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21 – 30 of 317
Book part
Publication date: 18 January 2023

Shane W. Reid, Aaron F. McKenny and Jeremy C. Short

A growing body of research outlines how to best facilitate and ensure methodological rigor when using dictionary-based computerized text analyses (DBCTA) in organizational…

Abstract

A growing body of research outlines how to best facilitate and ensure methodological rigor when using dictionary-based computerized text analyses (DBCTA) in organizational research. However, these best practices are currently scattered across several methodological and empirical manuscripts, making it difficult for scholars new to the technique to implement DBCTA in their own research. To better equip researchers looking to leverage this technique, this methodological report consolidates current best practices for applying DBCTA into a single, practical guide. In doing so, we provide direction regarding how to make key design decisions and identify valuable resources to help researchers from the beginning of the research process through final publication. Consequently, we advance DBCTA methods research by providing a one-stop reference for novices and experts alike concerning current best practices and available resources.

Book part
Publication date: 5 January 2016

Abstract

Details

Storytelling-Case Archetype Decoding and Assignment Manual (SCADAM)
Type: Book
ISBN: 978-1-78560-216-0

Book part
Publication date: 5 January 2016

Abstract

Details

Storytelling-Case Archetype Decoding and Assignment Manual (SCADAM)
Type: Book
ISBN: 978-1-78560-216-0

Book part
Publication date: 5 January 2016

Abstract

Details

Storytelling-Case Archetype Decoding and Assignment Manual (SCADAM)
Type: Book
ISBN: 978-1-78560-216-0

Book part
Publication date: 5 January 2016

Abstract

Details

Storytelling-Case Archetype Decoding and Assignment Manual (SCADAM)
Type: Book
ISBN: 978-1-78560-216-0

Book part
Publication date: 5 January 2016

Abstract

Details

Storytelling-Case Archetype Decoding and Assignment Manual (SCADAM)
Type: Book
ISBN: 978-1-78560-216-0

Book part
Publication date: 22 November 2023

Chapman J. Lindgren, Wei Wang, Siddharth K. Upadhyay and Vladimer B. Kobayashi

Sentiment analysis is a text analysis method that is developed for systematically detecting, identifying, or extracting the emotional intent of words to infer if the text…

Abstract

Sentiment analysis is a text analysis method that is developed for systematically detecting, identifying, or extracting the emotional intent of words to infer if the text expresses a positive or negative tone. Although this novel method has opened an exciting new avenue for organizational research – mainly due to the abundantly available text data in organizations and the well-developed sentiment analysis techniques, it has also posed a serious challenge to many organizational researchers. This chapter aims to introduce the sentiment analysis method in the text mining area to the organizational research community. In this chapter, the authors first briefly discuss the central role of sentiment in organizational research and then introduce the traditional and modern approaches to sentiment analysis. The authors further delineate research paradigms for text analysis research, advocating the iterative research paradigm (cf., inductive and deductive research paradigms) that is more suitable for text mining research, and also introduce the analytical procedures for sentiment analysis with three stages – discovery, measurement, and inference. More importantly, the authors highlight both the dictionary-based and machine learning (ML) approaches in the measurement stage, with special coverage on deep learning and word embedding techniques as the latest breakthroughs in sentiment and text analyses. Lastly, the authors provide two illustrative examples to demonstrate the applications of sentiment analysis in organizational research. It is the authors’ hope that this chapter – by providing these practical guidelines – will help facilitate more applications of this novel method in organizational research in the future.

Details

Stress and Well-being at the Strategic Level
Type: Book
ISBN: 978-1-83797-359-0

Keywords

Abstract

Details

Storytelling-Case Archetype Decoding and Assignment Manual (SCADAM)
Type: Book
ISBN: 978-1-78560-216-0

Article
Publication date: 30 May 2023

R.V. ShabbirHusain, Atul Arun Pathak, Shabana Chandrasekaran and Balamurugan Annamalai

This study aims to explore the role of the linguistic style used in the brand-posted social media content on consumer engagement in the Fintech domain.

Abstract

Purpose

This study aims to explore the role of the linguistic style used in the brand-posted social media content on consumer engagement in the Fintech domain.

Design/methodology/approach

A total of 3,286 tweets (registering nearly 1.35 million impressions) published by 10 leading Fintech unicorns in India were extracted using the Twitter API. The Linguistic Inquiry and Word Count (LIWC) dictionary was used to analyse the linguistic characteristics of the shared tweets. Negative Binomial Regression (NBR) was used for testing the hypotheses.

Findings

This study finds that using drive words and cognitive language increases consumer engagement with Fintech messages via the central route of information processing. Further, affective words and conversational language drive consumer engagement through the peripheral route of information processing.

Research limitations/implications

The study extends the literature on brand engagement by unveiling the effect of linguistic features used to design social media messages.

Practical implications

The study provides guidance to social media marketers of Fintech brands regarding what content strategies best enhance consumer engagement. The linguistic style to improve online consumer engagement (OCE) is detailed.

Originality/value

The study’s findings contribute to the growing stream of Fintech literature by exploring the role of linguistic style on consumer engagement in social media communication. The study’s findings indicate the relevance of the dual processing mechanism of elaboration likelihood model (ELM) as an explanatory theory for evaluating consumer engagement with messages posted by Fintech brands.

Details

International Journal of Bank Marketing, vol. 42 no. 2
Type: Research Article
ISSN: 0265-2323

Keywords

Article
Publication date: 27 December 2022

Vivian Ta-Johnson, Joel Suss and Brian Lande

Few studies have tested the efficacy of instruction based on cognitive load theory in police use-of-force (UoF) training due to limitations of existing cognitive load measures…

Abstract

Purpose

Few studies have tested the efficacy of instruction based on cognitive load theory in police use-of-force (UoF) training due to limitations of existing cognitive load measures. Although linguistic measures of cognitive load address these limitations, they have yet to be applied to police UoF training. This study aims to discuss the aforementioned issue.

Design/methodology/approach

Officers’ verbal behavioral data from two UoF de-escalation projects were used to calculate cognitive load and assess how it varied with officer experience level (less-experienced, experienced). The verbal data were further analyzed to examine specific thinking patterns that contributed to heightened cognitive load across officer experience levels.

Findings

Across both studies, responses from less-experienced officers contained greater usage of cognitive language than responses from experienced officers. Specific cognitive processes that contribute to cognitive load in specific situations were also identified.

Originality/value

This paper enables police trainers to facilitate the development of adaptive training strategies to improve police UoF training via the reduction of cognitive load, and also contributes to the collective understanding of how less-experienced and experienced officers differ in their UoF decision-making.

Details

Policing: An International Journal, vol. 46 no. 2
Type: Research Article
ISSN: 1363-951X

Keywords

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