The in-depth interview

The purpose of the in-depth semi-structured interview was to uncover the IS competencies and IS capabilities for preventive care and to find out the mechanism whereby these IS resources were being used – specifically how the resources are structured, bundled and leveraged for preventive care. The in-depth interview was held in two phases with 40 participants from the two clinical departments – surgical and ophthalmology and three non-clinical departments – a dietetic unit, physiotherapy unit and health-education unit resulting in a total of 60 interviews (refer Table 3.6). This research ended up with a total of 40 participants that represented everyone in these departments and was sufficient because this is the number when saturation or redundancy is reached (Merriam 2009). Moreover, the number of participants exceeded the minimum level number of 15 participants generally recommended by the SPS approach, thus avoiding criticism and biased reporting (Pan & Tan 2011). The interviews were conducted in two-rounds with a total of 60 interviews. First-round interviews were conducted from 22 August 2016 until 4 November 2016. The number of interviews conducted was 42. A semi-structured interview was conducted using English and Malay. All interviews were audio recorded and later transcribed and coded manually. Second-round interviews were conducted from 16 February 2017 to 11 March 2017 for follow ups and triangulation, with 18 interviews as shown in Table 3.6. The follow-up interviews with the same participants served as multiple sources of evidence, thus increasing the validity of the data (refer to Section 3.7.1).

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Table 3.6: Summary of the participants, their roles and the number of interviews conducted

Role Number of

participants

First-round interview Second-round interview Number of interviews Number of follow-up interviews Number of interviews Number of follow-up interviews Dietician 1 1 – – 1 Physiotherapist 5 5 – 3 Pharmacist 4 4 1 1 Doctors: Consultant 3 1 – 2 – Senior 4 3 - 1 - Junior 5 5 - 1 Nurses: Sister 4 3 1 1 1 Senior 3 3 1 - 2 Junior 5 5 - - - Counsellor – Diabetic clinic 1 1 2 2 Counsellor – Quit- smoking clinic 1 – – 1 1 Head of IT Department 1 1 – – – Head of Education Department 1 1 1 – 1 Gatekeeper 1 1 1 – – IT officer 1 1 – – – Total 40 35 7 5 13 Total interviews 42 18

Data-analysis procedures and methods

Data analysis is a process of making sense out of data to find answers to the research questions (Merriam 2009). The data analysis is conducted together with the process of data collection (Pan & Tan 2011). In this research, I used data analysis to develop categories and themes to interpret the meaning of the data. The data analysis was an interactive framing cycle between eventual case data, theoretical lens and models (Pan & Tan 2011). This, in turn, provided the answers to the research question. This research adopted a five-phase cycle from Yin (2016) to perform the data analysis. I used Yin’s structured analysis phase because this phase helped me to continuously concentrate on the link between data collection and the research question (Pan & Tan

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2011; Yazan 2015). Moreover, Yin suggests that there is a need for the case study researcher to plan for data-analysis steps as this element is ‘one of the least developed aspects’ in producing analytic result (Yin 2014, p. 133). The following are Yin’s five analysis phases: 1. compile database 2. disassemble data 3. reassemble data 4. interpret data 5. conclude

Phase 1 started with data compilation and sorting. Compilation is defined as a technique to organise or arrange data and notes in a useful manner. The completed compilation is termed a database. In this research, several files were created in the database, consisting of interview data and other related information (see Figure 3.4). The purpose of the compiling phase is to become refamiliarised with, and review, the data and field notes, and re-read the transcripts and re-listen to the audio record.

Figure 3.4: Example of folders for compiling database

Phase 2, disassembling data, involved dividing the compiled interview data into smaller pieces. During this phase, codes or labels were assigned to the pieces of data, and this process was repeated many times. The codes or labels were also refined several times. I utilised two levels of codes assigned to the data. Level 1 codes or open codes were formed from the participant’s transcripts.

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I used two types of Level 1 codes as a technique to assign codes that can be applied by a beginner researcher. They are in-vivo and holistic codes (Saldana 2013), which help to find Level 2 codes. In this research, the in-vivo code is referred to the key word that originally comes from the participant’s responses. The following is a sample of how in-vivo code that is peri team was assigned to the interview data transcribed:

Dr H #22: Okay, let say the patient is in the medical unit and suddenly the patient has bleeding from inside stomach, so we have to refer to the surgical department. We will talk to their medical officer, present the patient case and request for a special team to come and help us examine this patient in the medical unit. We call them peri team.

There are also other types of coding used for analysing data, such as holistic coding. The holistic code is formed by choosing several lines or a paragraph from the participant’s responses as a code. The following is an example of assigning a holistic code to the interview data:

Nurse Sf #17: We have great tasks and are very close to our patients. We are like middle person between the doctor and the patients. Patients are comfortable to tell us about their condition.

Level 2 codes are achieved when the coding proceeds to a higher set of codes that belong to the same group or categories that are called category codes (Merriam 2009; Yin 2016). For example, in this research, after several codes were assigned to the data, the assigned codes that belonged to similar groups were placed under a category. This phase can go back and forth while compiling the data in Phase 1. Figure 3.5 illustrates finding Level 2 codes from Level 1 codes.

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Figure 3.5: Example of assigning Level 1 codes to find Level 2 codes

Phase 3, reassembling data, involves finding patterns from the codes and categories. From a group of codes (Level 1 codes), a few categories (Level 2 codes) are developed, which are then given themes. Themes are a higher conceptual phase, also known as Level 3 and Level 4 codes. Thus, finding the themes for this research required reassembling or searching for patterns. The reassembling procedure included reorganising and recombining pieces of data into different groupings. The

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data were rearranged and recombined graphically, or by grouping or displaying them in the form of lists and tables, such as tables to reassemble data (see Figure 3.6) to develop themes. This phase can be repeated several times in turn with disassembling data in Phase 2. The following is a sample of a table to reassemble data to find themes, showing an example for Theme 1.

Figure 3.6: Sample of table to reassemble data to find patterns for theme development

Phase 4, interpreting the reassembled data, is supported by lists and tables. In this phase, the database can be recompiled (Phase 1), disassembled (Phase 2) or reassembled (Phase 3). For this research, I used analytic techniques, such as explanation building and logic models (Yin 2012), and analytic display methods such as a matrix display (see Appendix 11) and a table of resource mapping (see Appendix 12) (Miles, Huberman & Saldana 2014) to facilitate an overall view of how resources, IS competencies and IS capabilities are developed for preventive care.

These techniques and methods are used as the tools to interpret and write up the research findings in Phase 5, which involves concluding the entire research.

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Data validity and reliability

There are numerous ways to evaluate and enhance the rigour of overall qualitative research quality provided by methodologists (Creswell 2014; Merriam 2009; Yin 2014). This research followed Yin’s (2014) specific tactics, systematically provided in each phase of the research, which helped me develop a rigorous and robust case. Yin (2014) suggests four tests to establish research validity and reliability – construct validity, internal validity, external validity and reliability. Table 3.7 presents the tactics used in this research for dealing with these four tests.

Table 3.7: Data validity and reliability (Yin 2014)

Test Case study tactics The phase of research in which

tactics occurs

Construct validity Use multiple sources of evidence Establish a chain of evidence Have a key informant to review draft case study report

Data collection Data collection Composition

Internal validity Do explanation building Use logic models

Data analysis Data analysis External validity Use theory in single case study Research design Reliability Use case study protocols

Develop a case study database

Data collection Data collection