Research Philosophy

In social science research, a series of research methods exist that allow the different types of researches to be conducted. Nevertheless, all the research methods can be divided into two types, namely qualitative research methods and quantitative research methods (Cassell and Symon, 2004). The adoption of a research method depends on the nature of the research. This section will discuss the philosophy behind the research methods and justify the choice of the research method employed in this research.

5.1.1. Research Focus Areas

factors that obstruct the innovation activities in automotive IJVs in China. Considering this, this research has two main research focus areas. First, it finds it important to understand the innovation behaviour in automotive IJVs in China as it can provide an overall picture of the patterns of innovation activities in the automotive IJVs. A number of factors have been concluded as a theoretical framework in Chapter 2 and Chapter 3 to understand innovation and specifically the innovation behaviour in the context of IJVs. This research will look at the innovation behaviour of IJVs in accordance to these factors. On the other hand, understanding the process in the innovation activities in the automotive industry in China is also considered significant as it can provide insights for local firms on how to better engage in the innovation activities, as well as an evaluation of the current Chinese government policy.

Based on these two areas, this research will reconsider the feasibility of the IJV as a vehicle to drive indigenous innovation capacity by researching the potential factors that obstruct the innovation activities within automotive IJVs in China. The result of this research is expected to fill the theoretical gap by identifying the current literature’s underlying assumptions on the capacity of IJVs to drive innovation. Based on the identified factors, this research can evaluate the effectiveness of IJVs in enhancing the innovation capacity in the automotive industry in China.

5.1.2. The Qualitative Research Method

To investigate the innovation behaviour in automotive IJVs in China, this study requires collecting data from the in-depth study of a number of chosen automotive IJVs in China. The data includes individual interviews with each partner of the automotive IJVs to fully capture the picture of the innovation behaviour. Nevertheless, this study finds that the types of data needed to achieve the research objectives are mainly based on the understanding of highly complex and dynamic human/organisational behaviour. Realistically, quantifying such an understanding effectively can be extremely difficult. Moreover, there are only 33 automotive IJVs in China; hence, the sample pool is not large enough to draw a statistically meaningful conclusion regarding the patterns and trends based on the quantitative data. Instead, to explain the limited innovation performance within the context of

automotive IJVs in China, this research can be classified by a number of studies as a bottom-up method based on the qualitative data from the real-life phenomenon (Liu, 2009; Yin, 2003; Silverman, 2011). This study is designed to focus on factors associated with the collaborative innovation process by observing different perception/views of involved partners of the automotive IJVs. It is considered appropriate to generate a new ‘ought to be’ grounded theory suggested by Cohen (1980), which is in turn derived from the detailed understanding of the cases studied (Cohen, 1980; Silverman, 2011). Therefore, qualitative data analysis, which provides the contextual understanding required by the research subjective through the interview respondents, was considered a more valid approach than a quantitative data analysis. This argument is supported by a number of studies as qualitative data is better used when the research focuses on the wide and deep understanding of the subjective ‘why’ and ‘how’ questions on the basis of the complicated social phenomenon (Cohen, 1980; Sukamolson, 2001; Liu, 2009). Despite the limitation of the subjective nature of qualitative data, as recognised by Bryman (2001), this study finds qualitative data to be appropriate to understanding the highly complex organisational behaviour given the nature of this research. In this research, such qualitative data mainly represents a detailed analysis of the interview data of both of the local and foreign partners involved in the selected IJVs to understand the innovation behaviour.

5.1.3. Case Study as a Qualitative Research Method

The qualitative case study research method is widely applied to research like sociology, law or management that require a detailed investigation of the phenomenon within certain contexts based on a period of observation or qualitative data collection (Kohlbacher, 2006; Zainal, 2007). The case study research method is generally seen as being able to explain the highly complex social phenomenon involving ‘why’ and ‘how’ based research because of the focus on a few carefully selected cases (Zainal, 2007). Qualitative data provides multiple levels of analysis under the context and processes that highlight the theoretical issues and test/generate new theories under empirical investigation (Yin, 2003; Cassel and Symon, 2004; Eisenhardt, 1989). Indeed, understanding innovation behaviour in

Chinese automotive IJVs is an example of research of organisational behaviour referred to by Silverman (2011); therefore, the case study research method can be applied in this type of research.

One of the advantages of the case study research method is that it can be flexible, which allows the researcher to be both deductive and inductive (Yin, 2014). This is appropriate in this research as both induction and deduction are needed to achieve the research objective. Specifically, this research first aims to find the underlying assumptions against the current theoretical suggestions as the current theories do not fully explain the real-life phenomenon, which represents the deductive approach. This can lead to the explanation for the limited innovation performance in the automotive IJVs. This study focuses on a number of chosen empirical examples as case studies to generate the explanation for this. On the other hand, this research also aims to enhance the current theories by identifying behaviour, processes and/or factors not previously seen in the current literature (the inductive approach); it could, therefore, help the automotive IJVs in China better innovate.

5.1.4. The Case Selection Strategy

The studied cases should be selected according to the nature and objective of the study (Cassel and Symon, 2004; Punch, 2005). In order to explain the limited innovation performances in automotive IJVs in China as a general phenomenon, this research finds it necessary to focus on multiple cases. In fact, both Yin (2003) and Punch (2005) suggested that the multiple case study design enables the researcher to understand more about the studied phenomenon, population and condition. Consequently, the research could result in stronger effects in terms of generalisability within the wider context if more cases were studied (Yin, 2014). In deciding the number of cases to be selected from the empirical automotive IJVs, this research considers the generalisability issue of the qualitative case study research method and therefore mainly considers the issue of literal replication (Yin, 2003) in selecting the cases.

Yin (2014) also suggests a comparative case study design, which compares the cases according to certain defined features. The similarities and differences in the

comparison can generate relatively clearer results based on several selected cases. Eisenhardt (1989) also suggests a comparative approach based on a polar-type case selection strategy. Such a strategy compares absolutely opposite cases to each other. According to Eisenhardt (1989), such ‘transparently observable cases’ can be easily compared in terms of their key features (e.g. the most innovative case with the least innovative case) and generate a clear and valid research result.

However, the nature of the qualitative case study research method would mean there is relatively limited generalisability (Yin, 2014). Therefore, ensuring the selected cases have characteristics that are broadly representative of Chinese automotive IJVs in China is important. In this respect, this study finds a number of commonalities across the automotive industry in China, which gives a considerable potential for literal replication among the automotive industry in China. First, this study recognises that all automotive firms are subject to the same competitive environment, which gives the exact same background to all selected cases. Additionally, this study finds that all automotive IJVs are broadly alike in their nature. For example, most of the automotive IJVs have an equally distributed shareholding (50% for local firms and 50% for foreign firms). Most of the local automotive firms with IJV partnerships are state-owned, which are substantially supported and influenced by the government; and most of the foreign parent companies are MNCs which are highly capable of advanced technologies. The commonalities and similarities in the automotive industry in China means that the research results can be broadly applicable to all Chinese automotive IJVs. On the other hand, a considerable number of differences are also present among the automotive firms, such as duration of time established and corporate business strategy. The differences can show the different strategies and actions that the IJVs take and the consequences of these differences. The differences of the selected cases enable the research to look at the important variables that could represent the key elements of variability in the context of automotive IJVs in China.

In light of the arguments above, this research employs a multiple case design in order to have a more generalisable and representative result. Moreover, the selected cases will follow the principle of both comparative cases and polar-type cases to ensure

the clarity of research results. The details of case selection will be presented in the next section.