Management and Strategy - HKU Business School

26 January

Does Emotion Expression Variability Help or Hinder Leaders? Effects of Variability in Leaders’ Emotion Displays on Followers’ Perceptions of Leader Dysregulation, Authenticity, and Effectiveness

SPEAKER

Ms. Zaijia Liu
PhD Candidate in the Management Department (Organizational Behavior)
Columbia Business School

ABSTRACT

Does variability in leaders’ displays of emotion benefit or harm employees’ perceptions of their leaders? Prior research has provided mixed answers. Variability in leaders’ emotion expressions might undermine perceived leadership effectiveness because it makes leaders appear dysregulated. Yet, leaders’ emotion expression variability might also increase perceived leadership effectiveness because leaders who display more variability in their emotion expressions are perceived as more authentic. Six studies found support for the benefits of emotion expression variability in leadership judgments, including surveys with MBA students rating their most recent manager (Study 1a), full-time employees rating their long-term managers (Studies 1b and 5), a lab experiment involving a carefully controlled manipulation of emotion expression variability (Study 2), ratings of video-recorded professionals giving leadership speeches (Study 3), and ratings of leaders in real team competitions (Study 4). In each study, we found that variability in leaders’ emotion expression was associated with greater judgments of authenticity and consequently perceptions of greater leadership effectiveness. Theoretical and practical implications for emotion expression, impression management, and leader effectiveness are discussed.

21 January

Lifted Up or Feet on the Ground? Leader Emotional Balancing, Developmental Feedback, and Employee Learning

SPEAKER

Ms. Siyan Guo
PhD Candidate in Organizational Behavior and Human Resource Management
Robert H. Smith School of Business
University of Maryland

ABSTRACT

In this dissertation, the concept of developmental feedback (DFB) is extended to include two dimensions, gap identification and gap elimination. I focus on the affective mechanisms underlying the DFB – learning relationship and identify trade-offs in each of the DFB dimensions. I argue that while gap elimination elicits employee positive affect (PA) that facilitates learning via increased learning self-efficacy, it undermines learning via PA and decreased learning need recognition. In addition, gap identification induces employee negative affect (NA) that works in the opposite way. Emotional balancing, or leaders’ dynamic engagement in both affect improving and affect worsening behaviors, is proposed to attenuate the negative mechanisms. I conducted a three-wave, multisource field study to test my theoretical model. The findings largely support my proposed model. The results indicate that gap identification induces employee NA, while gap elimination induces PA. Gap identification has a positive effect on learning via employee learning need recognition, but a negative effect on learning via employee NA and learning self-efficacy. I also find that gap elimination positively affects learning through PA and improved self-efficacy in learning. Importantly, the results demonstrate the beneficial effects of emotional balancing, which significantly moderates the outcomes of PA and NA. Taken together, these findings indicate that receiving DFB is a highly emotional experience that creates a tension between being lifted up and keeping feet on the ground, leaders can use emotional balancing to manage employee affect in order to facilitate learning.

17 January

Governing Social Issues on Platforms: Evidence from a Field Experiment

SPEAKER

Dr. Wesley W. Koo
Assistant Professor of Strategy
INSEAD

ABSTRACT

How to understand and address social issues is a topic of growing interest in the field of management and organizations. An important organizational form in the modern era is that of multi-sided platforms, which provide the interface for interactions among different types of participants. In this study, we investigate the ability of platforms to address social issues through private governance. We conducted a field experiment in collaboration with a Singapore-based online platform that connects domestic helpers with employers/families. This setting is characterized by various forms of socially harmful behavior from employers (e.g., not giving helper enough food, yelling at helpers). In the treatment conditions, we communicated to helpers and employers that the platform plans to implement a new rating system that allows helpers to rate employers. There are two sets of findings. First, employers did not react positively to the rating system, and employers’ dislike of the rating system is especially pronounced among high-income employers. Second, surprisingly, helpers did not like the rating system either, even though it was designed to help and empower them. In particular, the most vulnerable helpers (those who likely had experienced socially harmful behavior in the past) were especially likely to disapprove of the rating system. This study shows that significant frictions exist to impede a platform’s governance of social issues. Whereas the theory of indirect network effects predicts that governance that benefits one side of the platform would make the platform more attractive for both sides, this study shows that either side has their idiosyncratic reasons to reject platform governance.

14 January

To engage or not to engage with AI for critical judgments: How professionals deal with opacity when using AI for medical diagnosis

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.

13 January

Horizontal Salary Comparison, Distributive Justice and Employee Withdrawal

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.

22 December

Assembling the Optimal Project Portfolio: Career Consequences of Content and Collaboration Specialization

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.

22 November

Preferences and Productivity in Organizational Matching: Theory and Empirics from Internal Labor Markets

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.

18 November

The Haunting Past: Nationalism, Career Concerns, and Local Politicians’ Actions towards Japanese FDI in China

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.

12 November

Automation, Specialization, and Productivity: Field Evidence

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.

10 November

Using Machine Learning to Generate Novel Insights in the Organisational Sciences: Applications to Innovation and Entrepreneurship

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.