SPEAKER

Dr. Hila Lifshitz-Assaf
Associate Professor of Information, Operations and Management Sciences
Stern School of Business
New York University

ABSTRACT

Artificial intelligence (AI) technologies promise to transform how professionals conduct knowledge work by augmenting their capabilities for making professional judgments. We know little, however, about how human-AI augmentation takes place in practice. Yet gaining this understanding is particularly important when professionals use AI tools to form judgments on critical decisions. We conducted an in-depth field study in a major US hospital where AI tools were used in three departments by diagnostic radiologists making breast cancer, lung cancer, and bone age determinations. The study illustrates the hindering effects of opacity that professionals experienced when using AI tools and explores how these professionals grappled with it in practice. In all three departments, this opacity resulted in professionals experiencing increased uncertainty because AI tool results often diverged from their initial judgment without providing underlying reasoning. Only in one department (of the three), did professionals consistently incorporate AI results into their final judgments, achieving what we call engaged augmentation. These professionals invested in AI interrogation practices – practices enacted by human experts to relate their own knowledge claims to AI knowledge claims. Professionals in the other two departments did not enact such practices and did not incorporate AI inputs into their final decisions, which we call un-engaged “augmentation.” Our study unpacks the challenges involved in augmenting professional judgment with powerful, yet opaque, technologies and contributes to literature on AI adoption in knowledge work.

SPEAKER

Dr. Xiaomin Xu
Lecturer in Work, Organisation, and Management
University of Liverpool Management School

ABSTRACT

Relative salary compared with referent others has well-established implications for employee attitudes and behaviors at work. Yet, how employees process information on comparisons, particularly when internal and external comparison information is incongruent, remains controversial. In this paper we integrate the model of dispositional attribution and equity theory to predict how the incongruence of internal and external salary comparisons affects perceptions of distributive justice and subsequent employee withdrawal behavior. We hypothesized that the effect of salary comparisons on perceived distributive justice follows a hierarchically restrictive schema in which a lower salary in comparison to a referent has a greater effect than a higher salary. This further affects employee withdrawal such as psychological withdrawal, turnover intention and actual turnover. Two studies were conducted to test our hypotheses: a quasi-experimental study (N = 130) and a time-lagged survey (N = 307). Consistent with our framework, we observed that when comparison information was incongruent, information indicating disadvantage more strongly affected perceived distributive justice than did information indicating advantage. Moreover, the impact on perceived distributive justice was negatively related to employee withdrawal. The theoretical and practical implications of these findings are discussed.

SPEAKER

Dr. Matthew Bidwell
Associate Professor of Management
The Wharton School
University of Pennsylvania

ABSTRACT

Research on careers often focuses on understanding the sequence of jobs that people move through. In project-based organizations, though, different career trajectories tend to reflect differences in the kinds of projects that people work on over time. In this paper we explore two aspects of the project portfolios that people assemble – variety in the content that they involve and variety in the collaborators worked with. Drawing on theories of human and social capital and careers, we propose that increased diversification in both the kinds of projects that an employee works on and the collaborators that they work with are likely to lead to faster promotion. We also suggest that the effects of increased content diversity and collaborator diversity are likely to offset each other, so that the benefits of content diversity are less when employees work with a greater variety of collaborators. We explore this question using project data from a professional services firm. We, leverage variation in client demand as an exogenous source of variation in project portfolios to generate instruments for project variety. We find that increased diversification in content and collaborators is associated with increased promotion and that their effects do offset each other. We also find strong non-linearities in the effects, as increases in content diversity show strongly diminishing returns, while returns from collaborator diversity are highest for those who have the most collaborators.

SPEAKER

Dr. Bo Cowgill
Assistant Professor
Graduate School of Business
Columbia University

ABSTRACT

We study the design of managerial practices for matching workers to divisions. Our methods use both sides’ preferences to match with each other, and on the employer’s expectations about resulting productivities. Our model derives boundary conditions for when dictating assignments outperforms delegating matching preferences to worker/division preferences (and vice versa). Our model highlights the tradeoffs between the coordination benefits of dictating versus informational advantages of delegating. We then turn to a large organization’s internal labor market for empirics. We find that optimal matching is highly productive. Using the organization’s preferred metric, the optimal match is 36% more productive than randomly assigned matches within job categories. However, it achieves this through negative assortative matching, and by placing a majority of workers and managers with assignments they did not rank. By contrast, preference-based matches (using deferred acceptance) are much less productive (only 3% better than random), and feature positive assortative matching. Workers and managers are significantly more likely to be assigned to a preferred partner. We show how a novel method — integrating both firm and employees/division preferences — can improve firms’ matchmaking.

SPEAKER

Dr. Yanbo Wang
Associate Professor of Management and Strategy
HKU Business School

ABSTRACT

Nationalism as an exclusionist sentiment is an important driver in policy formation that shapes cross border economic activities. Extensive studies assume that nationalism derived from traumatic history in the host country magnifies the liability of foreignness for multinational corporation operations through today’s hostile public opinions. Yet even casual observation suggests that the blocking role of nationalism in economic domains is not self-evident (e.g., the thriving Japanese FDI in China and South Korea; the solid trade partnership between France and the United Kingdom). In recognition that public opinions are subject to politicians’ manipulation (i.e., endogenous to policy formation), we argue instead that enactment of the nationalism against FDI is driven by the sentiment of individual politicians, which depends on the linkage between FDI introduction and their promotion. We test our ideas using the context of the city-level bureaucratic system of China. The city-level governments have a dual leadership structure comprising the Party secretary and the mayor. These two positions have a unique contrast between their incentive structures and their associated behavioral imperatives. We expose the stress between the individual and their organizational cage through our investigation of how a powerful nationalist sentiment imprinted by the Second Sino-Japanese War leads to variations in actions between these top two decision makers towards localized Japanese FDIs. We find that the Party secretary’s exclusionist sentiment changes behavior towards Japanese FDIs: when the Party secretary has stronger historical exposure to the war, Japanese FDIs are fewer. However, for a mayor imprinted in the same manner, this effect does not exist. The difference in actions emerges because the incentive structure in the Party secretary and mayor positions are different, even though they have responsibilities over the same jurisdiction.

SPEAKER

Dr. Jie Gong
Assistant Professor
Department of Strategy and Policy
NUS Business School, National University of Singapore

ABSTRACT

Becker and Murphy (1992) proposed that job specialization would increase productivity but is limited by the costs of coordinating workers. They reasoned that technology facilitates coordination, and so, increases specialization and productivity. Here, we propose a different role for technology. Automation substitutes machines for workers in particular tasks, leaving workers to specialize in the non-automated tasks, hence not requiring coordination. Specialization reduces the marginal cost of effort, and so, workers increase effort and productivity. The proposition is supported by a field experiment. Conventionally, supermarket cashiers perform two tasks – scan purchases and collect payment. Singapore supermarkets divided the job, with humans scanning and machines collecting payment. The new job design increased cashier productivity in scanning by over 10 percent. Productivity rose by increasing effort in scanning, rather than through learning or reducing task-switching.

SPEAKER

Dr. Krishna Savani
Associate Professor of Leadership, Management, and Organization
Nanyang Business School
Nanyang Technological University

ABSTRACT

When management researchers want to explain important outcomes (e.g., employees’ job performance, countries’ innovation, and investors’ support for new ventures), they typically focus on one or a few antecedents suggested by prominent theoretical frameworks in the field. However, researchers might or might not identify the best explanation through this process. Machine learning methods are ideally suited for identifying the best explanation—they learn to predict the outcome of interest using all the available information and can identify the most important antecedents. I argue that machine learning can thus complement the traditional hypothetico-deductive reasoning that dominates the field. In the first project that I will describe today, I trained deep learning models to predict countries’ innovation scores and support for crowdfunding from residents’ responses to 680 attitudes, values, and beliefs included in the World Values Survey. The model could do so with 90% accuracy. Follow-up analyses revealed national pride as antecedents of both country-level innovation and support for crowdfunding. Follow-up experiments verified that increasing people’s national pride increased their creativity, a key driver of innovation, and increased their support for crowdfunding initiatives. Overall, this research highlights that machine learning methods can generate novel theoretical insights in the organizational sciences.

SPEAKER

Prof. Lamar Pierce
Professor of Organization & Strategy
Olin Business School
Washington University in St. Louis

ABSTRACT

Behavioral economists have proposed that incentive contracts result in higher productivity when bonuses are “loss framed”—prepaid then clawed back if targets are unmet. We test this claim in a large-scale field experiment. Holding financial incentives fixed, we randomized the pre- or post-payment of sales bonuses at 294 car dealerships. Prepayment was estimated to reduce sales by 5%, generating a revenue loss of $45 million over 4 months. We document, both empirically and theoretically, that negative effects of loss framing can arise due to an increase in incentives for “gaming” behaviors. Based on these claims, we reassess the common wisdom regarding the desirability of loss framing.

SPEAKER

Prof. David Hsu
Richard A. Sapp Professor of Management
The Wharton School
University of Pennsylvania

ABSTRACT

How does inventor team commingling, which we define as integrating human capital from the target and acquiring firm for R&D collaboration, impact innovation outcomes in technology acquisitions? Organizing post-acquisition R&D production teams in this manner holds the potential of sidestepping the classical integration-autonomy tradeoff. Structural integration facilitates task coordination but may dampen individual motivation, while an autonomous post-acquisition organization presents the opposite tradeoff. We argue that commingling is especially suited to technology acquisition integration and innovation, as it helps address task uncertainty (not just task coordination) while at the same time facilitating organizational and human know-how recombination (not just motivation). We assemble a sample of technology acquisitions, with some firms also experiencing prior R&D alliances with the acquirer. We find that inventor commingling and structural integration are substitutes as related to innovation outcomes. Furthermore, innovation outcomes are strongly increasing post-merger for firms with more intensive inventor commingling. All of these effects are distinct from team knowledge diversity. We instrument direct flights between the acquisition party locations to address the issue of endogenous commingling, and find consistent results. This supports a causal interpretation of commingling on innovation. Finally, as initial evidence that the commingling design may also depend on managerial authority and control, we find that the same individuals who engaged in post-acquisition commingling and pre-acquisition R&D alliance collaboration experienced greater innovation outcomes under the former structure.

SPEAKER

Professor Ivan Png
Distinguished Professor in Strategy & Policy, Economics
National University of Singapore

Miss Yun Hou
PhD Student in Department of Strategy & Policy
National University of Singapore

ABSTRACT

How do businesses respond to benchmarking information? Benchmarking — information about peers on relative performance and practices — is widely used but there is little scientific information on its effectiveness. Theoretically, benchmarking can help new entrepreneurs to resolve uncertainty about their own ability. Performance depends on the entrepreneur’s own ability, practices, and market-level uncertainty common to all entrepreneurs. By revealing the performance and practices of high performers, benchmarking enables subjects to infer the market-level uncertainty and deduce their own ability.

To investigate the causal impact of benchmarking, we carried out a randomized controlled experiment among owners of food stalls. 194 business owners operating food stalls at 17 hawker centres across Singapore were recruited into the study. Both the control and treatment owners were informed of their own performance. Additionally, treatment owners were told their relative performance and best practices. The experiment started in September 2019, and finished in December 2020.

Empirically, on the extensive margin, benchmarking affected exit. Compared to control owners, treatment owners who performed relatively poorly were more likely to exit and less likely otherwise. The treatment effects were more pronounced among those who were less informed about their ability. On the intensive margin, benchmarking increased the adoption of back-of-house management practices, both benchmarked and non-benchmarked, with the effects more pronounced among more educated owners and those with lower costs of adoption. Benchmarking also substantially increased performance, but the estimates were not statistically significant, perhaps due to the Covid-19 pandemic and small sample.

BIOGRAPHY

Yun Hou is a PhD student in the Department of Strategy & Policy, National University of Singapore. She received her bachelor’s degree in 2014 and Master’s in 2016, both from Peking University. She enjoys researching innovation and entrepreneurship.

 Ivan Png is a Distinguished Professor at the National University of Singapore. His research focuses on the economics of innovation and productivity.  He is the author of Managerial Economics, which has been published in multiple editions. He is the Principal Investigator of SPIRE, a S$4.75 million research project on service productivity. In free time, he exercises with his wife and plays tennis and the violin (both badly).