{"id":{"repo_id":"otago-polytech","oai_identifier":"oai:null:10652/6445"},"canonical_url":"https://search.dev.ndltd.org/etd/otago-polytech/oai:null:10652/6445","repository":{"repo_id":"otago-polytech","name":"Otago Polytechnic","base_url":"https://www.researchbank.ac.nz/server/oai/request"},"display":{"title":"Exploring the role of AI in enhancing sustainability within New Zealand's hospitality industry: A study on knowledge, applicability, and perception in reducing food waste","abstract":"The hospitality industry in New Zealand is a noteworthy contributor towards the country’s Gross Domestic Product, however, it is also responsible for a significant amount of food waste (FW). In recent years, there has been a growing interest globally in artificial intelligence (AI) to curb FW in the hospitality industry. This study explores the role of AI in reducing FW and enhancing sustainability within New Zealand's hospitality industry, specifically, it focuses on the factors critical for its implementation - knowledge, attitudes, and perceptions of hospitality stakeholders. The study adopted a mixed-methods approach involving quantitative and qualitative data collection from 131 industry professionals. The data suggests that the sample is skewed towards the lower end of FW, requiring caution due to inconsistent self-reported data with audits (Chisnall, 2017). A quarter were unsure or found it difficult to estimate avoidable food waste (AFW), potentially indicating a lack of awareness or difficulty measuring FW accurately. Inaccurate demand forecasting is the primary cause of FW in catering services and cafes/coffee shops, while portion control and plate waste are cited as the main causes of FW in fine-dining restaurants and pubs. A lack of FW awareness is the leading cause of FW in hotels/resorts and fast-food outlets. The diverse range of challenges faced by the hospitality industry provides insights into the areas where improvements can be made to increase efficiency, reduce waste, and enhance customer satisfaction. The study found a general lack of awareness and knowledge about AI among hospitality professionals, yet an openness to adopting AI-driven technologies for FW reduction. Challenges such as cost-effectiveness and proven effectiveness are key causes hindering AI adoption. Increased awareness and promotion of AI's return on investment could decrease scepticism and facilitate effective integration into FW reduction strategies. Finally, this research underlines the need for collaborative efforts among industry professionals, policymakers, and technology developers to overcome existing hurdles and leverage AI for sustainable practices in the hospitality sector in New Zealand.","abstract_html":"The hospitality industry in New Zealand is a noteworthy contributor towards the country’s Gross Domestic Product, however, it is also responsible for a significant amount of food waste (FW). In recent years, there has been a growing interest globally in artificial intelligence (AI) to curb FW in the hospitality industry. This study explores the role of AI in reducing FW and enhancing sustainability within New Zealand's hospitality industry, specifically, it focuses on the factors critical for its implementation - knowledge, attitudes, and perceptions of hospitality stakeholders. The study adopted a mixed-methods approach involving quantitative and qualitative data collection from 131 industry professionals. The data suggests that the sample is skewed towards the lower end of FW, requiring caution due to inconsistent self-reported data with audits (Chisnall, 2017). A quarter were unsure or found it difficult to estimate avoidable food waste (AFW), potentially indicating a lack of awareness or difficulty measuring FW accurately. Inaccurate demand forecasting is the primary cause of FW in catering services and cafes/coffee shops, while portion control and plate waste are cited as the main causes of FW in fine-dining restaurants and pubs. A lack of FW awareness is the leading cause of FW in hotels/resorts and fast-food outlets. The diverse range of challenges faced by the hospitality industry provides insights into the areas where improvements can be made to increase efficiency, reduce waste, and enhance customer satisfaction. The study found a general lack of awareness and knowledge about AI among hospitality professionals, yet an openness to adopting AI-driven technologies for FW reduction. Challenges such as cost-effectiveness and proven effectiveness are key causes hindering AI adoption. Increased awareness and promotion of AI's return on investment could decrease scepticism and facilitate effective integration into FW reduction strategies. Finally, this research underlines the need for collaborative efforts among industry professionals, policymakers, and technology developers to overcome existing hurdles and leverage AI for sustainable practices in the hospitality sector in New Zealand.","abstract_has_math":false,"creators":["Perera, Melanie"],"institution":"Otago Polytechnic | Te Pūkenga","degree_name":"Master of Applied Management","degree_level":"Masters","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024","date_published":"2024","updated_at":"2026-07-27T20:28:40Z","subjects":["sustainability","hospitality industry","food waste","artificial intelligence","perception","New Zealand"],"languages":["en"],"rights":["This thesis is publicly available under a Creative Commons Attribution-NonCommercial-NoDerivatives licence CC BY-NC-ND 4.0 International. This licence applies except where otherwise indicated, especially for images. https://creativecommons.org/licenses/by-nc-nd/4.0/"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10652/6445","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Perera, Melanie"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-08-15T23:14:40Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-08-15T23:14:40Z"]},{"key":"dc:date.issued","label":"Date","values":["2024"]},{"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 | Te Pūkenga"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["sustainability","hospitality industry","food waste","artificial intelligence","perception","New Zealand"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["This thesis is publicly available under a Creative Commons Attribution-NonCommercial-NoDerivatives licence CC BY-NC-ND 4.0 International. 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The study adopted a mixed-methods approach involving quantitative and qualitative data collection from 131 industry professionals. The data suggests that the sample is skewed towards the lower end of FW, requiring caution due to inconsistent self-reported data with audits (Chisnall, 2017). A quarter were unsure or found it difficult to estimate avoidable food waste (AFW), potentially indicating a lack of awareness or difficulty measuring FW accurately. Inaccurate demand forecasting is the primary cause of FW in catering services and cafes/coffee shops, while portion control and plate waste are cited as the main causes of FW in fine-dining restaurants and pubs. A lack of FW awareness is the leading cause of FW in hotels/resorts and fast-food outlets. The diverse range of challenges faced by the hospitality industry provides insights into the areas where improvements can be made to increase efficiency, reduce waste, and enhance customer satisfaction. The study found a general lack of awareness and knowledge about AI among hospitality professionals, yet an openness to adopting AI-driven technologies for FW reduction. Challenges such as cost-effectiveness and proven effectiveness are key causes hindering AI adoption. Increased awareness and promotion of AI's return on investment could decrease scepticism and facilitate effective integration into FW reduction strategies. Finally, this research underlines the need for collaborative efforts among industry professionals, policymakers, and technology developers to overcome existing hurdles and leverage AI for sustainable practices in the hospitality sector in New Zealand."]},{"key":"dc:title","label":"Title","values":["Exploring the role of AI in enhancing sustainability within New Zealand's hospitality industry: A study on knowledge, applicability, and perception in reducing food waste"]}]}],"canonical_facts":{"dc:creator":["Perera, Melanie"],"dc:date.accessioned":["2024-08-15T23:14:40Z"],"dc:date.available":["2024-08-15T23:14:40Z"],"dc:date.issued":["2024"],"dc:description.abstract":["The hospitality industry in New Zealand is a noteworthy contributor towards the country’s Gross Domestic Product, however, it is also responsible for a significant amount of food waste (FW). In recent years, there has been a growing interest globally in artificial intelligence (AI) to curb FW in the hospitality industry. This study explores the role of AI in reducing FW and enhancing sustainability within New Zealand's hospitality industry, specifically, it focuses on the factors critical for its implementation - knowledge, attitudes, and perceptions of hospitality stakeholders. The study adopted a mixed-methods approach involving quantitative and qualitative data collection from 131 industry professionals. The data suggests that the sample is skewed towards the lower end of FW, requiring caution due to inconsistent self-reported data with audits (Chisnall, 2017). A quarter were unsure or found it difficult to estimate avoidable food waste (AFW), potentially indicating a lack of awareness or difficulty measuring FW accurately. Inaccurate demand forecasting is the primary cause of FW in catering services and cafes/coffee shops, while portion control and plate waste are cited as the main causes of FW in fine-dining restaurants and pubs. A lack of FW awareness is the leading cause of FW in hotels/resorts and fast-food outlets. The diverse range of challenges faced by the hospitality industry provides insights into the areas where improvements can be made to increase efficiency, reduce waste, and enhance customer satisfaction. The study found a general lack of awareness and knowledge about AI among hospitality professionals, yet an openness to adopting AI-driven technologies for FW reduction. Challenges such as cost-effectiveness and proven effectiveness are key causes hindering AI adoption. Increased awareness and promotion of AI's return on investment could decrease scepticism and facilitate effective integration into FW reduction strategies. Finally, this research underlines the need for collaborative efforts among industry professionals, policymakers, and technology developers to overcome existing hurdles and leverage AI for sustainable practices in the hospitality sector in New Zealand."],"dc:identifier.uri":["https://hdl.handle.net/10652/6445"],"dc:language.iso":["en"],"dc:rights":["This thesis is publicly available under a Creative Commons Attribution-NonCommercial-NoDerivatives licence CC BY-NC-ND 4.0 International. This licence applies except where otherwise indicated, especially for images. https://creativecommons.org/licenses/by-nc-nd/4.0/"],"dc:subject":["sustainability","hospitality industry","food waste","artificial intelligence","perception","New Zealand"],"dc:title":["Exploring the role of AI in enhancing sustainability within New Zealand's hospitality industry: A study on knowledge, applicability, and perception in reducing food waste"],"dc:type":["Masters Thesis"],"thesis:degree_level":["Masters"],"thesis:degree_name":["Master of Applied Management"],"thesis:institution_name":["Otago Polytechnic | Te Pūkenga"]},"updated_at":"2026-07-27T20:28:40Z"}