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http://hdl.handle.net/10603/598809
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DC Field | Value | Language |
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dc.coverage.spatial | ||
dc.date.accessioned | 2024-10-30T10:09:54Z | - |
dc.date.available | 2024-10-30T10:09:54Z | - |
dc.identifier.uri | http://hdl.handle.net/10603/598809 | - |
dc.description.abstract | In recent years, technological changes and advancements have forced Fast Moving Consumer Goods (FMCG) industries, especially food processing, to redesign their functionality. This includes the integration of technologies like Artificial Intelligence (AI) to enhance performance. Future trends in the food processing industry will be shaped by sustainability, efficiency, traceability, wellness, safety, hygiene, health, and newlinetransparency. Food processing industries are compelled to embrace digitalization in the newlinecurrent era of globalization and digital transformation. AI encompasses programs, newlinealgorithms, robotics, drones, data mining, cloud computing, sensors, driver-less newlinevehicles, the internet of things, digital platforms, and machines, representing a new newlinelevel of intelligence. AI aims to replicate human reasoning and problem-solving newlinecapabilities, leading to task automation, increased efficiency, and reduced human newlineeffort. The growth of AI is reshaping the food processing industry, with potential newlineapplications spanning from cultivation, supply chain management, storage and safety, newlineHuman Resource Management (HRM), and Customer Relationship Management (CRM). Integrating and adopting AI in food processing can address unique challenges and offer substantial benefits across these functions. While large-scale food processing newlineindustries have made significant progress in adopting AI systems, small and mediumscale food industries are also integrating AI technology. The current research study employs a quantitative research methodology and obtained data from 320 small and medium-scale food processing industries employees in the city of Bengaluru. The primary surveyed data were analyzed using the Structural Equation Modeling (SEM) approach through AMOS 26. The research used the UTAUT 2 model to measure the usage and adoption of Artificial Intelligence (AI) among the employees of small and medium-scale food processing industries. | |
dc.format.extent | xviii, 214p.; | |
dc.language | English | |
dc.relation | 301 | |
dc.rights | university | |
dc.title | Adoption and usage of artificial intelligence in food processing industries | |
dc.title.alternative | ||
dc.creator.researcher | Marvin, Paul Frank | |
dc.subject.keyword | Artificial Intelligence, | |
dc.subject.keyword | Automation, Robotics. | |
dc.subject.keyword | Business | |
dc.subject.keyword | Economics and Business | |
dc.subject.keyword | Food Processing Industry, | |
dc.subject.keyword | Social Sciences | |
dc.subject.keyword | UTAUT 2, | |
dc.description.note | ||
dc.contributor.guide | George, Ginu | |
dc.publisher.place | Bangalore | |
dc.publisher.university | CHRIST University | |
dc.publisher.institution | Department of Commerce | |
dc.date.registered | 2020 | |
dc.date.completed | 2024 | |
dc.date.awarded | 2024 | |
dc.format.dimensions | A4 | |
dc.format.accompanyingmaterial | None | |
dc.source.university | University | |
dc.type.degree | Ph.D. | |
Appears in Departments: | Department of Commerce |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 182.91 kB | Adobe PDF | View/Open |
02_prelim pages.pdf | 888.18 kB | Adobe PDF | View/Open | |
03_abstract.pdf | 156.29 kB | Adobe PDF | View/Open | |
04_table_of_contents.pdf | 156.08 kB | Adobe PDF | View/Open | |
05_chapter1.pdf | 698.59 kB | Adobe PDF | View/Open | |
06_chapter2.pdf | 309.54 kB | Adobe PDF | View/Open | |
07_chapter3.pdf | 188.96 kB | Adobe PDF | View/Open | |
08_chapter4.pdf | 840.51 kB | Adobe PDF | View/Open | |
09_chapter5.pdf | 267.79 kB | Adobe PDF | View/Open | |
10_annexures.pdf | 2.47 MB | Adobe PDF | View/Open | |
80_recommendation.pdf | 450.09 kB | Adobe PDF | View/Open |
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