Methods to Improve Our Field:
Offering innovative ideas that explore how strategy and management methodology can be developed, Methods to Improve Our Field considers approaches that range from the re-imagining of secondary data in the digital age and Interpretive Phenomenological Analysis (IPA) to Machine Learning and Artificial...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Bingley
Emerald Publishing Limited
2023
|
| Ausgabe: | 1st ed |
| Schriftenreihe: | Research Methodology in Strategy and Management Series
v.14 |
| Schlagworte: | |
| Online-Zugang: | DE-2070s |
| Zusammenfassung: | Offering innovative ideas that explore how strategy and management methodology can be developed, Methods to Improve Our Field considers approaches that range from the re-imagining of secondary data in the digital age and Interpretive Phenomenological Analysis (IPA) to Machine Learning and Artificial Intelligence |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (193 Seiten) |
| ISBN: | 9781804553664 |
Internformat
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| 505 | 8 | |a Cover -- METHODS TO IMPROVE OUR FIELD -- RESEARCH METHODOLOGY IN STRATEGY AND MANAGEMENT -- METHODS TO IMPROVE OUR FIELD -- Copyright -- CONTENTS -- ABOUT THE EDITORS -- ABOUT THE CONTRIBUTORS -- Introduction to Methods to Improve Our Field -- IntroductionNTRODUCTION -- VolumeOLUME ContributionsONTRIBUTIONS -- ClosingLOSING ReflectionsEFLECTIONS -- ReferencesReferences: -- Reimagining Ssecondary Ddata in a Ddigital Aage -- Abstract -- Introduction -- Troublesome Ttypologies -- Secondary Ddata as Rreuse -- Beyond Rreuse: Rreimagining Ssecondary Ddata in a Ddigital Aage -- Critical Challenges and Considerations in Uusing Ddigital Mmethods -- Acknowledgements -- ReferencesReferences: -- Insights Ffrom the Application of Interpretative Phenomenological Analysis in Management Research -- Abstract -- Introduction -- Understanding Interpretative Phenomenological Analysis -- The IPA Process -- Sampling and Data Collection -- Data Analysis -- Demonstrating Research Quality -- Conclusion -- Note -- References -- SynthesizingYNTHESIZING BestEST PracticesRACTICES forFOR ConductingONDUCTING DictionaryICTIONARY-BbasedBASED ComputerizedOM ... -- Abstract -- TheHE ComputerOMPUTER-AaidedAIDED TextextEXT AnalysisNALYSIS LandscapeANDSCAPE -- KeyeyEY ConsiderationsONSIDERATIONS forFOR ConductingONDUCTING DBCTA ResearchesearchESEARCHey -- First Phase: Study Design and Text Collection -- Second Phase: Text Normalization -- Third Phase: Dictionary-BbBased Computerized Text Analysis -- Fourth Phase: Interpreting and Publishing Results -- DiscussionISCUSSION -- Notes -- ReferencesReferences: -- Using Mixed- Effect Growth Models tTo Examine Time aAs aA Predictor oOf Interest aAnd Between-Firm Differences OoOver Time -- Abstract -- GrowthROWTH ModelsODELS -- What Aare Growth Models? -- Illustrative Panel Data | |
| 505 | 8 | |a Initial Models: Comparing GLS to Multilevel Models of Time as a Linear Growth Trend -- Using Multilevel Growth Models to Tests Between- Firm Effects on Differences in Firm Slopes -- DiscontinuousISCONTINUOUS GrowthROWTH ModelsODELS -- Leveraging the Discontinuous Growth Models to Test Variations in Rates of Change -- Using the DGM to Test Between- Firm Effects on Differences in Time, Trans, and Post Slopes -- Theoretical and Methodological Implications for Strategic Management Research -- Conclusion -- Notes -- ReferencesReferences: -- Garbage in, Garbage out: A Theory-Driven Approach to Improve Data Handling in Supervised Machine Learning -- Abstract -- Introduction -- Machine Learning and Measurement -- Broad Approaches to ML and Typical ML Tasks -- Distinguishing ML From Statistical Modeling -- Common ML Algorithms -- Applying ML in Strategy Research -- A Theory-Driven Approach to Supervised ML -- Preimplementation Data Handling -- Step 1: Establish the Relevance of ML for the Focal Construct -- Step 2: Establish the "Ground Truth" of the Training Data -- Step 3: Apply Theoretical Prepruning -- ML Implementation -- Step 4: Select Appropriate ML Techniques for the Focal Construct -- A Demonstration Using McCLelland's Needs -- ML as a Method to Measure CEOs' Motivational Needs -- Training Data -- Feature Extraction -- ML Techniques -- Model Performance -- Postimplementation Reporting and Refinement -- Reporting on Hyperparameters -- Performance Metrics -- Continuous Improvement of ML Tools -- Conclusion -- Notes -- References -- ArtificialRTIFICIAL IntelligenceNTELLIGENCE and THEAND OperationalizationPERATIONALIZATION ofOF PsychologicalSYCHOLOGICAL C ... -- Abstract -- ArtificialRTIFICIAL IntelligenceNTELLIGENCE -- IllustrativeLLUSTRATIVE EmpericalMPIRICAL ContextONTEXT: EmotionsMOTIONS andAND EmotionalMOTIONAL AuthenticityUTHENTICITY D. | |
| 505 | 8 | |a Venture Pitches -- Basic Emotions -- Emotional Authenticity -- ArtificialRTIFICIAL IntelligenceNTELLIGENCE toTO DetectETECT AuthenticityUTHENTICITY ofOF BasicASIC EmotionsMOTIONS -- AI-bBased Operationalization of Basic Emotions -- Basic Emotions and Facial Expressions -- Facial Expressions and AI-Bbased Emotion Detection -- AI-BbBased Emotional Authenticity Detection -- DiscussionISCUSSION -- Multimodal AI Systems -- Machine Learning -- ConclusionONCLUSION -- Note -- ReferencesReferences: -- PechaKucha-Bbased Participatory Video for Organizational Research -- Abstract -- Literature Review -- Using PechaKucha to Bring PV to Organizational Research -- PechaKucha Presentations as a Multimodal Participatory Video Data Source -- Method Description -- Creating and Collecting PV Data Wwith PechaKucha -- Analysis of PechaKucha Data -- Possible Organizational Research Projects Using PechaKucha-Bbased Participatory Video -- Discussion -- Conclusion -- References | |
| 520 | |a Offering innovative ideas that explore how strategy and management methodology can be developed, Methods to Improve Our Field considers approaches that range from the re-imagining of secondary data in the digital age and Interpretive Phenomenological Analysis (IPA) to Machine Learning and Artificial Intelligence | ||
| 650 | 4 | |a Business planning | |
| 700 | 1 | |a McKenny, Aaron F. |e Sonstige |4 oth | |
| 700 | 1 | |a O'Kane, Paula |e Sonstige |4 oth | |
| 700 | 1 | |a Paroutis, Sotirios |e Sonstige |4 oth | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Hill, Aaron D. |t Methods to Improve Our Field |d Bingley : Emerald Publishing Limited,c2023 |z 9781804553657 |
| 912 | |a ZDB-30-PQE | ||
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Datensatz im Suchindex
| _version_ | 1810600639118442496 |
|---|---|
| adam_text | |
| any_adam_object | |
| author | Hill, Aaron D. |
| author_facet | Hill, Aaron D. |
| author_role | aut |
| author_sort | Hill, Aaron D. |
| author_variant | a d h ad adh |
| building | Verbundindex |
| bvnumber | BV049874469 |
| collection | ZDB-30-PQE |
| contents | Cover -- METHODS TO IMPROVE OUR FIELD -- RESEARCH METHODOLOGY IN STRATEGY AND MANAGEMENT -- METHODS TO IMPROVE OUR FIELD -- Copyright -- CONTENTS -- ABOUT THE EDITORS -- ABOUT THE CONTRIBUTORS -- Introduction to Methods to Improve Our Field -- IntroductionNTRODUCTION -- VolumeOLUME ContributionsONTRIBUTIONS -- ClosingLOSING ReflectionsEFLECTIONS -- ReferencesReferences: -- Reimagining Ssecondary Ddata in a Ddigital Aage -- Abstract -- Introduction -- Troublesome Ttypologies -- Secondary Ddata as Rreuse -- Beyond Rreuse: Rreimagining Ssecondary Ddata in a Ddigital Aage -- Critical Challenges and Considerations in Uusing Ddigital Mmethods -- Acknowledgements -- ReferencesReferences: -- Insights Ffrom the Application of Interpretative Phenomenological Analysis in Management Research -- Abstract -- Introduction -- Understanding Interpretative Phenomenological Analysis -- The IPA Process -- Sampling and Data Collection -- Data Analysis -- Demonstrating Research Quality -- Conclusion -- Note -- References -- SynthesizingYNTHESIZING BestEST PracticesRACTICES forFOR ConductingONDUCTING DictionaryICTIONARY-BbasedBASED ComputerizedOM ... -- Abstract -- TheHE ComputerOMPUTER-AaidedAIDED TextextEXT AnalysisNALYSIS LandscapeANDSCAPE -- KeyeyEY ConsiderationsONSIDERATIONS forFOR ConductingONDUCTING DBCTA ResearchesearchESEARCHey -- First Phase: Study Design and Text Collection -- Second Phase: Text Normalization -- Third Phase: Dictionary-BbBased Computerized Text Analysis -- Fourth Phase: Interpreting and Publishing Results -- DiscussionISCUSSION -- Notes -- ReferencesReferences: -- Using Mixed- Effect Growth Models tTo Examine Time aAs aA Predictor oOf Interest aAnd Between-Firm Differences OoOver Time -- Abstract -- GrowthROWTH ModelsODELS -- What Aare Growth Models? -- Illustrative Panel Data Initial Models: Comparing GLS to Multilevel Models of Time as a Linear Growth Trend -- Using Multilevel Growth Models to Tests Between- Firm Effects on Differences in Firm Slopes -- DiscontinuousISCONTINUOUS GrowthROWTH ModelsODELS -- Leveraging the Discontinuous Growth Models to Test Variations in Rates of Change -- Using the DGM to Test Between- Firm Effects on Differences in Time, Trans, and Post Slopes -- Theoretical and Methodological Implications for Strategic Management Research -- Conclusion -- Notes -- ReferencesReferences: -- Garbage in, Garbage out: A Theory-Driven Approach to Improve Data Handling in Supervised Machine Learning -- Abstract -- Introduction -- Machine Learning and Measurement -- Broad Approaches to ML and Typical ML Tasks -- Distinguishing ML From Statistical Modeling -- Common ML Algorithms -- Applying ML in Strategy Research -- A Theory-Driven Approach to Supervised ML -- Preimplementation Data Handling -- Step 1: Establish the Relevance of ML for the Focal Construct -- Step 2: Establish the "Ground Truth" of the Training Data -- Step 3: Apply Theoretical Prepruning -- ML Implementation -- Step 4: Select Appropriate ML Techniques for the Focal Construct -- A Demonstration Using McCLelland's Needs -- ML as a Method to Measure CEOs' Motivational Needs -- Training Data -- Feature Extraction -- ML Techniques -- Model Performance -- Postimplementation Reporting and Refinement -- Reporting on Hyperparameters -- Performance Metrics -- Continuous Improvement of ML Tools -- Conclusion -- Notes -- References -- ArtificialRTIFICIAL IntelligenceNTELLIGENCE and THEAND OperationalizationPERATIONALIZATION ofOF PsychologicalSYCHOLOGICAL C ... -- Abstract -- ArtificialRTIFICIAL IntelligenceNTELLIGENCE -- IllustrativeLLUSTRATIVE EmpericalMPIRICAL ContextONTEXT: EmotionsMOTIONS andAND EmotionalMOTIONAL AuthenticityUTHENTICITY D. Venture Pitches -- Basic Emotions -- Emotional Authenticity -- ArtificialRTIFICIAL IntelligenceNTELLIGENCE toTO DetectETECT AuthenticityUTHENTICITY ofOF BasicASIC EmotionsMOTIONS -- AI-bBased Operationalization of Basic Emotions -- Basic Emotions and Facial Expressions -- Facial Expressions and AI-Bbased Emotion Detection -- AI-BbBased Emotional Authenticity Detection -- DiscussionISCUSSION -- Multimodal AI Systems -- Machine Learning -- ConclusionONCLUSION -- Note -- ReferencesReferences: -- PechaKucha-Bbased Participatory Video for Organizational Research -- Abstract -- Literature Review -- Using PechaKucha to Bring PV to Organizational Research -- PechaKucha Presentations as a Multimodal Participatory Video Data Source -- Method Description -- Creating and Collecting PV Data Wwith PechaKucha -- Analysis of PechaKucha Data -- Possible Organizational Research Projects Using PechaKucha-Bbased Participatory Video -- Discussion -- Conclusion -- References |
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| dewey-full | 658.1 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.1 |
| dewey-search | 658.1 |
| dewey-sort | 3658.1 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Wirtschaftswissenschaften |
| edition | 1st ed |
| format | Electronic eBook |
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| id | DE-604.BV049874469 |
| illustrated | Not Illustrated |
| indexdate | 2024-09-19T05:22:06Z |
| institution | BVB |
| isbn | 9781804553664 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035213927 |
| oclc_num | 1358088191 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (193 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Emerald Publishing Limited |
| record_format | marc |
| series2 | Research Methodology in Strategy and Management Series |
| spelling | Hill, Aaron D. Verfasser aut Methods to Improve Our Field 1st ed Bingley Emerald Publishing Limited 2023 ©2023 1 Online-Ressource (193 Seiten) txt rdacontent c rdamedia cr rdacarrier Research Methodology in Strategy and Management Series v.14 Description based on publisher supplied metadata and other sources Cover -- METHODS TO IMPROVE OUR FIELD -- RESEARCH METHODOLOGY IN STRATEGY AND MANAGEMENT -- METHODS TO IMPROVE OUR FIELD -- Copyright -- CONTENTS -- ABOUT THE EDITORS -- ABOUT THE CONTRIBUTORS -- Introduction to Methods to Improve Our Field -- IntroductionNTRODUCTION -- VolumeOLUME ContributionsONTRIBUTIONS -- ClosingLOSING ReflectionsEFLECTIONS -- ReferencesReferences: -- Reimagining Ssecondary Ddata in a Ddigital Aage -- Abstract -- Introduction -- Troublesome Ttypologies -- Secondary Ddata as Rreuse -- Beyond Rreuse: Rreimagining Ssecondary Ddata in a Ddigital Aage -- Critical Challenges and Considerations in Uusing Ddigital Mmethods -- Acknowledgements -- ReferencesReferences: -- Insights Ffrom the Application of Interpretative Phenomenological Analysis in Management Research -- Abstract -- Introduction -- Understanding Interpretative Phenomenological Analysis -- The IPA Process -- Sampling and Data Collection -- Data Analysis -- Demonstrating Research Quality -- Conclusion -- Note -- References -- SynthesizingYNTHESIZING BestEST PracticesRACTICES forFOR ConductingONDUCTING DictionaryICTIONARY-BbasedBASED ComputerizedOM ... -- Abstract -- TheHE ComputerOMPUTER-AaidedAIDED TextextEXT AnalysisNALYSIS LandscapeANDSCAPE -- KeyeyEY ConsiderationsONSIDERATIONS forFOR ConductingONDUCTING DBCTA ResearchesearchESEARCHey -- First Phase: Study Design and Text Collection -- Second Phase: Text Normalization -- Third Phase: Dictionary-BbBased Computerized Text Analysis -- Fourth Phase: Interpreting and Publishing Results -- DiscussionISCUSSION -- Notes -- ReferencesReferences: -- Using Mixed- Effect Growth Models tTo Examine Time aAs aA Predictor oOf Interest aAnd Between-Firm Differences OoOver Time -- Abstract -- GrowthROWTH ModelsODELS -- What Aare Growth Models? -- Illustrative Panel Data Initial Models: Comparing GLS to Multilevel Models of Time as a Linear Growth Trend -- Using Multilevel Growth Models to Tests Between- Firm Effects on Differences in Firm Slopes -- DiscontinuousISCONTINUOUS GrowthROWTH ModelsODELS -- Leveraging the Discontinuous Growth Models to Test Variations in Rates of Change -- Using the DGM to Test Between- Firm Effects on Differences in Time, Trans, and Post Slopes -- Theoretical and Methodological Implications for Strategic Management Research -- Conclusion -- Notes -- ReferencesReferences: -- Garbage in, Garbage out: A Theory-Driven Approach to Improve Data Handling in Supervised Machine Learning -- Abstract -- Introduction -- Machine Learning and Measurement -- Broad Approaches to ML and Typical ML Tasks -- Distinguishing ML From Statistical Modeling -- Common ML Algorithms -- Applying ML in Strategy Research -- A Theory-Driven Approach to Supervised ML -- Preimplementation Data Handling -- Step 1: Establish the Relevance of ML for the Focal Construct -- Step 2: Establish the "Ground Truth" of the Training Data -- Step 3: Apply Theoretical Prepruning -- ML Implementation -- Step 4: Select Appropriate ML Techniques for the Focal Construct -- A Demonstration Using McCLelland's Needs -- ML as a Method to Measure CEOs' Motivational Needs -- Training Data -- Feature Extraction -- ML Techniques -- Model Performance -- Postimplementation Reporting and Refinement -- Reporting on Hyperparameters -- Performance Metrics -- Continuous Improvement of ML Tools -- Conclusion -- Notes -- References -- ArtificialRTIFICIAL IntelligenceNTELLIGENCE and THEAND OperationalizationPERATIONALIZATION ofOF PsychologicalSYCHOLOGICAL C ... -- Abstract -- ArtificialRTIFICIAL IntelligenceNTELLIGENCE -- IllustrativeLLUSTRATIVE EmpericalMPIRICAL ContextONTEXT: EmotionsMOTIONS andAND EmotionalMOTIONAL AuthenticityUTHENTICITY D. Venture Pitches -- Basic Emotions -- Emotional Authenticity -- ArtificialRTIFICIAL IntelligenceNTELLIGENCE toTO DetectETECT AuthenticityUTHENTICITY ofOF BasicASIC EmotionsMOTIONS -- AI-bBased Operationalization of Basic Emotions -- Basic Emotions and Facial Expressions -- Facial Expressions and AI-Bbased Emotion Detection -- AI-BbBased Emotional Authenticity Detection -- DiscussionISCUSSION -- Multimodal AI Systems -- Machine Learning -- ConclusionONCLUSION -- Note -- ReferencesReferences: -- PechaKucha-Bbased Participatory Video for Organizational Research -- Abstract -- Literature Review -- Using PechaKucha to Bring PV to Organizational Research -- PechaKucha Presentations as a Multimodal Participatory Video Data Source -- Method Description -- Creating and Collecting PV Data Wwith PechaKucha -- Analysis of PechaKucha Data -- Possible Organizational Research Projects Using PechaKucha-Bbased Participatory Video -- Discussion -- Conclusion -- References Offering innovative ideas that explore how strategy and management methodology can be developed, Methods to Improve Our Field considers approaches that range from the re-imagining of secondary data in the digital age and Interpretive Phenomenological Analysis (IPA) to Machine Learning and Artificial Intelligence Business planning McKenny, Aaron F. Sonstige oth O'Kane, Paula Sonstige oth Paroutis, Sotirios Sonstige oth Erscheint auch als Druck-Ausgabe Hill, Aaron D. Methods to Improve Our Field Bingley : Emerald Publishing Limited,c2023 9781804553657 |
| spellingShingle | Hill, Aaron D. Methods to Improve Our Field Cover -- METHODS TO IMPROVE OUR FIELD -- RESEARCH METHODOLOGY IN STRATEGY AND MANAGEMENT -- METHODS TO IMPROVE OUR FIELD -- Copyright -- CONTENTS -- ABOUT THE EDITORS -- ABOUT THE CONTRIBUTORS -- Introduction to Methods to Improve Our Field -- IntroductionNTRODUCTION -- VolumeOLUME ContributionsONTRIBUTIONS -- ClosingLOSING ReflectionsEFLECTIONS -- ReferencesReferences: -- Reimagining Ssecondary Ddata in a Ddigital Aage -- Abstract -- Introduction -- Troublesome Ttypologies -- Secondary Ddata as Rreuse -- Beyond Rreuse: Rreimagining Ssecondary Ddata in a Ddigital Aage -- Critical Challenges and Considerations in Uusing Ddigital Mmethods -- Acknowledgements -- ReferencesReferences: -- Insights Ffrom the Application of Interpretative Phenomenological Analysis in Management Research -- Abstract -- Introduction -- Understanding Interpretative Phenomenological Analysis -- The IPA Process -- Sampling and Data Collection -- Data Analysis -- Demonstrating Research Quality -- Conclusion -- Note -- References -- SynthesizingYNTHESIZING BestEST PracticesRACTICES forFOR ConductingONDUCTING DictionaryICTIONARY-BbasedBASED ComputerizedOM ... -- Abstract -- TheHE ComputerOMPUTER-AaidedAIDED TextextEXT AnalysisNALYSIS LandscapeANDSCAPE -- KeyeyEY ConsiderationsONSIDERATIONS forFOR ConductingONDUCTING DBCTA ResearchesearchESEARCHey -- First Phase: Study Design and Text Collection -- Second Phase: Text Normalization -- Third Phase: Dictionary-BbBased Computerized Text Analysis -- Fourth Phase: Interpreting and Publishing Results -- DiscussionISCUSSION -- Notes -- ReferencesReferences: -- Using Mixed- Effect Growth Models tTo Examine Time aAs aA Predictor oOf Interest aAnd Between-Firm Differences OoOver Time -- Abstract -- GrowthROWTH ModelsODELS -- What Aare Growth Models? -- Illustrative Panel Data Initial Models: Comparing GLS to Multilevel Models of Time as a Linear Growth Trend -- Using Multilevel Growth Models to Tests Between- Firm Effects on Differences in Firm Slopes -- DiscontinuousISCONTINUOUS GrowthROWTH ModelsODELS -- Leveraging the Discontinuous Growth Models to Test Variations in Rates of Change -- Using the DGM to Test Between- Firm Effects on Differences in Time, Trans, and Post Slopes -- Theoretical and Methodological Implications for Strategic Management Research -- Conclusion -- Notes -- ReferencesReferences: -- Garbage in, Garbage out: A Theory-Driven Approach to Improve Data Handling in Supervised Machine Learning -- Abstract -- Introduction -- Machine Learning and Measurement -- Broad Approaches to ML and Typical ML Tasks -- Distinguishing ML From Statistical Modeling -- Common ML Algorithms -- Applying ML in Strategy Research -- A Theory-Driven Approach to Supervised ML -- Preimplementation Data Handling -- Step 1: Establish the Relevance of ML for the Focal Construct -- Step 2: Establish the "Ground Truth" of the Training Data -- Step 3: Apply Theoretical Prepruning -- ML Implementation -- Step 4: Select Appropriate ML Techniques for the Focal Construct -- A Demonstration Using McCLelland's Needs -- ML as a Method to Measure CEOs' Motivational Needs -- Training Data -- Feature Extraction -- ML Techniques -- Model Performance -- Postimplementation Reporting and Refinement -- Reporting on Hyperparameters -- Performance Metrics -- Continuous Improvement of ML Tools -- Conclusion -- Notes -- References -- ArtificialRTIFICIAL IntelligenceNTELLIGENCE and THEAND OperationalizationPERATIONALIZATION ofOF PsychologicalSYCHOLOGICAL C ... -- Abstract -- ArtificialRTIFICIAL IntelligenceNTELLIGENCE -- IllustrativeLLUSTRATIVE EmpericalMPIRICAL ContextONTEXT: EmotionsMOTIONS andAND EmotionalMOTIONAL AuthenticityUTHENTICITY D. Venture Pitches -- Basic Emotions -- Emotional Authenticity -- ArtificialRTIFICIAL IntelligenceNTELLIGENCE toTO DetectETECT AuthenticityUTHENTICITY ofOF BasicASIC EmotionsMOTIONS -- AI-bBased Operationalization of Basic Emotions -- Basic Emotions and Facial Expressions -- Facial Expressions and AI-Bbased Emotion Detection -- AI-BbBased Emotional Authenticity Detection -- DiscussionISCUSSION -- Multimodal AI Systems -- Machine Learning -- ConclusionONCLUSION -- Note -- ReferencesReferences: -- PechaKucha-Bbased Participatory Video for Organizational Research -- Abstract -- Literature Review -- Using PechaKucha to Bring PV to Organizational Research -- PechaKucha Presentations as a Multimodal Participatory Video Data Source -- Method Description -- Creating and Collecting PV Data Wwith PechaKucha -- Analysis of PechaKucha Data -- Possible Organizational Research Projects Using PechaKucha-Bbased Participatory Video -- Discussion -- Conclusion -- References Business planning |
| title | Methods to Improve Our Field |
| title_auth | Methods to Improve Our Field |
| title_exact_search | Methods to Improve Our Field |
| title_full | Methods to Improve Our Field |
| title_fullStr | Methods to Improve Our Field |
| title_full_unstemmed | Methods to Improve Our Field |
| title_short | Methods to Improve Our Field |
| title_sort | methods to improve our field |
| topic | Business planning |
| topic_facet | Business planning |
| work_keys_str_mv | AT hillaarond methodstoimproveourfield AT mckennyaaronf methodstoimproveourfield AT okanepaula methodstoimproveourfield AT paroutissotirios methodstoimproveourfield |