In the following chapter the underlying methodology of this thesis is discussed. First, the research philosophy is defined followed by the research approach. Subsequently, the research as well as the survey design are outlined. This is followed by describing the method of data collection and the subsequent data analysis. Finally, the research quality is discussed.
To conduct a well-structured, comprehensible and scientifically substantiated research, basic assumptions of the research philosophy need to be determined in the beginning. These assumptions shape and define the viewpoint of the authors as well as they provide an understanding of how research objects are studied (Saunders, Lewis, & Thornhill, 2016). First, the ontological conception which refers to assumptions about the nature and existence of reality was defined (Easterby-Smith, Thorpe, & Jackson, 2015; Saunders et al., 2016). This thesis follows the ontology of relativism which accepts multiple “truths” that “vary from place to place and from time to time” (Easterby-Smith et al., 2015, p. 50). We assumed that the importance of critical operations capabilities differs amongst environments as well as different types of manufacturing firms. Thus, the determination of the importance of critical operations capabilities depends on the viewpoint of the observer, respectively the participating manufacturing firms and is valid for a specific context in a specific period.
After determining the viewpoint of the study, we defined the approach of enquiring into the physical and social world by choosing an epistemological position. Epistemology studies the theory of knowledge by defining how valid and legitimate knowledge is generated (Saunders et al., 2016). In our thesis we aimed to expose the importance of operations capabilities in high-cost environments by deducting from existing theory. Hence, we followed the epistemological position of positivism as we intended to test theory (Easterby-Smith et al., 2015). Starting with the proposition that the importance of operations capabilities differs within market environments, we empirically tested the statement and provided an explanation for the theory. This approach is a characteristic of positivism (Bryman & Bell, 2011)
This thesis is based on an existing framework from previous academic research and complemented by testing and evaluating the framework empirically. Thus, our research strategy follows a deductive approach as part of the epistemological position of positivism (Bryman & Bell, 2011; Saunders et al., 2016). To collect data, a mono-method of data collection was adopted using a quantitative method to gather numerical data in the form of a survey. Adopting a quantitative method is also strongly connected to the positivism epistemology as well as the deductive approach using data to test theory (Saunders et al., 2016). Quantitative research allows for operationalisation of the operations capabilities enabling measuring these concepts by their importance, which is an essential part of a deductive approach (Bryman & Bell, 2011; Saunders et al., 2016).
Using the mono-method quantitative study, data was collected through a single data collection technique, and the matching quantitative analysis technique (Saunders et al., 2016). The technique implemented to gather data was a questionnaire, with statistical analytical procedures. Before conducting the quantitative data collection, a sample group was carefully selected to generate an appropriate sample for the questionnaire. By attaining the appropriate sample, generalisation of the findings was enabled which is an important characteristic of deduction (Saunders et al., 2016)
In order to create a link between the research philosophy and the methods of data collection and analysis, an appropriate research design was defined. Research design acts as the methodological link between those elements and is the basis for answering the research questions as well as for fulfilling the research purpose (Easterby-Smith et al., 2015; Saunders et al., 2016). Hence, research design shapes the quality of the research since assumptions of the research philosophy become comprehensible (Easterby-Smith et al., 2015). This study is evaluative since it intends to contribute to existing theory by evaluating an existing framework of operations capabilities (Hilletofth & Sansone, 2018) in a high-cost environment (Saunders et al., 2016).
As introduced in the previous section, a deductive research approach was adopted to validate the theoretical propositions of the existing framework. In combination with the evaluative design, this research approach is appropriate for the survey strategy as it is a suitable tool for comparison (Saunders et al., 2016). Conducting survey research is also clearly linked to the epistemological position of positivism (Bryman & Bell, 2011). Survey research allows time-efficient collection of primary data from a small part of a large population in order to achieve generalisation and replicability (Rea & Parker, 2014; Saunders et al., 2016). Underlying reasons to apply survey research is the lack of available adequate secondary data, the need to generalise from a small to a large population, an accessible respondent sample as well as the requirement to retrieve self-reported data (Rea & Parker, 2014). This research fulfils the requirements of the abovementioned reasons since available secondary data of important critical operations capabilities in high-cost environments was lacking. Furthermore, generalisation of important operations capabilities for competitive manufacturing was obtained and an accessible respondent sample existed. Lastly, the collected data was of a self-reported and personal nature.
To develop and conduct the survey, a multistage process was applied to generate valid, reliable and robust results. Following the stages of a survey process taken from Rea and Parker (2014), we selected a web-based survey for data collection. Choosing the web-based design was reasonable due to its beneficial characteristics of rapid data collection, convenience for respondents, low pressure on respondents, ease of follow-up as well as usefulness of targeting specialised populations (Rea & Parker, 2014). The next step was the process of determining the survey sample. For sampling, a non-probability sample was chosen since the respondent base was unknown and hence, clustering was not plausible. Thus, this thesis provides a generalisation of important operations capabilities for competitive manufacturing in high-cost environments but not on a statistical ground (Saunders et al., 2016).
To obtain valuable and robust results with regards to the purpose and research questions, the perspective of the management level from manufacturing firms was required. The management level is involved in the decision-making process regarding operations strategy as well as it possesses comprehensive knowledge of the firm and its environment. Hence, it was chosen as the sample frame. To access the management level the initial sampling stage was performed through convenience sampling to approach manufacturing firms. Convenience sampling is conducted through selecting sample units based on their accessibility (Easterby-Smith et al., 2015), hence business social networking services (for example LinkedIn) was used to reach out to manufacturing firms. Subsequently, after the sample units were established, the potential sample members were approached using inclusion criteria to ensure that the participants are eligible. The inclusion criteria was to be employed in a management position (first-line, middle or top management) at a manufacturing company with domestic production and have at least one year of management experience. This sampling technique is called purposive sampling, in which the researchers create eligibility criteria using theory to ensure that sample members are suited for the survey (Easterby-Smith et al., 2015).
Conducting the convenience and purposive sampling techniques resulted in identifying several manufacturing firms and in turn 38 sample members. Even though this sample size may appear small, the survey responses yielded high quality and credibility due to the well-defined inclusion criteria. A larger sample size would not necessarily increase the credibility of the responses if it is poorly designed or if respondents are trained and supervised insufficiently (Fowler, 2009). With regards to the time frame of this thesis, an adequate weighting between those factors was achieved.
Developing the questionnaire to be straightforward, easy to answer and time-efficient for respondents, the questions were designed as closed-ended questions using a Likert Scale. Adopting a Likert Scale is beneficial when researchers seek to derive attitudinal data regarding a specific subject using a continuum from one to five with two extremes to guide the respondent (Bryman & Bell, 2011; Rea & Parker, 2014). A Likert Scale was applied in the questionnaire to obtain attitudinal data of the importance of specific operations capabilities with “Not Important” as one extreme and “Very Important” as the other extreme to rate the importance. As the Likert Scale is commonly used as an interval scale for analysis purposes (Sekaran & Bougie, 2016), the subsequent analysis method was based on interval scales. Applying closed-ended questions in a questionnaire provides uniformed answers facilitating comparisons between respondent types and variables (Bryman & Bell, 2011; Rea & Parker, 2014). Hence, it is suitable for identifying the importance of operations capabilities amongst different manufacturing firms. Furthermore, closed-ended questions facilitate clear questions, simplicity of answering and quick responses (Bryman & Bell, 2011; Rea & Parker, 2014). Since closed-ended questions are pre-coded in terms of fixed answers, it enables easy processing of data for analysis (Bryman & Bell, 2011). Thus, the codes were used to derive data for analysis. To minimise the risk of reflexive and automatic answering, the operations capabilities were written in italics which is known as sensitising (Rea & Parker, 2014).
Once the questionnaire was developed, it was pre-tested to identify flaws and feasibility and then adjusted. After sampling and designing the questionnaire, the data collection was conducted to complete the survey research (Fowler, 2009). The web-based survey design of the research allowed to conduct data collection in a brief period of tim
Using the existing framework, the structure of the questionnaire was designed. To generate respondents’ interest it is vital to convey the importance of the study as well as the value of participation by providing the purpose of the study (Rea & Parker, 2014). Accordingly, the questionnaire introduces the purpose of the study to provide insight and clarity, conveying the usefulness for both parties. Furthermore, the questionnaire provided clearness in assuring confidentiality and privacy of the respondents. After introducing the study background, short introductory questions were subsequently presented to derive the respondent’s background and firm characteristics. Introductory questions are used to gather basic factual information about the respondent (Rea Parker, 2014). Following that, the study topic related questions were introduced and created. These questions are short, easy to understand, easy to answer and precise, making it easy and interesting for the respondent to answer (Bryman & Bell, 2011; Rea & Parker, 2014).
After constructing the first draft of the questionnaire, a pre-test was performed. A pre-test is conducted to ensure the overall quality of the questionnaire regarding question clarity, comprehensiveness, confidentiality and accuracy (Bryman & Bell, 2011; Fowler, 2009; Rea & Parker, 2014). Hence, it provided the required insights into the questionnaires quality, validity and reliability. As the pre-test does not seek to validate statistical accuracy, it is important to consider the time aspect and hence, a small number of respondents outside the research target population is sufficient (Bryman & Bell, 2011; Rea & Parker, 2014). Thus, the pre-test was sent out to five respondents outside the targeted population for evaluation. Based on the results of the pre-test, the questionnaire was revised and perfected afterwards. This resulted in the final questionnaire for the full-scale survey on 38 respondents from various manufacturing firms within different departments.
To draw conclusions and confirm theory, the quantitative data gathered through the survey had to be analysed and interpreted. For generating valid and reliable results, a two-step approach was applied (Easterby-Smith et al., 2015). In the first step the data was summarised and analysed afterwards to identify patterns and generate inferences about the importance of critical operations capabilities. Starting with the summary, data was collected in a data matrix containing the separate variables in the columns and the survey responses in the matrix rows. Adhering to this standardised layout, data can be evaluated using analysis software afterwards (Saunders et al., 2016). As mentioned in the previous section, the questionnaire collected numerical data from pre-coded closed-ended questions. Thus, answers are precise and can be assigned to a fixed position on a numerical scale (Saunders et al., 2016).
Once data was processed in the data matrix, an error check was conducted before starting the exploratory data check. An error check aims to identify data inconsistencies such as illogical relationships to ensure quality of the data set (Saunders et al., 2016). To provide an example, inconsistency appears when the overall importance of a critical operations capability dimension is rated very low, but all underlying operations capabilities are rated very high. The error check identified five incomplete, respectively illogical answers (all questions answered with the same value) which were not considered in the analysis. After conducting the error check, individual variables were analysed in detail to identify patterns. This included the calculation of the range, defined as the distance between the largest and the smallest answering score (Easterby-Smith et al., 2015). Hence, minimum and maximum scores were identified.
To approximate to a distinct statement about the importance of critical operations capabilities, further statistical tools were used. First, the interquartile range showing the middle 50% of the observations was calculated. This method recognises significant outliers below or above the interquartile range. Following that, the mean of each question was determined by adding all scores and dividing them through the number of data points. However, since the mean does not allow a statement about the distribution of the answers, the standard deviation was calculated subsequently. The standard deviation measures the average spread around the mean (Easterby-Smith et al., 2015). Using the standard deviation therefore provides a statement about the uniqueness of the answers, respectively different views on a specific question. Both, mean and standard deviation are the most common tools for describing interval scaled data (Sekaran & Bougie, 2016).
To visualise the results, diagrams were applied as they are a suited tool to explain, interpret and understand quantitative data (Bryman & Bell, 2011). Hence, box-and-whisker plots were created to illustrate the frequency of occurrence based on the Likert Scale using a continuum from one to five. These diagrams show the central tendency, percentiles and deviation of each question (Sekaran & Bougie, 2016). Thus, diagrams can be used for triangulation by linking processed quantitative data to previous qualitative research (Saunders et al., 2016).
Finally, rankings of the important operations capabilities were created (for example Table 2 and Table 3). The rankings were based on the mean of each question and show the importance in descending order. The first ranking is a general ranking for manufacturing firms in high-cost environments while the second ranking distinguish amongst different types of manufacturing firm characteristics in high-cost environments in order to fulfil RQ2. Next to the ranking, both tables contain the standard deviation for each operations capability which was considered in the overall quantitative evaluation.
To ensure high research quality, reliability, validity and ethical considerations were carefully considered throughout the study. Transparency regarding the research procedures facilitates replication for other observers (Saunders et al., 2016). Thus, it is vital for reliable research. Disclosing all methodological structures as well as research stages provides full transparency for others to judge and replicate the study is an important part of ensuring reliability in a deductive approach (Saunders et al., 2016). Subsequently, external reliability is reached through replicability of data collection and analysis, producing similar findings and proofing consistency of the research (Easterby-Smith et al., 2015; Saunders et al., 2016). To ensure that similar findings could be produced when replicating the research, the questionnaire is provided next to the research methodology.
Apart from external reliability and consistency in research findings, it is important to guarantee consistency of the research process, commonly known as internal reliability. Internal reliability can be achieved through data analysis and collection performed by multiple researchers (Saunders et al., 2016). Additionally, stability in coding and interpreting the data needs to be established (Saunders et al., 2016). This research was conducted by two Master students carrying out data collection and analysis. Regular cross-checks were accomplished during the entire research process. Stability in coding and data interpretation was established through designing pre-coded answers in the questionnaire.
Next to the research reliability, validity is a key characteristic for research quality (Saunders et al., 2016). Referring to the questionnaire in a positivist research, pre-testing measures the reliability of the instruments and therefore validates whether the questions measure the research topic (Easterby-Smith et al., 2015; Saunders et al., 2016).Thus, reliability and validity complete each other. Fulfilling the requirements of validity, a pre-test was conducted followed by a revision of the questionnaire. Furthermore in a questionnaire-based survey of a positivist research, a set of questions is linked to statistical analytical factors or outcomes, generating validity (Saunders et al., 2016). Hence, the outcome of the questionnaire-based survey is connected and used to answer the research questions of this thesis.
Besides research quality, ethical considerations were taken into account to protect the interests of research participants and to adhere to scientific standards. To ensure ethical research, the overall principle is to protect the dignity as well as avoiding any harm of research participants by ensuring confidentiality of collected data (Easterby-Smith et al., 2015; Rea & Parker, 2014; Sekaran & Bougie, 2016). Protection of research participants was guaranteed by anonymising the collected data. Except the job position and the years of work experience, no personal information of the respondents was collected. To ensure confidentiality for participating manufacturing firms, no company names were used. Instead, the Statistical classification of economic activities in the European Community (NACE) was used to describe the area of operations of each firm. Furthermore, a fully informed consent was established by providing an information package containing relevant information such as the motivation and the purpose of the thesis as well as the research questions. Contact information were attached to be available for further questions or needs for clarification from participants. Participation in the survey was completely voluntarily, no pressure was applied on participants at any time
1.2 Problem discussion
1.3 Purpose and Research questions
1.4 Scope and delimitation
2. Research methodology
2.1 Research philosophy
2.2 Research approach
2.3 Research design
2.4 Research quality
3. Literature Review
3.1 Changing market environments
3.2 Competitive advantage of manufacturing firms
3.3 Operations strategy
3.4 Operations capability framework
4. Empirical findings
4.1 Evaluation of high-cost environments
4.2 Evaluation of manufacturing firm characteristics
6.1 Research questions and purpose
6.3 Limitations and further research
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