Making Things Happen
General intelligence, personality traits, and motivation as predictors of performance, potential, and rate of advancement of Royal Navy senior officers
Mike Young & Victor Dulewicz
Military Psychology, forthcoming
This paper assesses the impact of general intelligence, as well as specific personality traits, and aspects of motivation, on performance, potential, and advancement of senior leaders. A questionnaire survey was conducted on the full population of 381 senior officers in the Royal Navy with an 80% response rate. Performance, potential, and rate of advancement were established direct from the organization's appraisal system; intelligence, personality traits and motivation were assessed, at the time of the study, using the Verify G+ Test, Occupational Personality Questionnaire, and Motivation Questionnaire. Findings suggest differences in motivation are more important than differences in general intelligence, or personality traits, in predicting assessed performance, potential within, and actual rate of advancement to, senior leadership positions. This is a rare example of a study into very senior leaders, validated against both formal appraisal data and actual rates of advancement. As a consequence of this study the Royal Navy has started to use psychometric-based assessments as part of the selection and development of its most Senior Officers.
Ideas Are Dimes A Dozen: Large Language Models For Idea Generation In Innovation
Karan Girotra et al.
University of Pennsylvania Working Paper, July 2023
Large language models (LLMs) such as OpenAI's GPT series have shown remarkable capabilities in generating fluent and coherent text in various domains. We compare the ideation capabilities of ChatGPT-4, a chatbot based on a state-of-the-art LLM, with those of students at an elite university. ChatGPT-4 can generate ideas much faster and cheaper than students, and the ideas are on average of higher quality (as measured by purchase-intent surveys) and exhibit higher variance in quality. More important, the vast majority of the best ideas in the pooled sample are generated by ChatGPT and not by the students. Providing ChatGPT with a few examples of highly rated ideas further increases its performance. We discuss the implications of these findings for the management of innovation.
Andrew Caplin et al.
NBER Working Paper, September 2023
Jobs increasingly require good decision-making. Workers are valued not only for how much they can do, but also for their ability to decide what to do. In this paper we develop a theory and measurement paradigm for assessing individual variation in the ability to make good decisions about resource allocation, which we call allocative skill. We begin with a model where agents strategically acquire information about factor productivity under time and effort constraints. Conditional on such constraints, agents' allocative skill can be defined as the marginal product of their attention. We test our model in a field survey where participants act as managers assigning fictional workers with heterogeneous productivity schedules to job tasks and are paid in proportion to output. Allocative skill strongly predicts full-time labor earnings, even conditional on IQ, numeracy, and education, and the return to allocative skill is greater in decision-intensive occupations.
Inspiring, Yet Tiring: How Leader Emotional Complexity Shapes Follower Creativity
Jakob Stollberger, Yves Guillaume & Daan van Knippenberg
Organization Science, forthcoming
Moods and emotions are an important influence on creativity at work, and recent developments point to emotional complexity as a particularly relevant influence in this respect. We develop this line of research by shifting focus from emotional complexity as an intrapersonal influence to emotional complexity as an interpersonal influence between leader and subordinate. Specifically, we integrate the social-functional approach to emotions with theory on self-regulation to shed light on the effects of leader emotional complexity (LEC), operationalized as alternations between leader displays of happiness and anger, on follower creativity. Three studies, two video experiments (Studies 1 and 2) and a multisource experience sampling study (Study 3), revealed that, on one hand, LEC stimulated creativity by enhancing the cognitive flexibility of followers; on the other hand, LEC led to heightened self-regulatory resource depletion, which compromised follower creativity. Our results also showed that trait epistemic motivation strengthened the positive effects of LEC on creativity via cognitive flexibility, the negative effects via self-regulatory resource depletion were also stronger for followers with higher trait epistemic motivation. Combined, results suggest that leader displays of emotional complexity can be tiring but are even more inspiring.
Marijuana use and perceptions of employment suitability
Michael Tews, Sydney Pons & Heyao Yu
Journal of Personnel Psychology, forthcoming
The present study extends research on marijuana social acceptance and employment suitability in the United States by examining how hiring managers view substance use-related content in social networking profiles. Specifically, this research focused on the weight hiring managers placed on social networking posts containing recreational marijuana content, medicinal marijuana content, and alcohol content when assessing potential candidates. With a sample of 405 hiring managers who evaluated experimentally manipulated social networking profiles, the results demonstrated a modest negative hiring bias against recreational marijuana content, with stronger negative effects for alcohol content. At the same time, social networking content related to medicinal marijuana content did not have a significant effect on perceptions of employment suitability. These findings highlight the nuanced nature of substance use stigma in today's evolving society.
Inverted Apprenticeship: How Senior Occupational Members Develop Practical Expertise and Preserve Their Position When New Technologies Arrive
Matthew Beane & Callen Anthony
Organization Science, forthcoming
New technologies create a dilemma for senior members of occupations. Traditionally, practical expertise and position are considered correlates, yet when new technologies arrive, they may be knocked out of alignment. This means that senior members must develop new expertise lest their position be threatened. However, because position often signifies expertise, developing new practical expertise may be challenging. Indeed, senior members face strong pressures not to appear to nor actually devote time to comprehensive formal training as they are booked with complex problems using prior methods, they are responsible for the learning of junior members, and they have passed early career training windows. Through comparative ethnographic field studies of urological surgery and investment banking, we show that "inverted apprenticeships," defined as configured struggle and restructured interactions with junior members that allow senior members to develop practical expertise with new technologies while maintaining their position, resolve this dilemma. We identify four pathways that senior experts took to structure these inverted apprenticeships, including seeking, stalling, leveraging, and confronting. We uncover the conditions of each pathway and trace their consequences. Although these pathways allowed senior members to enhance or preserve their position, they generated widely varying practical expertise with the new technology. Furthermore, the majority of these pathways undermined the learning of those most junior, who were supposed to be developing expertise through their interactions with seniors.
Managerial Prosocial Preferences and Automation Innovation
Columbia University Working Paper, July 2023
Automation, even while increasing aggregate employment in the long run, can displace and cause significant harm to incumbent employees. We propose that managers' prosocial preferences, specifically their desire not to displace and harm their employees, deter investing in automation innovation. Using both novel and previously-used proxies of prosocial preferences (e.g., CEO's use of 'we' vs. 'they' pronouns during earnings calls, employee-prosocial vs. shareholder-centric language in annual reports, Machiavellianism score, and charity engagement), we show that managerial prosociality decreases automation innovation in US public firms while having a weaker effect on non-automation innovation. The negative effect is stronger when financial slack provides managers with greater discretion and weaker when social safety nets reduce the harm from losing a job. In a complementary laboratory experiment, we show that aversion to harming employees underpins the negative relation. Our study highlights prosocial preferences as a novel source of heterogeneity that shapes the direction of firm technological investment and provides a richer psychological foundation beyond self-interest and career concerns.
Can Technology Startups Hire Talented Early Employees? Ability, Preferences, and Employee First Job Choice
Michael Roach & Henry Sauermann
Management Science, forthcoming
Early stage technology startups rely critically on talented scientists and engineers to commercialize new technologies. And yet these startups compete with established technology firms to hire the best workers. Theories of ability sorting predict that high-ability workers will choose jobs in established firms that offer greater complementary assets and higher pay, leaving low-ability workers to take lower paying and riskier jobs in startups. We propose an alternative view in which heterogeneity in both worker ability and preferences enable startups to hire talented workers who have a taste for a startup work environment even at lower pay. Using a longitudinal survey that follows 2,394 science and engineering PhDs from graduate school into their first industrial employment, we overcome common empirical challenges by observing ability and stated preferences prior to entry into the labor market. We find that both ability and career preferences strongly predict startup employment with high-ability workers who prefer startup employment being the most likely to work in a startup. We show that this partly reflects dual selection effects whereby worker preferences result in a large pool of startup job applicants and startups make job offers to the most talented workers. Additional analyses confirm that startup employees earn approximately 17% lower pay. This gap is greatest for high-ability workers and persists over workers' early careers, suggesting that they accept a negative compensating differential in exchange for the nonpecuniary benefits of startup employment. Data on job attributes and stated reasons for job choice further support this interpretation.
Automotive Procurement Under Opaque Prices: Theory with Evidence from the BMW Supply Chain
Danko Turcic et al.
Management Science, forthcoming
Several features of automotive procurement distinguish it from the prototypical supply chain in the academic literature: pass-through pricing that reimburses suppliers for raw material costs, market frictions that prohibit cost transparency and imbue suppliers with pricing power, and contractual commitments that span multiple production periods. In this context, we formalize a procurement model by considering an automaker that buys components from an upstream supplier to assemble cars over several production periods in an environment where period demands and raw material costs are both stochastic. Our paper clarifies how information asymmetry and market factors that amplify or weaken this asymmetry affect the firms' procurement protocol preferences. Then, using proprietary contract and supplier data from BMW, we empirically validate this model and show that it reflects BMW's reality: the factors that should theoretically go into automotive procurement decisions do so. Our analysis also reveals that existing contracting protocols in this context are not optimal for procurement under asymmetric information, and so we propose an alternative contracting method. We calibrate our model and estimate an automaker's performance improvement from this optimal contract over the status quo.