People Are More Open to Algorithmic Judgment Than You Might Think

People Are More Open to Algorithmic Judgment Than You Might Think

When it comes to making judgments based on large data sets, machines are often superior to humans, but many business leaders remain skeptical of the guidance produced by their organizations’ data analytics programs, particularly when it comes to talent analytics. That skepticism derives largely from doubts about the quality of the data the organization is collecting, but there is also a natural tendency among people who make strategic decisions for a living to reject the notion that an algorithm could do parts of their job as well as or better than they can.

While this may be true of executives and high-level professionals, some recent research suggests that most people are actually comfortable with the decisions algorithms make and even more trusting of them than of judgments made by humans. A new study from the Harvard Business School, led by post-doctoral fellow Jennifer M. Logg, finds that “lay people adhere more to advice when they think it comes from an algorithm than from a person”:

People showed this sort of algorithm appreciation when making numeric estimates about a visual stimulus (Experiment 1A) and forecasts about the popularity of songs and romantic matches (Experiments 1B and 1C). Yet, researchers predicted the opposite result (Experiment 1D). Algorithm appreciation persisted when advice appeared jointly or separately (Experiment 2). However, algorithm appreciation waned when people chose between an algorithm’s estimate and their own (versus an external advisor’s—Experiment 3) and they had expertise in forecasting (Experiment 4). Paradoxically, experienced professionals, who make forecasts on a regular basis, relied less on algorithmic advice than lay people did, which hurt their accuracy.

Our colleagues here at Gartner have also investigated consumers’ attitudes toward AI and found that these attitudes are more welcoming than conventional wisdom might lead you to believe. The 2018 Gartner Consumer AI Perceptions Study found that overall, consumers are not skeptical of the potential usefulness of AI, though they do have some concerns about its impact on their skills, social relationships, and privacy. The study was conducted online during January and February 2018 among 4,019 respondents in the US and UK. Respondents ranged in age from 18 through 74 years old, with quotas and weighting applied for age, gender, region, and income.

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How Can We Design Flexibility to Meet Different Employees’ Work-Life Balance Needs?

How Can We Design Flexibility to Meet Different Employees’ Work-Life Balance Needs?

In a meta-analysis of recent studies on flexible work policies, professors Ellen Ernst Kossek and Brenda A. Lautsch looked at whether these programs had consistent benefits for all types of workers: e.g., hourly or salaried, managerial or professional, and high- or low-income. Discussing their findings at the Harvard Business Review, Kossek and Lautsch register their dismay that in most of these studies, these distinctions weren’t even explored. “Despite the many studies on the topic,” they write, “it is rare for scholars to consider occupational differences across workers in the need for, and experience of, work-life flexibility.”

That’s a problem, the authors underscore, because employees in different roles and circumstances diverge significantly in terms of access to flexibility and other work-life balance programs, with varying consequences for their quality of life and work:

What exactly do we know about how kinds of work-life flexibility benefit employees in different jobs the most? First, not every employee faces the same work-life challenges, has access to the same types of flexibility, or experiences outcomes from them in the same way. For example, retail, food, and other workers in hourly jobs that pay at or close to the minimum wage often struggle to get sufficient predictable (and sometimes enough) work hours to care for their families. They would benefit from being able to control their work hours through flex time and having greater control over schedules and time off, as well as the ramping up of hours when it fits their lives. Yet these are the workers who rarely have access to control over when they work.

In addition, access to other work-life flexibility practices that affect the ability to take time off and the continuity of work, like paid sick and parental leaves, is critical to these hourly workers. It is also largely unavailable to them.

These authors’ point about how employees differ in their work-life challenges and the kinds of benefits they need resonates with something we’ve observed in our research at CEB, now Gartner, over the past several years and that is coming into ever greater focus in our ongoing work: Work-life balance is a broad category of need, for which no HR department can possibly design a one-size-fits-all solution.

Last week, we hosted Genentech’s Head of People Analytics Chase Rowbotham for a webinar. One of the projects he described was an analysis his team did to understand the effects of commute times on employees’ likelihood of leaving. Based on those findings, Genentech is rolling out a new “Working Flexibly” philosophy and toolkit, among a series of initiatives geared toward improving the employee experience. It’s intentionally a philosophy, not a policy, precisely because of this variation in what working flexibly can and should look like for different segments of the workforce. (CEB Corporate Leadership Council members who missed the webinar can watch a replay of it on our member site.)

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A Lesson in Using Predictive Analytics to Drive Retention

A Lesson in Using Predictive Analytics to Drive Retention

When we talk to HR leaders about predictive analytics, the first thing they usually want to do with this new advanced tool is improve retention. That’s definitely easier said than done, especially if you want the project to actually drive results, instead of just being an interesting research topic. Aliah Wright at SHRM highlights the success story of one organization that had a strong need to retain its highly skilled employees and used predictive analytics to help meet that goal:

When a top employee at the Anderson Center for Autism, a private school in Staatsburg, N.Y., handed in her resignation, the school’s HR department was expecting her. The HR staff had been using a predictive analytics program to help them gauge retention. “The software is so good that we were developing a retention plan for her as she was preparing to resign,” said Gregg Paulk, director of information technologies for the 92-year-old nonprofit organization. After HR staff spoke with her, “she actually rescinded her resignation,” he added. …

In 2001, the school undertook a new technology initiative spurred and funded by the No Child Left Behind legislation. Using Ultimate Software’s UltiPro, Paulk said the company “grew … and kept head count flat, reduced paper [processes] by 95 percent, and increased the time spent on employee development by 30 percent. The software also allows staff to manage time and attendance from anywhere [and yields] improved reporting and compliance.

“The software also helped us avoid the loss of key talent with predictive tools. It’s really powerful, and it’s astonishing the results we’ve seen,” Paulk said. “[The tools] helped us understand our challenges and put the puzzle pieces together.”

It looks like Anderson has done a couple of things really well, which makes it a great example of how to apply analytics most effectively.

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The Challenge of Mainstreaming Human Capital Reporting

The Challenge of Mainstreaming Human Capital Reporting

David McCann at CFO Magazine takes a look at two groups—Principles for Responsible Investment and the Human Capital Management Coalition—that are working to encourage more organizations to publicly disclose human capital metrics like turnover, absenteeism, and employee engagement. It’s an uphill battle for these advocates, McCann writes, as not everyone is convinced that these metrics are worth using or disclosing:

[A] former finance and marketing executive waves off the notion that such data is a good barometer of management quality. Tom McGuire, now talent strategy leader at Talent Growth Advisors, says: “Whether a company is well-run is a good question, but a more relevant one is, how do its people impact its value? To understand that, you need to look at the company’s intellectual capital—patents, brands, and proprietary technologies and methodologies. The only source of any of those things is people.”

For that reason, McGuire also quarrels with the idea that disclosure about a company’s entire workforce has much value. … Similarly, [Jeff Higgins, CEO of the Human Capital Management Institute,] points out, if you lose 20% of management in a year, that’s way too high. Losing 20% of your call-center workers is OK. It’s also fine if 20% of a retailer’s customer-facing staff is lost. But it’s disastrous if a professional services firm has 20% turnover among customer-facing professionals. The metrics that come out of the investor groups’ engagements with retailers may be used to compare the companies with one another, but it’s unknown how granular the information is, so therefore it’s unknown how useful such comparisons will be.

This discussion shows that investors are starting to understand the importance of human capital when looking at the value of a company and looking for ways to get data and information related to that. But actually understanding what the metrics are saying and how to interpret them is harder than it may seem. This might be a place where talent analytics professionals and HR leaders more broadly can step in and educate their peers in corporate leadership about not only which metrics to share but also how to think about them. People data is only going to be as strong as the story built around it.

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What’s in a Job Title?

What’s in a Job Title?

A recent survey commissioned by the staffing firm Spherion looks at how employees feel about their job titles. One quarter of employees, the survey found, “consider non-traditional job titles unprofessional and are against the idea of having one,” while 23 percent also said these types of titles don’t accurately describe their roles:

Although not every company may have a “Chief Happiness Officer” on the payroll, Spherion found that creatively-named roles are merely a small part of employees’ overall professional title dissatisfaction. Nearly half (42 percent) of today’s workers feel their job title does not accurately reflect their true roles and responsibilities. However, even employees in favor of more traditional titles believe they could use improvement, as 14 percent consider monikers such as “Project Manager” or “Specialist” too generic.

“Employees take great pride in their job titles, and in some cases, a title that is considered limiting or hard to describe can significantly impact their job satisfaction,” said Sandy Mazur, Spherion Division President. “As businesses face greater pressure to retain and recruit top workers, reexamining how different titles are perceived and applied can make a big difference in building morale and positioning a company as a favorable place to work.”

Job titling is an area of frustration from the employer perspective too. At CEB, we asked HR leaders last year if they were planning to reduce the number of job titles in their organizations. About two-thirds of HR leaders reported that they had done so, or would consider doing so. Their goals were to create consistent titling nomenclature across the organization, increase clarity of career opportunities for employees, emphasize titles that provide clarity and impact in external markets, reduce the gap between junior- and senior-level employees (in other words, flatten organizational hierarchy), and reduce overspecialization of roles and responsibilities. On the flip side, only 2 percent of organizations were letting employee choose their own job titles (though another 17 percent said they might consider it).

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What’s Next for Performance Reviews?

What’s Next for Performance Reviews?

In the past year or two, many organizations have radically reformed their approach to performance reviews, in some cases ditching them altogether, and in many cases keeping the review but getting rid of performance scores or ratings, as GE became the latest major employer to do in July. On the other hand, our research at CEB has found that removing ratings tends to diminish the quality of the review process and doesn’t help performance; others have questioned this new trend as well. Looking at all the changes going on in this field (and drawing heavily on CEB research!), Knowledge@Wharton considers what’s working, what’s not, and what the future holds for performance management:

While the traditional annual performance review is surely dying, [Peter] Cappelli, who is also director of Wharton’s Center for Human Resources, has a wait-and-see attitude about whether employers will really create a different kind of relationship with employees, or end up doing less performance appraisal and replacing it with nothing instead.

“For a lot of companies that are thinking about this change, they are just copying what other companies are doing,” he says. “We will see a lot of false starts on this thing, and then they will discover their relationship with employees is worse off. The thing I would watch is to what extent this is an ideological battle. Is it all about the money, all about rewarding people — that [this is] how things get done, we have to punish the bad employee and fire them? Is it all about the economic incentive? Or is it much more about relationships and developing people and encouraging them to perform better? It’s an ideological divide that has to do with human nature. And to some extent that’s at the heart of this whole issue.” …

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Analytics Initiatives Demand Enterprise Leaders, Not C-Suite Politicians

Analytics Initiatives Demand Enterprise Leaders, Not C-Suite Politicians

Going over some new research on the impact analytics has had on their clients, EY’s Chris McShea, Dan Oakley, and Chris Mazzei write at the Harvard Business Review that “efforts to adopt analytics upset the balance of power in the C-suite, and this shift often had a negative impact on analytics initiatives”:

Shaped by history, personalities, and events, levels of influence the members of the C-suite were not all equal. But in order to function effectively, the rivalries and politics had evolved to a tacit equilibrium. While skirmishes occurred constantly on recurring allocation matters (i.e., budgets and plans), the balance of power proved to be quite resilient. This benefited these organizations in many ways, including providing a stable direction for employees.

But the commitment to advanced analytics disrupted this equilibrium. Since there was no natural owner of analytics within the traditional organizational structure, multiple executives competed hard to own the new capability. While not every C-suite member wanted to manage such a high-stakes opportunity, the most powerful members were eager to oversee an influential new pool of talent and command more time on the board’s agenda. With the exception of the “winner,” a feeling of vulnerability settled over the other executive team members when the analysis conducted by the analytics group revealed inefficiencies and missed opportunities in their respective functions.

This is another example of why organizations need enterprise leaders.

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