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Corporate Diversity Statements and Employees’ Online DEI Ratings: An Unsupervised Machine-Learning Text-Mining Analysis
Journal of Business and Psychology ( IF 3.7 ) Pub Date : 2022-05-31 , DOI: 10.1007/s10869-022-09819-x
Wei Wang , Julie V. Dinh , Kisha S. Jones , Siddharth Upadhyay , Jun Yang

Following the deaths of many Black Americans in spring 2020, public consciousness rose around the societal mega-threat of racism. In response, many organizations released public statements to condemn racism and affirm their stance on diversity, equity, and inclusion (DEI). However, little is known about the specific thematic contents covered in such diversity statements and their implications on important organizational outcomes. Taking both inductive and deductive approaches, we conducted two studies to advance our understanding in this area. Study 1 employed structural topic modeling (STM)—an advanced unsupervised machine-learning text-mining technique—and comprehensively analyzed the latent semantic topics underlying the diversity statements publicly released by Fortune 1000 companies in late May and early June 2020. The results uncovered six underlying latent semantic topics: (1) general DEI terms, (2) supporting Black community, (3) acknowledging Black community, (4) committing to diversifying the workforce, (5) miscellaneous words, and (6) titles and companies. Furthermore, drawing from the identity-blindness and identity-consciousness theoretical frameworks and leveraging millions of data points of employees’ DEI ratings retrieved from Glassdoor.com, Study 2 further tested and supported hypotheses that companies were more positively rated by their employees on organizational diversity and inclusion if they (1) released (vs. did not release) diversity statements and (2) emphasized identity-conscious (vs. identity-blind) topics in their diversity statements. Our findings shed light on important theoretical implications for the current research and offer practical recommendations for organizational scientists and practitioners in diversity management.



中文翻译:

公司多元化声明和员工在线 DEI 评级:无监督机器学习文本挖掘分析

在 2020 年春季许多美国黑人死亡之后,公众意识在种族主义的社会巨大威胁周围兴起。作为回应,许多组织发布了公开声明,谴责种族主义并申明他们对多样性、公平和包容 (DEI) 的立场。然而,人们对此类多样性声明中涵盖的具体主题内容及其对重要组织成果的影响知之甚少。我们采用归纳法和演绎法,进行了两项研究以加深我们对这一领域的理解。研究 1 采用了结构主题建模 (STM)——一种先进的无监督机器学习文本挖掘技术——并全面分析了财富 1000 强公司在 2020 年 5 月下旬和 6 月初公开发布的多样性声明背后的潜在语义主题。一般 DEI 术语,(2)支持黑人社区,(3)承认黑人社区,(4)致力于使劳动力多样化,(5)杂词,以及 (6)头衔和公司. 此外,研究 2 借鉴了身份盲和身份意识理论框架,并利用从 Glassdoor.com 检索到的数百万个员工 DEI 评级数据点,进一步检验并支持了以下假设,即员工在组织多样性方面对公司的评价更为积极如果他们 (1) 发布(相对于未发布)多样性声明,并且 (2) 在其多样性声明中强调身份意识(相对于身份盲)主题,则包括在内。我们的研究结果阐明了对当前研究的重要理论意义,并为组织科学家和多元化管理从业者提供了实用建议。

更新日期:2022-06-01
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