{"id":864603,"date":"2022-07-25T13:29:01","date_gmt":"2022-07-25T20:29:01","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/"},"modified":"2022-07-25T13:31:32","modified_gmt":"2022-07-25T20:31:32","slug":"mis-quarterly","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/mis-quarterly\/","title":{"rendered":"MIS Quarterly"},"content":{"rendered":"
Researchers employ methodologies that rely on contemporary technologies to study phenomenon. Recent advances in artificial intelligence (AI), particularly machine learning (ML), have intensified the speed, and our abilities, to create and deploy new knowledge for constructing theories (Abbasi et al. 2016). The availability of big data and ML tools is no longer the sole domain of academics. Business processes generate large amounts of data and now practitioners also deploy ML enabled methodologies to create new knowledge through working theories.<\/p>\n","protected":false},"excerpt":{"rendered":"
Researchers employ methodologies that rely on contemporary technologies to study phenomenon. Recent advances in artificial intelligence (AI), particularly machine learning (ML), have intensified the speed, and our abilities, to create and deploy new knowledge for constructing theories (Abbasi et al. 2016). The availability of big data and ML tools is no longer the sole domain 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