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UID:pretalx-2026-THR3GA@conferences.acspri.org.au
DTSTART;TZID=AEST:20261125T161500
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DESCRIPTION:A social licence to operate (SLO) refers to the ongoing accepta
 nce of organisations and their activities by communities and the wider pub
 lic. Moving beyond basic regulatory compliance and evolving into broader s
 ocial accountability\, this concept helps to explain why and how communiti
 es come to approve or reject the organisations that affect them. However\,
  applying a framework of social licence used in one industry to another pr
 esents a methodological challenge: identifying the common drivers of accep
 tance while adapting the approach and interpretation to the local\, social
 \, and institutional contexts that are unique to each industry and communi
 ty.\n\nDrawing on a methodology developed in CSIRO\, Australia’s nationa
 l science agency\, and then advanced and operationalised by Voconiq for th
 e extractive industries\, this presentation outlines how social research c
 an be integrated with data science to measure and track SLO across differe
 nt sectors. The method combines intentional survey design\, repeated measu
 rement\, reproducible data processing\, and theory-driven path modelling. 
 Path analyses examine relationships between trust\, acceptance\, perceived
  impacts\, fairness\, and industry responsiveness (among other factors)\, 
 while qualitative comments provide an additional lens into community exper
 ience. This approach allows for the recurring versus circumstantial predic
 tors of social performance to be systematically explored. \n\nIncorporatin
 g close to a decade of longitudinal data\, this presentation highlights th
 e Australian egg industry’s community research program as an applied cas
 e study. A core set of survey questions has enabled comparison of national
  attitudes and perceptions of the egg industry over 9 years\, using repres
 entative adult samples. In 2026\, industry responsiveness ranked first amo
 ng the modelled drivers of trust\, while clear labelling was the strongest
  predictor of industry support when considering the relative costs and ben
 efits of its operation. Findings from this body of work illustrate how a c
 onceptual framework first applied in mining can guide research in other in
 dustries to understand the shared and distinct relationships underpinning 
 social performance.\n\nAs part of the case study’s discussion\, particul
 ar attention goes to sampling and recruitment\, measurement comparability\
 , longitudinal inference\, and interpretation of path models within their 
 broader setting. Overall\, we demonstrate how social science theory and co
 mputational methods can combine to compare SLO across contexts\, distingui
 shing drivers of community trust and acceptance that are broadly transfera
 ble from those that provide unique community insights.
DTSTAMP:20261013T070134Z
LOCATION:Cullen Room
SUMMARY:Measuring Social Licence Across Contexts: Integrating Data Science 
 with Social Research Theory - Djamila Eliby\, PhD
URL:https://conferences.acspri.org.au/2026/talk/THR3GA/
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