This chapter presents the methodology, method, and sampling strategy chosen for conducting the research, and also covers the methodology of data analysis as well as research quality and ethical considerations.
The utmost aim of this study is to use the developed model to describe the retention tools and techniques of Finnish IT firms, and then utilize the findings by enhancing said model to create recommendations for best practices. For this goal to be achieved, we must first gain an understanding of the current practices within the industry. To form a complete picture of the tools and techniques and the logic behind them, data must be gathered from the employers, rather than relying on the employees’ personal perspectives. The first step of this process was determining the philosophical stance to be taken. Research philosophy bears significance to the entire research project as it has an impact on the researchers’ worldview. It is useful in clarifying and evaluating research designs and informs the reflexive role of researchers in research methods (Easterby-Smith, Thorpe & Jackson, 2015; Saunders, Lewis & Thornhill, 2016). In other words, the philosophical standing influences the way we go about answering the research question.
At the very core of the research process is ontology, which relates to the nature of reality and existence (Easterby-Smith et al., 2015; Saunders et al., 2016). Easterby-Smith et al. (2015) present realism, internal realism, relativism, and nominalism as four different ontological positions. As discussed in the introductory chapter, we are aware that the theme of retention has already been studied, but with the focus being on different industries, cultures, and countries from various perspectives. Based on the state of current research, we have highlighted the need for current research in the IT industry with a North European perspective. This suggests that there is no single definitive answer as to what are the tools and techniques enabling success in retention, but rather that the answer alters depending on the context. As Easterby-Smith et al. (2015) state, there are a myriad of approaches, reflecting the view that there are many truths and that facts depend on the viewpoint of the observer. The truth therefore varies between one time and place and another (Easterby-Smith et al., 2015), implying that the ontological stance of this thesis is relativism. This position is further underlined by the fact that we have chosen to study the specific issue of retention from the management’s perspective, acknowledging that adopting a certain perspective changes the entire research process and its outcomes (Saunders et al., 2016). The consequence of relativism is that unlike with realism, we do not seek the absolute truth as it does not exist. As we collect and analyze data, we are aware that our findings are subjective and represent one truth out of many.
After settling on relativism, the next step was to determine the epistemological position taken. Epistemology studies the nature of knowledge and refers to the general set of assumptions of the most appropriate way of inquiring into the nature of the world (Easterby-Smith et al., 2015; Saunders et al., 2016). There are two contrasting views: positivism and social constructionism. Positivism’s key idea is that the social world exists externally, and it can be measured by objective methods, disregarding subjective inferences through intuition, sensation, and reflection. In juxtaposition, social constructionism is built on the idea that reality is not determined by objective and external factors, but by people and their experiences (Easterby-Smith et al., 2015).
As implied by the research question, in aiming to understand the retention tools and techniques used by IT companies in Finland, we are looking at social phenomena with subjective meanings. This means that we are aiming to increase our general understanding of the situation by focusing upon its details and the reality behind them (Easterby-Smith et al., 2015; Saunders et al., 2016). We acknowledge the existence of different realities and objective knowledge (Easterby-Smith et al., 2015), trying to build our knowledge through developing our own retention model. Further, the research question is such that we are unquestionably a part of the research process. It is not possible to acquire the necessary insight from independent observations, so we must interact with individuals in order to gain a deeper understanding of their perspectives and thoughts, and to obtain as many details as possible in order to gather rich data. In light of these characteristics, social constructionism is the more suitable position for our purpose.
Having settled on the philosophical standing of relativism and social constructionism, the next step was identifying what type of a purpose this research project fulfils. Saunders et al. (2016) state that the formulation of the research question will determine the research purpose, and present four distinct types of studies: exploratory, descriptive, explanatory, and evaluative. In short, exploratory studies are used to discover what is happening, gain insight, and to clarify the understanding of a research topic. Descriptive studies, on the other hand, are useful when there is already a clear understanding of the research topic, and the goal is to gain an accurate profile of it. Explanatory studies are applied when the researchers wish to clarify causal relationships between variables, and the aim of Evaluative studies is to determine how well something is working (Saunders et al., 2016).
It is also possible to conduct a study combining two or more purposes, either by having a research design that uses mixed methods, or by utilizing a single method in a way that facilitates more than one purpose (Saunders et al., 2016). We opted for the latter approach, with a combination of exploratory and descriptive purposes. This was determined to be the most suitable option for us, as the research question features two distinct aims.
The first is to describe the retention tools and techniques used by Finnish IT firms, with the empirical study being based on existing literature. The purpose of this part is therefore clearly descriptive. However, the aim of the research is taken further in the second part of the research question, which seeks to offer novel theoretical and practical contributions on the issue of retaining IT knowledge workers. For the goal to be achieved, it is important that there is flexibility to change direction and focus as a result of the empirical findings. Since a descriptive study does not allow for this, combining it with an exploratory study is the only suitable choice for fulfilling the dual purpose of the thesis.
After defining the philosophical position and purpose of the research project, it is important to understand that research can be conducted through two approaches: inductive or deductive. When adopting the deductive approach, the aim is theory testing, with researchers first developing a theory and a hypothesis, and then designing the research strategy to test their hypothesis. When using the inductive approach, the process is inverted, and the aim is theory building. In this case researches first collect data, and then construct a theory through analyzing it (Saunders et al., 2016).
However, as Moutinho and Hutcheson (2011) state, deductive and inductive approaches are not reciprocally exclusive. This is also the premise for our standing, as this research cannot be classified as belonging purely to one category or the other, rather residing somewhere in between. First, it is true that we followed a deductive approach by using existing theory to formulate the research question, and the research strategy was designed to answer it. This makes the study primarily deductive. However, we somewhat moved toward being more inductive by opening up the possibility for novel insights through semi-structured interviews, where numerous open questions are used. This can be seen in the topic guide for the interviews, which is located in the appendix. Through this inductive activity, we allowed for the possibility of expanding on our theory through collected data. This means that after gathering and analyzing the data, we investigated whether there was a need to modify the pre-developed model to fit the discovered reality. On top of the new addition to the model, the inductive element is further supported by utilizing the empirical findings to illustrate the role and relative significance of each category.
As Easterby-Smith et al. (2015) state, the choice of research design needs to fit the underlying philosophical position. There are two concepts widely used to distinguish both data analysis procedures and data collection techniques in business and management research: qualitative and quantitative approaches. One way to differentiate these terms is based on whether the focus is on numeric or non-numeric data (Saunders et al., 2016). Their main difference is that while the goal of quantitative research is to test objective theories by examining the relationship among dependent and independent variables within a population, qualitative research seeks to understand the context, processes, or the significance people attach to actions (Easterby-Smith et al., 2015). Another important difference is that quantitative research aims at statistical generalizability (conclusions beyond those that have been examined), whereas qualitative research aims at internal generalizability (ability to explain what has been researched in a given environment). For this study, a qualitative approach was adopted as we seek to achieve in-depth knowledge of retention tools and techniques in the specific context of the IT industry. In other words, we aim for internal generalization. Additionally, this approach is strongly supported by our philosophical standing as qualitative research acknowledges subjectivity (Easterby-Smith et al., 2015), the core premise of relativism and social constructionism.
It is important to note that the qualitative approach has its limitations, first of which is related to the restricted sample size due to the time and costs involved. Overall, data collection, analysis, and interpretation require a large amount of time and carefulness. Second, as the nature of qualitative data is subjective, and it is originated in a single context, concerns of validity and reliability arise in terms of replication and generalizability. These issues in relation to this thesis are addressed in the section about research quality. At the same time, this approach offers researches multiple benefits. Because the researcher is so closely involved, they are able to gain a deeper view into the matter and can identify subtleties and complexities that quantitative research might miss. This type of research can also form a strong basis for suggesting possible relationships and dynamic processes, and as a reflection of social reality qualitative analysis allows for ambiguities and contradictions in the data (Easterby-Smith et al., 2015). For our research, it was essential to be able to gain a deep, nuanced understanding about the retention tools and techniques in the IT industry through interactions and close involvement with the respondents.
After these decisions had been made, the most appropriate method for gathering data had to be identified. Multiple methods for conducting the research were considered, and their strengths and weaknesses for our specific purpose are addressed next. Two methods, focus groups and secondary data, were ruled out nearly right away. Finding a common time and place for a focus group of human resource (HR) managers would be a feat in itself, and since the topic can be considered as sensitive information, there is no guarantee that the managers would openly share their views and experiences with their competitors. Secondary data was also decided against as the available information on retention tools and techniques is very limited, difficult to access, and it is not possible to delve deeper into the data and the thought process behind it through additional questions.
Next, we contemplated using the popular method of questionnaires. Open-ended questionnaires can be utilized for qualitative research, and they can gather data about the behaviour and opinions of a large amount of people especially when distributed online. No matter the medium, a large number of participants is required in order to have a body of data representative of the population (Easterby-Smith et al., 2015). This posed an issue in our research, as the number of potential informants is limited. More importantly, as the research question already implies, the aim is to gain a deeper insight into the current tools and techniques used in Finnish IT companies for retention, in order to provide suggestions for its enhancement. By using a questionnaire, we would have been limited in the amount and quality of data received as it is not possible to use further inquiries to focus on certain themes and elicit a more thorough answer. Due to these limitations, questionnaires were not chosen as the research method.
Finally, we considered conducting the research through interviews. Interviews assist the researcher in exploring an experience or topic in depth, and the aim of seeking a more thorough understanding remains the same no matter how interviews are conducted (Easterby-Smith et al., 2015). Interviews can be structured, semi-structured, or unstructured, depending on the research problem at hand (Easterby-Smith et al., 2015; Saunders et al., 2016). The specific technique chosen for this research was to conduct semi-structured interviews in person. While it would have been possible to interview the managers through email, phone or a video call, the personal interaction with the respondents suited our purposes the best. When meeting the interviewee face-to-face, it is much easier to build trust and rapport and to keep focus on the topic of the discussion, and to capture nuances such as emotions, verbal, and nonverbal cues like body language. Semi-structured interviews, featuring a topic guide, were chosen because there is a list of certain issues we wished to cover during the interviews, but at the same time we wanted to have the freedom to flexibly deviate from the structure as needed, pose follow-up questions, and to encourage the participants to reply with open-ended answers and share their thoughts and experiences. The usage of semi-structured interviews also allowed us to accommodate inductiveness and provide scope for including an exploratory purpose in addition to the otherwise descriptive study.
Once the single method research design had been decided on, the next task was to craft a sampling strategy and choose between probability and non-probability sampling. In many cases within business research probability sampling is either not feasible as there is no sampling frame, or adequate to answer the research question (Saunders et al., 2016). These issues also applied to this research project, and thus the chosen sampling strategy is non-probabilistic, purposive, and theoretical, as per the used theory. Non-probability sampling is subjective, with the researcher choosing the sample based on predetermined criteria. To participate in this study, the following criteria had to be met:
First, the individual had to be currently employed by a business organization operating in the field of IT, and their responsibilities must include HR matters to a large extent. This criterion was included to make sure that we reached informants with a high degree of knowledge of the HR function and its practical applications within the company. Second, the companies themselves must have been founded in Finland, ensuring that the roots of the organizational cultures stem from a homogeneous cultural context. Third, the minimum size for the companies was set at 10 employees to make certain that some HR practices have been established. No other restriction on size or sub-field of business within IT was put in place, so that we could receive as comprehensive of an understanding of the population as possible through interviewing representatives from firms of different sizes and not be limited to only consulting companies, for example.
After determining the criteria, a total of thirty companies were contacted via email, inquiring about their interest to participate in an interview on the research topic. This number was chosen because both the number of IT companies fitting the criteria and the time for conducting the interviews were limited. Generally, there is a need to collect data until the additional collection and analysis of data are less likely to add new or pertinent information (Easterby-Smith et al., 2015). However, this is difficult to estimate in advance. Therefore, considering the limited timeframe and lack of guarantee of how many companies would be open to the request, we set a goal to conduct ten interviews. Taking between one and two hours each, this amount would be feasible in terms of time, and would also provide sufficiently deep insight into the research problem, with a sample size large enough to offer some generalizability.
In the end, we were able to conduct 10 in-person interviews at the company premises, and the interview guide used in each interview can be found as an appendix. The interviews were conducted in Finnish as it is the informants’ native language, making it easier to build trust between the interviewer and interviewee and ensure complex and thorough answers without the risk of miscommunication. The interviewees were open and gave rich answers to each question, and each interview lasted approximately 90 minutes. The data was then carefully translated into English for the analysis. As the majority of respondents requested to remain anonymous, all companies are kept completely anonymous.
The sample characteristics were satisfactory; there were informants from both consulting and product-oriented organizations, representing a variety of sizes in a balanced manner, and although it was not a predetermined criterion, all companies were financially healthy. Hence, there were no major differences between the initial plan and the outcome. Reflecting upon the sampling strategy, we could have aimed for a larger number of interviews, but at the time this was decided against as it was seen as important to ensure that enough time was left for thorough analysis and discussion of the findings.
Data Analysis Methodology
The collected data has to be analyzed and the meaning comprehended in order for it to be useful. As qualitative data is non-standardized and expressed through words, there is no single standardized procedure for analyzing it. However, generally the process has three parts: summarizing, categorizing, and (re-)structuring / ordering (Saunders et al., 2016). In summarizing data, large amounts of texts are transformed into fewer, rephrased words which include the main points from the interviews. In categorizing, categories or codes are used to group the data, and they emerge either from the collected data or a theoretical framework. Finally, during (re-)structuring / ordering, data is arranged into a meaningful order which makes it easier to be analyzed.
These steps also form the base for template analysis, a method that allows researchers to tailor it to fit their particular requirements. This flexibility in the analysis process was seen as particularly valuable for this research, as with semi-structured interviews certain themes are already known beforehand, but others cannot be predicted.
Since research philosophy is connected to the way data is analyzed, it is important to keep it in mind during this step. In this case there was no misconnection between the two, as template analysis can be used with different epistemological positions and is suited for both positivism and social constructionism. Further, template analysis combines both the inductive and deductive approach by enabling codes to be determined in advance, and through allowing changes or additions to be made as the data are collected and analyzed. Similarly, this enables the exploratory purpose to be fulfilled.
In practice, there is a list of categories or codes that constitute the themes revealed from the collected data (King, 2012; Saunders et al., 2016), which are organized in a hierarchical structure to represent the relationships between themes (King, 2012). In our case, the categories were mostly pre-determined based on our theoretical framework. However, we also remained open for new categories that emerged from the empirical material, and as data collection proceeded the template was revised as part of the qualitative analysis.
There are five ways for revision: inserting a new code into the hierarchy, deleting a code from the hierarchy, altering the scope of a code, unification of codes that were initially considered distinctive, and reclassifying a code into another category. Whenever a revision takes place, it is followed by a verification of this action and its implications to previous coding (King, 2012; Saunders et al., 2016). In our study, new codes were added to the hierarchy, and the scope of some existing codes were accordingly altered as a result. After these revisions were completed, the finalized template provided the basis for analyzing the empirical data. Organized under the codes, the collected data was then carefully analyzed so that key insights and patterns could be identified.
As Saunders et al. (2016) state, there are a number of issues related to quality that must be considered in qualitative research. These include reliability, forms of bias, validity and generalizability. To begin with, reliability is connected to the lack of standardization and whether other researches would find similar information. This cannot be fully solved as both the context of the research and the researchers’ active role always have an impact on the findings of the study (Easterby-Smith et al., 2015; Saunders et al., 2016). Even so, to minimize this issue, we have included all of the relevant information on how the study was conducted in order to demonstrate how the conclusions were reached. Additionally, this thesis follows social constructionism, which has an inherent assumption on the existence of multiple truths. Due to this, it is implied that the concern about coding reliability is not relevant. However, matters such as the attempt to approach the theme from various perspectives, the researchers’ reflexivity, and the richness of the generated description are significant requirements (King, 2012). We have taken care to meet all of these requirements and minimize data quality issues by being aware of our interactive role in the research process, and through conducting interviews generating rich data in numerous IT companies.
Reliability is also connected to issues of bias (Saunders et al., 2016), as the nature of qualitative data is subjective (Easterby-Smith et al., 2015). To ensure that the research findings weren’t influenced by bias, two main preventive actions were taken. First, during the interviews we constantly repeated our understanding back to the participants, to confirm that we had understood them as intended. Second, the interview findings were transcribed and discussed in detail before the analysis, to avoid relying on memory alone and to ensure mutual understanding.
Validity is generally not seen as an issue in qualitative research due to the fact that the questions can be clarified, meanings of responses examined, and topics viewed from different angles (Saunders et al., 2016). Within this point, the major implication for this thesis is that there was a risk of misunderstanding or misinterpreting the data, as all of the interviews were translated. To avoid this problem, both time and thought was put into the translation process, and attention was paid to words and the meanings behind them. Through this, it was ensured that the nuances in the data were not lost. Finally, it is not feasible to make statistical generalizations about the entire population, as this qualitative research is based on a small number of companies within one country. However, internal generalization is achievable, as the thesis looks at retention tools and techniques in the environment of the IT industry.
Research ethics are a significant aspect of developing a research study. Compared to quantitative research, a qualitative approach is likely to lead to a greater range of ethical concerns (Saunders et al., 2016). This is why the authors have carefully considered the implications of their activities before collecting data. The ten key principles from Easterby-Smith et al. (2015) were kept in mind when developing the topic guide, conducting the interviews, and reporting the findings.
Translated in terms this thesis, the key principles are realized as follows. First, participants were informed of the research project and its nature, aims and scope, and how the data is used. This ensured that they were fully informed, and their informed consent could be attained. The interviewees were treated with utmost dignity and were assured that the data is confidential in that no one but the authors have access to the data before it has been anonymized so that no company or individual can be identified. Informants were also asked if they wish to remain anonymous and as a majority did, all participating companies are kept fully anonymous in order to prevent harm, and to protect their privacy.
Furthermore, the authors are honest and transparent in the communication about the research, and the final version of the thesis will be sent to the participating firms. This research does not receive any funding, and there are no affiliations or conflicts of interest. Finally, the research findings are reported truthfully and to their full extent and there is no misleading or false reporting. Through these actions, the authors protect the research participants and the integrity of the research community.
Table of Contents
2.1 Frame of Reference
2.3 Industry Context
2.4 Model for Retention
3.1 Research Philosophy
3.2 Research Purpose
3.3 Research Approach
3.4 Research Design
3.5 Data Collection
3.6 Data Analysis Methodology
3.7 Research Quality
3.8 Ethical Considerations
4. Empirical Findings
4.1 Context Description
4.2 Retention in General
4.3 Retention Tools and Techniques
4.4 Concluding Remarks
5.1 Importance of Retention .
5.2 Retention Tools and Techniques
5.3 Concluding Remarks
5.4 Model Development
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What Makes Talent Stay? Enhancing the Retention of IT Knowledge Workers