Academic Articles

What can machine learning do? Workforce implications
Erik Brynjolfsson and Tom Mitchell. 12/22/2017. “What can machine learning do? Workforce implications.” Science, 358, 6370, Pp. 1530-1534. Publisher's VersionAbstract
Digital computers have transformed work in almost every sector of the economy over the past several decades (1). We are now at the beginning of an even larger and more rapid transformation due to recent advances in machine learning (ML), which is capable of accelerating the pace of automation itself. However, although it is clear that ML is a “general purpose technology,” like the steam engine and electricity, which spawns a plethora of additional innovations and capabilities (2), there is no widely shared agreement on the tasks where ML systems excel, and thus little agreement on the specific expected impacts on the workforce and on the economy more broadly. We discuss what we see to be key implications for the workforce, drawing on our rubric of what the current generation of ML systems can and cannot do [see the supplementary materials (SM)]. Although parts of many jobs may be “suitable for ML” (SML), other tasks within these same jobs do not fit the criteria for ML well; hence, effects on employment are more complex than the simple replacement and substitution story emphasized by some. Although economic effects of ML are relatively limited today, and we are not facing the imminent “end of work” as is sometimes proclaimed, the implications for the economy and the workforce going forward are profound.
Small Cues Change Savings Choices
James J.Choi, Emily Haisley, Jennifer Kurkoski, and Cade Massey. 2017. “Small Cues Change Savings Choices.” Behavioral Evidence Hub. Publisher's VersionAbstract


Researchers tested the effects of including cues, anchors, and savings goals in a company email encouraging employee contributions to their 401(k).


Researchers found that providing high contribution rate or savings goal examples, or highlighting high savings thresholds created by the 401(k) plan rules, increased 401(k) contribution rates by 1-2% of income per pay period.

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Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them
Berkeley Dietvorst, Joseph P. Simmons, and Cade Massey. 6/13/2015. “Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them.” SSRN. Publisher's VersionAbstract
Although evidence-based algorithms consistently outperform human forecasters, people often fail to use them after learning that they are imperfect, a phenomenon known as algorithm aversion. In this paper, we present three studies investigating how to reduce algorithm aversion. In incentivized forecasting tasks, participants chose between using their own forecasts or those of an algorithm that was built by experts. Participants were considerably more likely to choose to use an imperfect algorithm when they could modify its forecasts, and they performed better as a result. Notably, the preference for modifiable algorithms held even when participants were severely restricted in the modifications they could make (Studies 1-3). In fact, our results suggest that participants’ preference for modifiable algorithms was indicative of a desire for some control over the forecasting outcome, and not for a desire for greater control over the forecasting outcome, as participants’ preference for modifiable algorithms was relatively insensitive to the magnitude of the modifications they were able to make (Study 2). Additionally, we found that giving participants the freedom to modify an imperfect algorithm made them feel more satisfied with the forecasting process, more likely to believe that the algorithm was superior, and more likely to choose to use an algorithm to make subsequent forecasts (Study 3). This research suggests that one can reduce algorithm aversion by giving people some control - even a slight amount - over an imperfect algorithm’s forecast.
The Bright Side of Being Prosocial at Work, and the Dark Side, Too
Mark C. Bolino and Adam Grant. 2016. “The Bright Side of Being Prosocial at Work, and the Dark Side, Too.” The Academy of Management Annals. Publisher's VersionAbstract
More than a quarter century ago, organizational scholars began to explore the implications of prosociality in organizations. Three interrelated streams have emerged from this work, which focus on prosocial motives (the desire to benefit others or expend effort out of concern for others), prosocial behaviors (acts that promote/protect the welfare of individuals, groups, or organizations), and prosocial impact (the experience of making a positive difference in the lives of others through one’s work). Prior studies have highlighted the importance of prosocial motives, behaviors, and impact, and have enhanced our understanding of each of them. However, there has been little effort to systematically review and integrate these related lines of work in a way that furthers our understanding of prosociality in organizations. In this article, we provide an overview of the current state of the literature, highlight key findings, identify major research themes, and address important controversies and debates. We call for an expanded view of prosocial behavior and a sharper focus on the costs and unintended consequences of prosocial phenomena. We conclude by suggesting a number of avenues for future research that will address unanswered questions and should provide a more complete understanding of prosociality in the workplace.
Shifts and Ladders: Comparing the Role of Internal and External Mobility in Managerial Careers
Matthew Bidwell and Ethan Mollick. 10/5/2015. “Shifts and Ladders: Comparing the Role of Internal and External Mobility in Managerial Careers.” Organization Science, 26, 6, Pp. 1553-1804. Publisher's VersionAbstract
Employees can build their careers either by moving into a new job within their current organization or else by moving to a different organization. We use matching perspectives on job mobility to develop predictions about the different roles that those internal and external moves will play within careers. Using data on the careers of master of business administration alumni, we show how internal and external mobility are associated with very different rewards: upward progression into a job with greater responsibilities is much more likely to happen through internal mobility than external mobility; yet despite this difference, external moves offer similar increases in pay to internal, as employers seek to attract external hires. Consistent with our arguments, we also show that the pay increases associated with external moves are lower when the moves take place for reasons other than career advancement, such as following a layoff or when moving into a different kind of work. Despite growing interest in boundaryless careers, our findings indicate that internal and external mobility play very different roles in executives’ careers, with upward mobility still happening overwhelmingly within organizations.
Job Titles as Identity Badges: How Self-Reflective Titles Can Reduce Emotional Exhaustion
Adam Grant, Justin Berg, and Daniel Cable. 2014. “Job Titles as Identity Badges: How Self-Reflective Titles Can Reduce Emotional Exhaustion.” Academy of Management Journal, 57, 4, Pp. 1201–1225. Publisher's VersionAbstract
Job titles help organizations manage their human capital and have far-reaching implications for employees’ identities. Because titles do not always reflect the unique value that employees bring to their jobs, some organizations have recently experimented with encouraging employees to create their own job titles. To explore the psychological implications of self-reflective job titles, we conducted field research combining inductive qualitative and deductive experimental methods. In Study 1, a qualitative study at the Make-A-Wish Foundation, we were surprised to learn that employees experienced self-reflective job titles as reducing their emotional exhaustion. We triangulated interviews, observations, and archival documents to identify three explanatory mechanisms through which self-reflective job titles may operate: selfverification, psychological safety, and external rapport. In Study 2, a field quasiexperiment within a health care system, we found that employees who created selfreflective job titles experienced less emotional exhaustion five weeks later, whereas employees in two control groups did not. These effects were mediated by increases in self-verification and psychological safety, but not external rapport. Our research suggests that self-reflective job titles can be important vehicles for identity expression and stress reduction, offering meaningful implications for research on job titles, identity, and emotional exhaustion.
Deliberate Practice Spells Success: Why Grittier Competitors Triumph at the National Spelling Bee
Angela Lee Duckworth, Teri A. Kirby, Eli Tsukayama, Heather Berstein, and K. Anders Ericsson. 10/4/2010. “Deliberate Practice Spells Success: Why Grittier Competitors Triumph at the National Spelling Bee.” Social Psychological and Personality Science, 2, 2, Pp. 174–181. Publisher's VersionAbstract

The expert performance framework distinguishes between deliberate practice and less effective practice activities. The current longitudinal study is the first to use this framework to understand how children improve in an academic skill. Specifically, the authors examined the effectiveness and subjective experience of three preparation activities widely recommended to improve spelling skill. Deliberate practice, operationally defined as studying and memorizing words while alone, better predicted performance in the National Spelling Bee than being quizzed by others or reading for pleasure. Rated as the most effortful and least enjoyable type of preparation activity, deliberate practice was increasingly favored over being quizzed as spellers accumulated competition experience. Deliberate practice mediated the prediction of final performance by the personality trait of grit, suggesting that perseverance and passion for long-term goals enable spellers to persist with practice activities that are less intrinsically rewarding—but more effective—than other types of preparation.

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Doing Better but Feeling Worse: Looking for the “Best” Job Undermines Satisfaction
Sheena S. Iyengar, Rachael E. Wells, and Barry Schwartz. 2/1/2006. “Doing Better but Feeling Worse: Looking for the “Best” Job Undermines Satisfaction.” Psychological Science, 17, 2, Pp. 143–150. Publisher's VersionAbstract

Expanding upon Simon's (1955) seminal theory, this investigation compared the choice-making strategies of maximizers and satisficers, finding that maximizing tendencies, although positively correlated with objectively better decision outcomes, are also associated with more negative subjective evaluations of these decision outcomes. Specifically, in the fall of their final year in school, students were administered a scale that measured maximizing tendencies and were then followed over the course of the year as they searched for jobs. Students with high maximizing tendencies secured jobs with 20% higher starting salaries than did students with low maximizing tendencies. However, maximizers were less satisfied than satisficers with the jobs they obtained, and experienced more negative affect throughout the job-search process. These effects were mediated by maximizers' greater reliance on external sources of information and their fixation on realized and unrealized options during the search and selection process.

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Necessary Evils and Interpersonal Sensitivity in Organizations
Joshua Margolis and Andrew Molinsky. 4/1/2005. “Necessary Evils and Interpersonal Sensitivity in Organizations.” The Academy of Management Review, 30, 2, Pp. 245-268. Publisher's VersionAbstract

In order to produce a beneficial result, professionals must sometimes cause harm to another human being. To capture this phenomenon, we introduce the construct of "necessary evils" and explore the inherent challenges such tasks pose for those who must perform them. Whereas previous research has established the importance of treating victims of necessary evils with interpersonal sensitivity, we focus on the challenges performers face when attempting to achieve this prescribed standard in practice.

See paper summary below, link to full article here

Predicting students' happiness from physiology, phone, mobility, and behavioral data
Natasha Jaques. 2015. “Predicting students' happiness from physiology, phone, mobility, and behavioral data”. Publisher's VersionAbstract

In order to model students' happiness, we apply machine learning methods to data collected from undergrad students monitored over the course of one month each. The data collected include physiological signals, location, smartphone logs, and survey responses to behavioral questions. Each day, participants reported their wellbeing on measures including stress, health, and happiness. Because of the relationship between happiness and depression, modeling happiness may help us to detect individuals who are at risk of depression and guide interventions to help them. We are also interested in how behavioral factors (such as sleep and social activity) affect happiness positively and negatively. A variety of machine learning and feature selection techniques are compared, including Gaussian Mixture Models and ensemble classification. We achieve 70% classification accuracy of self-reported happiness on held-out test data.

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