Methods to Improve Our Field

by Aaron D. Hill (Editor), Aaron F. McKenny (Editor), Paula O'Kane (Editor), Sotirios Paroutis (Editor)

Overview

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.

Product Details

ISBN-13:	9781804553657
Publisher:	Emerald Publishing Limited
Publication date:	01/18/2023
Series:	Research Methodology in Strategy and Management , #14
Pages:	192
Product dimensions:	5.98(w) x 9.02(h) x 0.60(d)

About the Author

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 UniversityBloomington, 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.

Table of Contents

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