Harvard Business Review: Better People Analytics

Better People Analytics

Artificial Intelligence and Ethics

Artificial Intelligence and Ethics

How the Eagles Followed the Numbers to the Super Bowl

How the Eagles Followed the Numbers to the Super Bowl

How People Analytics Can Change Process, Culture, and Strategy

How People Analytics Can Change Process, Culture, and Strategy

University Took Uncommonly Close Look at Student-Conduct Data

Rutgers

Dodgers, Brewers show how analytics is changing baseball

Baseball

Little Privacy in the Workplace of the Future

Little Privacy in the Workplace of the Future

Google's Culture of Self-Surveying

Google

The Resume of the Future

The Resume of the Future

More Academic Articles

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

PROJECT SUMMARY

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

IMPACT

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.
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More Popular Press

There will be little privacy in the workplace of the future
3/28/2018. “There will be little privacy in the workplace of the future”.Abstract

Walk up a set of steep stairs next to a vegan Chinese restaurant in Palo Alto in Silicon Valley, and you will see the future of work, or at least one version of it. This is the local office of Humanyze, a firm that provides “people analytics”. It counts several Fortune 500 companies among its clients (though it will not say who they are). Its employees mill around an office full of sunlight and computers, as well as beacons that track their location and interactions. Everyone is wearing an ID badge the size of a credit card and the depth of a book of matches. It contains a microphone that picks up whether they are talking to one another; Bluetooth and infrared sensors to monitor where they are; and an accelerometer to record when they move.

“Every aspect of business is becoming more data-driven. There’s no reason the people side of business shouldn’t be the same,” says Ben Waber, Humanyze’s boss. The company’s staff are treated much the same way as its clients. Data from their employees’ badges are integrated with information from their e-mail and calendars to form a full picture of how they spend their time at work. Clients get to see only team-level statistics, but Humanyze’s employees can look at their own data, which include metrics such as time spent with people of the same sex, activity levels and the ratio of time spent speaking versus listening.

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A.I. as Talent Scout: Unorthodox Hires, and Maybe Lower Pay
Noam Scheiber. 12/6/2018. “A.I. as Talent Scout: Unorthodox Hires, and Maybe Lower Pay.” The New York Times. Publisher's VersionAbstract

One day this fall, Ashutosh Garg, the chief executive of a recruiting service called Eightfold.ai, turned up a résumé that piqued his interest.

It belonged to a prospective data scientist, someone who unearths patterns in data to help businesses make decisions, like how to target ads. But curiously, the résumé featured the term “data science” nowhere.

Instead, the résumé belonged to an analyst at Barclays who had done graduate work in physics at the University of California, Los Angeles. Though his profile on the social network LinkedIn indicated that he had never worked as a data scientist, Eightfold’s software flagged him as a good fit. He was similar in certain key ways, like his math and computer chops, to four actual data scientists whom Mr. Garg had instructed the software to consider as a model.

The idea is not to focus on job titles, but “what skills they have,” Mr. Garg said. “You’re really looking for people who have not done it, but can do it.”

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They're Watching You at Work
Don Peck. 12/2013. “They're Watching You at Work.” The Atlantic. Publisher's VersionAbstract

What happens when Big Data meets human resources? The emerging practice of "people analytics" is already transforming how employers hire, fire, and promote.

in 2003, thanks to Michael Lewis and his best seller Moneyball, the general manager of the Oakland A’s, Billy Beane, became a star. The previous year, Beane had turned his back on his scouts and had instead entrusted player-acquisition decisions to mathematical models developed by a young, Harvard-trained statistical wizard on his staff. What happened next has become baseball lore. The A’s, a small-market team with a paltry budget, ripped off the longest winning streak in American League history and rolled up 103 wins for the season. Only the mighty Yankees, who had spent three times as much on player salaries, won as many games. The team’s success, in turn, launched a revolution. In the years that followed, team after team began to use detailed predictive models to assess players’ potential and monetary value, and the early adopters, by and large, gained a measurable competitive edge over their more hidebound peers.

That’s the story as most of us know it. But it is incomplete. What would seem at first glance to be nothing but a memorable tale about baseball may turn out to be the opening chapter of a much larger story about jobs. Predictive statistical analysis, harnessed to big data, appears poised to alter the way millions of people are hired and assessed.

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Meet Your New Boss: An Algorithm

Meet Your New Boss: An Algorithm

A.I. as Talent Scout: Unorthodox Hires, and Maybe Lower Pay

A.I. as Talent Scout: Unorthodox Hires, and Maybe Lower Pay

The Performance Management Revolution

Performance Management

Amazon scrapped 'sexist AI' tool

Amazon AI

Making it easier to discover datasets

Google AI

HR Must Make People Analytics More User-Friendly

HR Must Make People Analytics More User-Friendly

More Harvard Business Review

The Most Valuable People in Your Network
Rob Cross. 3/8/2011. “The Most Valuable People in Your Network.” Harvard Business Review. Publisher's VersionAbstract

Too often new collaborative technologies — though intended to connect employees seamlessly and enable work to get done more efficiently — are misused in ways that impede innovation and hurt performance.

Age-old wisdom suggests it is not what but whom you know that matters. Over decades this truism has been supported by a great deal of research on networks. Work since the 1970s shows that people who maintain certain kinds of networks do better: They are promoted more rapidly than their peers, make more money, are more likely to find a job if they lose their own, and are more likely to be considered high performers.

But the secret to these networks has never been their size. Simply following the advice of self-help books and building mammoth Rolodexes or Facebook accounts actually tends to hurt performance as well as have a negative effect on health and well-being at work. Rather, the people who do better tend to have more ties to people who themselves are not connected. People with ties to the less-connected are more likely to hear about ideas that haven’t gotten exposure elsewhere, and are able to piece together opportunities in ways that less-effectively-networked colleagues cannot.

If bigger is not better in networks, what is the actual impact of social media tools in the workforce? The answer: They are as likely to actually hurt performance and engagement as they are to help — if they simply foist more collaborative demands on an already-overloaded workforce. In most places, people are drowning in collaborative demands imposed by meetings, emails, and phone calls. For most of us, these activities consume 75% to 90% of a typical work week and constitute a gauntlet to get to the work we must do. In this context, new collaborative technologies, when not used appropriately, are over-loading us all and diminishing efficiency and innovation at work.

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The Performance Management Revolution
Peter Cappelli and Anna Tavis. 10/2016. “The Performance Management Revolution.” Harvard Business Review. Publisher's VersionAbstract

When Brian Jensen told his audience of HR executives that Colorcon wasn’t bothering with annual reviews anymore, they were appalled. This was in 2002, during his tenure as the drugmaker’s head of global human resources. In his presentation at the Wharton School, Jensen explained that Colorcon had found a more effective way of reinforcing desired behaviors and managing performance: Supervisors were giving people instant feedback, tying it to individuals’ own goals, and handing out small weekly bonuses to employees they saw doing good things.

Back then the idea of abandoning the traditional appraisal process—and all that followed from it—seemed heretical. But now, by some estimates, more than one-third of U.S. companies are doing just that. From Silicon Valley to New York, and in offices across the world, firms are replacing annual reviews with frequent, informal check-ins between managers and employees.

How We Got Here

Historical and economic context has played a large role in the evolution of performance management over the decades. When human capital was plentiful, the focus was on which people to let go, which to keep, and which to reward—and for those purposes, traditional appraisals (with their emphasis on individual accountability) worked pretty well. But when talent was in shorter supply, as it is now, developing people became a greater concern—and organizations had to find new ways of meeting that need.

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Reinventing Talent Management: How GE Uses Analytics to Guide a More Digital, Far-Flung Workforce
Steven Prokesch. 9/2017. “Reinventing Talent Management: How GE Uses Analytics to Guide a More Digital, Far-Flung Workforce.” Harvard Business Review. Publisher's VersionAbstract

During Jeff Immelt’s 16 years as CEO, GE radically changed its mix of businesses and its strategy.

Its focus—becoming a truly global, technology-driven industrial company that’s blazing the path for the internet of things—has had dramatic implications for the profile of its workforce. Currently, 50% of GE’s 300,000 employees have been with the company for five years or less, meaning that they may lack the personal networks needed to succeed and get ahead. The skills of GE’s workforce have been rapidly changing as well, largely because of the company’s ongoing transformation into a state-of-the-art digital industrial organization that excels at analytics. The good news is that GE has managed to attract thousands of digerati. The bad news is that they have little tolerance for the bureaucracy of a conventional multinational. As is the case with younger workers in general, they want to be in charge of their own careers and don’t want to depend solely on their bosses or HR to identify opportunities and figure out the training and experiences needed to pursue their professional goals.

What’s the solution to these challenges? GE hopes it’s HR analytics. “We need a set of complementary technologies that can take a company that’s in 180 countries around the world and make it small,” says James Gallman, who until recently was the GE executive responsible for people analytics and planning. The technologies he’s referring to are a set of self-service applications available to employees, leaders, and HR. All the apps are based on a generic matching algorithm built by data scientists at GE’s Global Research Center in conjunction with HR. “It’s GE’s version of Match.com,” quips Gallman. “It can take a person and match him or her to something else: online or conventional educational programs, another person, or a job.”

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