Many companies, such as GE, the Gap, 9 9. What these companies want to build—objectives that are more fluid and changeable than annual goals, frequent feedback discussions rather than annual or semiannual ones, forward-looking coaching for development rather than backward-focused rating and ranking, a greater emphasis on teams than on individuals—looks like the exact opposite of what they are abandoning.
Identifying clear overperformers and underperformers is important, but conducting annual ratings rituals based on the bell curve will not develop the workforce overall. Instead, by getting rid of bureaucratic annual-review processes—and the behavior related to them—companies can focus on getting much higher levels of performance out of many more of their employees. Good data are crucial to the new processes, not least because so many employees think that the current evaluation processes are full of subjectivity.
Rather than relying on a once-a-year, inexact analysis of individuals, companies can get better information by using systems that crowdsource and collect data on the performance of people and teams. Continually crowd-sourcing performance data throughout the year yields even better insights.
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For instance, Zalando, a leading European e-retailer, is currently implementing a real-time tool that crowd-sources both structured and unstructured performance feedback from meetings, problem-solving sessions, completed projects, launches, and campaigns. The system then weights responses by how much exposure the provider has to the requestor.
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For every kind of behavior that employees seek or provide feedback about, the system—a structured, easy-to-use tool—prompts a list of questions that can be answered intuitively by moving a slider on the touchscreen of a mobile device. Because the data are collected in real time, they can be more accurate than annual reviews, when colleagues and supervisors must strain to remember details about the people they evaluate. The tool facilitates requests for feedback and keeps a record of when it is received. GE is also changing the language of feedback to emphasize coaching and development rather than criticism.
GE employees get both quantitative and qualitative information about their performance, so they can readjust rapidly throughout the year. Crucially, the technology does not replace performance conversations between managers and employees. GE hopes to move most of its employees to this new system by the end of In other words, tools can automate activities not just to free up time that managers and employees now spend inefficiently gathering information on performance but also transform what feedback is meant to achieve.
The quality of the data improve, too. Because they are collected in real time from fresh performance events, employees find the information more credible, while managers can draw on real-world evidence for more meaningful coaching dialogues. As companies automate activities and add machine learning and artificial intelligence to the mix, the quality of the data will improve exponentially, and they will be collected much more efficiently. Finally, performance-development tools can also identify the top performers more accurately, though everyone already knows subjectively who they are.
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Managers could adjust the size of the pool of top performers to capture, say, the best 8 or 12 percent of employees. Managers will therefore have a fact-based, objective way to identify truly distinctive employees. Companies can also use such systems to identify those who have genuinely fallen behind. Relatively easy and inexpensive to build or to buy and customize , such performance-development applications are promising—but challenging see the exhibit for a generic illustration of such an app.
Employees could attempt to game systems to land a spot among the top 10 percent or to ensure that a rival does not. Artificial intelligence and semantic analysis might conceivably distinguish genuine from manicured performance feedback, and raters could be compared with others to detect cheating. Some employees may also feel that Big Brother is watching and evaluating their every move. These and other real-life challenges must be addressed as more and more companies adopt such tools.
The next step companies can take to move performance management from the industrial to the digital era is to take the anxiety out of compensation. But this move requires managers to make some counterintuitive decisions. Conventional wisdom links performance evaluations, ratings, and compensation. This seems completely appropriate: most people think that stronger performance deserves more pay, weaker performance less.
To meet these expectations, mean performance levels would be pegged around the market average. Overperformance would beat the market rate, to attract and retain top talent. And poor scores would bring employees below the market average, to provide a disincentive for underperformance. This logic is appealing and consistent with the Gaussian view.
In fact, the distribution guide, with its target percentages across different ratings, gives companies a simple template for calculating differentiated pay while helping them to stay within an overall compensation budget. No doubt, this is one of the reasons for the prevalence of the Gaussian view. This approach, however, has a number of problems. First, the cart sometimes goes before the horse: managers use desired compensation distributions to reverse engineer ratings.
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To pay Tom x and Maggie y , the evaluator must find that Tom exceeds expectations that Maggie merely meets. They breed cynicism, demotivate employees, and can make them combative, not collaborative. Second, linking performance ratings and compensation in this way ignores recent findings in the cognitive sciences and behavioral economics. The research of Nobel laureate Daniel Kahneman and others suggests that employees may worry excessively about the pay implications of even small differences in ratings, so that the fear of potential losses, however small, should influence behavior twice as much as potential gains do.
Although this idea is counterintuitive, linking performance with pay can demotivate employees even if the link produces only small net variances in compensation.
Since only a few employees are standouts, it makes little sense to risk demotivating the broad majority by linking pay and performance. More and more technology companies, for instance, have done away with performance-related bonuses.
Success with significance
Employees are free to focus on doing great work, to develop, and even to make mistakes—without having to worry about the implications of marginal rating differences on their compensation. Still, companies can remove a major driver of anxiety for the broad majority of employees. Finally, researchers such as Dan Pink say that the things which really motivate people to perform well are feelings like autonomy, mastery, and purpose.
In our experience, these increase as workers gain access to assets, priority projects, and customers and receive displays of loyalty and recognition. Snapping the link between performance and compensation allows companies to worry less about tracking, rating, and their consequences and more about building capabilities and inspiring employees to stretch their skills and aptitudes. A large Middle Eastern technology company recently conducted a thorough study of what motivates its employees, looking at combinations of more than variables to understand what fired up the best people.
Variables studied included multiple kinds of compensation, where employees worked, the size of teams, tenure, and performance ratings from colleagues and managers. The company found that meaning —seeing purpose and value in work—was the single most important factor, accounting for 50 percent of all movement in the motivation score. In some cases, higher-paid staff were markedly less motivated than others. Many of the companies that have moved in this direction use generous stock awards that make employees up and down the line feel not only well compensated but also like owners.
Companies lacking shares as currency may find it harder to make the numbers work unless they can materially boost corporate performance. The growing need for companies to inspire and motivate performance makes it critical to innovate in coaching—and to do so at scale. Many companies and experts are exploring how to improve coaching—a topic of the moment. Concrete vignettes, made available just in time by handy tools—and a shared vocabulary for feedback—provide a helpful scaffolding.
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But managers unquestionably face a long learning curve for effective coaching as work continues to change and automation and reengineering configure job positions and work flows in new ways. Companies in high-performing sectors, such as technology, finance, and media, are ahead of the curve in adapting to the future of digital work. More companies will need to follow—quickly. They ought to shed old models of calibrated employee ratings based on normal distributions and liberate large parts of the workforce to focus on drivers of motivation stronger than incremental changes in pay.
Meanwhile, companies still have to keep a keen eye on employees who are truly outstanding and on those who struggle.
##Audiobook## Human Performance Consulting: Transforming Human Potent…
Ironically, companies like GE are using technology to democratize and rehumanize processes that have become mechanistic and bureaucratic. Embed Size px. Start on. Show related SlideShares at end. WordPress Shortcode. Published in: Business.
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