Advisor

Antonie J. Jetter

Date of Award

2-6-2020

Document Type

Dissertation

Degree Name

Doctor of Philosophy (Ph.D.) in Technology Management

Department

Engineering and Technology Management

Physical Description

1 online resource (xvi, 326 pages)

Abstract

During concept development, product developers consider product users and their future experience, cost, development and manufacturing efficiency, product function/quality, and differentiation of the product in the market. Development teams often struggle to adequately address all of these considerations, due to the following reasons: (1) Differences in technological and experiential knowledge, methods used, and communication styles that make it difficult for customers/user, marketing, and engineering to communicate effectively. As a result, important factors may not be sufficiently considered. (2) Product design factors, including technological alternatives, functions, features, benefits, and customer value are interdependent: in some cases, customers are willing to sacrifice a feature for improvement in another factor, in other cases, they only value particular design factors if other factors are also present. Design factors can therefore only be understood in the context of other factors. However, current concept development methods fail to adequately model the system of concept development decisions. (3) The structural complexity of products hinders teams from assessing how a change impacts all other design factors and future customer value, which can cause teams to ignore indirect effects and unintended consequences of product concept decisions.

This research, therefore, presents novel method, cognitive distance reduction method (CDRM), that allows teams to systematically, holistically, and iteratively assess alternative product concepts and their respective impact on customer value by modeling them as combinations of product design factors. Teams can thus identify and select product concepts that achieve high customer value, given existing constraints. CDRM captures the mental models of engineers and marketing professionals about the elements and interdependencies of the development project (e.g., product features, benefits, customer value, and technologies) and represents them as quantitative system models to simulate future system states. CDRM consists of six steps (Basic PDF Elicitation, Model Formation, Model Synthesis, Scenario Building, Simulation, and Result Analysis & Interpretation). CDRM is based on a system modeling approach, namely fuzzy cognitive mapping (FCM), that is gaining popularity in many fields but is still largely unused in product innovation. Two studies, both using robotic vacuum cleaners as the product concept of interest, are used to implement, test and assess the proposed CDRM: a pilot study, focused on feasibility and an experimental workshop, focused on the impact of CDRM on product development teams. Results show that CDRM is capable of representing a new product as a system, comprised of product design factors and relationships among them. Complexity is managed by creating the customer-focused Need Map and the engineering-focused Tech Map independently and integrating them to construct a group mental model, so-called PDF Map. The various maps capture the worldviews of PD team members and serve as a communication tool. Moreover, CDRM can also be used as a simulation tool and helps teams identify and select product concepts that achieve high customer value, given existing constraints. As part of the CDRM analysis and simulation, sensitivity analysis helps product development avoid overengineering or not meeting minimum requirements by identifying PDFs that do not contribute to further improvements of customer preference or might even have detrimental effects.

The primary contribution of this research is practical by providing a novel approach for helping product development engineers capture and understand customer knowledge for successful concept development activities. CDRM can improve current concept development practice by improving engineers' understanding of customer requirements and select product concepts that best fulfill customer needs. To make these practical contributions, theoretical and methodological improvements were necessary. Regarding theory, the work provides a comprehensive discussion of several phenomena that plague early product development and knowledge sharing and provides clear differentiation between uncertainty, complexity, and equivocality, describes how they impact team mental models and provides an explanation of cognitive distance. The work integrates several current research perspectives. Regarding methodological innovations, this work provides several approaches for measuring cognitive distance based on survey data and Fuzzy Cognitive Maps that can be used by practitioners and researchers who wish to understand if teams interpret a complex system in a similar way or, instead, suffer from equivocality.

Persistent Identifier

https://archives.pdx.edu/ds/psu/32598

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