{"id":{"repo_id":"otago-polytech","oai_identifier":"oai:researchbank.ac.nz:10652/7191"},"canonical_url":"https://search.dev.ndltd.org/etd/otago-polytech/oai:researchbank.ac.nz:10652/7191","repository":{"repo_id":"otago-polytech","name":"Otago Polytechnic","base_url":"https://www.researchbank.ac.nz/server/oai/request"},"display":{"title":"The AI revolution: Investigating how small and medium-sized enterprises (SMEs) are adapting to Artificial Intelligence (AI) in the manufacturing sector in New Zealand","abstract":"Small and medium-sized enterprises (SMEs) are the backbone of New Zealand’s economy and dominate the manufacturing sector, yet evidence on how they are adopting Artificial Intelligence (AI) remains limited. Focusing on the two largest subsectors, Food & Beverage and Machinery & Equipment, this thesis examines how these businesses are adapting to AI technologies, where AI is being applied, what drives its implementation, what barriers exist, and which strategies enable an effective transition. Using an exploratory mixed-methods design, this study primarily combines on qualitative evidence from semi-structured interviews with 10 manufacturing SME leaders and 3 AI consultants, complemented by quantitative insights from an online survey completed by 23 respondents. The literature review and analysis, grounded in the Technology–Organisation–Environment (TOE) framework, integrate current adoption practices and perspectives among New Zealand manufacturing SMEs and AI consultants. Findings indicate that adoption is at an early stage, with use concentrated in administrative processes, marketing, quality related tasks. Constraints encompass knowledge gaps, limited digital capacity and awareness, and overall low organizational readiness. Enablers include capability building, leadership, expert guidance, supportive policy and ecosystem factors. Actionable recommendations are offered for decision-makers to guide firm-level practice and national initiatives aimed at strengthening SMEs’ resilience through transformative AI adoption. The study also provides one of the first empirical accounts within the academic literature of AI adoption among New Zealand manufacturing SMEs, while extending the TOE framework to smaller, resource-constrained contexts. Limitations include a small sample drawn from two of seven manufacturing subsectors, reliance on self-reported data, and a focus on New Zealand, which may minimize generalisability to other settings.","abstract_html":"Small and medium-sized enterprises (SMEs) are the backbone of New Zealand’s economy and dominate the manufacturing sector, yet evidence on how they are adopting Artificial Intelligence (AI) remains limited. Focusing on the two largest subsectors, Food & Beverage and Machinery & Equipment, this thesis examines how these businesses are adapting to AI technologies, where AI is being applied, what drives its implementation, what barriers exist, and which strategies enable an effective transition. Using an exploratory mixed-methods design, this study primarily combines on qualitative evidence from semi-structured interviews with 10 manufacturing SME leaders and 3 AI consultants, complemented by quantitative insights from an online survey completed by 23 respondents. The literature review and analysis, grounded in the Technology–Organisation–Environment (TOE) framework, integrate current adoption practices and perspectives among New Zealand manufacturing SMEs and AI consultants. Findings indicate that adoption is at an early stage, with use concentrated in administrative processes, marketing, quality related tasks. Constraints encompass knowledge gaps, limited digital capacity and awareness, and overall low organizational readiness. Enablers include capability building, leadership, expert guidance, supportive policy and ecosystem factors. Actionable recommendations are offered for decision-makers to guide firm-level practice and national initiatives aimed at strengthening SMEs’ resilience through transformative AI adoption. The study also provides one of the first empirical accounts within the academic literature of AI adoption among New Zealand manufacturing SMEs, while extending the TOE framework to smaller, resource-constrained contexts. Limitations include a small sample drawn from two of seven manufacturing subsectors, reliance on self-reported data, and a focus on New Zealand, which may minimize generalisability to other settings.","abstract_has_math":false,"creators":["Canon Iriarte, Alexia Gabriel"],"institution":"Otago Polytechnic Auckland International Campus","degree_name":"Master of Applied Management","degree_level":"Masters","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025","date_published":"2025","updated_at":"2026-07-27T20:28:17Z","subjects":["Artificial intelligence","SMEs","manufacturing","New Zealand","technology-organisation-environment framework"],"languages":["en"],"rights":["CC BY-NC-ND Attribution-NonCommercial-NoDerivs 4.0 International"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10652/7191","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Canon Iriarte, Alexia Gabriel"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-04-08T00:08:58Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-04-08T00:08:58Z"]},{"key":"dc:date.issued","label":"Date","values":["2025"]},{"key":"dc:type","label":"Dc Type","values":["Masters Thesis"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Management"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Otago Polytechnic Auckland International Campus"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Artificial intelligence","SMEs","manufacturing","New Zealand","technology-organisation-environment framework"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["CC BY-NC-ND Attribution-NonCommercial-NoDerivs 4.0 International"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10652/7191"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Small and medium-sized enterprises (SMEs) are the backbone of New Zealand’s economy and dominate the manufacturing sector, yet evidence on how they are adopting Artificial Intelligence (AI) remains limited. Focusing on the two largest subsectors, Food & Beverage and Machinery & Equipment, this thesis examines how these businesses are adapting to AI technologies, where AI is being applied, what drives its implementation, what barriers exist, and which strategies enable an effective transition. Using an exploratory mixed-methods design, this study primarily combines on qualitative evidence from semi-structured interviews with 10 manufacturing SME leaders and 3 AI consultants, complemented by quantitative insights from an online survey completed by 23 respondents. The literature review and analysis, grounded in the Technology–Organisation–Environment (TOE) framework, integrate current adoption practices and perspectives among New Zealand manufacturing SMEs and AI consultants. Findings indicate that adoption is at an early stage, with use concentrated in administrative processes, marketing, quality related tasks. Constraints encompass knowledge gaps, limited digital capacity and awareness, and overall low organizational readiness. Enablers include capability building, leadership, expert guidance, supportive policy and ecosystem factors. Actionable recommendations are offered for decision-makers to guide firm-level practice and national initiatives aimed at strengthening SMEs’ resilience through transformative AI adoption. The study also provides one of the first empirical accounts within the academic literature of AI adoption among New Zealand manufacturing SMEs, while extending the TOE framework to smaller, resource-constrained contexts. Limitations include a small sample drawn from two of seven manufacturing subsectors, reliance on self-reported data, and a focus on New Zealand, which may minimize generalisability to other settings."]},{"key":"dc:title","label":"Title","values":["The AI revolution: Investigating how small and medium-sized enterprises (SMEs) are adapting to Artificial Intelligence (AI) in the manufacturing sector in New Zealand"]}]}],"canonical_facts":{"dc:creator":["Canon Iriarte, Alexia Gabriel"],"dc:date.accessioned":["2026-04-08T00:08:58Z"],"dc:date.available":["2026-04-08T00:08:58Z"],"dc:date.issued":["2025"],"dc:description.abstract":["Small and medium-sized enterprises (SMEs) are the backbone of New Zealand’s economy and dominate the manufacturing sector, yet evidence on how they are adopting Artificial Intelligence (AI) remains limited. 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Constraints encompass knowledge gaps, limited digital capacity and awareness, and overall low organizational readiness. Enablers include capability building, leadership, expert guidance, supportive policy and ecosystem factors. Actionable recommendations are offered for decision-makers to guide firm-level practice and national initiatives aimed at strengthening SMEs’ resilience through transformative AI adoption. The study also provides one of the first empirical accounts within the academic literature of AI adoption among New Zealand manufacturing SMEs, while extending the TOE framework to smaller, resource-constrained contexts. 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