Data collection

Both primary and secondary data were collected for this study. Primary sources include 11 semi -structured interviews supplemented with secondary data in order to get a broader perspective on the phenomenon. Secondary data was mainly in the form of official reports, scientific articles that complemented the primary data collection in making sense of the main perspectives through different scientific- and societal angles.

3.3.1 Interviews

According to hermeneutic phenomenology, as previously mentioned, the researcher’s unavoidable pre-understanding forms the foundation for their perception of a phenomenon (Sloan et al., 2013).

Realizing that my pre-understanding would affect the way I select and interpret knowledge, it was

imperative for me to keep an open mind when it came to different understandings, which was best

achieved through semi-structured interviews. The collected empirical data through interviews, can

aid a researcher in becoming aware of his/her own pre-understandings, while at the same time

creating new understandings on the phenomenon (Sloan et al., 2013). Semi-structured interviews

are ‘non-standardized’ interviews, where the researcher will follow a list of themes and key

questions during the interview. However, that does not mean that questions are fixed and stay the

same between interviews. Questions may vary given the context and interviewee and the order of

questions may vary depending on the flow of the conversation (Saunders et al., 2012). I had

prepared a list of about 22 primarily open-ended questions to keep the conversation on track and

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to ensure that the topic of conversation did not stray too far from the objective (Appendix 12.).

Initially, these questions were heavily influenced by my pre-understanding on the subject, however, throughout the interviewing process, my understanding evolved as I acquired more knowledge.

Therefore, the open-ended questions were slightly altered between interviews.

I conducted 11 thematically semi-structured interviews between September and November 2021 with an average duration of 35 minutes. To promote credibility, I provided participants with information regarding interview themes and the purpose of the study (Saunders et al., 2012). After 11 interviews, I ceased collecting additional empirical data due to time restraints and due to having reached relative thematic data saturation. I use the term "relative" as no two women will have had exactly the same experiences, however, several themes had become apparent and repeated themselves between interviews. To be able to focus on the dialogue, I asked permission to audio-record our conversations, enabling me to transcribe said audio-recordings at a later time. Upon completion of said transcripts, I began coding the transcripts thematically as a way to structure the data collected. Due to the explorative nature of the study, I engaged in inductive coding. Through the bottom-up approach of inductive coding I was able to derive codes from the data itself. In doing so, I was able to let the data dictate the emerging narrative. The purpose of using inductive coding is (Thomas, 2003, p. 1):

1. “To condense extensive and varied raw text data into a brief, summary format 2. To establish clear links between the research objectives and the summary findings

3. To develop a model or theory about the underlying structure of experiences or processes which are evident in the raw data”

Throughout the interviewing process, I noticed how five thematic categories would reemerge with

every interview. Therefore, I initiated my coding process by making an Excel sheet based on those

categories. Thus, the data was grouped into the five major categories of introduction, education,

stereotypes, perception, and management. Sorting the data of each individual category, I

established a number of sub-categories, which I formulated as questions. Thereby, I was able to sort

the data more accurately, by filling in passages that would answer the question of the sub-category,

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into the spreadsheet. To ensure the anonymity of the respondents, they will be referred to through pseudonyms.

3.3.2 Recruitment of interview participants

The 11 interview participants were selected based mainly on availability, but also a set of demographic criteria, in order to diversify responses and get a nuanced view on the phenomenon.

I tried to recruit participants from various age groups and with different backgrounds. In that way, the data collected would not accommodate a single group of women in IT, but paint a broader picture. Participant experience in the field varies between a few months and 40 years. Some of them began studying and working with IT immediately, while some gradually shifted their careers in that direction. Some of the participants have children while others do not. Participants were recruited through different means. Some of them were personal acquaintances/colleagues from within my network or acquaintances of my acquaintances, others were recruited through a linked-in post that was shared among IT consultants. There were no criteria in terms of specifics when classifying what constituted IT work, be it data analysis, programming or IT management. Although many of the participants graduated from IT-specific study lines, having a university degree in IT was also not a criteria. These criteria were avoided to ensure a larger pool of participants, but also enabled a broader spectrum to be analyzed. In the following, the 11 participants are briefly described:

Nora is a software designer at BK Medical, where she works with software development. This is her

first job after completing her education in Medicine and Technology at DTU (Appendix 1.).

Clara is an AC clerk (academic employee) at Bygningsstyrelsen, where she works with data analysis.

Before working at Bygningsstyrelsen, after completing her Masters in Mathematics at KU, she began working with data management at the Danish Municipal Hospital (Appendix 2.).

Fillippa is a software engineer at BK Medical, where she works with software development. While

studying Computer Science at KU, she had a student job where she worked with web development.

Her job at BK Medical is her first full time job (Appendix 3.)

33 Olivia is an IT employee at Bygningsstyrelsen, where she works with technical data and software

documentation. She has worked with IT&D for the last 40 years in many different jobs, however, she is actually a trained electrician and only had EDB courses in tenth grade (Appendix 4.).

Emma works as a Bid Manager at EPICO IT, where she manages her department and is responsible

for the company's supply. Before working at EPICO IT, she worked a few years at a small IT company called IT Dan and before that, she was a consultant for IBM for 13 years. She has a Bachelor in Marketing and Sales and does, therefore, not have an IT-specific education (Appendix 5.).

Sophia is the team leader of the IT department at Bygningsstyrelsen, where she mainly coordinates

and manages at an operational level. She has a Masters in IT and Organization, a Masters in Information Science and has worked with IT for 16 years. Three of those were at Bygningsstyrelsen and the rest were in the Danish Military, where she also worked with IT project management (Appendix 6.).

Charlotte is a senior business developer at Nordea, where she mainly works at the business end of

operations, but works with IT on the side. She has a Cand.merc.it degree from CBS and while she was studying for it, she worked part-time for a company called Proggresive IT. After ending her studies and before working at Nordea, worked for an IT consultant company called CGI (Appendix 7.).

Amelia is a freelance, technical project manager. She works with the IT aspects of mergers and

acquisitions. She is a trained data mechanic, her first IT job was at the Danish Airforce, and afterwards, she worked as a PC Supporter at Novo Nordisk. Since then, she has worked with most aspects of IT in several different companies for a total of 32 years (Appendix 8.).

Isabella is a specialist consultant at Udviklings- og Forenklingsstyrelsen, where she works with

software development. She has worked with IT for approximately 9 years, 4 of which she worked as

a consultant in Sweden and 5 of which she has worked for Udviklings - og Forenklingsstyrelsen in

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Denmark. Before that, she studied Computer Engineering in Sweden - she is a Swedish national (Appendix 9.).

Mia is a senior tester at Netcompany, where she is in charge of test management and test coordination. While completing her Masters in Information Science, she worked part-time at a game developer. Her job at Netcompany is her first full-time job, but in between her Masters and her employment at Netcompany, she worked about one and a half year on a research project (Appendix 10.).

Emily is a UX designer at BK Medical, where she works with user experience and user interfaces of

ultrasound scanners. This is her first full-time job after completing her Masters in Digital Design and Interactive Technologies at ITU. While studying, she worked part-time at Nordea as a UX designer and at the media agency IUM as a digital designer (Appendix 11.).

3.3.3 Secondary data

Secondary data is classified as data that has been collected previously, typically with a different purpose (Saunders et al., 2012). In this study, secondary data includes various external data sources, in the form of reports and scientific papers, that contributed with general knowledge on topics relevant for the study.

According to Saunders et al. (2012), the advantages of applying secondary data sources for research

purposes include the time saved through the ease of acquiring this data. Furthermore, secondary

data sources allow for the reviewing of data from longitudinal studies that would not have been

feasible for the researcher to conduct themselves, given resource and time restraints. However,

Saunders et al. (2012) also posit the disadvantages of applying secondary data sources, including

how data is typically collected for different purposes that do not necessarily match the needs of the

researcher and therefore only addresses parts of the research question. Additionally, definitions or

phenomena may have changed since the time of data collection by the secondary source, which

could leave the data outdated. Another disadvantage of applying secondary data is how the

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researcher has no control over data quality and that data has been interpreted by the collector of the data, wherefore the objectivity of the data cannot necessarily be ensured.

My choice for applying secondary data in this study aligne with the advantages described by Saunders et al., and I took precautionary measures when evaluating secondary data sources in accordance with the disadvantages presented by Saunders et al.. These precautionary measures include my attempt to incorporate peer reviewed sources to the widest extent possible, i n order to ensure the reliability and validity of the applied secondary data sources. Furthermore, I sought to review these sources critically and deselected data that could have been collected with a non-academic agenda.

It should be noted that most literature available focuses on women in STEM and is not necessarily very IT specific. However, since IT is a large aspect of STEM, arguments presented in STEM literature will in some instances be regarded as valid for explaining IT-specific phenomena. Furthermore, most literature on women in IT is not very current given the growing importance of diversity studies and the fast changing dynamics of the IT field. However, current statistics on employment by gender in the IT fields show that the percentage of female IT workers has not seen a significant increase at any point. Therefore, it can be assumed that the arguments presented in older literature are still relevant.

3.3.4 Limitations to the data collection

As this is a phenomenological study, readers should take note that I am a male researcher. This could have had various implications for my research. First, I assume that my understanding of a male-dominated environment will be very different from the understanding that most of the interview participants will have. Therefore, I may not have asked participants sufficient questions that allowed them to express their perceptions and experiences in a male dominated environment.

Furthermore, as a man seeing the world from a woman’s perspective, I may have emphasized or

deemphasized aspects of the participants' experiences that are actually of insignificant or significant

importance. Additionally, studies show that men can appear to be emotionally unavailable towards

women (Affleck et al., 2012). Whether this is due to biology, gender socialization or social

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constructionism, it could have influenced the responses given to me by the interview participants.

It is possible that participants withheld information or devalued their experiences if they got uncomfortable sharing their stories with me. Thus, my credibility as a researcher could have been affected by my gender, which could have led participants to become sensitive to the exploration of certain themes. As a result, participants might only have painted a partial picture of their experiences and perceptions.

According to Saunders et al. (2012), there are four types of data quality issues related to the data collection method of semi-structured interviewing; reliability, forms of bias, generalizability, and validity. This study is not very reliable in the sense that other researchers may not arrive at the same conclusions. However, the data collected through interviews reflect reality from a single point in time. If someone was to research the phenomenon again at a future point in time, circumstances may have changed. The complexities and dynamics of the research issue is specifically what made this form of data collection method valuable (Saunders et al., 2012). With regard to bias, my gender, as a researcher, could have affected both questions and responses as previously mentioned.

However, in an attempt to eliminate interviewer bias, I strived towards asking open-ended

questions with the purpose of illuminating subjects necessary for my research, rather than to

confirm my own pre-understandings of the phenomenon. Since the study is based on the personal

experiences and perceptions of 11 women, it could be argued that the study is not very

representative of the experiences of all women in IT in Denmark. Had I not been cons trained by the

limited recruitment time, I would have sought to interview more women and tried to get around a

wider spectrum of demographics and variations of IT jobs. However, I believe the data collected to

be sufficient in terms of exploring responses and themes from a variety of angels. Due to the

phenomenological nature of this study, and the purpose explaining the underrepresentation of

women in IT through the experiences of women in the field, the validity of the study is reflected in

my account of the experiences of the participants.

In document THE GENDER GAP IN THE DANISH IT SECTOR (Sider 31-37)