Del 14 i serien Research Methodology in Strategy and Management

Methods to Improve Our Field

Inbunden, Engelska, 2023

AvAaron D. Hill,Aaron F. McKenny,Paula O'Kane,Sotirios Paroutis

1 429 kr

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Research Methodology in Strategy and Management advances understanding of the methods used to study organizations – including managers, strategies, and how firms succeed.Offering innovative ideas that explore how strategy and management methodology can be developed, the chapter authors in Volume 14, Methods to Improve Our Field, consider 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.Methods to Improve Our Field is a necessity for both academics and researchers interested in the progression and cutting-edge studies of management, strategy, international business, entrepreneurship, and organization theory.

Produktinformation

Utgivningsdatum2023-01-18
Mått152 x 229 x 15 mm
Vikt395 g
FormatInbunden
SpråkEngelska
SerieResearch Methodology in Strategy and Management
Antal sidor192
FörlagEmerald Publishing Limited
ISBN9781804553657

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Aaron D. Hill is an Associate Professor in the Management Department of the Warrington College of Business at the University of Florida, USA.Aaron F. McKenny is an Assistant Professor at the Kelley School of Business, Indiana University Bloomington, USA.Paula O’Kane is a Senior Lecturer in the Department of Management at the Otago Business School, University of Otago, New Zealand.Sotirios Paroutis is Professor of Strategic Management at Warwick Business School, the University of Warwick, UK.

Chapter 1. Introduction to Methods to Improve Our Field; Aaron D. Hill, Aaron F. McKenny, Paula O'Kane, and Sotirios Paroutis Chapter 2. Reimagining Secondary Data in a Digital Age; Katrina Pritchard Chapter 3. Insights from the Application of Interpretative Phenomenological Analysis in Management Research; Yulia Taylor and Fiona Edgar Chapter 4. Synthesizing Best Practices for Conducting Dictionary-Based Computerized Text Analysis Research; Shane W. Reid, Aaron F. McKenny, and Jeremy C. Short Chapter 5. Using Mixed Effect Growth Models To Examine Time as a Predictor of Interest and Between-Firm Differences Over Time; Donald J. Schepker and Paul D. Bliese Chapter 6. Garbage In, Garbage Out: A Theory-Driven Approach To Improve Data Handling In Supervised Machine Learning; Steven J Hyde, Eric Bachura, and Joseph Harrison Chapter 7. Artificial Intelligence and Operationalization of Psychological Constructs: The Case of Emotions and Emotional Authenticity of Entrepreneurs during Pitch Presentations; Andreas Schwab, Yanjinlkham Shuumarjav, Jake B. Telkamp, and Jose R. Beltran Chapter 8. PechaKucha-based Participatory Video for Organizational Research; Timothy Madden, Laura T. Madden, and Anne D. Smith