Does mental model similarity equal innovation team performance?

Purpose This paper addresses challenges and opportunities of interdisciplinary teamwork in innovation management, focusing on how team mental models (TMMs) can foster more effective collaboration among team members from diverse backgrounds. The research examines the relationship between TMM similarity and team performance in interdisciplinary innovation teams. Design/methodology/approach An exploratory study was conducted with 55 participants across 15 teams. TMM similarity was measured by analyzing team interactions using a web-based interface that facilitated contrasted comparison ratings. These ratings were first processed into individual graphs via a shortest-node-finding pathfinder algorithm, then compared. Subject matter experts evaluated team performance. Findings The results reveal a significant positive correlation between TMM similarity and team performance in interdisciplinary innovation teams, suggesting that greater alignment in team members' mental models enhances overall innovation project performance. Research limitations/implications Although the academic programs sampled replicate the environment, challenges and various other aspects of innovation projects, they can only be considered proxies for innovation projects within real organizations. Further research within professional environments, using a larger sample, is recommended. Practical implications The findings highlight the value of assessing and fostering TMM similarity to improve teamwork and performance in interdisciplinary innovation projects. The interface and code used are publicly available to encourage their implementation in organizations. Originality/value This research provides novel insights into the application of TMMs within interdisciplinary innovation teams, extending the concept beyond its traditional use in unidisciplinary and structured task settings.

Authors

Institutions

Publication Details

Journal
Innovation & Management Review
Published
2025-11-20
DOI
https://doi.org/10.1108/inmr-09-2023-0152
Primary Topic
Cognitive Science and Mapping
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Does mental model similarity equal innovation team performance?

Florian Huber, Sabine Kaupp
Innovation & Management Review
Cognitive Science and Mapping
article

Does mental model similarity equal innovation team performance?

Florian Huber, Sabine Kaupp
article en

Abstract

Purpose This paper addresses challenges and opportunities of interdisciplinary teamwork in innovation management, focusing on how team mental models (TMMs) can foster more effective collaboration among team members from diverse backgrounds. The research examines the relationship between TMM similarity and team performance in interdisciplinary innovation teams. Design/methodology/approach An exploratory study was conducted with 55 participants across 15 teams. TMM similarity was measured by analyzing team interactions using a web-based interface that facilitated contrasted comparison ratings. These ratings were first processed into individual graphs via a shortest-node-finding pathfinder algorithm, then compared. Subject matter experts evaluated team performance. Findings The results reveal a significant positive correlation between TMM similarity and team performance in interdisciplinary innovation teams, suggesting that greater alignment in team members' mental models enhances overall innovation project performance. Research limitations/implications Although the academic programs sampled replicate the environment, challenges and various other aspects of innovation projects, they can only be considered proxies for innovation projects within real organizations. Further research within professional environments, using a larger sample, is recommended. Practical implications The findings highlight the value of assessing and fostering TMM similarity to improve teamwork and performance in interdisciplinary innovation projects. The interface and code used are publicly available to encourage their implementation in organizations. Originality/value This research provides novel insights into the application of TMMs within interdisciplinary innovation teams, extending the concept beyond its traditional use in unidisciplinary and structured task settings.

Innovation & Management ReviewVol. 22(4)
LMU Klinikum (DE), Technische Hochschule Ingolstadt (DE)
Openalex Percentile: Top 81%
Cognitive Science and Mapping
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.