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3. Research design and methodology

3.4. Data analysis strategy

To conduct this research the Gioia methodology is chosen (Gioia et al., 2012). The methodology emphasizes a systematic approach to new concept development and grounded theory articulation. One of the reasons Gioia et al. (2012) designed a methodology is to help researchers with a systematic approach to conduct research that leads to credible interpretations.

Furthermore, theory development following the traditional scientific method engages more in the extension and refining of existing knowledge. Goia et al. (2012, p. 16): “Advances in knowledge that are too strongly rooted in what we already know delimit what we can know.”

Therefore, a systematic inductive approach is established with the purpose to apprehend concepts related to human organizational experience which is suitable for people who encounter that experience and to the experience’s scientific theorizing (Gioia et al., 2012).

The data analysis technique used in this thesis is coding, all the quotes will be coded according to the Gioia method (Gioia et al., 2012). Coding ‘represents the operations by which data are broken down, conceptualised, and put back together in new ways. It is the central

process by which theories are built from data’ (Strauss & Corbin, 1990, p. 57). Data will be analyzed in two parts via an inductive approach. First, the aim is to understand the level of the managers terms and codes, and second, at the abstract theoretical level of themes. The first-analysis focuses to maintain the informants’ terms as they are being said, and are not condensed into more generalized categories. After this process the categories are labeled. The second-order analysis focuses attention on whether emerging themes suggest concepts that explain observed phenomena. Second-order themes can condense into second-order aggregate dimensions. In appendix A, B, and C, the coding schemes are presented for all the data.

3.6 Data structure

The first-order terms, second-order themes, and aggregate dimensions are visualized in a data structure, illustrated in figure 3. The first-order codes in the data structure show from which first-order codes the concepts come from. This illustrates the first-order codes are linked to the theoretical second-order codes, and provides evidence the theory is grounded in the data.

Second-order themes are the concepts to build the grounded model. Other investigators can review the data based on the database and retrieve conclusions from it rather than be limited to the written reports, this results in an increase of the reliability of the case study.

Figure 3: Data structure (Gioia et al., 2012).

1st-order codes 2nd-order themes Aggregate dimensions

Uncertainty in current environment Transition to new technology Competition

Legal issue Increase revenue Decrease revenue

Large shareholders sell their shares Investment of customers

Continued technology improvement Strong believe in technology

Technology will add value for customer Technology implementation

New technology crucial for market needs Customers' demand for new

technology

Technology as enabler Technology importance

Customer recognition and readiness of technology

Customers' requirements of new technology

Internal confidence in technology Added value for customers EUV roadmap

Technology improves performance Technology explanation

Operational proven technology End customer behavior Competitive technology

Confident in leadership position in the semiconductor equipment marketplace

Industry has turned the corner on EUV Technology adoption

Future demand of customers Operational targets will be met soon Uncertainty in technology transition There is a probability the customer will choose for EUV

Technological complexity Technology progress Continuation of technology improvement is needed Phases technology introduction Transition to new technology

Increasing customer confidence in EUV for manufacturing readiness is critical

Decrease stock price Increase stock price Trust investors

Market share First large order EUV First order EUV China

Order EUV

Outcome Financial results

Driver

Confidence and incremental framing

Confidence and radical framing Process Process Sales of shares

Market dynamics

Careful and radical framing Process Careful and incremental framing Process

Stock price

3.7 Reliability and validity

The trustworthiness of a research is important, therefore this paragraph discusses the benefits and limitations of the study. The data collected from the earning conference calls transcripts are not affected by my research purpose, this can be considered a benefit, as the data is not influenced. Nevertheless, the limitation of the fact that this data was not collected for the sake of my research agenda (Bowen, 2009) is that I should read specifically and filter whether the information concerns the correct topic to answer the research question. The earning conference calls discuss several topics such as financial results of past quarter, vision of the company for the upcoming quarters, but also practical information about technologies. When senior managers frame technological changes to the audience they might include output details in terms of orders or revenue in order to legitimize its salience. Therefore, it is crucial to be alert whether the communication is intended to legitimize technological changes. In this study Ayres’s (1985) definition of technological change is applied to assess the communication of ASML’s senior managers, which means that technological changes include the research and development, introduction of new products and processes (innovation), and improvement of existing products and processes.

Despite these limitations, an advantage would be that I have no prejudices concerning the conduction of document analysis nor the technologies discussed by the company.

Experienced researchers may have a more predetermined way data analysis conduction, as I am new to document analysis my attitude and work manner is more open minded. Moreover, documents are stable and suitable to review multiple times and includes detailed information such as names and details of events from documents give an advantage in the research process.

As I am not familiar with ASML’s technologies, the interpretation will develop over time, the first time I read a document I might not consider a quote as technological changing framing strategy, but after reading a lot of documents, this might change as I gain a better understanding

of the complex technologies. That is one of the reasons why the study benefits highly from the possibility to analyze data over a longer period of time. In this matter, framing practices of senior managers are comparable during different company phases. Lastly, the use of secondary data is valuable when time and resources are limited, the process is more cost-effective as the data is already available (Johnston, 2017). Due to my limited time to conduct this research, this is an important advantage to take into account.

The Gioia methodology is designed to seek qualitative rigor in inductive research (Gioia et al., 2012). Within qualitative research, researchers struggle with the credibility of their findings as well as the sceptical attitudes of their readers about the conclusion’s plausibility and defensibility (Gioia et al., 2012). In order to achieve credibility, the data collection’s procedures are explained extensively. The data that will be collected from the documents, will be displayed in an Excel file and available for reanalysis. However, in qualitative research, the analysis is largely depending on the researcher’s subjective interpretation. Therefore, the researcher bias may arise as I become the instrument to interpret the data myself and might be biased because of my own belief system. I might be biased towards the case study as I consider the company as innovative and progressive. The Gioia methodology helps to combat this bias by analyzing the data in two separate phases, the first-order analysis maintains the terms that the person has used, rather than directly generalizing the terms into categories. When there is a better understanding of the phenomenon, the second-order of analysis is conducted and categories are placed within themes, which are theoretical concepts. By analyzing the data in two phases, the informants’ terms have remained and there is more availability for the investigator to interpret the data, which helps to avoid jumping into conclusions. The Gioia analysis method increases the study’s credibility, as it establishes confidence in the truth value of the findings.

In the previous paragraph, a detailed description is given on how the data is collected and analyzed. The purpose of the detailed elaboration is to ensure the data collection and

analysis is dependable, this means another person should find the same data and come to the same conclusion under the same conditions (Clark et al., 1981). Furthermore, as this study concerns a single case study there is limited evidence to provide scientific generalization.

However, ASML is a typical company for the industry and therefore generalization is possible for other cases in the industry. Besides, the study has a more empirical perspective and establishes a deeper understanding of the phenomenon.