Sustainability in Leadership: The Implicit Associations of the First-Person Pronouns and Leadership Effectiveness Based on Word Embedding Association Test

Qu Yao, Yingjie Zheng and Jianhang Chen ()
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Qu Yao: Business School, Hohai University, Nanjing 211100, China
Yingjie Zheng: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Jianhang Chen: College of Information Science and Engineering, Hohai University, Changzhou 213000, China

Sustainability, 2024, vol. 16, issue 15, 1-15

Abstract: The first-person pronoun is an indispensable element of the communication process. Meanwhile, leadership effectiveness, as the result of leaders’ leadership work, is the key to the sustainable development of leaders and corporations. However, due to the constraints of traditional methods and sample bias, it is challenging to accurately measure and validate the relationship between first-person pronouns and leadership effectiveness at the implicit level. Word Embedding Association Test (WEAT) measures the relative degree of association between words in natural language by calculating the difference in word similarity. This study employs the word and sentence vector indicators of WEAT to investigate the implicit relationship between first-person pronouns and leadership effectiveness. The word vector analyses of the Beijing Normal University word vector database and Google News word vector database demonstrate that the cosine similarity and semantic similarity of “we-leadership effectiveness” are considerably greater than that of “I-leadership effectiveness”. Furthermore, the sentence vector analyses of the Chinese Wikipedia BERT model corroborate this relationship. In conclusion, the results of a machine learning-based WEAT verified the relationship between first-person plural pronouns and leadership effectiveness. This suggests that when leaders prefer to use “we”, they are perceived to be more effective.

Keywords: leadership effectiveness; first-person pronouns; machine learning; word embedding (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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