Page 10 - To share_Farrugia_Dissertation_Summary_

The author selected a qualitative research and designed a set of

specific questions supported by findings through the literature review

(grouped by theme and by BD, AI and analytics within the wine
industry life cycle stages). The author selected a sample breadth of 60

wine related organisations from Kent, UK (stratified by acreage size)

and selected another set of various 180 wine related organisations

from Tuscany in Italy.

Italy is renowned for its wine, whilst the UK growing wine market is

still in its infancy but growing rapidly. Most wine related organisations

cover the full wine life cycle (vineyard management up to and including
the sales). The author contacted the UK wine related organisations’

SMEs via email and phone calls. In contrast, the author contacted the

Italy wine related organisations SMEs via email and social media.

A set of questions were designed (figure 12) to probe SMEs as the basis

for the interviews. A set of questions were adapted based on the

participants response, adding questions/change question sequence as

required (as not all wine organisations apply BD, AI and Analytics
applications).

The author conducted in-dept interviews with the UK and Italy
participants to identify the application of BD, AI and analytics with the

wine industry life cycle stages. The author also explored the lack of

BD, AI and analytics related applications, educating the SMEs with the

potential application of BD, AI and analytics, to identify where the SME
would potential apply of BD, AI and analytics related applications.

Upon gathering the information, the author classified the responses

against the framework composed from the literature review (shown in

table 10, 11, 12, 13, 14, 15, 16, 17 in the appendix), highlighting the
actual and potential BD, AI and analytics related applications, as shown

in the Data Analysis section.

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