3.1 Interpretivism
The research has an interpretivist subjectivist epistemology. Interpretivism from the philosophical viewpoint is things not being straightforward and predictable in the social sciences and having taken an interpretivist approach to study means expecting multiple interpretations and understandings (Bassot, 2022). In Interpretivism, the subject matter in social sciences different from natural sciences and reality is established by human action and meaning making instead of existing objectively and externally (Bell et al., 2019).
According to Goldkuhl (2012), the social world of people is based on subjective and shared meaning and the idea of interpretivism is to work with these subjective meanings. Interpretivism is also related with how and why of social actions and the process where things happen (Bell et al., 2019). Saunders and Lewis (2018) add that it studies social phenomena in their natural environment as business situations, in addition to being complex are also unique. It is accepted that there will be multiple realities instead of one single reality or solution, thus the epistemology and ontology are subjectivist (Bassot, 2022). The interpretive approach assumes that the social world is continuously constructed through group interactions, making social reality understandable through the perspectives of individuals engaged in meaning-making activities (Hesse-Biber, 2017).
In contrast, according to Bassot (2022) positivism is fully about application of science and looks for universal laws based on a hypothesis. Positivism assumes the existence of objective world which can be observed, defined and measured (Buchanan & Huczynski, 2019). But, human behaviour is near impossible to learn using methods that apply to natural objects and events (Buchanan & Huczynski, 2019).
3.2 Qualitative Approach
Understanding these kinds of complex behaviours cannot be done through a quantitative approach. Clark et. al. (2021) argue that to understand or rationalise the behaviour of members of a social group without appreciating the specific context, relation, networks, and environments that they come from is difficult. Qualitative researchers explore phenomena in their natural environments, aiming to understand and interpret these occurrences based on the meanings people assign to them (Denzin & Lincoln, 2018). Qualitative research focuses on the social meanings, people assign to their experiences, circumstances, and situations (Hesse-Biber, 2017).
According to Clark et. al. (2021), qualitative researchers often provide detailed descriptions in their findings but also focus on explanations, with their emphasis on asking “why?” frequently being understated. Qualitative research places primarily emphasis on an inductive approach to the connection between theory and research (Bell et al., 2019). Inductive is exploratory in nature and not confirmatory (Hesse-Biber, 2017).
Qualitative research seeks to generate deep insights into specific topics through thoughtful engagement with places and social actors, including individuals, communities, organisations, or institutions (Clark et al., 2021). Based on Bell et al. (2019), qualitative research is a research strategy with emphasis on words and images in contrast to quantification in collection and analysis of data. Qualitative research is exploration by collecting data like language, words, observations, pictures, etc which is analysed and interpreted (Bassot, 2020).
3.3 Secondary Data
The research utilises secondary data and is more viable than primary data. Secondary data research involves using and interpreting data collected by others to gain new insights related to one’s own research question and involves examining numerous studies in a given area to reach some conclusion (Bassot, 2022). Secondary study is further analysis of an existing dataset that offers interpretations, conclusions, or knowledge beyond or different from those presented in the initial report of the inquiry and its main results (Hakim, 1982).
According to Chandola and Booker (2021), the benefits of using secondary data include cost savings and time efficiency, as most secondary data can be accessed quickly and typically come from larger, high-quality sample sizes whereas collecting primary data through a high-quality survey of a large group can be quite expensive, even with the use of internet surveys, and the time required to receive responses can be lengthy. There are benefits of primary data like high level of control and accuracy but access to people working at strategy level would be near impossible because of their schedule.
However, there are challenges to secondary data as it might also not fully explain details on how it was collected, and it may not be specifically related to the research question. According to Bassot (2022), relying on poor-quality data such as those lacking sufficient information or data that exists in a context different from one’s own study can be misleading and should be avoided. Misinformation in the secondary data can be found either deceitfully or accidentally and, in both cases, researchers should be aware about this and handle the analysis accordingly (Sloan & Quan-Hasse, 2022). Copyright issues can also be a challenge when collecting data from the internet and data can disappear from the internet without noticing which makes it essential to create a local backup of those data (Bassot, 2022). Additionally, biases might exist in data sources and it’s important to know the source of funding of the data that we are utilising. Chandola and Booker (2021) emphasise that researchers should be cautious if the data is funded by a specific interest group that may seek to influence its quality.
3.4 Data Gathering
As noted by Hesse-Biber (2017), qualitative research utilises a variety of methods, which is one of its defining features, allowing for a wide array of potential research topics and questions. However, the selection of high-quality data for the selected topic is highly important as it can affect the quality of the research. Also, every research project should have clear focus and make decisions about the topic they will be covering to stay on track, have realistic project size, and not to deviate or digress into other areas (Bassot, 2022).
Journal articles and books were used as written resources. Written resources have many advantages like they can be easily downloaded and saved for coding purposes while needing quite a lot of time required to read (Bassot, 2022). Additionally, company websites, game store data, blog posts, industry databases, and news articles from reliable sources has been used for secondary text data. Blogs are current documents that provide rich and spontaneous accounts of everyday life experiences (Hookway & Snee, 2017).
Interviews and talks conducted with founders and teams working in independent game companies by reliable games podcast and events were particularly relevant which included both audio and video format. These were relevant with the fast-changing nature of the game industry as well.
3.5 Purposive Sampling
Purposive sampling ensures that the data collected is closely aligned with the question of this research in comparison to other random sampling methods. By deliberately choosing data sources with specific insights or experiences, the research can achieve a deeper understanding of complex phenomena or specialised topics. The data collection in qualitative research has a relatively smaller sample size (Denny & Weckesser, 2022). Purposive sampling is used to ensure that the sample aligns closely with the research objectives, which strengthens the study’s rigour and increases the credibility of its results (Campbell et. al, 2020).
Based on Saunders and Lewis (2018), non-probability sampling uses a researcher’s judgement to select data that contributes best to answer the research question. Purposive sampling is the most popular form of non-probability sampling where data is selected based on certain criteria, which includes both judgement and quota sampling techniques (Sibona et. al., 2020). It is important to exclude data that are not relevant to your research question (Saunders & Lewis, 2018). According to Bassot (2022, with the vast amount of available data, it’s crucial to focus on the most relevant information for your study, which may require being selective and discarding or ignoring certain resources at various stages.
It was very important for this study to select companies that were both financially independent and have produced at least a single successful game. To filter out these companies two approaches were used. In the first approach, games that were commercially successful were filtered out from the vast array of games that are out there. Then I tracked out the companies behind each game. If the companies were financially independent, they would be in the list, otherwise they would be out of the list. Several companies like thatgamecompany, Obsidian, CD Projekt Red, etc. which seemed like financially independent at first but when researched deeper were backed by someone else; thus excluded from this study.
Another approach was to search for independent companies first. Then out of these companies, it was confirmed to see if they have put on at least a successful game in the market. Only those which has financially successful games in their belt were put in the list.
Out of these companies, I gathered interviews, articles, industry events and talks related to these companies and filtered the ones who seem to be following emergent strategy principle. Finally, the selected data were of the companies that ensured that they could be analysed effectively and relatively easy on a practical level with enough depth (Bassot, 2022).
3.6 Thematic Analysis
Thematic analysis was used to analyse the data in this study due to its flexibility and capacity to capture rich, detailed, and nuanced descriptions of the data. ‘Thematic analysis is a method for identifying, analysing, and interpreting patterns of meaning (‘themes’) within qualitative data’ (Clarke & Braun, 2017). The purpose of thematic analysis isn’t just to summarise the data, but to recognise and interpret significant aspects of it, though not necessarily all, guided by the research question and the emphasis is on producing rigorous and high-quality analysis (Clarke & Braun, 2017). Bell et. al. (2019) states that a pattern within the data can be considered a theme when there is a lot of repetition. Based on Clark et al. (2021), something can be considered a theme if it is a category of interest identified by research or related to research question. Coding was left out as it has the downside of data losing valuable context because of fragmentation, process being laborious and time-consuming (Clark et al., 2021).
Once thematic analysis was complete and themes have been identified, the research moved into integrating these themes into a coherent narrative or argument. This involved comparing and contrasting themes, identifying relationships between them, and drawing broader conclusions. Since, it is important to analyse the data in its context, it is decided to not keep the results and discussion as separate section.
In contrast, thematic analysis relies heavily on the researcher’s interpretation, which can introduce bias. To mitigate this, efforts were made to remain reflexive throughout the analysis process, constantly questioning assumptions and interpretations.
3.7 Case Studies
Case studies are used for studying various companies. According to Hesse-Biber (2017), a case study which can be about an individual, event, program, organisation or society and it as an approach that provides researchers with a holistic understanding of an issue or phenomenon within its social context. It also explores complexity and uniqueness of such things deeply from multiple points of view (Simon, 2009). Its goal is to develop understanding by addressing research questions and continuously triangulating rich descriptions with interpretations of those descriptions in an iterative process (Stake, 2005). In qualitative research, case study emphasises the experiential knowledge of the case while carefully considering its social, political, and contextual influences, requiring detailed analysis of its activities to enhance understanding for any audience (Stake, 2005). According to Hesse-Biber (2017), the primary goal of a case study is to achieve a meaningful and nuanced understanding of the subject.
3.8 Ethics and Trustworthiness
Ethical consideration has been paramount through the research. According to Bassot (2022) , ethics deals with morals or how people are meant to behave and tries to answer broad terms like good and evil, right and wrong. Ethics is ‘doing the right thing because it is the right thing to do’ (Russell, 2017). The ethical considerations inherent in qualitative research design present unique challenges regarding informed consent, confidentiality and privacy, social justice, and practitioner involvement (Shaw, 2008). These secondary data were protected and processed in an ethical manner and complied with all regulations and laws that apply to such data (Chandola & Booker, 2021). Whatever data gathered is represented honestly and accurately and is not misrepresented in any way. All ethical procedures have been followed. Hesse-Biber (2017) adds that the ethical integrity of the researcher is a crucial factor in guaranteeing that the research process and the findings are trustworthy and valid
Additionally, validity and reliability are crucial components of research and are more straightforward to assess in quantitative studies while the qualitative approach poses greater challenges, as there is no agreement on how to measure these factors (Bassot, 2022). As the research takes an interpretivist approach, the concepts of validity and reliability are generally applied to the researcher instead of the study (Bassot, 2022). According to Clark et al. (2021), qualitative also has criticism of being too subjective, difficult to replicate and generalise and not sufficiently transparent. In qualitative research, reliability relates to the degree of consistency applied during data analysis (Noble and Smith, 2015). The term trustworthiness is preferred in qualitative research which means can the findings be trusted (Bassot, 2022). A qualitative researcher must ensure trustworthiness by carefully showing that their research was carried out systematically and with honesty (Bassot, 2022). All these have been taken into consideration.