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Top1. Introduction
Managing innovation is important if organisations are to renew themselves, transform stagnant businesses, and increase market share (Hardy, 1994). New products can only be successfully developed when departments such as marketing, sales, R&D and manufacturing are able to reconceptualise and create knowledge and expertise for processes that cut across departments (Lau et al., 2010). New product development (NPD) process on its own is dynamic capabilities (DC) since these capabilities are to develop relevant strategies, processes and skills through collaboration with external and internal supply chain partners in order to support the NPD process and also enhance NPD performance (Verona, 1999; Mishra and Shah, 2009). To facilitate systematic innovation, it is necessary to focus on micro-foundations of DC that need to be operationalised for reinventing business processes and building entirely new markets that meet untapped customer demand (Eisenhardt & Martin, 2000; Teece, 2007). These micro-foundations underlying organizational and managerial processes include not only investment on R&D and intellectual property protection, but also complementary assets needed to achieve and sustain competitive advantage (Teece et al., 1997; Tecce, 2007). For this purpose, companies need to focus on higher order organisational capabilities such as DC which involve organisational routines and organisational integrations in order to outperform competitors and gain and access value capturing and creating strategy though innovation (Teece, 2018). Although, the DC based on their strategies support NPD process, it can be used to explain different firm performances (Marsh & Stock, 2006). Effective management of DC can also increase the NPD success and long-term competitive advantage (Hullova et al., 2019).
Teece (2007) defined a comprehensive generic model to capture numerous management practices in the field of DC. The DC model of Teece (2007) has been selected due to the reasons that alleviate the shortcoming of the existing DC models reported in the DC literature. There are some drawbacks surrounding the existing DC models. First of all, these models have deficiency to explain how firms achieve sustainable competitive advantage through micro-foundations. Secondly, they do not show the comprehensive view of management practices that integrate strategic and innovation management. Many previous studies have looked at only a thin slice of DC or uni-dimensional DC. According to the literature review, there is lack of empirical research to measure DC at micro-foundation level. Although a large number of criteria and indicators have been identified in many prior studies, many of them do not provide integration or aggregation in a structured and quantitative manner.
The originality of the research framework attributes to the observation that there was shortage of a comprehensive measure of DC designed specifically for the automotive industry. The focus of this study was placed on measuring DC by taking both present and future concerns of relevant purchasing, production, and R&D managers. By refining and structuring relevant criteria and indicators, this study is the first of its kind to operationalise multi-dimensional DC framework which includes the three main interrelated capacities of the DC model of Teece (2007), namely: sensing capacity, seizing capacity and reconfiguration capacity, with respect to the micro-foundations that relate to the organisational and managerial process for the NPD process. The study mainly focus on specific type of DC at supply chain (SC) relationship level where suppliers involve in NPD process extensively. The aim is to propose the DC performance self-assessment for manufacturing companies using the evidential reasoning (ER) approach as a powerful multi-criteria decision analysis (MCDA) tool that is useful for dealing with both quantitative and qualitative information with subjective judgments of ambiguity. The ER approach is implemented in the intelligent decision system (IDS) software tool for multiple criteria performance assessment (Xu & Yang, 2001). The proposed MCDA method provides an operational means to assess a company’s DC performance. Regarding the novelty of methodology, the ER approach and the IDS software are employed to generate the average performance scores of DC. This research also identify the relevant main decision variables and refined measurement items through case studies and the literature review, which are related to suppliers, customers and internal functions in the context of NPD process, thereby contributing to the existing knowledge in the SC and NPD literature. The MCDA framework for company performance self-assessment is validated and tested in manufacturing companies.