Better decision making for evaluating new medical device projects: A real options approach

Johal, Sukhvinder; Oliver, Peter; Williams, Hywel C

doi:10.1057/palgrave.jmm.5050116

Cited by 12 publications

(11 citation statements)

References 20 publications

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“…The importance of the rational decision-making dimension has large support in the literature and is emphasized in theoretical approaches like behavioral analysis (Dean and Sharfman, 1993a), mathematical game theory ( Jekunen, 2014) and real options reasoning (Child and Hsieh, 2014;Johal et al, 2007). Yet, a growing number of recent research publications stresses the notion that rational decision-making may often be intermingled with, or combined with intuitive and political decision-making, especially in innovation decision-making processes (Calabretta et al, 2017;Elbanna, 2006;Kester et al, 2009;Stanczyk et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

Contingent use of rational, intuitive and political decision-making in R&D

Kolbe

Bossink

Man

2019

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Purpose The purpose of this paper is to gain insight into the contingent use of rational, intuitive and political decision-making in R&D. Design/methodology/approach This research is based on a study in an R&D department of a multinational high-tech firm in the Netherlands. The study consists of a case study design, focusing on four embedded cases, longitudinally studying each case. Findings The literature distinguishes three dimensions of innovation decision-making processes: rational, intuitive and political. By studying these interwoven dimensions over time, this study finds that the dominant use of each of these dimensions differs across the innovation process. There is an emphasis on intuitive decision-making in an early phase, followed by more emphasis on political decision-making, and moving to more emphasis on rational decision-making in a later phase of the R&D process. Furthermore, the predominant choice in a specific innovation phase for one of the three decision-making dimensions is influenced by the decision-making dimension that is dominantly employed in the preceding phase. Research limitations/implications This study contributes to the innovation decision-making literature by developing and applying a model that distinguishes rational, intuitive and political decision-making dimensions, the interactions among these dimensions in innovation decision-making in R&D, and the contingency of these dimensions upon the innovation phase. It calls for further research into the contingent nature of innovation decision-making processes. Practical implications For practitioners this study has two relevant insights. First it highlights the importance and usefulness of intuitive and political decision-making in addition to the prevailing emphasis on rational decision-making. Second, practitioners may be more alert to consciously changing their dominant decision-making approach across the phases of the innovation process. Third, companies may adjust their human resource policies to this study’s findings. Originality/value The literature on rational, intuitive and political decision-making is quite extensive. However, research has hardly studied how these decision-making dimensions develop in conjunction, and over time. This paper reports on a first study to do so and finds that the dominant use of these dimensions is contingent upon the phase of the R&D process and on the decision-making dimensions used in earlier phases. The study suggests that using a contingency approach can help to further integrate the debate in research and practice.

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Section: Discussionmentioning

confidence: 99%

Contingent use of rational, intuitive and political decision-making in R&D

Kolbe

Bossink

Man

2019

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“…The early‐stage value of the PMCF is the maximum that could rationally be invested in the development process. We have avoided the complexities of a full investment analysis based on the options approach [24–26] and focused instead on a simple model with just two decision gates. Additional gates can only increase value.…”

Section: Discussionmentioning

confidence: 99%

Early-Stage Valuation of Medical Devices: The Role of Developmental Uncertainty

2010

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A model based on two decision gates can lead to lower bounds (underestimates) for product value that can serve to support a decision to develop the product. Quantifiable uncertainty that can be resolved before the device is brought to market will generally enhance early-stage valuations of the device, and this remains true even when some components of uncertainty cannot be fully described. Clinical trials and other evidence-gathering activities undertaken as part of the development process can contribute to early-stage estimates of value.

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“…Continuing in the effort to build a bridge between theory and practice, Ijzerman and Steuten [9] researched the question of how likely Health Technology Assesment (HTA) is to be utilized by development companies in the decision-making process. Surveys conducted in selected companies [10] suggest that among commonly used methods informing the decision-making, there are discounted cash flow (DCF), unstructured peer reviews, scoring checklists, and decision tree analyses. Bootman [11] pointed out the differences between the various types of economic evaluation, from cost-effectiveness analysis (CEA), through cost-utility analysis (CUA), to cost-benefit analysis (CBA).…”

Section: Introductionmentioning

confidence: 99%

“…Danner et al [12] characterize the analytical hierarchy process (AHP) as a multi-criterial analysis type which takes into account both qualitative and quantitative factors. Its drawbacks include possible confusions regarding the numerical scales used in the analysis, as their relationship with the verbal descriptions is not rooted in any theoretical foundation [10]. Furthermore, this method forces the decision-maker to choose always one decision from a given set of possible decisions, regardless of whether the decisions offered are acceptable at all.…”

Section: Introductionmentioning

confidence: 99%

“…An expert system is an artificial intelligence technique that uses an input knowledge base and inference engine to solve a problem [13]. The knowledge base required by the system is time consuming, and it may be difficult for experts to express knowledge in terms of specific rules and confidence factors [10]. Moreover, the MD companies use a range of other methods to decide which new products are likely to result in good financial returns, including fewer tangible approaches such as a 'gut feeling' based on the experience in the marketplace [14].…”

Section: Introductionmentioning

confidence: 99%

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Complexity Stage Model of the Medical Device Development Based on Economic Evaluation—MedDee

et al. 2020

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The development of a new product is essential for the progress and success of any company. The medical device market is very specific, which is challenging. Therefore, this paper assesses an economic model for medical device evaluation using the economic, health, technology regulatory, and present market knowledge to enable the cost–time conception for any applicant. The purpose of this study is to propose a comprehensive stage model of the medical device development to subsequently describe the financial expenditure of the entire development process. The identification of critical steps was based on the literature review, and analysis, and a comparison of the available medical device development stages and directives. Furthermore, a preliminary assessment of the medical device development steps and procedures on the basis of the interviews was performed. Six interviews were conducted with an average duration of one hour, focusing on areas: relevance and level of detail of the medical device development stages, involvement of economic methods, and applicability of the proposed model. Subsequently, the improvement and modification of the medical device investment process, based on respondents’ responses, were conducted. The authors have proposed the complexity model MedDee—Medical Devices Development by Economic Evaluation. This model is comprised of six phases: initiation, concept, design, production, final verification, and market disposition in which the economic methods are incorporated.

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Better decision making for evaluating new medical device projects: A real options approach

Abstract: He joined the university after three years in industry where he worked in new product development in the high technology sector. His current research interests include real options analysis, decision-making tools and health economics.

Cited by 12 publications

References 20 publications

Contingent use of rational, intuitive and political decision-making in R&D

Contingent use of rational, intuitive and political decision-making in R&D

Early-Stage Valuation of Medical Devices: The Role of Developmental Uncertainty

Complexity Stage Model of the Medical Device Development Based on Economic Evaluation—MedDee

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