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for the BD, AI and analytics, the author will probe the SME to identify
the reasons for not applying relevant tools. This method will provide

the author with the specific application (of BD, AI and analytics) within

the wine life cycle stages, providing gap analysis whereby the author
believes that the respective organisation could potentially apply BD, AI

and analytics related applications. The data gathered from the

interview findings will be evaluated using a thematic analysis within

the interview transcript to identify any common occurring themes.

After conducting the interviews, the author will create a high-level view

table (for each participant) showing the application of BD, AI and

analytics tools against the wine life cycle stages. See tables 10, 11,
12, 13, 14, 15, 16,17).

The author will apply the conceptual model classified by themes to

represent the wine life cycle stages, with each theme identifying the
application of BD, AI and analytics applied tools. The author will

analyse the interview gathered data (transcripts for each participant,

see table 4, 5, 6 7), codify the identified themes (table 19,20,21,22)

compose a consolidate a table to show the codified interview themes
against the literature view (as per the conceptual model), shown in

table 8. The latter will display the interview codified findings to identify

the BD, AI and analytics applications against the conceptual model
themes (classed by BD, AI and analytics) and define gaps. Then, the

author will tag the interview codified analysis against the thematic

literature review showing further analysis as to the contribution of BD,

AI and analytics related tools. This will highlight potential areas that
wine organisations need to focus their effort in applying such tools to

ensure growth and streamline their operations.

3.8. *taken out before sharing*

3.9. *taken out before sharing*

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