Research Methodology and Design

The choice of a research design and methods have to be dovetailed with the specific research question being investigated (vom-Hofe & Chen 2006; Bryman &

Bell 2007). In addition, Rocha (2004) argued that in industrial-cluster study, the choice of research method should consider the concept of the cluster and the level of analysis carried out by the study. For this study, the concept of the industrial cluster was adopted from Becattini‗s concept (2004), which views industrial clusters as a socio-geographical group of micro, small, and medium enterprises, and emphasises not only their economic aspects but also their social and culture aspects.

In doing so, this study employs a qualitative approach for several reasons.

First, the research questions in this study require exploration and begin with ―how‖

or ―what‖, so that the researcher can gain an in-depth understanding of what is going on relative to the topic (Yin 2003). For this study, the participants‘ experiences with surviving their business in industrial cluster was explored by the researcher by asking (a) what are the driving factors of MSMEs survival in industrial cluster, and (b) why and (c) how have these driving factors been established by MSMEs in industrial clusters? Second, a qualitative study allows the researcher to explore phenomena, such a feelings or thought processes that are difficult to extract through conventional research methods (Corbin & Strauss 2008). For this study, the researcher explored participants‘ perceptions and experiences of keeping their MSMEs alive in industrial clusters. Third, qualitative research methods are the best approach when studying phenomena in their natural settings and when striving to understand social processes

in context (Denzin & Lincoln 2003). This study focused on the driving factors influencing the survival of MSMEs in industrial clusters. Fourth, qualitative methods emphasise the researcher‘s role as an active participant in the study (Creswell 2007).

For this study, the researcher was the key instrument in data collection, and the interpreter of data findings. Qualitative research methods used in this study included:

purposive sampling, semi-structured interviews, and systematic and concurrent data collection and data analysis.

The case study methodology is a strategy of inquiry in which the researcher explores in-depth a program, event, activity, process or one or more individuals (Yin 2009). Cases are bounded by time and activity, and researchers collect detailed information using a variety of data-collection procedures over a sustained period of time. For this study, the phenomenon under investigation was the driving factors of MSMEs survival in industrial clusters. The cases for this study were the furniture and footwear industrial clusters in East Java province, Indonesia. For this study, the researcher collected data through in-depth interviews, and additionally reviewed documents provided to government officers where the study was conducted.

Specifically, interviews were conducted and audio-taped, tapes were transcribed into Word documents, local government documents were reviewed, and data was coded for emergent themes. Another component of case studies is the unit of analysis, defined as the area of focus of the study (Yin 2009). For this study, this unit of analysis was micro, small, and medium firms participating in the study.

Although the case study has apparently been adapted to several fields, its research design has not been codified well and there is no standard approach. Thus, there is no standard research design of case study. However, the research design proposed by (Yin 2009) was adapted in this study (Figure 4.1). The firs stage of the study consists of theory development, case selection and the design of the data-collection protocol. The development of cluster theory is fruitful for selecting cases in areas that have been under studied, defining a complete description of MSME clusters, and stipulating rival theories in explaining why efficient collective operation, social capital and policy inducements do or do not affect cluster development (Yin 2003). Furthermore, developing a theory allows the researcher to measure constructs more accurately and in turn to properly design a data collection protocol (Eisenhardt & Graebner 2007). The second step is preparing, collecting and

analysing data. The activities in this step will be interwoven between data collection and analysis. Hence, the data could be well organised and more deeply analysed.

This working method may also be fruitful to energise the fieldwork and help in developing the interim report required in the study (Miles & Huberman 1994).

Figure 4.1 Research Design

Define and Design Preparation, Collection, and Analysis Analyse and Conclude

Draw cross-case conclusions Select cases Conduct 1st case Write individual case

(Bukir Pasuruan) report Modify theory

Develop

theory Develop policy

implication Design data Conduct 2nd case Write individual case

collection protocol report

(Sooko Mojokerto)

Write cross-case report

Adapted from Yin (2009)

Furthermore, as this study uses multiple cases in self-contained studies, data collection and analysis will be conducted separately in each (Yin 2009). Each case thus may reveal a significant finding. The emergence of a significant finding may lead to the need to redesign the cases‘ data-collection protocol shown as a dashed-line feedback loop Figure 4.1. The last steps are cross-case analysis that relies on the individual case results, and drawing conclusion. The analysis will indicate the extent of replication logic and will explain why each case has certain results. Rival theories will also used to explain any differences between cases (Yin 2009). Modifying the theory and proposing policy implications will be performed before drawing conclusions from each case. Finally, cross-case analysis will be generated..