DOI: 10.5281/zenodo.7381974
title: LiftWEC Social Acceptability Dataset

description: This document contains LiftWEC’s social acceptability dataset. The dataset consists of interview transcripts sourced from semi-structured discussions conducted by a LiftWEC researcher and a range of stakeholders relevant to the field of marine renewable energy production. Semi-structured interviews were conducted with relevant actors between September and November of 2021. A snowball sampling approach was used for the selection of actors to be interviewed. Beginning with a small population of socio-political actors, this study developed a larger sample by learning from initial participants and identifying others who were relevant to the study. Interviewees were drawn up through a process of mapping, ensuring that a variety of actors holding different roles and located in different European nations engaged with the study and provided insight. Participants were then selected based upon the likelihood of having a detailed understanding of the emerging problems confronting marine renewable energy. Informed by literature, it was decided to categorise actors within three distinct profiles; (i) <em>socio-political actors</em>, (ii) <em>market actors</em>, and (iii) <em>community actors</em>. Three examples of interview transcripts, one from an actor relating to each of the aforementioned profiles, are presented in this dataset. The interviews were designed from the outset to allow for the analysis of debate regarding the social acceptance of novel and emerging marine renewable energy. Interviewees were prompted to discuss their perceptions of the current challenges and opportunities facing marine renewable energy technologies and their experience of how social acceptance issues are managed, and provide recommendations for the future. To support the free development and uptake of individual opinions from a variety of stakeholders, interviews were conducted on a one-to-one basis. A semi-structured interview guideline – an example is presented in section 3 of this dataset – was developed to gather data regarding the specific research objectives of the study. The interview guidelines helped to ensure comparability across interviews, especially across different countries and contexts. The questions that are part of the guideline are open questions, i.e., interview partners did not have fixed options for answering them. This provided interviewees with the possibility of freely choosing which aspect they wanted to put an emphasis or which aspects they wanted to mention. Furthermore, semi-structured interviews enabled the interviewer to spontaneously rephrase or add questions if the answers provided by the interviewee left too much room for interpretation or were not fully clear. All interviews lasted for a duration of between 40 minutes and one hour. This study that these interviews spawned from was conducted in line with the guidelines and standards set by the Queen’s University of Belfast’s Code of Conduct and Integrity in Research and its Policy and Principles on the Ethical Approval of Research. Free and informed consent was obtained from all participants prior to the collection of data from online interviews. All interviewees were provided with a project information sheet and a consent form prior to meeting, and participants were fully briefed on what the research involves. It was also explained how anonymity and confidentiality will be achieved. Permission was also sought for the audio of the meetings to be recorded and participants were made aware of their right to withdraw within one month of data gathering without penalty. Consent was also obtained for the data to be used for research purposes and for future publication. Confidentiality, a hugely important consideration in research, was ensured at all times during the course of the research.
description: This dataset is associated with LiftWEC Deliverable LW-D09-03: Scoping report of the social acceptance of LiftWEC. This work was produced as part of the LiftWEC Project. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 851885. This output reflects the views only of the author(s), and the European Union cannot be held responsible for any use which may be made of the information contained therein.