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similarities and differences and identify gaps. Thematic analysis
provides a summarisation of key information, providing clear

structured data analysis. However, Holloway & Todres, (2003) stated

that thematic analysis flexibility could potentially lead to
inconsistencies when defining themes.

Thematic grouping – gathering and grouping relevant subjects into

themes derived from transcript coding shown in table 18, supported by
the conceptual framework (figure 1). This is supported by the

association between the identified interview coding (from transcripts)

and the conceptual framework (shown in table 3).

After conducting the interviews, the author created a set of transcripts

for each interview participant (shown in tables 19, 20, 21, 22). Then,

the transcripts were used to create a theme coding key table (shown

in table 18). A table for each participant was created, categorising the
findings against the identified coded themes, shown in tables: 4,5,6,7.

The values in tables 4,5,6,7 was applied against the values with the

conceptual framework (shown in figure 1); creating a new table to

summarise the codified interview findings against the conceptual
framework (shown in table 8). The latter shows the overall

participants’ application of BD, AI and analytics by codified interview

findings.

4.3. Thematic analysis

The author will classify the findings from the transcripts by theme for

each participant, selecting the best quotes from each participant to
justify the interview findings.

Participant 1 from Italy: The supporting transcript for Italy participant

1 (shown on table 4) stated “they do not believe AI could be applied in
Italy”. However, Italy participant 1 indicated that the participants’

organisation applied tools that incorporate both AI, BD and analytics:

applications mainly within fertigation, viticulture, marketing and retail

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