Developing and validating historiometric measures of leader individual differences by computerized content analysis of documents

Spangler, William D.; Gupta, Alka; Kim, Dong Ha; Nazarian, Serima

doi:10.1016/j.leaqua.2012.11.002

Cited by 20 publications

(7 citation statements)

References 89 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To begin, using the eight categories of speech charisma, a coding scheme is developed to guide coders in the analysis of the content. The coding scheme comprises the list of categories, their definitions, and rules for identifying which words and phrases fit into each category, based not only on the theoretical categories in Table 2 but also on related content analysis studies of speech charisma (Awamleh and Gardner, 1999;Spangler et al, 2012). The coding scheme is the foundation upon which validity is determined, whether it be face or construct (Poole and Folger, 1981;Potter and Levine-Donnerstein, 1999).…”

Section: Methodsmentioning

confidence: 99%

“…To begin, using the eight categories of speech charisma, a coding scheme is developed to guide coders in the analysis of the content. The coding scheme comprises the list of categories, their (Awamleh and Gardner, 1999;Spangler et al, 2012). The coding scheme is the foundation upon which validity is determined, whether it be face or construct (Poole and Folger, 1981;Potter and Levine-Donnerstein, 1999).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Making sense of text: artificial intelligence-enabled content analysis

Lee

Dabirian

McCarthy

et al. 2020

EJM

View full text Add to dashboard Cite

Purpose The purpose of this paper is to introduce, apply and compare how artificial intelligence (AI), and specifically the IBM Watson system, can be used for content analysis in marketing research relative to manual and computer-aided (non-AI) approaches to content analysis. Design/methodology/approach To illustrate the use of AI-enabled content analysis, this paper examines the text of leadership speeches, content related to organizational brand. The process and results of using AI are compared to manual and computer-aided approaches by using three performance factors for content analysis: reliability, validity and efficiency. Findings Relative to manual and computer-aided approaches, AI-enabled content analysis provides clear advantages with high reliability, high validity and moderate efficiency. Research limitations/implications This paper offers three contributions. First, it highlights the continued importance of the content analysis research method, particularly with the explosive growth of natural language-based user-generated content. Second, it provides a road map of how to use AI-enabled content analysis. Third, it applies and compares AI-enabled content analysis to manual and computer-aided, using leadership speeches. Practical implications For each of the three approaches, nine steps are outlined and described to allow for replicability of this study. The advantages and disadvantages of using AI for content analysis are discussed. Together these are intended to motivate and guide researchers to apply and develop AI-enabled content analysis for research in marketing and other disciplines. Originality/value To the best of the authors’ knowledge, this paper is among the first to introduce, apply and compare how AI can be used for content analysis.

show abstract

Section: Methodsmentioning

confidence: 99%

“…To begin, using the eight categories of speech charisma, a coding scheme is developed to guide coders in the analysis of the content. The coding scheme comprises the list of categories, their (Awamleh and Gardner, 1999;Spangler et al, 2012). The coding scheme is the foundation upon which validity is determined, whether it be face or construct (Poole and Folger, 1981;Potter and Levine-Donnerstein, 1999).…”

Section: Methodsmentioning

confidence: 99%

Making sense of text: artificial intelligence-enabled content analysis

Lee

Dabirian

McCarthy

et al. 2020

EJM

View full text Add to dashboard Cite

show abstract

“…For example, there are studies using self-reports of top managers' personalities and related traits (e.g., Peterson et al, 2009;Pryor et al, 2019;Nadkarni & Herrmann, 2010), employee ratings of CEO personality (e.g., Giberson et al, 2009;O'Reilly et al, 2014), and top management team member ratings of CEO leadership styles purported to be influenced by personality (Colbert et al, 2014;Judge & Bono, 2000). Beyond selfratings and subordinate ratings, researchers have gathered personality ratings through proxies derived from content and textual analyses of founders' crowdfunding pitches (Anglin et al, 2018), CEOs' letters to shareholders, interviews, speeches, and press releases (Gamache et al, 2015;Harrison et al, 2019;Nadkarni & Chen, 2014), or news articles (Resick et al, 2009), videometric analyses in which third parties code personality based on video footage (Petrenko et al, 2019;Recendes et al, 2022), and many other unobtrusive measures (for reviews, see Hill et al, 2014;Spangler et al, 2012). Given the need to rely on multiple alternative measures, we contend that applying multiperspective frameworks to this literature would benefit several areas of theoretical development.…”

Section: Moving Beyond the Big 5: Multiperspective Framework And Othe...mentioning

confidence: 99%

An integrative conceptual review of multiperspective frameworks in personality research and a roadmap for extended applications in organizational psychology.

Smith,

Wu,

Holmes

et al. 2024

Journal of Applied Psychology

View full text Add to dashboard Cite

Multiperspective frameworks, such as the social relations model, socioanalytic theory, the realistic accuracy model, the self-other knowledge asymmetry model, and the trait-reputation-identity model, have advanced understanding of personality over the last 40 years. Due to a resurgence of interest in multiperspective research on personality and other constructs in organizational psychology, we conducted an integrative conceptual review of these specific multirater frameworks and their application in work settings. Our review identifies similarities and differences in these frameworks and suggests that they collectively represent an invaluable resource for personality researchers and the broader field of organizational psychology. Through our review, we distinguish multiperspective frameworks from similar approaches (e.g., multirater designs), track the evolution of these frameworks, and leverage current applications of these frameworks to craft a future research agenda. Our review serves as a roadmap to help scholars apply multiperspective logic more thoroughly and systematically in personality research and beyond. We close with a discussion of practical implications.

show abstract

“…Of these, the most commonly employed method is frequency counting, in which the researcher identifies and counts the incidence of words in the text that indicate a variable of interest. Although frequency counting has been used in some HMA studies (e.g., Spangler, Gupta, Kim, & Nazarian, 2012), this application is exceedingly uncommon. In contrast, nearly 85% of content analysis studies identified by Duriau and colleagues (2007) employed frequency counting as their primary method of relationship testing.…”

Section: Hma As a Content Analysis Packagementioning

confidence: 99%

Historiometry in Organizational Science

Crayne

Hunter

2017

Organizational Research Methods

View full text Add to dashboard Cite

Historiometric analysis (HMA), an organized set of content analytic techniques, allows researchers to convert historical information into numeric data that are appropriate for complex statistical analyses and modeling. The HMA method has been present in the social sciences for more than a century, yet is largely absent from the management and organizational sciences literatures. In this article, we make the case for increased attention to HMA in organizational research, and describe research scenarios for which the techniques prescribed by HMA are particularly well-suited. We also provide a comprehensive guide for conducting historiometric analysis including practical guidelines, procedural instructions, and analysis of potential obstacles to the research process.

show abstract

Developing and validating historiometric measures of leader individual differences by computerized content analysis of documents

Cited by 20 publications

References 89 publications

Making sense of text: artificial intelligence-enabled content analysis

Making sense of text: artificial intelligence-enabled content analysis

An integrative conceptual review of multiperspective frameworks in personality research and a roadmap for extended applications in organizational psychology.

Historiometry in Organizational Science

Contact Info

Product

Resources

About