The subsequent sections delve in the methodologies used and touched upon in this thesis.
Qualitative vs. quantitative
Research methods are often placed under two different categories. Qualitative research or quantitative research.
Firstly quantitative research involves gathering information that is focused on numbers and statistics. Quantitative data are that are measurable and can often be used in graphs or ta-bles. Methods that can be used in quantitative data collection are experiments, question-naires and observations. McLeod (2008) writes that the result should either be able to fit in-to categories or be counted.
Qualitative research is compared to quantitative more focused on descriptive data that could be obtained through a number of ways. Data collection techniques used to gather qualitative data is interviews, unstructured observations, diary accounts or case studies. Qualitative data is often more difficult to analyze compared to quantitative data. That is be-cause it is more complex and could involve analyzing in an accurate way different behav-iors (McLeod, 2008).
In this thesis we will do a qualitative research as we are focusing on using data collected from interviews and secondary literature connected to our subject. As we will get elaborate answers from our data collection qualitative research is the best option as we intend to ana-lyze our data in depth (Yin, 2010).
Reliability & validity
Reliability and validity is an important aspect in any research to be able to make a contribu-tion. To have result being reliable the result should be able to be replicated and have almost the identical outcome. Reliability can be measured by doing the same test twice or using different methods to get the same results (Saunders et al., 2009)
Validity refers to the ability to measure what something is supposed to measure. The accu-racy of a study or test in a research needs to be valid if the results will be of any signifi-cance. If a result can be properly referenced and there is good evidence of the outcome then you have validity in a form. In our study we want to have both reliability and validity to the most extent. By doing the same type of data collection on different subjects we can have a sort of consistency in our work and also try to use both primary and secondary data sources.
There are a number of data collection techniques that one can use when doing a qualitative study. One could also use different methods in the same study. This is called triangulation and this helps to have more validity in the research. Adding more collection techniques Closing IT projects: A Swedish public sector perspective helps to check each result and compare with one another (Saunders, Thornhill & Lewis, 2007).
There are different types of collecting data and these should be considered when doing a research. For qualitative research the common is to use case studies, observations or interviews because this gives a more deep understanding of a specific scenario. Especially applicable to smaller samples. In this research we will mix interviews with secondary literature search within our topic.
Primary and secondary data
There are two main data sources that are used in most publications. Primary data which are obtained directly and with more supervision. Primary data, in our case, are the usage of in-terviews which we know come directly from the source. Other primary data are obtained from observations, surveys, company reports and emails (Saunders et al., 2007).
In our thesis we will also use secondary data in form of publications related to our topic. Secondary data can be apart from research papers other publications such as books, articles and newspapers. One could say that secondary data are collected from sources that already exist rather than sources that have been created by the researcher for example (Saunders et al., 2007).
Interviews in terms of qualitative research aims to describe and interpret what the inter-viewee says. The main task is to connect the result of the interview/conversation to an ana-lytic stage to get results that are significant to the subject. Interviews can be done in differ-ent ways, preferably face to face but it is possible with telephone and by other means like – skype for example (GOA, 1991)
There are many types of interviews that one can do as a researcher. It is important to think about which one is best for the situation in the research. Saunders et al. (2007) list the dif-ferent types of interviews:
- Informal interviews – no planned questions, the interviewer and the interviewee have more of a conversation on the predetermined topic.
- Standardized open ended interviews – There are a number of planned questions asked to every interviewee.
- Closed fixed response interview – The interviewees are asked the same questions which are planned but can only answer with an answer provided in a list.
- Semi structured interview – A somewhat planned interview with some predeter-mined questions and topic but with a lot of room for open discussion.
There are both strengths and weaknesses with doing interviews. The biggest advantage is that one can get deeper insights about the topic when interviewing someone. You can get much more to interpret than just words. Voice, emotions and body expressions are also part of the interpretation that could be used in the analysis later. Another advantage is that you as a researcher can steer the interview in terms of the type of answers you will receive. The perk of being able to plan a interview with the questions that seem most appropriate. A researcher can also write better more clearer reports with interview data because of the detailed data obtained (GOA, 1991).
There are some disadvantages as well when it comes to interviews. It is mostly connected to the pre phase before actually doing the interview. It can be hard getting a proper time and place to actually have a decent interview. There is also the possibility to miss some in-formation i.e. forgetting to ask some questions or running out of time. Interviews can be time consuming especially in the analyze phase as it is very time consuming to code the da-ta and transcribe it properly (Saunders et al., 2007).
The interviews we have done in this study has been done in the public sector. Our initial approach was to send out as many request as possible to known IT departments. We got answer from Jönköpings kommun and after the first interview with their IT portfolio man-ager Svein Lister we decided to narrow our scope to the public sector. We then got in con-tact with another person in Jönköpings kommun that worked closely with our first contact. Lastly we did get in contact with a manager at Domstolsverket. Both of these public sector organizations have a big IT infrastructure and many customers to maintain.
All the interviews was semi-structured interview with some questions we had in mind be-fore, but there was always room for discussion and follow up questions. Two of the inter-views were done in English and one interview was done in Swedish. The interviews was recorded and the content shown in the thesis is approved. The interview transcription can be seen in the Appendix.
Deduction is what you can call scientific research. It is often called the testing theory, be-cause it involves having a specified theory or hypothesis that explains what is expected from the research. Deduction is commonly expressed by five stages developed by (Robson, 2002):
- Deducting a hypothesis
- Expressing the hypothesis in operational terms so you can measure results.
- Testing the hypothesis
- Examining the results of the test
- If needed, modify the theory
There are many ways to do deductive research. It is very important that the researcher is independent of what is being observed to have good reliability and be critical to the re-search (Saunders et al., 2007).
Induction compared to deduction is where the researcher builds own theory. For example going through series of interviews and observation on a sample to build a theory on a spe-cific area. The collected data would be the ground to the analysis that you go through as a researcher in an inductive approach. Inductive research is often more appropriate on a smaller sample size because of the observation and concentrated research of individuals or small groups of people is well suited for an inductive approach (Saunders et al., 2007).
In this thesis we will have an inductive approach as we are doing interviews on a smaller sample size of organizations. There are many researchers that use an inductive research be-cause of their usage of different collecting methods and in that sense inductive approach is likely a better choice than deduction is in that scenario (Saunders et al., 2007).
For this thesis we will be using an inductive approach as we intend to form our own theory based on the data collecting we will conduct. As our main data collection technique will be interviews it is best that we have an open mind towards the results and observation from the collecting.
The analysis technique is an important part as it is the method which you get your results. There are many ways to analyze data. In our thesis we are focusing on to explore and find out something new to contribute with. There are other ways as confirmatory that has as a goal to confirm an already stated theory. To our qualitative research we are doing an ex-ploratory analysis (Saunders et, al. 2007).
We will use data reduction and data display analysis in our thesis. Data reduction is an anal-ysis technique that takes the collected amount of data and reduces the amount to meaning-ful data that is significantly smaller. The theory is to quickly reduce the data to the most important categories and concepts of an interview or an observation for example. Apart from reducing the data we want to display is using data display where the reduced data is visualized and organized to be easier to read and understand. This method will make it eas-ier to draw conclusions from the data obtained. The advantage being that we save time when doing our analysis (Yin, 2010).
Important for us is that the analysis will take everything said in the interviews in considera-tion when doing the data reduction. It is crucial that we do not miss anything, especially when we the time frame is limited we want to get as much good data out as possible under a short period of time.
1.4 Research questions
2 Theoretical framework
2.1 Project management
2.2 Project closure
2.3 Project ending competencies
2.4 Project closing activities
2.5 Factors affecting project closure
2.6 Private versus public project handling
3.1 Qualitative vs quantitative
3.2 Reliability & validity
3.3 Data collection
3.4 Research approach
3.5 Research analysis
4.2 Findings from interview
5.1 Research questions revisited
5.2 Project closure model
6.1 Results discussion
6.2 Methods discussion
6.3 Implications for research
6.4 Implications for practice
6.5 Further research
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Closing IT projects A Swedish public sector perspective