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.
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.
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.
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.
You want to know which teams are at the forefront of analytics? Just look around at the teams still playing.
Once upon a time, there was the Oakland Athletics and a sacred tome called "Moneyball." It was about baseball teams winning with statistics. Only it wasn't about that at all. It was about market inefficiency. Then John Henry bought the Boston Red Sox, hired Bill James, made Theo Epstein his general manager, and Moneyball spread to a big market.
We're several iterations past all of that. Things move fast in technology, so fast it can even carry a tradition-based industry like baseball into the digital age. These days, every team is playing Moneyball. All of them, as in 30 for 30.
"At this point, I think everyone assumes that their counterpart is smart," Brewers general manager David Stearns said. "And everyone is doing what they can do to unearth competitive advantages." To call it Moneyball is not right, either. Michael Lewis is still turning out ground-breaking work, but to fully capture what is happening in big league front offices, circa 2018, the next inside look at analytics and baseball would need to be authored by someone like the late Stephen Hawking. It's hard to say what you'd call it. "The Singularity" has already been taken.
Organizational Network Analysis (ONA) is the set of scientific methods and theories to help understand interactions within an organization. It helps executives and managers to intervene at critical times, increase performance, and reduce costs.
There’s increasing pressure on executives to drive sustained, long-term growth. Yet, they lack the information they need to make informed business decisions and successfully initiate change. As organizations restructure departments to have fewer hierarchical levels, work increasingly occurs between social networks, rather than though prescribed reporting structures. Research shows that employees look to their networks to find information and to solve problems. Communication no longer flows solely from senior management to individual contributors – information moves through social networks, between colleagues and different teams. Organizations can analyze social networks to assess how information flows between teams and to intervene at critical times in order to improve how work gets done.
– Explore the benefits of supporting organizational networks – How network analysis can impact company performance – How to interpret network graphs – Business applications of ONA for human resources, business processes, and corporate real estate decisions
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.
The most illuminating moment of the Eagles’ enchanted season was a Week 3 play ridiculed in Philadelphia but celebrated here by a small cadre of people who recognized its significance almost immediately.
What fueled the excitement among members of the EdjSports crew was not the outcome of the play — a 6-yard sack of Carson Wentz on fourth-and-8 that gifted the Giants good field position — but rather the call itself. Leading by 7-0 on the Giants’ 43-yard line a few minutes before halftime, the Eagles opted not to punt.
By keeping Philadelphia’s offense on the field in a situation almost always played safe in the risk-averse N.F.L., Coach Doug Pederson did not buck conventional wisdom so much as roll his eyes at it.
An intern at EdjSports, responding to a flurry of text messages from his colleagues about the play, ran the numbers at home. The Eagles, by going for it, improved their probability of winning by 0.5 percent. Defending his decision (again) at a news conference the next day, Pederson cited that exact statistic.
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.”
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.
Without models, making sense of data is hard. Data helps describe reality, albeit imperfectly. On its own, though, data can’t recommend one decision over another. If you notice that your best-performing teams are also your most diverse, that may be interesting. But to turn that data point into insight, you need to plug it into some model of the world — for instance, you may hypothesize that having a greater variety of perspectives on a team leads to better decision-making. Your hypothesis represents a model of the world.
Though single models can perform well, ensembles of models work even better. That is why the best thinkers, the most accurate predictors, and the most effective design teams use ensembles of models. They are what I call, many-model thinkers.