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New research fuels the race to make work better

In today’s fast-moving, competitive, and highly connected world, we know there’s a direct relationship between good work and successful outcomes. Good work helps organizations achieve their goals, and good work makes employees feel effective and engaged.

But how do we know what good—or better yet, great—looks like? In this article, we look at some of the leading science that Microsoft is partnering with researchers on to answer some of the big questions about how people work and to help improve their effectiveness and empowerment.

For instance, take collaboration. We know meetings are necessary for effective collaboration: to advance projects, to support processes, to get things done. But done badly, meetings can also be the Achilles’ heel of a company and workforce. Every one of us has been in ineffective meetings that make us want to run away screaming in frustration. Email presents a similar problem: we need it to communicate and to do our work. At the same time, many of us are drowning in it, taking time away from other critical tasks to deal with our Frankensteining inboxes.

Is there a ‘right’ level of meetings and email, at which employees are most productive and organizations perform well—something we should aim for?

Jeff Polzer, Harvard Business School researcher and UPS Foundation professor of Human Resource Management

We all want good collaboration. But what does that mean, and what measurable impact can better meetings and email behavior have on outcomes such as how companies perform?

Jeff Polzer wants to answer just that question. The Harvard Business School researcher and UPS Foundation Professor of Human Resource Management is interested in the challenge employees face trying to strike the right balance of collaborative activities. Is there a “right” level of meetings and email, at which employees are most productive and organizations perform well—something we should aim for? And what’s the tipping point—can we pinpoint when levels of collaboration become problematic? What happens when over-collaboration pushes people to multitask more? Polzer, whose research in this area is ongoing, wants to know.

It isn’t just random curiosity. Questions such as this have a huge impact on organizations and on the economy. Once we identify the leading drivers of business outcomes, we can better predict and control those outcomes. Companies can map a clear line from behaviors, processes, and previously ephemeral concepts such as “meeting culture” to their bottom line. On a macro level, this understanding can be applied to enrich whole systems of work, reshaping how we approach productivity and drive revenue.

Once we identify the leading drivers of business outcomes, we can better predict and control those outcomes.

Uncovering overload—and why it matters

This type of collaboration research also helps organizations and employees make positive change. To be effective, any company’s measurement and change programs must be tied to science, because this is not only what helps us understand how to change, but what gets people on board to reshape their behavior. People don’t want to feel like they are just a quota; they want to feel intrinsically valued. If employees feel that their company is only interested in measuring their performance, they will find ways to game any change system or intervention.

And the need to study the impacts of human behavior on work is ongoing, because as behavior changes, people, cultures, and perceptions shift, and our need for new knowledge continues.

At Microsoft, we’re invested in cutting-edge collaboration research and understanding the factors that influence everyday work so that we can apply new, deep understanding of behaviors and their economic impact to the products we create that help organizations and employees. Collaborating with leading academics including Polzer, we are tackling some of the biggest and most fascinating questions about productivity, networks, leadership, organizational agility, culture, workspace planning, and more. Our customers are part of this journey, too, partnering in academic efforts to understand more about their own organizations and make a mark helping to evolve our collective approach to work.

Take the collaboration question: Polzer and his research team found evidence across a large sample of deidentified firms that more meeting hours per week were associated with greater company revenue up to a point, supporting the purported merits of collaboration. However, the pattern of revenue in firms with high levels of meeting hours plateaued and then declined, consistent with anecdotal stories of collaboration overload. They found similar effects for the volume of emails sent by employees. The combination of high meeting hours and emails was especially troublesome, as was the tendency for some firms to have high levels of emails sent during meetings—a form of multitasking that, when overdone, was a signal of decreased performance. For any given level of meetings and emails, firms were better off if this collaborative burden was shared more equally across employees rather than disproportionately shouldered by a subset of people.

At Microsoft, we’re invested in cutting-edge research about how work happens and the factors that influence everyday work, so that we can apply new, deep understanding of behaviors and their economic impact to the products we create that help organizations and employees.

These large sample aggregate results highlight the potential downsides of too much collaboration.

The exact point at which declines in performance set in depended on industry, company size, and even the legacy of the period in which the company was founded. Many factors combine to determine the optimal level of collaboration, and therefore the level at which it becomes counterproductive, highlighting the need for more granular research to understand how this phenomenon operates in specific companies.

Investing in research to drive innovation

We can also look at a topic like innovation. Companies want to be innovative, and so they need their employees to innovate. But what factors enable this? From research, we’ve learned one factor is something called employee voice.

Ethan Burris, professor of Management and the Chevron Centennial Fellow at the University of Texas at Austin’s McCombs School of Business, worked with us to study this. Economists, governments, and company leaders alike are very interested in the idea of innovation. Many a midsize city is asking, “How do we become a little bit like Silicon Valley—how do we bring good jobs and economic growth to our cities?” They want to know what innovative work looks like and how to get it.

Previously this was near impossible for most organizations to measure. Now, though, we can. We have data signals—such as those coming from email inboxes and other collaboration—that we can quantify to see how work happens.

By predicting employee voice, organizations can do more to empower employees and create the kind of culture that fosters desired outcomes such as innovation.

Ethan Burris, professor of Management and the Chevron Centennial Fellow at the University of Texas at Austin’s McCombs School of Business

What does this have to do with employee voice? Research has shown that when employees feel they have a voice in their company, they are empowered to do things like speak out when they see a problem and speak up when they see a better way of doing something or have an innovative idea. When people trust that if they use their voice they will be listened to, contribute value, and maybe even be rewarded, that spurs them to put forth the effort. Employee voice is key to an innovative culture.

By looking at large, anonymized datasets and working with Microsoft Workplace Analytics, Burris was able to show through his study a correlation between the number of meetings employees take part in and their sense of having a voice in their workplace. By predicting employee voice, organizations can do more to empower employees and create the kind of culture that fosters desired outcomes such as innovation.

Another area of study helping to make our workplace intelligence tools more accurate is country culture. Many organizations want to crystallize a benchmark for good—what’s an ideal meeting length, or an ideal division of collaboration and focus time? But for metrics to be useful, they need to be translated and applied within the context and values of cultures and people. How often people want to meet is impacted, for instance, by their culture’s norms. Piling into a room for eight hours on a Monday might seem like a death knell for one team in one country; for another somewhere else it might seem well within the norm for how things get done.

To help companies as cross-cultural work and digital collaboration increases, Polzer wanted to better understand how the cultural values of the countries in which people work affect their patterns of meeting and email activity. For instance, do organizations that operate in collectivistic societies spend more time in meetings and send more emails than their individualistic counterparts? Using Geert Hofstede’s measures for culture and aggregated, anonymized datasets that spanned workforces across more than 50 countries, Polzer discovered that the country culture impacts meetings and email patterns. Companies originating from countries with a high level of individualism, like the United States, tend to email more and meet less often. Companies operating in cultures that are more collectivistic meet more often and email less. When companies merge from these individualistic and collectivistic cultures, the individualistic based employees meet more often. Polzer reported his findings in the Academy of Management Proceedings.

Research such as this is important because it can be used to better understand meeting culture when looking for healthy patterns.

From data to action

By now you can likely see how excited we get by harnessing data intelligence to develop all types of solutions and interventions that sit at the intersection of science, technology, and the future of work. Research can answer all kinds of interesting questions related to how we work and how changing the way we work based on research findings can point us in the right direction to improve both the employee experience and business results. They also help us here at Microsoft build better tools for that purpose.

For instance, we’re partnering with leading turnover prediction experts to develop a data model to help companies predict in real time who is likely to quit. Working with Brooks C. Holtom, professor of Management, McDonough School of Business at Georgetown University​ and David G. Allen, associate dean for Graduate Programs, TCU Neeley School of Business, we set out​ to help companies save money and stay competitive by retaining top talent. By using big data and machine learning, we’ve been able to identify risk signals that can empower leaders to proactively engage valued employees to increase the odds that they stay. The potential here for insights that help both companies and employees is huge; work is early, and research is ongoing.

Organizations and workers today have endless metrics at their fingertips. To gain transformative insights, those that have real value and can predictably and powerfully shape how we work, we need to contextualize data and the behaviors it quantifies with science. It’s essential to measure things like productivity, agility, employee engagement, and innovation.

By using big data and machine learning, we’ve been able to identify risk signals that can empower leaders to proactively engage valued employees to increase the odds that they stay.

Brooks C. Holtom, professor of Management, McDonough School of Business at Georgetown University​<br>David G. Allen, associate dean for Graduate Programs, TCU Neeley School of Business

But to impact business outcomes, we need to define these concepts that we measure—to arrive at a collective definition backed by science. We are doing that with our academic partners and applying learnings to our products. And we’re excited that customers can also be part of this impactful journey to create new science on how work works. To that end, we’ll regularly update this space with opportunities for Microsoft customers to get involved in the newest studies on work, and we’ll share key findings as this fascinating research progresses.

Categories:

Workplace Analytics
Portrait of Chantrelle Nielsen

Chantrelle Nielsen

Chantrelle Nielsen directs research and strategy for Workplace Analytics, a new organizational productivity category at Microsoft. She led product management, marketing, and several other functions at VoloMetrix as they grew and were acquired by Microsoft.

Portrait of Neha Shah

Neha Shah

Neha Shah is a Director of Solution Design in Microsoft's Workplace Intelligence organization. Before joining Microsoft, she was a management professor at Rutgers Business School. Her expertise focuses on social networks in the workplace, particularly on linking network science with organizational and management behavior. Prior to her academic work, Neha was a strategy consultant. She has a PhD in organizational behavior from UCLA and is based in New York.

Portrait of Chris Gideon

Chris Gideon

Chris Gideon is Principal Data Science and Research Manager on Microsoft's Modern Workplace Transformation team. Using unique Microsoft 365 collaboration data Chris helps customers gain deep insights into the way their people work and adapt to the rapid pace of change. In addition, Chris works with academic researchers to analyze behavioral data from collaboration.