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As Glaser predicted, the extra time involved in open coding full interviews, rather than
coding just the important concepts, was substantial (ranging from 40 to 60 hours each
for the first few one-hour interviews to eight to 20 hours each for the last few). However,
this also allowed me to relive the interviews and the detailed analysis helped me to acquire
a deeper understanding of the issues. This understanding facilitated the emergence by
discovery of the core concept and made me, the researcher, more comfortable with the
coding activity.
It is probable that without recording and coding literal transcriptions I could have saved
some time; however, listening to the actors often triggered theoretical memos and facil-
itated the finding of relations - therefore, it was a productive activity, not a wasteful
one. Moreover, listening and reading the interviews matched my cognitive style and
therefore facilitated emergence.
While I found re-listening to the interviews and analysing the full text very rewarding
and interesting, it must be recognised that Glaser is correct in his assertions - neither
recording nor taking extensive notes are necessary activities for conceptualisation.
Nevertheless, not recording is too risky a strategy for a PhD student to follow. Above
and beyond fulfilling the need for evidence in a PhD study by recording and transcribing
interviews, researchers can revisit and re-code text as more evidence emerges and patterns
are detected. The ability to have access to the full transcription and to replay the inter-
view at any time is a distinct advantage, especially in studies of organisational cases that
are conducted over a long period of time, at different points in the life cycle of the ana-
lysed phenomena. In any case, the iterative nature of grounded theory demands the
constant comparison of incidents with already collected data. In doing this, previously
undetected incidents are likely to emerge. These new incidents benefit the study and
therefore justify the extra effort required to record, transcribe, and code potentially ir-
relevant data.
Using qualitative data coding tools in GTM research
Glaser (1998, pp. 185-6) also alerts against the 'technological traps' of data analysis tools
such as NVivo or ATLAS.ti because they create unnecessary restrictions, inhibit the
researcher's development of skills and impose time-consuming learning curves. Glaser
perceives computing technology as an easy way out and as a hindrance rather than an
aid to creativity. This is not my experience. Yet computing tools can be used in many
ways and some of those ways will indeed have the negative consequences Glaser has
mentioned.
Using ATLAS.ti in my study for open coding and memoing was a substantial advantage.
It provided a fast way of checking and comparing incidents and the flexibility of export-
ing data to other tools as I perceived appropriate. The software's ability to collect memos
allowed the efficient writing, analysis, and retrieval of memos at any time in the process.
It is also true that ATLAS.ti was not everything I needed. I used additional techniques
and tools: butcher's paper and a whiteboard to draw box diagrams representing the in-
terrelation of emerging concepts; notepads and flowcharting software to draw many
diagrams; a word processor to combine and analyse sets of incidents and memos; and
mind-mapping software (MindManager) to organise ideas and themes.
Therefore, Glaser is correct in asserting that this is creative work, yet the generalisation
that technology restricts creativity was falsified by this study's experience, as people
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