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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">PBES</journal-id><journal-title-group><journal-title>Proceedings of Business and Economic Studies</journal-title></journal-title-group><issn>2209-2641</issn><eissn>2209-265X</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/pbes.v7i4.8056</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Intelligent Manufacturing Engineers’ Knowledge Transfer and Innovation Capability: From the Perspective of Big Data Acceptance Attitude</title><url>https://artdesignp.com/journal/PBES/7/4/10.26689/pbes.v7i4.8056</url><author>XiaoYan</author><pub-date pub-type="publication-year"><year>2024</year></pub-date><volume>7</volume><issue>4</issue><history><date date-type="pub"><published-time>2024-08-26</published-time></date></history><abstract>In the face of intelligent manufacturing (or smart manufacturing) human resource shortage, the training of industrial engineers in the field of intelligent manufacturing is of great significance. In academia, the positive link between learning transfer and knowledge innovation is recognized by most scholars, while the learner’s attitude toward big data decision-making, as a cognitive perception, affects learning transfer from the learner’s experienced engineering paradigm to the intelligent manufacturing paradigm. Thus, learning transfer can be regarded as a result of the learner’s attitude, and it becomes the intermediary state between their attitude and knowledge innovation. This paper reviews prior research on knowledge transfer and develops hypotheses on the relationships between learner acceptance attitude, knowledge transfer, and knowledge innovation.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Mingyue Talk, 2024, Artificial Intelligence will affect 60% of Jobs in the United States and Exacerbate Global Inequality. 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