Academic articles
Practitioner articles
Working papers
Books
Book chapters
Case studies
Other publications
Subject(s)
Technology, R&D management
Keyword(s)
augmentation, automation, task routinization, human capital, crowd science, algorithmic management
Artificial intelligence (AI) is diffusing rapidly throughout the economy, raising questions regarding its impact on human workers. Current theories predict that the degree to which humans are replaced (automation) or supported (augmentation) will depend on characteristics of the task such as its degree of routinization and manual vs. cognitive nature. Yet, existing empirical evidence tends to come from aggregate occupation data or individual case studies, with limited research using larger samples of projects or tasks. Moreover, whether and how AI is implemented may not only depend on task characteristics but also on managers’ objectives, which may go beyond narrow efficiency to include benefits resulting from human employment itself (“employment goals”). We provide novel empirical evidence using data from more than 1,200 research projects that involve crowd members in different tasks such as data collection, data analysis, or creative problem solving. We confirm that the use of AI is associated with task characteristics, although the patterns are more nuanced than those shown in work on prior automation technologies such as computers and robots. Moreover, we find that managers pursuing employment goals are less likely to use AI for automation, while they are just as likely as others to use AI to augment the work of human workers. We contribute to the literature on AI adoption in organizations by providing rare evidence from project-level data, and by highlighting that the path towards “good AI” or “bad AI” is, partly, a matter of human choice.
With permission of the Academy of Management
Volume
2024
ISSN (Online)
2151-6561
ISSN (Print)
0065-0668
Subject(s)
Technology, R&D management
Keyword(s)
research questions, experiential knowledge, crowd science
Recently, organizations started exploring the use of crowdsourcing not only to solve pre-defined problems, but also to identify novel problems worth solving – not least in the hopes of more effectively aligning research and innovation agendas with issues of societal relevance. Yet, a key challenge is that many crowd-identified problem statements are not novel and simply re-state well-known problems, resulting in an ineffective way to organize problem identification. In this study, we theorize that the extent of “user knowledge” among crowd members, which can be acquired both through one's own experience as someone affected by a particular situation (e.g., as a patient) and through engagement with the experiences of others (e.g., as a caretaker or medical professional), increases the novelty of problem statements. In addition, we explore whether the novelty of crowd-identified problem statements might be improved by providing crowd members with two types of complementary knowledge related to the problem space: declarative and procedural. Our preliminary results from a large-scale online experiment show a significant positive relationship between “user knowledge” of crowd members and the novelty of the problem statements they submit. Providing crowd members with complementary knowledge related to the problem space does not lead to significant novelty improvements, according to our preliminary analysis. Nevertheless, these initial findings indicate that the impact of experiential knowledge in problem-solving also applies to problem finding. This has significant implications for organizations aiming to effectively utilize crowdsourcing for establishing their research and innovation agendas.
With permission of the Academy of Management
Volume
2024
ISSN (Online)
2151-6561
ISSN (Print)
0065-0668
Subject(s)
Technology, R&D management
Keyword(s)
augmentation, automation, task routinization, human capital, crowd science
In this symposium, we will examine a wide array of questions and hypotheses that focus on the people who conduct science -- as a complement to more established research traditions that focus on the publications and patents that people produce. Talks will cover topics that relate to a variety of career stages and background characteristics such as: What are the characteristics of scientists who are also inventors? How is Artificial Intelligence being integrated into crowd science projects? How does media coverage about research variably impact the authors of the research? Talks will also feature innovative data resources including one presentation that is able to examine the ways in which External Letters variably influence academic careers with respect to tenure and promotion decisions (particularly in relation to faculty who seek and gain one or more patents).
With permission of the Academy of Management
Volume
2024
ISSN (Online)
2151-6561
ISSN (Print)
0065-0668
Subject(s)
Health and environment; Human resources management/organizational behavior
Keyword(s)
physician leadership, planetary health
Subject(s)
Human resources management/organizational behavior
Keyword(s)
hybrid work, new work, middle managers
ISSN (Print)
0015-6914
Subject(s)
Diversity and inclusion; Ethics and social responsibility
Keyword(s)
Course design and delivery, Women in higher education, Feature article, Europe
JEL Code(s)
124
Subject(s)
Diversity and inclusion; Health and environment; Technology, R&D management
Keyword(s)
open Innovation in Science (OIS), open science, citizen science, crowd science, crowd paradigms
Volume
9
Journal Pages
1–12
ISSN (Online)
2057-4991
Subject(s)
Human resources management/organizational behavior
Keyword(s)
leadership, leadership transitions, career transitions
ISSN (Print)
0015-6914
Subject(s)
Diversity and inclusion
Keyword(s)
workplace visibility, career development, self-promotion, emotional barriers, societal norms, professional growth, networking strategies, gender expectations, personal branding, collective responsibility, behavioral change, head heart and hands
ISSN (Print)
0015-6914