2023
DOI: 10.1002/jcpy.1380
|View full text |Cite
|
Sign up to set email alerts
|

Speedy activists: How firm response time to sociopolitical events influences consumer behavior

Abstract: Organizations face growing pressure from their consumers and stakeholders to take public stances on sociopolitical issues. However, many are hesitant to do so lest they make missteps, promises they cannot keep, appear inauthentic, or alienate consumers, employees, or other stakeholders. Here we investigate consumers' impressions of firms that respond quickly or slowly to sociopolitical events. Using data scraped from Instagram and three online experiments (N = 2452), we find that consumers express more positiv… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
5

Relationship

0
5

Authors

Journals

citations
Cited by 6 publications
(1 citation statement)
references
References 56 publications
0
1
0
Order By: Relevance
“…Further, we observe no clear correlation between the use of field data and more sophisticated natural language processing (NLP) methods among the special issue articles: using archival field data does not necessarily require advanced methods. Special issue articles used methods that ranged from relatively straightforward applications of easy‐to‐use sentiment analysis and dictionaries (e.g., Nam et al, 2023; Wan et al, 2023) to more complex methods like grammar dependency parsing or deep learning neural network approaches (e.g., Park et al, 2023). Such methods are applied when doing so help solve specific research challenges, such as the difficulty of accurately measuring passive voice (Sepehri et al, 2023) or subjectivity in language (Park et al, 2023).…”
Section: Introducing the “Consumer Insights From Text Analysis” Speci...mentioning
confidence: 99%
“…Further, we observe no clear correlation between the use of field data and more sophisticated natural language processing (NLP) methods among the special issue articles: using archival field data does not necessarily require advanced methods. Special issue articles used methods that ranged from relatively straightforward applications of easy‐to‐use sentiment analysis and dictionaries (e.g., Nam et al, 2023; Wan et al, 2023) to more complex methods like grammar dependency parsing or deep learning neural network approaches (e.g., Park et al, 2023). Such methods are applied when doing so help solve specific research challenges, such as the difficulty of accurately measuring passive voice (Sepehri et al, 2023) or subjectivity in language (Park et al, 2023).…”
Section: Introducing the “Consumer Insights From Text Analysis” Speci...mentioning
confidence: 99%