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Research Papers

# Design Team Convergence: The Influence of Example Solution Quality

[+] Author and Article Information
Katherine Fu

Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213kfu@cmu.edu

Jonathan Cagan1

Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213cagan@cmu.edu

Kenneth Kotovsky

Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213kotovsky@cmu.edu

The \$50 gift certificate was awarded randomly, and the overall best design was not intended to be assessed.

The inter-rater reliability calculation was done by taking the difference between the two quality ratings for each attribute, which ranged from −2 to 2, and then by dividing that difference by 4 to obtain an effective “percentage” difference within the span of the scale. For example, if one rater gave a design a value of 1 and the second rater gave a design a value of 0, the percentage difference within the span of values was calculated to be 0.25, i.e., $(1−0)/4$. These values were summed across the attributes for each design evaluated and then averaged for the set of double coded designs. Finally, this value was subtracted from one to get the percent agreement, as opposed to the percent disagreement if it had not been subtracted from one.

1

Corresponding author.

J. Mech. Des 132(11), 111005 (Nov 03, 2010) (11 pages) doi:10.1115/1.4002202 History: Received July 07, 2009; Revised July 21, 2010; Published November 03, 2010; Online November 03, 2010

## Abstract

This study examines how engineering design teams converge upon a solution to a design problem and how their solution is influenced by information given to them prior to problem solving. Specifically, the study considers the influence of the type of information received prior to problem solving on team convergence over time, as well as on the quality of produced solutions. To understand convergence, a model of the team members’ solution approach was developed through a cognitive engineering design study, specifically examining the effect of the introduction of a poor example solution or a good example solution prior to problem solving on the quality of the produced solutions. Latent semantic analysis was used to track the teams’ convergence, and the quality of design solutions was systematically assessed using pre-established criteria and multiple evaluators. Introducing a poor example solution was shown to decrease teams’ convergence over time, as well as the quality of their design solution; introducing a good example solution did not produce a statistically significant different effect on convergence compared with the control (with no prior example solution provided) but did lead to higher quality solutions.

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Topics: Design , Teams

## Figures

Figure 2

Schematic of example solution, given to subjects in good example condition

Figure 3

Pictorial timeline of events

Figure 4

Illustration of six pairwise comparisons made with LSA for one group of four participants, averaged for each writing session

Figure 5

Average level of semantic convergence within groups; error bars show ±1 standard error

Figure 6

Example pairs of documents from the writing sessions with low convergence and high convergence

Figure 7

Illustration of four comparisons between individual group members and collaborative final design solution text made with LSA for one group of four participants, averaged for each writing session

Figure 8

Average level of semantic convergence to final design solution description text; error bars show ±1 standard error

Figure 9

Average quality of final design solutions; error bars show ±1 standard error

Figure 10

Energy sources for removing shell

Figure 11

Energy sources for separating nut and broken shell as identified in final designs in each condition

Figure 12

Average number of features from poor example and good example included in final designs in each condition

Figure 1

Schematic of example solution, given to subjects in poor example condition (31)

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