FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3037880470> ?p ?o ?g. }

• W3037880470 endingPage "105" @default.
• W3037880470 startingPage "91" @default.
• W3037880470 abstract "This article discusses the pitfalls and opportunities of AI in marketing through the lenses of knowledge creation and knowledge transfer. First, we discuss the notion of “higher-order learning” that distinguishes AI applications from traditional modeling approaches, and while focusing on recent advances in deep neural networks, we cover its underlying methodologies (multilayer perceptron, convolutional, and recurrent neural networks) and learning paradigms (supervised, unsupervised, and reinforcement learning). Second, we discuss the technological pitfalls and dangers marketing managers need to be aware of when implementing AI in their organizations, including the concepts of badly defined objective functions, unsafe or unrealistic learning environments, biased AI, explainable AI, and controllable AI. Third, AI will have a deep impact on predictive tasks that can be automated and require little explainability, we predict that AI will fall short of its promises in many marketing domains if we do not solve the challenges of tacit knowledge transfer between AI models and marketing organizations." @default.
• W3037880470 created "2020-07-02" @default.
• W3037880470 creator A5017837767 @default.
• W3037880470 creator A5051548449 @default.
• W3037880470 creator A5060761466 @default.
• W3037880470 creator A5072741569 @default.
• W3037880470 creator A5083338507 @default.
• W3037880470 date "2020-08-01" @default.
• W3037880470 modified "2023-10-10" @default.
• W3037880470 title "Artificial Intelligence and Marketing: Pitfalls and Opportunities" @default.
• W3037880470 cites W1001370459 @default.
• W3037880470 cites W1901616594 @default.
• W3037880470 cites W1982052652 @default.
• W3037880470 cites W1988198833 @default.
• W3037880470 cites W2008653298 @default.
• W3037880470 cites W2013418317 @default.
• W3037880470 cites W2015122554 @default.
• W3037880470 cites W2017561954 @default.
• W3037880470 cites W2021074941 @default.
• W3037880470 cites W2025726856 @default.
• W3037880470 cites W2029869759 @default.
• W3037880470 cites W2050263393 @default.
• W3037880470 cites W2073186871 @default.
• W3037880470 cites W2082360792 @default.
• W3037880470 cites W2088731121 @default.
• W3037880470 cites W2091025306 @default.
• W3037880470 cites W2097192888 @default.
• W3037880470 cites W2111837988 @default.
• W3037880470 cites W2132454116 @default.
• W3037880470 cites W2167605048 @default.
• W3037880470 cites W2257979135 @default.
• W3037880470 cites W2295797531 @default.
• W3037880470 cites W2403097949 @default.
• W3037880470 cites W2435892841 @default.
• W3037880470 cites W2471138382 @default.
• W3037880470 cites W2487586139 @default.
• W3037880470 cites W2550925836 @default.
• W3037880470 cites W2790974113 @default.
• W3037880470 cites W2793560204 @default.
• W3037880470 cites W2797763839 @default.
• W3037880470 cites W2806410959 @default.
• W3037880470 cites W2894528744 @default.
• W3037880470 cites W2899856450 @default.
• W3037880470 cites W2912380945 @default.
• W3037880470 cites W2915955953 @default.
• W3037880470 cites W2922129458 @default.
• W3037880470 cites W2942858056 @default.
• W3037880470 cites W2945976633 @default.
• W3037880470 cites W2963095307 @default.
• W3037880470 cites W2963849010 @default.
• W3037880470 cites W2995289083 @default.
• W3037880470 cites W3016964845 @default.
• W3037880470 cites W3026440221 @default.
• W3037880470 cites W3027672271 @default.
• W3037880470 cites W3027937825 @default.
• W3037880470 cites W3121632328 @default.
• W3037880470 cites W3123754374 @default.
• W3037880470 cites W4238354545 @default.
• W3037880470 cites W4238485280 @default.
• W3037880470 cites W4245888765 @default.
• W3037880470 cites W2023355288 @default.
• W3037880470 doi "https://doi.org/10.1016/j.intmar.2020.04.007" @default.
• W3037880470 hasPublicationYear "2020" @default.
• W3037880470 type Work @default.
• W3037880470 sameAs 3037880470 @default.
• W3037880470 citedByCount "114" @default.
• W3037880470 countsByYear W30378804702020 @default.
• W3037880470 countsByYear W30378804702021 @default.
• W3037880470 countsByYear W30378804702022 @default.
• W3037880470 countsByYear W30378804702023 @default.
• W3037880470 crossrefType "journal-article" @default.
• W3037880470 hasAuthorship W3037880470A5017837767 @default.
• W3037880470 hasAuthorship W3037880470A5051548449 @default.
• W3037880470 hasAuthorship W3037880470A5060761466 @default.
• W3037880470 hasAuthorship W3037880470A5072741569 @default.
• W3037880470 hasAuthorship W3037880470A5083338507 @default.
• W3037880470 hasBestOaLocation W30378804701 @default.
• W3037880470 hasConcept C108583219 @default.
• W3037880470 hasConcept C119857082 @default.
• W3037880470 hasConcept C150899416 @default.
• W3037880470 hasConcept C154945302 @default.
• W3037880470 hasConcept C2522767166 @default.
• W3037880470 hasConcept C2779561248 @default.
• W3037880470 hasConcept C41008148 @default.
• W3037880470 hasConcept C50644808 @default.
• W3037880470 hasConcept C56739046 @default.
• W3037880470 hasConcept C81363708 @default.
• W3037880470 hasConceptScore W3037880470C108583219 @default.
• W3037880470 hasConceptScore W3037880470C119857082 @default.
• W3037880470 hasConceptScore W3037880470C150899416 @default.
• W3037880470 hasConceptScore W3037880470C154945302 @default.
• W3037880470 hasConceptScore W3037880470C2522767166 @default.
• W3037880470 hasConceptScore W3037880470C2779561248 @default.
• W3037880470 hasConceptScore W3037880470C41008148 @default.
• W3037880470 hasConceptScore W3037880470C50644808 @default.
• W3037880470 hasConceptScore W3037880470C56739046 @default.
• W3037880470 hasConceptScore W3037880470C81363708 @default.
• W3037880470 hasLocation W30378804701 @default.

• next