Interview Data Analysis

Referring to Figure 3.1, six phases of thematic analysis were used for the interview data analysis in this study. Each of these phases is explained in this section.

3.6.1.1 First phase: Familiarising the data

In the first phase, interview transcription, reading and re-reading of the data, and formulating initial ideas were undertaken. The researcher transcribed the first interview immediately after its completion to ensure that the memory was fresh with its content and assist in making sense of the interview (Gillham, 2005). Moreover, it also gave the researcher enough opportunity to engage in self-reflection on the

interview results and think about strategies to effectively elicit answers from next participants (McMillan & Schumacher, 2010). The rest of the interview data was transcribed by professional transcribers.

As the present research involved the use of English and Indonesian, it was necessary to translate the data. All the Indonesian data were translated into English following the back-translation procedure (Liamputtong, 2010). Two sworn-translators were employed. One translated from Indonesian to English and the other subsequently translated back the data from English to Indonesian without knowing the original Indonesian version. The researcher, who is an Indonesian-English bilingual and was previously a professional translator, then compared the back-translation result with the original version in Indonesian. It was found that the comparability was 94.69% at the sentence level. After the translation process, there were in total 140,453 words in the interview transcripts. The data analysis was then conducted on the English version of the transcripts considering that the analysis result had to be finally presented in English. As suggested by McMillan and Schumacher (2010), the interview data were read in entirety before initiating coding to let interconnections of ideas shape in the researcher’s mind. Figure 3.2 graphically illustrates the progression of the coding process from Phase 2 to the identification of patterns in Phase 5. Each box denotes the analysis result from each phase and the arrows show the analysis progression to the next phase.

Figure 3.2. Progression of coding process based on the data analysis phases.

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3.6.1.2 Second phase: Generating initial codes

In the second phase, initial coding was conducted. The researcher coded the data with a word or phrase that described and captured its essence (McMillan &

Schumacher, 2010). Both manual and computer-assisted coding were utilised. In the first coding attempt, each possible line of the interview transcript was manually coded as computer-assisted coding could constrain the options for marking up a text (Rapley, 2011). The researcher utilised a-priori codes (see Table 3.6) only as far as the data supported them. Besides using the a-priori codes, the manual coding also generated 141 inductive codes. Figure 3.3 is an example of manual coding from IU-B Faculty Officer 1 extract about the benefits of DDP. The extract was coded with initials or word at the right-hand margin. A-priori codes were indicated by the initials of CB, CUR, ITS, EK, and UP. There was also an inductive code: accreditation.

Figure 3.3. Manual coding example.

For the computer-assisted coding, the a-priori codes were used. The manual coding result was not consulted while conducting the computer-assisted coding in order to maintain the independence of the second analysis. New inductive codes were then produced from this exercise, resulting in 48 inductive codes. Figure 3.4 presented an example of codes used in the NVivo analysis, also from IU-B Faculty Officer 1. The a-priori codes were curriculum (green strip), explicit knowledge (orange strip), capacity development purpose (pink strip), and ICT-mediated communication (blue strip). Inspiration (purple strip) was an inductive code. As noted by Gomm (2004), a first attempt with coding may result in numerous codes, but many can be merged to form more general themes as explained in the next phase.

Figure 3.4. NVivo coding example.

3.6.1.3 Third phase: Searching for themes

In the third phase, the various codes were organised into several themes. For the manual coding results, to expedite the overall thematic analysis, the manual inductive codes and extracts were transferred to NVivo. The manually-generated codes were then analysed together with the NVivo-based codes to generate themes.

At the beginning of the third phase, there were 189 inductive codes (i.e., 141 manual inductive codes and 48 NVivo-based inductive codes).

Developing themes in NVivo was done by separating major vs. minor codes and significant vs. insignificant codes (McMillan & Schumacher, 2010). Identifying major and minor codes was done with the assistance of NVivo to compute the number of sources (i.e. research participants) and references (i.e. interview extracts) of each code. From the 189 inductive codes, 62 codes were considered minor and insignificant as they were applied to data extracts from single participants. As some inductive codes carried similar ideas with the existing a priori codes, 35 of these inductive codes were merged with the a-priori codes. This is symbolised with a green arrow connecting the inductive codes and the a-priori codes in Figure 3.2.

The inductive codes also showed some evidence of rival explanation (Yin, 2014). Twenty six inductive codes carried information about participants’ views that did not support the theoretical framework. These codes were grouped together and were designated as X codes in NVivo. For example, data extracts showing that there was no revenue purpose for establishing DDP were coded as XRR and those showing

no ramp-up stage were coded as XRUS, hence the designation as X codes. The result of collating all of these X codes were then taken into account when reviewing themes and generating patterns in Phase 4.

Among the remaining inductive codes, there were similarities and overlaps in their extracts. Thus these similar codes were merged with each other, and the codes that had the most number of sources and references as well as pertinent to the theoretical framework were considered significant. As a result of this internal merger between the inductive codes, there were 13 significant inductive codes that were included in further analysis for theme identification. Combining the significant inductive codes with the a-priori codes, there were 48 significant codes used as the basis for generating themes. Subsequently, there were 12 potential themes identified and they are listed in Table 3.8 with the supporting codes originating from the inductive codes italicised.

Table 3.8 Potential Themes and Supporting Codes in Phase 3

Potential themes Supporting significant codes

Purposes for establishing DDP CP, IP, RP, International accreditation, Student Benefits and disadvantages of DDP CB, IB, RB, D, Halo effect

Commodification of HE REC, MD, SUB, Financial matters, Government, Lowering standards

Managerialism EC, ET, TD

Set-up of programs SU, History, Job description

Structured KT processes INS, IMS, RUS, ITS, Knowledge management, Lecturer, Research Unstructured KT processes AK, FK, CK

KT mechanisms ICT, FTF

Types of knowledge TK, EK

Inter-university dynamics TR, RM, UP, Culture, Factors influencing KT Intention and orientation COO, COM, INT

Units of analysis CUR, TA, PBA, MAR

3.6.1.4 Fourth phase: Reviewing the themes

To refine the potential themes in the fourth phase, it was necessary to examine which themes were more prominent than the others by following two levels of reviewing and refining the themes. In the first level, the coded data extracts for each potential theme were reviewed again to examine whether or not they formed a coherent pattern. During the process, inconsistencies were identified and addressed, resulting in further mergers of some significant codes and themes. For example, the codes lecturer and student were too general and did not address the research questions well. The extracts under these codes were subsequently coded with other more significant codes. The units of analysis were excluded from the codes and themes in this first level of analysis as from the methodological perspective, their function was to bring more focus to the case study, quite different from the other

themes which reported the participants’ views. The separation of units of analysis from the rest of the codes and themes is depicted with a unidirectional arrow from the major-significant codes box in Figure 3.2. In the subsequent phase, these units of analysis were analysed in light of the refined themes, codes, and research questions to generate patterns. The themes and codes were then organised visually into temporary thematic maps to see the interconnections between the themes until a satisfactory result was achieved (Creswell, 2007; Johnson & Christensen, 2008).

In the second level, the validity of the refined potential themes and the temporary thematic maps was examined against the interview data set and the documents. Re-reading the whole interview data was undertaken to ascertain whether or not the identified themes were applicable for the entire data, not only the selected coded extracts. Moreover, particular attention was given to the grouping of participants based on the three levels of university structure. For example, the entire interview data sets were read to examine the salience and relevance of the theme of purposes for establishing DDPs across the three groups of participants at the three participating universities to provide a richer understanding of the distinct views held by the participants at university-faculty-school levels between and within the universities. The coding process was repeated when the potential themes and thematic map did not adequately match the entire data set (Braun & Clarke, 2006).

As for the document data analysis, the refined themes and codes were used to code the documents and find support for these themes and codes. The refined themes were then re-aligned with the research questions and theoretical framework to examine differences and similarities between the theoretical framework and the resulting themes, and ensure that the themes addressed the research questions.

At this fourth phase, contrary evidence and other plausible patterns were addressed (McMillan & Schumacher, 2010). As noted earlier, codes that did not support the theoretical framework (X Codes) were not discarded as they could be a basis for rival explanations. Acknowledging these rival explanations is important to build credibility of the themes and patterns (Creswell, 2012). For instance, extracts from 19 participants were coded with no KT code. Based on these extracts, the Indonesian universities did not seek KT in regards to the teaching-learning approach because they claimed to have applied teaching-learning approaches that were comparable with their Australian counterpart. Such rival explanation was taken into

account in the analysis of the research findings to establish the credibility of the case study (Yin, 2014), and ensure that the final result was not only based on evidence supportive of the theoretical framework. At the end of phase four, the number of significant codes was reduced to 20 major-significant codes and the potential themes to 7 major-significant themes as shown in Figure 3.2.

3.6.1.5 Fifth phase: Defining and naming themes

In the fifth phase, upon completion of a satisfactory thematic map, the 7 major-significant themes were clearly defined. The essence of each theme was identified, and each theme was assigned its permanent name (Guest, MacQueen, & Namey, 2012). Similarly, the 20 major-significant codes were defined and their relationships to the overarching theme were clarified. The definitions for the codes and themes can be found in Appendix B. Afterwards, data extracts that were coded as units of analysis were examined again in relation to the research questions, major-significant themes, and codes. Overlaps between data extracts coded with the themes, codes, and the units of analysis were used in this examination. For example, concerning the Curriculum unit of analysis, the researcher re-examined how the purposes of establishing DDP impacted the process of DDP curriculum development, and how the KT process was enacted in regards to curriculum knowledge. Evidence in the documents and interview transcripts stating that curriculum KT benefited Indonesian universities was sought. This was repeated for the remaining units of analysis and conclusions were drawn about KT in each unit, paying attention to the similarities and differences in the KT processes between IU-A and IU-B with AU. In the previous phases of the data analysis, the data from IU-A, IU-B, and AU as the sub-elements of the case study were analysed together to find the most salient themes and codes in the single case study. Upon identification of the major-significant themes and codes, distinctions between these participating universities were taken into account starting from this stage to provide detailed descriptions of the distinct KT that took place between the different Indonesian universities with their common Australian partner. Based on the codes, themes, research questions, and units of analysis, five patterns of findings were generated. Figure 3.5 in the next page presents the final thematic map depicting the progression from data extracts to codes, themes, and patterns, taking into account the units of analysis.

4. Knowledge acquisition did not materialise for in some units of analysis because of adequacy of existing knowledge and miscommunication problems (RQ 3).

5. Universities selectively utilised the acquired tacit and explicit knowledge in accordance with the local context and integrated that knowledge with the support of internal knowledge management (RQ 4).

1. Indonesian universities unstructured KT process to acquire tacit and explicit knowledge by means of complementary use of soft and hard KT mechanisms, as supported by positive inter-university dynamics and intention to acquire knowledge (RQ 3).

Units of Analysis

The 20 major significant codes were shown by the coloured boxes at the centre of the thematic map in Figure 3.5. They were applied on the supporting excerpts, which were not depicted in the thematic map given the high number of these excerpts. Relevant codes were grouped under the overarching themes that were depicted with seven bold-faced boxes above the codes. These themes were connected with arrows to the units of analysis to signify that the conclusions taken from the data analysis results for each theme were re-examined in light of the units of analysis as explained in the preceding paragraph. From the re-examination of the themes and the units of analysis, five patterns emerged and they are represented by the five boxes at the top of the thematic map. Each pattern addresses a pertinent research question.

Further analyses on how the research questions are related to the patterns, themes, and units of analysis can be found in the Findings Chapter (see Section 4.4).

3.6.1.6 Sixth phase: Producing case study

At the sixth phase, a comprehensive description of each unit of analysis and the case as a whole was generated (Braun & Clarke, 2006). Relevant extracts from the interview and document data were chosen to show the essence of a pattern or theme that was being explained (Creswell, 2012). Comparisons and contrasts of how IU-A and IU-B conducted KT with AU in regards to the four units of analysis were also provided, leading to final conclusions of how KT processes took place among these Indonesian universities with their Australian DDP partner. As the explanation of the data analysis phases thus far has emphasised on the interview data, the focus now turns to how the document data analysis was done to corroborate the findings from the interviews.