Investigating the Influence of Designers’ Cognitive Characteristics and Interaction Behaviors in Design Concept Generation

2019 ◽  
Vol 141 (9) ◽  
Author(s):  
Kathryn W. Jablokow ◽  
Neeraj Sonalkar ◽  
Jonathan Edelman ◽  
Ade Mabogunje ◽  
Larry Leifer
Keyword(s):  
Cognitive Style ◽  
Specific Interaction ◽  
Design Teams ◽  
Concept Generation ◽  
Cognitive Characteristics ◽  
Team Outcomes ◽  
Team Interactions ◽  
Position Specificity ◽  
Interaction Behaviors ◽  
Team Concept

This paper investigates relationships among the cognitive characteristics, interaction behaviors, and ideation outcomes of 14 engineering design teams engaged in concept generation. Cognitive characteristics were measured using the Kirton Adaption-Innovation Inventory (KAI), which assesses an individual’s cognitive preference for structure in generating and working with ideas in problem solving. Team interactions were assessed using the Interaction Dynamics Notation (IDN), which allows interaction behaviors to be quantitatively analyzed, while team outcomes were measured in terms of ideation utterances (ideas and unique ideas). Our analyses revealed that cognitive style (KAI) did not correlate significantly with interaction response behaviors (IDN) or with the quantity of ideas/unique ideas produced. However, the cognitive style diversity of the teams did influence the number of topics they discussed, as well as the interconnectedness of those topics. In addition, several specific interaction responses were associated with the occurrence of ideas/unique ideas, although the sequences associated with those responses varied widely; the more adaptive teams also had greater position specificity in these sequences than the more innovative teams. Our findings highlight the importance of forming cognitively diverse design teams and suggest that specific interaction behaviors should be encouraged or taught as a means to increase the occurrence of ideas and/or unique ideas during team concept generation.

Design Whodunit: The Relationship Between Individual Characteristics and Interaction Behaviors in Design Concept Generation

2017 ◽  
Author(s):  
Neeraj Sonalkar ◽  
Kathryn Jablokow ◽  
Jonathan Edelman ◽  
Ade Mabogunje ◽  
Larry Leifer
Keyword(s):  
Engineering Design ◽  
Specific Interaction ◽  
Behavioral Model ◽  
Individual Characteristics ◽  
Concept Generation ◽  
Team Interactions ◽  
Responding To Change ◽  
Cognitive Behavioral Model ◽  
Interaction Behaviors ◽  
The Relationship

This paper investigates the relationship between interaction behaviors and the cognitive characteristics of participating individuals in engineering design teams engaged in concept generation. Individual characteristics were measured using the Kirton Adaption-Innovation inventory (KAI), which assesses an individual’s cognitive preference for structure in seeking and responding to change. Team interactions were measured using the Interaction Dynamics Notation (IDN), which allows interaction behaviors to be quantitatively analyzed. A correlation analysis revealed statistically significant correlations between individual characteristics and specific interaction behaviors, and ideation utterances. An interaction sequence analysis of the team data also revealed specific interaction sequences associated with greater probabilities of idea occurrence within the team. These findings serve as a first step towards building a cognitive-behavioral model of engineering design team performance.

A Study of Knowledge Construction and Mechanism of Cognitive Processing of Tibetan-Chinese Bilingual Pre-Service Teachers’

2014 ◽  
Vol 644-650 ◽  
pp. 6109-6113
Author(s):  
Jing Ling Qiu ◽  
Jing Li
Keyword(s):  
Preservice Teachers ◽  
Knowledge Management ◽  
Cognitive Style ◽  
Cognitive Processing ◽  
Questionnaire Survey ◽  
Knowledge Construction ◽  
Cognitive Characteristics

This study was based on cognitive characteristics and cognitive style of teachers in Tibet, using the questionnaire survey and other methods, carried on the investigation to many preservice teachers in Tibet, and on the basis of the theory and technology of knowledge management, explored the mechanism of Tibetan-Chinese bilingual pre-service teachers’ knowledge construction and the cognitive processing model.

A Validation Regarding Effectiveness of Scenario Graph

2011 ◽  
Author(s):  
Hiroyuki Yagita ◽  
Akira Tose ◽  
Madoka Nakajima ◽  
Sun K. Kim ◽  
Takashi Maeno
Keyword(s):  
Contextual Information ◽  
Idea Generation ◽  
Test Group ◽  
Problem Formulation ◽  
Service Development ◽  
Control Group ◽  
Design Teams ◽  
Concept Generation ◽  
Significant Difference

Scenario Graph is a structured mind mapping methodology that aids design teams to generate potential scenarios for new products and services while visually organizing contextual information. Since its introduction in industry and academia, the Scenario Graph has helped design teams to capture new values and behaviors of potential customers during the problem formulation stage. At the same time, the Scenario Graph, sharing a common challenge with various design methods, has faced difficulty regarding validation of its effectiveness as a design method. This paper describes a validation framework for a method in problem formulation stages and an experiment, which compare ideation results of 87 people — 43 people with the Scenario Graph method (as a test group) and 44 people with the Brainstorming (as a control group) — to solve an identical problem. While the results show no statistically significant difference in the number of ideas generated, the data reveals statistically significant differences in the quality of ideas. The test group, which used the Scenario Graph, yielded ideas that were more novel, feasible, and abstract than the control group, which used the Brainstorming, did. These metrics represent a way to measure the quality of ideas in the domain of engineering design. Our experiment confirms the hypotheses that the Scenario Graph is effective in improving the performance of idea generation sessions, which is consistent with qualitative evaluations. The lessons, gained from this experiment, provide an insight on how this method can be effectively used during the early stages of concept generation of a company’s process for product and/or service development.

CogniPGA: Longitudinal Evaluation of Picture Gesture Authentication with Cognition-Based Intervention

i-com ◽  
2019 ◽  
Vol 18 (3) ◽  
pp. 237-257
Author(s):  
Christina Katsini ◽  
Nikolaos Avouris ◽  
Christos Fidas
Keyword(s):  
Cognitive Style ◽  
Cued Recall ◽  
The Other ◽  
Visual Exploration ◽  
Visual Behavior ◽  
Longitudinal Evaluation ◽  
Cognitive Characteristics ◽  
Graphical Passwords ◽  
Authentication Schemes ◽  
Password Strength

AbstractThere is evidence that the visual behavior of users when creating graphical passwords affects the password strength. Adopting a cognitive style perspective in the interpretation of the results of recent studies revealed that users, depending on their cognitive style, follow different visual exploration paths when creating graphical passwords which affected the password strength. To take advantage of the inherent abilities of people, we proposed CogniPGA, a cued-recall graphical authentication scheme where a cognition-based intervention using gaze data is applied. This paper presents the longitudinal evaluation of the proposed scheme in terms of security, memorability, and usability from a cognitive style perspective. Results strengthen the assumptions that understanding and using the inherent cognitive characteristics of users could enable the design of user-first authentication schemes, where no compromises need to be made on security for benefiting usability or the other way around.

Do Social Constraints Inhibit Analytical Atheism? Cognitive Style and Religiosity in Turkey

2020 ◽  
Vol 20 (1-2) ◽  
pp. 1-21
Author(s):  
Catherine L. Caldwell-Harris ◽  
Sevil Hocaoğlu ◽  
Jonathan Morgan
Keyword(s):  
Social Norms ◽  
Cognitive Style ◽  
Social Environment ◽  
Cognitive Styles ◽  
Social Pressure ◽  
Social Constraints ◽  
Bilingual Students ◽  
Cultural Contexts ◽  
Cognitive Characteristics ◽  
Analogous Effect

Abstract Recent studies claim that having an analytical cognitive style is correlated with reduced religiosity in western populations. However, in cultural contexts where social norms constrain behavior, such cognitive characteristics may have reduced influence on behaviors and beliefs. We labeled this the ‘constraining environments hypothesis.’ In a sample of 246 Muslims in Turkey, the hypothesis was supported for gender. Females face social pressure to be religious. Unlike their male counterparts, they were more religious, less analytical, and their analytical scores were uncorrelated with religiosity. We had predicted an analogous effect for the comparison between monolingual and bilingual students, since English-proficient students are exposed to a wider social environment. The bilingual students were less religious than the monolingual students, yet they were also less analytical. Thus, being analytical was not the path to lower religiosity for the bilingual students. Cognitive styles need to be studied along with social norms in a variety of cultures, to understand religion-cognition relationships.

Automatic Facial Feature Extraction for Predicting Designers' Comfort With Engineering Equipment During Prototype Creation

2017 ◽  
Vol 139 (2) ◽  
Author(s):  
Shruthi Bezawada ◽  
Qianyu Hu ◽  
Allison Gray ◽  
Timothy Brick ◽  
Conrad Tucker
Keyword(s):  
Facial Feature ◽  
Specific Model ◽  
Comfort Level ◽  
Support Vector ◽  
Design Teams ◽  
Concept Generation ◽  
Engineering Equipment ◽  
Comfort Levels ◽  
Machine Learning Model ◽  
Before And After

Designers frequently utilize engineering equipment to create physical prototypes during the iterative concept generation and prototyping phases of design. Currently, evaluating designers' efficiency during prototype creation is a manual process that either involves observational or survey based approaches. Real-time feedback when using engineering equipment has the potential to enhance designers' efficiency or mitigate potential injuries that may result from incorrect use of equipment. Toward an automated approach to addressing these challenges, the authors of this work test the hypotheses that (i) there exists a difference in designers' comfort levels before and after they use a piece of engineering prototyping equipment and (ii) a machine learning model predicts the level of comfort a designer has while using engineering prototyping equipment with accuracies greater than random chance. It has been shown that the level of comfort that an individual has while completing a task impacts their performance. The authors investigate whether automatic tracking of designers' facial expressions during prototype creation predicts their level of comfort. A study, involving 37 participants using various engineering equipment, is used to validate the approach. The support vector machine (SVM) regression model yielded a range of R squared values from 0.82 to 0.86 for an equipment-specific model. A general model built to predict comfort level across all engineering equipment yielded an R squared value of 0.68. This work has the potential to transform the manner in which design teams utilize engineering equipment toward more efficient concept generation and prototype creation processes.

scholarly journals Concept Clustering in Design Teams: A Comparison of Human and Machine Clustering

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Chengwei Zhang ◽  
Youngwook Paul Kwon ◽  
Julia Kramer ◽  
Euiyoung Kim ◽  
Alice M. Agogino
Keyword(s):  
Machine Learning ◽  
Product Development ◽  
New Product Development ◽  
Learning Algorithm ◽  
Product Development Process ◽  
Design Teams ◽  
Concept Generation ◽  
Support Design ◽  
Concept Clustering ◽  
Student Teams

Concept clustering is an important element of the product development process. The process of reviewing multiple concepts provides a means of communicating concepts developed by individual team members and by the team as a whole. Clustering, however, can also require arduous iterations and the resulting clusters may not always be useful to the team. In this paper, we present a machine learning approach on natural language descriptions of concepts that enables an automatic means of clustering. Using data from over 1000 concepts generated by student teams in a graduate new product development class, we provide a comparison between the concept clustering performed manually by the student teams and the work automated by a machine learning algorithm. The goal of our machine learning tool is to support design teams in identifying possible areas of “over-clustering” and/or “under-clustering” in order to enhance divergent concept generation processes.

scholarly journals Problems and Challenges in the Interactions of Design Teams of Construction Projects: A Bibliometric Study

Buildings ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 461
Author(s):  
Eduardo I. Galaz-Delgado ◽  
Rodrigo F. Herrera ◽  
Edison Atencio ◽  
Felipe Muñoz-La Rivera ◽  
Clarissa N. Biotto
Keyword(s):  
Construction Industry ◽  
Collaborative Design ◽  
Construction Projects ◽  
Design Management ◽  
Design Team ◽  
Design Teams ◽  
Building Projects ◽  
Role Identification ◽  
Team Interactions ◽  
Three Stages

There is no comprehensive understanding of the problems that may impact the performance of the different actors that participate in the design of construction projects. In the absence of clarity about the problems and challenges that may impact the interactions, it is not possible to propose action plans to optimize the performance of the design teams. Therefore, this study proposes to identify the main problems and challenges in the interactions of design teams in building projects. A mixed review method is used to integrate bibliometric reviews, systematic reviews, and social network analysis to build a complete picture of the reviewed topic while highlighting certain key areas to ensure in-depth research. To achieve the objective of this work, the research was divided into three stages: (1) study of interactions in design teams; (2) identification of problems in design team interactions; and (3) study of problems in design team interactions. Through this study, four current major trends of research were identified: (1) Collaboration and BIM; (2) Design teams in the construction industry; (3) Design management; and (4) Collaborative design methodologies and processes. In addition, the most relevant problems or challenges within design team interactions arise in communication, collaboration, coordination, trust, and role identification.

scholarly journals Modelling the Dynamics of Influence on Individual Thinking during Idea Generation in Co-design Teams

2021 ◽  
Author(s):  
Harshika Singh
Keyword(s):  
Social Influence ◽  
Significant Role ◽  
Idea Generation ◽  
Current Work ◽  
Design Teams ◽  
Concept Generation ◽  
High Quality ◽  
Novel Approach ◽  
Idea Quality

Social influence is not evenly distributed in teams. Some individuals, referredto here as influencers, become more influential than others. Consequentially,these influencers play a significant role in shaping project performance.The current work simulates the presence of influencers duringidea generation in co-design teams to better understand emergent socio-cognitivephenomena. Besides providing, a novel approach for modelling learningin concept generation the model highlights the results related to individualcognition during idea generation. Idea quality and exploration of designspace are affected by the presence of influencers in design teams. Teamswith no well-defined influencers produce solutions with high general explorationbut less quality. In contrast, the agents in the teams with only oneinfluencer produce solutions high quality than those teams with no influencers.

scholarly journals Comparing Team Interactions in Traditional and BIM-Lean Design Management

Buildings ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 447
Author(s):  
Rodrigo F. Herrera ◽  
Claudio Mourgues ◽  
Luis F. Alarcón ◽  
Eugenio Pellicer
Keyword(s):  
Empirical Evidence ◽  
Management Approach ◽  
Design Management ◽  
Design Team ◽  
Design Teams ◽  
Lean Management ◽  
Team Members ◽  
Team Interaction ◽  
Study Selection ◽  
Team Interactions

There is qualitative evidence showing that design teams that use BIM-lean management have a higher level of interaction than design teams that do not use this management approach. However, there is no quantitative empirical evidence of this higher level of interaction. Therefore, the objective of this paper is to present quantitative empirical evidence of the differences among the various types of interactions of a design team. Two case studies were analyzed, and their design management was assessed from a lean BIM perspective while their team interactions were assessed using social network analysis (SNA). To achieve the aim of this paper, four steps were performed: (1) case study selection; (2) description of the design management of the projects from the lean design management and BIM perspectives; (3) assessment of design team interaction; and (4) comparison using SNA. The results show that the project that applied BIM-lean management exhibited higher levels of interactions among its design team members than the traditional team; transparent, orderly, and standardized information flows; a collaborative, trusting, and learning environment; and commitment management. None of these interaction elements were visible in the project that did not apply BIM-lean management. It is suggested that an analysis be performed on a representative sample of projects in the future so that conclusive statistical inferences could be made.

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