There are two important concepts behind collecting data. The first one data sources and second is the method for generating results from those sources (Mason, 1996). The data gathering for the interpretivism research paradigm is a communication procedure between the researcher and the participants (Fellows and Liu, 2003) The key factor of the data collection technique is the nature of the request and the data required in regard to a certain setting or context (Naoum, 2013). Therefore, different techniques might be appropriate to different methods and inquiries. This research identified people as data sources due to their knowledge, evidence and experience. However, there are many approaches for generating data from those people, such as interviews, questionnaires and observations (Saunders et al., 2009). As discussed earlier, the method of this research is mixed-method, implying a method which could collect understandings, opinions, interpretations and ideas of people who have been involved with construction analysis and design. Mason (1996) suggested four techniques for data gathering in interpretivism research: interview, observation, the use of documents and the use of visual data. However, the combination of personal interviews and a questionnaire was suggested by Naoum (2013) as the method that offers the best technique for understanding a participant’s opinion.
The epistemological position of this research suggests that the logical way to generate data is by interacting with experienced people. This research seeks to generate data from those people’s experiences in their current or past organisations, how they interpret the relationship between BIM tools and structural engineering design can improve the level of information quality regarding the development of the BIM concept. This research in the first step of the data collection process utilised semi-Structured interviews to collect qualitative data. The researcher conducted in-depth investigation into the issues and expected explanations and descriptions to match the key elements derived from the interviewees. The interview was deigned to allow the participants to exercise total control over the process in order to prevent bias as much as possible. The researcher prepared some questions for the interview and interviewees are free to mention their opinion in more depth when essential.
For conducting a piece of qualitative research the number of interviews is often a dilemma for researchers. The answer is dependent upon methodological aspect of research and the nature of research questions. Therefore to decide how many qualitative interviews is enough the researcher has to explore the purpose of his study by taking into consideration to this fact
98 “saturation is central to qualitative sampling” (Baker, 2012). This research in qualitative data
collection has adopted “non-probability” sampling which is appropriate for qualitative data collection and understands the deeply social phenomena. The interviewee’s have different background, years of experience and position in organisations (See table 4-3) and the central of qualitative sampling in this research relied on saturation of the responses. The number of interviews was continued to 12 when the researcher achieved saturation point, due to which the last interviewee’s responses were merely a repetition of the previous interviewees.
4.6.1 Qualitative Data Analysis Technique
This research adopted “content analysis” (Robson, 2002) as a technique to enable the researcher to identify keywords and the meaning of text in the context of information management challenges. According to Bryman (2004), content analysis is a technique “for the analysis of texts that seeks to quantify content in terms of predetermined categories and in a systematic and replicable manner”. The qualitative content analysis can provide codes for the data; those codes can be developed from the classification of texts into topics, themes or concepts. The contents came from communication between researcher and experts so that this research could apply qualitative content analysis in order to study the meaning of communication. Holsti (1969) classified content analysis into three fundamental categories: 1- Formulate inferences about the antecedents of the texts, 2- Describe the characteristics of the communication and 3- Describe the effect of the communication.
As has been mentioned before, interviews with experts were employed to determine the key challenges of interoperability during the design phase. Qualitative data analysis in this research used NVivo 10 software to collect, manage and represent the interview findings to achieve meaning. At the initial stage of the qualitative data analysis, the interviews were transcribed from an audio format into text for analysis and in the next stage the collected data was categorised into meaningful classification. The key words scanned from the text collected from expert interviews were used for the analysis based on the research’s questions, aim and objectives. The next section discussed quantitative data collection and analysis techniques which are adopted in survey research method.
Through the case study this research expected firstly, to explore more data about challenges in the context of UK-based structural engineering organisations. Secondly, the case study is expected to investigate the level of BIM adoption in UK-based structural organisations.
99 Finally, the impact of implementing BIM and information quality is also expected to be examined. The outcomes contribute to this research to identify the conceptual tags of final conceptual framework. The conceptual tags are the key criteria that structural engineering organisations need to consider to adopt efficient tools, workflows standards and strategies for human resource readiness, to enhance the quality of information. However the results from case study alone cannot be the only evidence to support the conceptual framework. The case study collected data only from large structural engineering organisations with specific capacities. Therefore the survey study employed after the case study collected data from other structural engineering firms with different capabilities and discussion between findings from case study and survey creates strong evidence to support the conceptual framework in this research.