The data analysis was divided into two phases (Figure 5). In the first phase (questionnaire analysis), the data generated from the questionnaire was compiled in Excel, in which the answers from the questionnaire were mapped systematically in a table structure. Each case was analyzed separately, and the data was analyzed by calculating the mean value for each capability which was illustrated in polar charts to facilitate visualization and the analysis process. The standard deviation for each capability was also calculated to identify the dispersion of values from the mean. A critical value (i.e. 4,00), was determined to differentiate which capabilities that were perceived as critical in a high cost environment. Consequently, each capability that received a rate higher than four was deemed as critical in the analysis. In order to identify critical improvement areas, the data regarding importance was compared against the data concerning performance and the results were illustrated in polar charts.
The difference between importance and performance highlighted improvement areas for each case. Improvement areas that exceeded the value 1,00 were perceived as critical in a high cost environment. The complete analysis of the results from the questionnaire was used as a base for the workshop in which respondents provided indepths reflections regarding the questionnaire results.
In the second phase (workshop analysis), the discussion from the workshop was analyzed in three steps (i.e. transcription of data, data familiarization and data categorization). In the first step (transcription of data), field notes and recordings were reviewed in which important viewpoints and quotes were highlighted. Each workshop was transcribed separately to avoid a mix-up of data. In the second step (familiarization of data), the authors read through the transcribed notes in order to interpret and understand the data. In the final step (data categorization), the data was categorized
using the framework as a reference point, in order to organize information according to each capability. Tables were used to structure the results of the qualitative data.
Validity and reliability are parameters that determine the research quality of a study (Williamson, 2002). Table 6 presents methods applied to increase the research quality of the study. The study’s internal validity was strengthened by applying both quantitative and qualitative data collection methods (i.e. questionnaires and workshops) through method triangulation (Yin, 2013). Each case included participants from multiple strategic functions to eliminate bias and to ensure that the data is generated from credible sources, which strengthens the study’s internal validity through source triangulation. Bias was further reduced by clarifying the study’s theoretical focus and concept through an initial meeting with case representatives, in order to avoid potential misunderstandings during data collection. The multiple case study approach increased the generalizability of the study and thus increased the external validity, as several cases were included in the study (Williamson, 2002). The external validity was further strengthened by adopting an existing and contemporary framework as main theory, from which conclusions were drawn (Yin, 2013). The application of source triangulation also increased the reliability as multiple strategic functions were involved in the study which generate more representative results and reduces bias. Empirical data was systematically collected in which data from each case was gathered and processed separately to avoid potential errors. The documentation and coding of data was conducted with both authors present in order to avoid numerical errors. A systematic and structured work process increased the reliability of the study which also strengthened the study’s replicability (Yin, 2007).
1.2 PROBLEM DESCRIPTION
1.3 PURPOSE AND RESEARCH QUESTIONS
1.4 SCOPE AND DELIMITATIONS
2 Theoretical background
2.1 INTRODUCTION TO THEORETICAL BACKGROUND
2.2 OPERATIONS STRATEGY
2.3 OPERATIONS CAPABILITIES
2.4 MANUFACTURING CONTEXT
3 Method and implementation .
3.1 RESEARCH PROCESS
3.2 RESEARCH APPROACH
3.3 RESEARCH STRATEGY
3.4 DATA COLLECTION
3.5 DATA ANALYSIS
3.6 RESEARCH QUALITY
4 Findings and analysis
4.1 OVERVIEW OF CASE FIRMS
4.2 WHICH OPERATIONS CAPABILITIES ARE CRITICAL FOR MANUFACTURING IN A HIGH COST ENVIRONMENT?
4.3 WHICH IMPROVEMENT AREAS ARE CRITICAL FOR COMPETITIVE MANUFACTURING IN A HIGH COST ENVIRONMENT?
4.4 RELATION TO THEORY
5 Concluding discussion
5.1 CONCLUSION .
5.2 THEORETICAL AND PRACTICAL IMPLICATIONS
5.3 LIMITATIONS AND FURTHER RESEARCH
GET THE COMPLETE PROJECT
Competitive manufacturing in a high cost environment