[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124953-en":3,"doc-seo-124953-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124953,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Machine Learning Based Decision-Making - A Sensemaking Perspective","Machine learning (ML) powers AI-enabled organizational systems that automate decisions or support decision-makers, yet how decision-makers meaningfully engage with ML-driven insights remains insufficiently explained. This study examines the interactive process between decision-makers and ML experts as they interpret environments and assemble business intelligence for action. Building on Weick’s sensemaking model, interviews with 31 ML experts and end-users identify sensemaking dimensions in ML utilization, resulting in a structured ML-driven sensemaking process model.","Machine Learning Based Decision-Making: ASensemaking Perspective  \nJingqi (Celeste) Li  \nThe University of Queensland, Brisbane, Queensland, Australia  \nMorteza Namvar  \nThe University of Queensland, Brisbane, Queensland, Australia [m.namvar@business.uq.edu.au](m.namvar@business.uq.edu.au)  \nGhiyoung P. Im  \nUniversity of Louisville, Louisville, Kentucky, USA  \nSaeed Akhlaghpour  \nThe University of Queensland, Brisbane, Queensland, Australia  \nAbstract  \nThe integration of machine learning (ML), functioning as the core of various artificial intelligence (AI)-enabled systems in organizations, comes with the assertion that ML models offer automated decisions or assist domain experts in refining their decision-making. The current research presents substantial evidence of ML’s positive impact on business and organizational performance. Nonetheless, there is a limited understanding of how decisionmakers participate in the process of generating ML-driven insights and enhancing their comprehension of business environments through ML outcomes. To enhance this engagement and understanding, this study examines the interactive process between decision-makers and ML experts as they strive to comprehend an environment and gather business insights for decision-making. It builds upon Weick’s sensemaking model by integrating ML’s pivotal role. By conducting interviews with 31 ML experts and ML end-users, we explore the dimensions of sensemaking in the context of ML utilization for decision-making. Consequently, this study proposes a process model which advances the organizational ML research by operationalizing Weick’s work into a structured ML-driven sensemaking model. This model charts a pragmatic pathway, outlining the interaction sequence between decision-makers and ML tools as they navigate through recognizing and utilizing ML, exploring opportunities, assessing ML model outcomes, and translating ML models into action, thereby advancing both the theoretical framework and its practical deployment in organizational contexts.  \nKeywords: Machine Learning (ML), decision-making, sensemaking.  \n1 Introduction  \nArtificial Intelligence (AI) is reshaping the foundation of business operations, fundamentally altering how companies operate and compete, as it facilitates smart services and automates tasks traditionally carried out by humans (Cui et al., 2022). Machine Learning (ML) serves asthe driving force for decision-making in a variety of AI systems (Namvar et al., 2022) . By training on vast datasets, learning intricate patterns, and generating predictive models, these AI systems autonomously analyze new data, recognize trends, and make accurate decisions based on their ‘learned knowledge’. As an illustration, ML is widely used for targeting prospective customers (Simester et al., 2020), making inventory replenishment decisions (M. Li & Li, 2022), or predicting and selecting hedge fund returns (Wu et al., 2021) . Decisions  \nstemming from AI systems, where insights are derived from applying ML to large datasets, might introduce an entirely innovative approach to solving business problems (van den Broek et al., 2021) . Consequently, both practitioners and researchers have emphasized the necessity of further research on the utilization of ML in organizational decision-making procedures (Enholm et al., 2021) .  \nUnlike conventional decision support tools, ML is distinguished by its unique characteristics (Collins et al., 2021). ML systems are noted for their advanced learning capacity (van den Broek et al., 2021), self-sufficiency (Teodorescu et al., 2021), and a remarkable level of opacity (Lebovitz et al., 2021), which sets them apart even from other intelligent technologies (Baird & Maruping, 2021) . These attributes contribute not only to ML’s effectiveness but also present certain challenges. For instance, the opacity of ML systems can raise issues regarding transparency and accountability in decision-making (de Laat, 2018) leading to potent","cbCaihqOEhZxf9lC","https://ap.wps.com/l/cbCaihqOEhZxf9lC","pdf",1758050,1,22,"English","en",105,"# Introduction\n## Background: ML-enabled decision-making in organizations\n## Distinctive characteristics and challenges of ML\n## Need for expert–end-user collaboration","[{\"question\":\"What problem does the study address about ML-driven decision-making?\",\"answer\":\"It addresses the limited understanding of how decision-makers participate in generating ML-driven insights and developing comprehension of business environments through ML outcomes.\"},{\"question\":\"How does the study relate its work to Weick’s sensemaking model?\",\"answer\":\"It builds on Weick’s sensemaking model by integrating ML’s pivotal role and operationalizing it into a structured ML-driven sensemaking process.\"},{\"question\":\"What data and participants does the study use?\",\"answer\":\"The study conducts interviews with 31 ML experts and ML end-users to explore sensemaking dimensions in ML utilization for decision-making.\"}]","Machine Learning Based Decision-Making - A Sensemaking Perspective | PDF",1785895578,55,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-based-decision-making-a-sensemaking-perspective","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-decision-making-a-sensemaking-perspective/124953/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address about ML-driven decision-making?","Question",{"text":75,"@type":76},"It addresses the limited understanding of how decision-makers participate in generating ML-driven insights and developing comprehension of business environments through ML outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study relate its work to Weick’s sensemaking model?",{"text":80,"@type":76},"It builds on Weick’s sensemaking model by integrating ML’s pivotal role and operationalizing it into a structured ML-driven sensemaking process.",{"name":82,"@type":73,"acceptedAnswer":83},"What data and participants does the study use?",{"text":84,"@type":76},"The study conducts interviews with 31 ML experts and ML end-users to explore sensemaking dimensions in ML utilization for decision-making.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]