Project Management in Product Development: Toward a Framework for Targeted Flexibility

As a discipline, project management has been accused of having lost its relevance for innovative initiatives because it emphasizes planning and control over the flexibility and learning-based strategies that are needed to succeed under uncertainty. Several authors therefore recommend adaptive project management practices - sometimes named “targeted flexibility” - that respond to project characteristics commonly found in innovation, namely novelty, complexity, speed and - as a result - uncertainty. This paper investigates how this proposed adaptation of project management occurs in a context with high levels of novelty that organizes work in projects and needs to accommodate projects of different pace, complexity and innovativeness: product development in small and medium enterprises that do research and development work in the same organizational unit. Results of a literature review and two exploratory studies, covering a total of 8 companies with multiple projects each, are presented. Implications for a future framework for targeted flexibility are developed, leading to the identification of the following needs for project management: (1) better understanding of the many ways in which project management impacts exploration and exploitation activities, (2) improved attention for the currently poorly supported pre-project and early initiation stages, (3) a shift of focus from monitoring against plans toward monitoring against achieved learning, and (4) the formulation of transition paths from current new product development practice to higher project management maturity

Heuristics in Decision Making

Heuristics are simple rules of thumbs for problem solving that follow a logic that is quite different from consequential logic. They have long been regarded, as an inferior technique for decision making that is the source of irrational decision behavior. Recently, decision making researchers have demonstrated that some heuristics are highly efficient and can compete with complex decision models in some application domains. This paper explores the different streams of research, summarizes the state of the art decision making model, and discusses its implications for complex decisions in engineering and technology management

Educating the Guess: Strategies, Concepts and Tools for the Fuzzy Front End of Product Development

Many companies lack efficient management of the early phases of new product development (NPD) - the socalled fuzzy front end (FFE). Rather than on structured methods, decision makers rely on ?gut ?feel? or ?guessing?. In an attempt to ?educate the guess? this paper discusses the activities and challenges of the FFE, as well as strategies to manage them successfully. It then briefly presents traditional and recent approaches to front-end management support. Based on the identified strengths and weaknesses of existing front-end solutions, the framework of a new management support system for the FFE is presented. Conceptually, the system is based on psychological findings about the process of action-regulation in complex decision environments. Methodologically, it uses Fuzzy Cognitive Maps (FCM) for modeling and simulation

Fuzzy Cognitive Maps for Engineering and Technology Management: What Works in Practice?

Due to a lack of available data, many early planning decisions in engineering and technology management have to be based on experts\u27 opinions and their qualitative statements about evolving technologies, markets and general business environments. Several authors have suggested the use of fuzzy cognitive maps (FCMs) to analytically support these decisions with simulation models that can cope with qualitative information. However, only little practice experience is documented. Based on multiple case studies and an extensive literature review, the paper reviews the state-of-the-art of FCM-practice and introduces a six-step guideline for practitioners and researchers who wish to apply FCMs to real-world problem

Do Maps Guide the Way to NPD Success? Theoretical and Practical Aspects of Knowledge Mapping in Product Development

New product development success largely depends on the ability to combine newly acquired information on customer demands and technological options with knowledge that exists within the company. Project organization and high employee turnover, however, make it difficult to be informed about what knowledge is available within the company and to access it successfully. Knowledge maps, a popular concept in present knowledge management, offer a possible solution by ?guiding the way to knowledge?. Their purpose, structure and content varies greatly, as does their ability to capture different aspects of knowledge. This paper investigates the theoretical basis of different types of knowledge maps and investigates their applicability in development projects

Fuzzy Cognitive Maps to Implement Corporate Social Responsibility in Product Planning: A Novel Approach

Product development can support proactive CSR strategies by changing product features, materials, and processes in order to reduce or even eliminate negative environmental and social impacts. However, the CSR literature provides little practical guidance for new product development, but promotes general principles for responding to environmental and social issues. One of these guiding principles is the concept of stakeholder engagement, but to date, few practical approaches for integrating stakeholder views and needs into product development exist. To address this gap, the paper discusses the use of Fuzzy Cognitive Map Modeling. The method, which has been applied in participatory stakeholder studies and in product development before, but never in conjunction, helps product planners to understand and assess stakeholder needs and to select product concepts that respond to them. It thus allows organizations to remain true to their CSR strategies

Explaining Health Technology Adoption: Past, Present, Future

One of the most pressing challenges of healthcare innovation today is the lack of technology adoption. Research that improves our ability to understand, predict, and advance technology adoption in health care needs to be based on well-tested theories. With the interest to conduct high quality research in health technology adoption in future, this study reviews the theories used in this context to either identify the superior theory(ies) and or discover the issues that need resolution for improving future HTA researches. To do that, the most popular [1][2] social cognitive theories conceived over the past four decades are reviewed analytically from the perspective of their capacity to explain, predict and intervene in health technology acceptance, adoption and adherence. While all these theories are instrumental in conducting adoption studies, and some like UTAUT (Unified Theory of Acceptance and Use of Technology) are better than others at it, there is no perfect theory to study HTA. Literature repeatedly suggests that while utilizing general theories that have successfully passed the test of time could serve as a strong foundation, there is a compelling need for new and more empirical theories. There is a need for health researchers to expedite theoretical evolution by conducting comprehensive observation and rigorous evaluation to 1) manipulate and expand existing theories and or 2) create new theories that better address the specific needs and challenges of health technology application to enhance the utility and better reflect empirical findings. The structure of this paper is as follows. After summarizing the specifics of health technology innovations, the primary challenges in its acceptance are categorized. From there the body of this paper is dedicated to the review of most popular social cognitive theories, as depicted in Figure 1, from: 1) general human behavior repeatedly applied in healthcare studies and rooted HTA researches, and 2) theories dedicated - o the study of technology acceptance behavior and applied as the prominent theories in studying HTA. Each theory is reviewed, followed by examples of its applications especially in modeling health technology adoption (HTA) behavior. Each theory is then evaluated based on the salient factors involved in the study of technology innovation in healthcare space in addition to the classical influencing concepts in technology adoption behavior. In the discussion section, these theories are compared and the applications studied are synthesized in the attempt to identify some of the best theories and state of the art practices used in the study of HTA. The conclusion section summarizes the findings of the literature and recommends best approaches for conducting empirical studies and planning effective processes that stimulate theoretical evolution in HTA and facilitate enhancement of acceptance of health technology innovations

Theoretical Framework for Managing the Front End of Innovation under Uncertainty

A growing body of research suggests that the fuzzy front-end of product development should not be managed with a one-size-fits-all standard process. Instead, projects with different market and technical uncertainties should be managed with one of five different processes (linear, recursive, evolving, selectionism, trial-and-error). Based on a review of the literature, the paper develops a theoretical framework for front-end management which provides the foundation for ongoing empirical research

Mergers and Acquisitions: Team Performance

Many mergers and acquisitions in high technology do not yield the expected results and acquired technologies fail to create value as planned. One explanation is the difficulty to transfer and integrate the tacit components of technological knowledge, when work groups and teams are disrupted. Mergers force work group and team members to redefine their roles, change their working approaches, and develop a shared vision and culture. The paper therefore researches high-tech mergers from a team perspective through an exploratory case study of two formerly separate Quality Assurance groups that were integrated into one. The case study identifies three factors that impact team performance after a merger: strong vision, clear communication, and operational synergy built on an open team culture and a common working approach

An Investigation on Fast and Frugal Model for New Project Screening

Research in psychology is increasingly interested in decision-makers\u27 use of heuristics or rules of thumb because they have accuracies close to more complex decision rules and seem particularly useful in difficult decision-making contexts when uncertainty is high and speed is of the essence. One particularly difficult decision setting is the fuzzy front-end of new product development because a large number of product ideas need to be screened to identify the few that should be developed further. This process is currently poorly supported through decision tools and mainly occurs on the basis of managerial “gut-feel”. This study explores managerial “gut-feel” by investigating the performance of simple project screening heuristics: two so-called Fast and Frugal (F&F) heuristics, Take-the-Best and Tallying, and three logistic regression models with 3, 5, and 7 decision variables are used to screen a simulated dataset of 52 projects. Each model\u27s ability to recognize successful projects and correctly reject poor projects is compared against the predictions of the other decision models. The results how that the logistic regression models outperform the F&F models in overall prediction quality and in the ability to predict project failure. However, the Tallying model has an overall performance that is close to the logistic regression and both F&F models are better at predicting success than the logistic regression model. Furthermore, the regression model that only takes 3 decision variables into consideration performs better than the regression models with 5 and all 7 decision variables. This indicates that a simple “less is more” decision approach, which is the basis of managerial “gut-feel”, can be a successful strategy for front-end screening