Xiang Zhou’s research while affiliated with Shenzhen University and other places

Organizations are increasingly introducing artificial intelligence (AI) into the workplace and encouraging employees to use AI to complete work. Correspondingly, research on AI usage predominantly focuses on the positive effects of AI usage on employees themselves. Drawing upon attribution theory and AI literature and taking an interpersonal perspective, this research challenges the prevailing consensus by investigating whether, when, and how employee AI usage would lead to negative coworker outcomes. We propose that when coworkers attribute employee AI usage as a way to slack off (i.e., slack attribution), employee AI usage is negatively related to coworkers' perceived morality of the employee, which in turn decreases coworkers' helping behavior toward the employee. Two experimental studies, a field survey study, and a field experiment provide substantial support for our hypotheses. This research adds new insights into the AI usage literature by revealing the negative coworker outcomes of employee AI usage.

Artificial intelligence (AI) is transforming human life. While some studies find that people prefer humans over AI (AI aversion), others find the opposite (AI appreciation). To reconcile these conflicting findings, we introduce the Capability–Personalization Framework. This theoretical framework posits that when deciding between AI and humans in a context, individuals focus on two dimensions: (a) perceived capability of AI and (b) perceived necessity for personalization. We propose that AI appreciation occurs when (a) AI is perceived as more capable than humans and (b) personalization is perceived as unnecessary in a given decision context, whereas AI aversion occurs when these conditions are not met. Our Capability–Personalization Framework is substantiated by a meta-analysis of 442 effect sizes from 163 studies (N = 82,078): AI appreciation occurs (d = 0.27, 95% CI [0.17, 0.37]) when AI is perceived as more capable than humans and personalization is perceived as unnecessary in a given decision context; otherwise, AI aversion occurs (d = −0.50, 95% CI [−0.63, −0.37]). Moderation analyses suggest that AI appreciation is more pronounced for tangible robots (vs. intangible algorithms), for attitudinal (vs. behavioral) outcomes, in between-subjects (vs. within-subjects) study designs, and in low unemployment countries, while AI aversion is more pronounced in countries with high levels of education and internet use. Overall, our integrative framework and meta-analysis advance knowledge about AI–human preferences and offer valuable implications for AI developers and users.

We introduce the concept of Artificial Intelligence Quotient (AIQ)—defined as a person’s ability to use AI to perform a wide variety of tasks—and provide evidence for its existence using five studies (archival, lab, and online) across different AIs and samples. Study 1 (an 18-year global dataset of human+AI chess tournaments) and Study 2 (a three-wave longitudinal study of human+AI renju games) show that individuals have stable human+AI performance over time (controlling for human’s own capability and AI’s capability), suggesting the existence of a stable human+AI capability. Study 3 shows that a general AIQ factor can be statistically extracted from individuals’ performance on a variety of tasks completed with ChatGPT, a more general AI tool. Besides replicating Study 3’s findings in larger samples, Study 4 and Study 5 (preregistered) show that the extracted AIQ factor has both concurrent validity and prospective validity. Regarding concurrent validity, the extracted AIQ factor can predict human+AI performance on a new task using the same AI (ChatGPT) on the same day. Regarding prospective validity, the extracted AIQ factor can predict human+AI performance on other new tasks using different AIs (renju AI or Gemini) in the future. Across studies, we ascertain the unique explanatory power of AIQ by controlling for individual’s IQ, social intelligence (SQ), AI literacy (knowledge about AI), and/or computer literacy. We also explored potential correlates of AIQ (e.g., personality traits, previous AI use, and demographics). Together, our findings suggest that AIQ exists and is measurable. By establishing this new type of intelligence (AIQ), we shed light on individual differences in the ability to use AI, which is increasingly important for individuals, organizations, and society.

COVID-19, as a crucial public health crisis, has affected our lives in nearly every aspect. Besides its major health threats, COVID-19 brings severe secondary impacts, one of which is the rise of social stigma. Although numerous studies have examined the antecedents and outcomes of COVID-19-related stigma, we still lack a systematic understanding of who is being stigmatized during the COVID-19 pandemic, what exacerbates COVID-19-related stigma, and what impacts COVID-19-related stigma has on victims. Therefore, this review aims to provide a systematic overview of COVID-19-related stigma. With 93 papers conducted with 126,371 individuals in more than 150 countries and territories spanning five continents, we identify three targets that have received the most research: Chinese/Asian people, (suspected) patients and survivors, and healthcare workers. Furthermore, we find that for each stigma target, characteristics of the stigmatized, stigmatizer, and context contribute to COVID-19-related stigma and that this stigma negatively influences victims' health and non-health outcomes. We call for future research to provide a more integrative, balanced, and rigorous picture of COVID-19-related stigma via conducting research on neglected topics (e.g., contextual factors that contribute to stigma toward HCWs) and stigma interventions and using a longitudinal design. In practice, we urge governments and institutions (e.g., ministries of public health, hospitals) to pay close attention to stigma issues and to promote safe and inclusive societies.

COVID-19, as a crucial public health crisis, has affected our lives in nearly every aspect. Besides its major health threats, COVID-19 brings severe secondary impacts, one of which is the rise of social stigma. Although numerous studies have examined the antecedents and outcomes of COVID-19-related stigma, we still lack a systematic understanding of who is being stigmatized during the COVID-19 pandemic, what exacerbates COVID-19-related stigma, and what impacts COVID-19-related stigma has on victims. Therefore, this review aims to provide a systematic overview of COVID-19-related stigma. With 96 papers conducted with 134,142 individuals in more than 150 countries and territories spanning five continents, we identify three targets that have received the most research: Chinese/Asian people, (suspected) patients and survivors, and healthcare workers. Furthermore, we find that for each stigma target, characteristics of the stigmatized, stigmatizer, and context contribute to COVID-19-related stigma and that this stigma negatively influences victims’ health and non-health outcomes. We call for future research to provide a more integrative, balanced, and rigorous picture of COVID-19-related stigma via conducting research on neglected topics and stigma interventions and using a longitudinal design. In practice, we urge governments and institutions to pay close attention to stigma issues and to promote safe and inclusive societies.