February 2, 2006 Intern 박지현

1. Analyzing shared and team mental models Janice Langan-Foxa Anthony Wirthb, Sharon Codea, Kim Langfield-Smithc, Andrew Wirthd February 2, 2006 Intern 박지현

2. C o n t e n t s 1 Mental models in organizations 2 The aims of the current paper 3 Background of the current study 4 Method 5 Results 6 Conclusion

3. Mental models in organizations Work teams have the potential to provide many benefits, such as increased flexibility and creativeness. Thus, organizations should focus on maximizing the potential of their teams. Ordinarily, a manager or expert should possess knowledge and experience that surpass that of any of his/her subordinates. Thus, a manager’s (or expert’s) mental model could be considered to represent the ‘ideal’ model of how the team should be functioning in order for the business unit to be successful. It could be hypothesized that when team members’ hold mental models that are similar to the manager/expert model, the team is more likely to be successful.

4. The aims of the current paper The aims of the current paper were… 1. within-team mental model similarity will be higher for structured task teams than for unstructured task teams, 2. structured task team members’ mental models of team functioning will differ from those of unstructured task team members, and 3. (a) team member–manager/expert mental model similarity will be higher for structured task teams than for unstructured task teams, and (b) team members’ mental models of team functioning will differ from those of experts and managers. Given that the statistical techniques were the focus of this paper, rather than the data.

5. Background of the current study X group :: Randomization test :: The similarity measure Imagine that we have two groups of people: 4 ‘X people’ and 3 ‘Y people’. & hypothesize that the Y people are heavier than the X people. H0 : MX=MY H1 : MX≤MY The weights in measuring order are ( 8, 7, 7, 10, 9, 11, 12 ) In general the p-value will be # of labelings with test statistic at least as extreme as real labeling total # of labelings (S≥32 H0)= =2/35=0.057 • 7point scale range Subject 1: (6,4,6,3) Subject 2: (5,5,2,1) Difference between two subject 1+1+4+2 or 1+1+16+4 If We define the difference within a group.. average of all dyadic differences Similarly, we define the difference in mental models between two groups A and B to be Ex Ex ex) Group A : Jan, Sharon, Tony Group B: Andrew, Kim SY=32

6. Method :: Main Study : 16 concepts which were most important to their team. ex) communication, interacting, information, change and uncertainty : 86 employees This included 72 shop floor team members (13 teams), 7 teamwork ‘experts’, and 7 managers. Structured task : well defined, unambiguous, routine Unstructured task : ill-defined, ambiguous, non-routine By the pair-wise rating task.. The scale ranges from 1 to 7, where 1 equals ‘not at all related’, and 7 equals ‘very related’. • Concepts • Subject • Team task context • Procedures

7. :: Randomization test :: The similarity measure Imagine that we have two groups of people: 4 ‘X people’ and 3 ‘Y people’. & hypothesize that the Y people are heavier than the X people. H0 : MX=MY H1 : MX≤MY The weights in measuring order are ( 8, 7, 7, 10, 9, 11, 12 ) In general the p-value will be # of labelings with test statistic at least as extreme as real labeling total # of labelings (S≥32 H0)= =2/35=0.057 • 7point scale range Subject 1: (6,4,6,3) Subject 2: (5,5,2,1) Difference between two subject 1+1+4+2 or 1+1+16+4 If We define the difference within a group.. average of all dyadic differences Similarly, we define the difference in mental models between two groups A and B to be Ex Ex ex) Group A : Jan, Sharon, Tony Group B: Andrew, Kim X group SY=32 Results :: Hypothesis 1: similarity of mental models within teams H0: There us no difference in the mean difference scores for structured and unstructured teams. H1: Unstructured task teams have a higher mean difference score. ( 135.8, 149, 150.8, 163, 172, 188.4, 188.9, 189, 217.1, 232.7, 251.4, 271.4 ) *the unstructured task teams in bold. There were labelings. p-value(L1) : 0.655 p-value(L2) : 0.885 There were clearly no grounds on which to reject the null hypothesis. • We conjectured that unstructured task team members would (on average) have a lower level of within-team mental model similarity than structured task teams. For each of the teams,

8. Results :: Hypothesis 2: similarity of structured task team members to unstructured task team members H0: the mental models came from a common population. H1: they were different in the sense defined by the test statistic There were 54 structured task members and 16 unstructured task members. There were labelings. p-value(L1) : 0.782 p-value(L2) : 0.649 We accepted that there was no significant difference between the groups. • We hypothesized that there would be some difference between the mental models of structured task team members and unstructured task team members.

9. Results :: Hypothesis 3: similarity of manager and expert mental models to team members’ mental models • We hypothesized (3a) that team member–manager/expert mental model similarity would be greater for structured task team members than it would be for unstructured task team members. There was no evidence to suggest greater similarity between mangers/experts and structured task team members than between managers/experts and unstructured task team members.

10. Results :: Hypothesis 3: similarity of manager and expert mental models to team members’ mental models • Our final hypothesis (3b) was that there might be some difference in mental models between either managers, experts or both (managers and experts combined), and the team members. The results support the hypothesis that managers of unstructured task teams have different mental models from unstructured team members. When experts are grouped in with managers the evidence is slightly stronger. No other comparisons were significant, even at the 10% level.

11. Conclusion The primary aim of the current paper was to describe the application of randomization tests as a new method for measuring mental model similarity at the team level, that is the measurement of team mental models. Given that the statistical techniques were the focus of this paper, rather than the data. Because it is possible that interpretation of the rating scale could vary across participants.