct effect on learning intentions: being male had a negative effect (sex B= -0.37, beta=-0.14, t (251) =-2.32 , p-value<0.05), high experience had a small negative effect (B= -0.05, beta=-0.20, t (251) =-2.09 , p-value<0.05 ), and being black also had a direct positive effect (compared to other race, so neither white nor black), and the effect is B=0.39 (beta=0.14, t (251) =1.97, p-value<0.10).

Thus, to conclude, without control variables in the model, hypothesis 12a is supported but none of the feedback valence variables influences learning intentions once some important control variables (like general affect and implicit theory) are added to the model while other control variables (demographics) make the effects more pronounced. In other words, hypotheses 2 (positive valence reduces learning while negative valence increases it), and 17a (inconsistency reduces learning; not supported because when significant the effect is opposite to what was hypothesized) are not supported in the SONA study but 12a only has tentative support (depending on control variables accounted for in the model).

b) BUSA study

In the BUSA study, the variance in learning behavior in stage 2 is shown in table F-1C in appendix F; and the variance in learning behavior in stage 3 is shown in table in F-1D. And as shown in tables F-1E and F-1F in appendix F, neither trend nor inconsistency had any effect on learning behavior at stage 2, even after controlling for mean performance. For instance, the R- square or variance explained when all three variables, mean, trend and inconsistency were inputted into the analysis is zero.

The same results are obtained even when adding (controlling for by adding later on)

initial feedback or performance score and when adding 5th _{round or year performance (see table}

F-1F), i.e. current feedback, to the model (so 5th _{year performance is not significant); when}

controlling for either, mean performance was removed from the analysis to avoid

multicollinearity problems not that keeping mean performance in the analysis when tried

changed results. However, the scale items used to measure learning behavior in the BUSA study asked respondents to think about their behavior in the game in general and so it is more past- oriented than future or present oriented. Thus, it makes more sense to test the effect that feedback patterns in the first five rounds has on learning behavior in stage 3 or in the next five rounds and not just the first five rounds. When this is done, trend becomes significant (it has a B coefficient of 0.01, with a p-value of 0.05; see table F-1G). However, the positive effect is minuscule since the R square this model explained is still zero; this does not change when adding initial valence which makes all variables in the model including trend insignificant.

The results for all independent variables are insignificant when looking at all ten rounds instead of just the first five (see table F-1H). So when testing the effect of trend and standard deviation in all rounds, controlling for mean performance in all ten rounds, all variables are insignificant as shown in table F-1H. Finally, when trend and standard deviation were broken down into two variables each, one for each survey (and so for instance trend for first five rounds and trend for last five rounds and the same for standard deviation) and all were entered into the model simultaneously, results did not change still- so all variables remained insignificant when tested with learning behavior at stage 3 as the outcome.

The model was also run with mean performance for first four then nine rounds controlled for, and current feedback still had an insignificant effect of learning behavior at stages 2 and 3. As for control variables, there was no effect on ant of the above discussed relationships when

adding any of them to the model. Control variables were tested using a smaller sample due to missing data but the relationship was tested using this smaller sample with and without the control variables (as would be replicated for the rest of the study to ensure accurate and valid results) and no change in any of the relationships was detected.

Finally, looking at whether there is a possibility for a moderation effect of learning effort in the feedback trend and consistency to learning behavior relationship, table F-1I shows no moderating effect even though there is a direct positive effect or association between learning effort and learning behavior in stage 3. Looking at the table, when controlling for mean performance in the first five years which had a significant but very small effect (coefficient= zero, p-value=0.01, t (77) =-2.70), trend in the second five rounds has a significant negative effect on learning behavior measured at stage 3 (B coefficient= -0.02, p-value=0.04, so p< 0.05, t (77) =-2.08). Tentatively, based on the results relating trend to learning behavior so far, it seems that trend has a negative effect on learning decisions and intentions to engage in learning-related behavior, especially when learning effort is also accounted for (or controlled) in the model. However, this conclusion needs to be considered caution and studied further before any valid argument can be made given how learning behavior was defined and measured in the BUSA study.

Learning effort also has a direct positive effect (B coefficient= 0.31, t (176) = 4.08, p- value=0.00) but not a moderating effect since looking at the interaction term between learning effort and trend as well as well as learning effort and standard deviation, both are found to be insignificant (p-value>0.10). Controlling for mean performance for the first 9 rounds and then adding year 10 performance, learning effort in last round and their interaction shows only that learning effort was significant with a B coefficient of 0.37, t (176) of 7.94 and a p-value of 0.00 with a R-square of 0.09 (i.e. 9% of the error variance in learning behavior in stage 3 is explained by learning effort; all other coefficients were zero with p-value >0.10). Thus, learning effort is not a moderator here but a direct predictor or is positively associated with learning behavior.

As for control variables, they were grouped in seven groups that are going to be used consistently when testing the effects of the control variables in the BUSA study throughout this paper. None of the control variables has any effect on the relationship between trend and consistency and learning behavior (remember that control variables will only be tested in relationships with the two main predictor variables in this study: trend and consistency while

controlling for mean performance) as shown in table F-1J. However, some of the control variables had a direct effect on learning behavior like PA towards team (beginning) and

performance goal (self, beginning). Also, when people are asked how much effort they expended in the five rounds (in terms of time and energy and separate from learning effort) and this is controlled for as a kind of supplementary control, the results show that again mean, trend and inconsistency of feedback have no effect while effort does (see table F-1J).

In terms of support for the hypotheses or lack of it, again looking at all the above results in the BUSA study there is generally no support for hypotheses 2 and 17a, like in the case of the SONA study. In the case of hypothesis 12a, there is some evidence that as trend increases, learning behavior decreases but again this conclusion is very tentative given that once initial valence is accounted for, trend loses this influence. The model with mean, trend and

inconsistency as predictors for learning behavior at stage 2 or after five rounds was also run with group-centered BUSA data and again none of the three predictors are significant at the 0.10 significance level. Moreover, even though learning effort has a direct positive effect on learning behavior, it shows no moderating effect on the relationship between any of the feedback variables and learning behavior.

Moreover, to test whether the valence rather than the value of current (5th _{year) feedback}

has any effect on learning behavior in stage 2 or after five rounds, 5th year performance scores were centered once per section (so group-centered) and once using the grand mean and scores were then designated as either positive (dummy variable used with positive equal 1 one time and was used as reference category another time)or negative (reference category first and then as dummy variable with 1 value afterwards) and in all cases, current feedback valence showed no

significant effect (p-value>0.10) when learning behavior is the outcome. For instance, when 5th

round valence (grand-centered) (positive=1 and negative =reference) is run with learning behavior as outcome, the B coefficient = -0.05(p-value=0.49). The same insignificant results

occur when learning behavior at stage 3 is the outcome. Thus, again when using valence for

current feedback rather than value, there is no support for hypothesis 2 with respect to learning behavior.

Finally, as additional analyses, the valence of trend (whether it is positive or negative compared once to the grand average and once to the group or section average expressed as dummy variables as in the case of current feedback valence; there are no neutral valences in this

data set) and the extent to which inconsistency is high or low compared once to the group average and once to the grand average (again high inconsistency and low inconsistency are expressed as dummy variables tested separately as predictors to explore the effect of each compared to the other) are tested as predictors and none have significant effects on learning

behavior in the BUSA study in stage 2 and also in stage 3. Again, these results support the

conclusion reached: there is very little evidence in this data set to support hypotheses 12a (depending on the control variables included in the model) and none at all to support hypothesis 2 (with respect to learning) and 17a.