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Top1. Introduction
New product development (NPD) is a means for automotive companies to survive in recent competitive land markets. The automotive industry has been faced with changes in strategies due to shorter product lifecycles and rapid technological advances compared to a few decades ago. Moreover, due to increasing market demand, customer tastes, rapid technological changes, and uncertainty, manufacturing companies need to adapt to a competitive business environment and meet customer demands. This necessitates a major focus on higher organisational capabilities such as dynamic capabilities (DC) that consist of relevant strategies; processes and skills at different levels in order to support the NPD process and also enhance NPD performance. In fact, top managers in R&D and purchasing functions require clear and operationalised disaggregated capacities of decision framework of DC and performance assessment of NPD projects.
Often DC are applied at firm level to enhance the performance of one single company and only few scholars have examined the relationship between DC and NPD performance (Beske & Seuring, 2014). Dynamic capabilities, which comprise internal and external capabilities and resources, enable firms to facilitate effective and efficient NPD (Pavlou & EI Sawy, 2011). Effective management of DC can increase NPD success and create a sustainable competitive advantage (Teece, 1982). To achieve NPD success, companies not only rely on existing resources and capabilities but also require a set of processes to prioritise, mobilise, coordinate, improve and reconfigure their critical NPD capabilities and resources (VanEchtelt et al., 2008). Furthermore, NPD performance depends on the continuous creation of new products, processes and the implementation of new organisational forms and business models which require DC to be supported by senior management involvement (Pavlou & EI Sawy, 2011).
DC models are often described in vague terms with criticisms such as the use of tautological definitions and the lack of empirical studies for their operationalisation (Eisenhardt & Martin, 2000; Priem & Butler, 2000; Williamson, 1999). For example, Arend and Bromiley (2009, p.75) provide a number of systematic critique points: 1) unclear value-addition relative to existing concepts; 2) lack of a coherent theoretical foundation; 3) weak empirical support; 4) unclear practical implications. Nevertheless, Helfat and Peteraf (2009, p. 92) argue that Arend and Bromiley (2009) “…fail to see ‘deficiencies’ or the tell-tale signs on early-stage development of an area of inquiry”. Faced with these difficulties of definition and operationalisation, the approach taken in this study is to model the nature of DC in NPD using a generic and hierarchical structure in the form of a multiple-criteria decision analysis (MCDA) problem. MCDA models assist senior managers to find appropriate responses to changes and uncertainties in their business environment (Yang, 2001) and could enable an improved understanding about the high failure rate of NPD projects, project overruns, and defect rates for managing NPD projects.
The study aims to propose the development of DC by operationalising each dimension of the DC model of Teece (2007), namely: sensing capacity, seizing capacity, and reconfiguration capacity concerning its contribution to NPD performance. The study attempts to answer the research question of what is the complex inter-relationship between DC and NPD assessment frameworks. For this purpose, regression analysis has been applied based on inputs collected from the respondents who filled the two structured online surveys. The MCDA method in this study is the evidential reasoning (ER) approach that aggregates all the qualitative and quantitative criteria regarding the two aforementioned frameworks for generating inputs for further analysis. Indeed, this research work tends to develop and test a new MCDA model of DC concerning their micro-foundations in the context of NPD.