There's no "I" in team, but getting your coworkers on the same "we" perspective can be tough. Here's why it's important, according to Rice University's research. Pexels

You just got a promotion — along with a brand-new work team whose members barely speak to one another. But first-rate cooperation is essential if you're going to deliver for your client. So you decide to spend a month getting to know each of your workers.

One is competent but bitter, frustrated by years of small mistakes by a colleague, mistakes that add to her own workload. Another, the one making the mistakes, seems so distracted he may as well be working at another company. Others have their own quirks. And to make matters worse, another department is set to merge its employees with your creaky, cranky team in a few months. How are you going to understand all these individuals, much less get them into shape as a unit?

For many managers, training and reading can help provide guidance. Others may hire an outside consultant and resort to team-building activities. But where does that outside expertise — not to mention training and reading — come from? It's based on academic research.

Rice Business professor Utpal Dholakia and colleagues René Algesheimer of the University of Zurich and Richard P. Bagozzi of the University of Michigan are among the scholars updating what we know about the dynamics of group decisions. Starting with classic group behavior theory, the scholars developed a series of sociologically-based models for analyzing small teams.

To better understand the existing shared intentions and attachment between teammates, Dholakia and his colleagues used a novel set of questions to survey 277 teams of computer gamers, each comprised of three people. They ran the survey responses through variations of a classic model called the Key Informant, which depends on the observations of group members about the social relationships inside a group.

Next, the researchers applied a sociological theory called Plural Subject Theory, focused on what's known as "we-attitude." That's exactly what it sounds like: verbally and actively treating an endeavor as a group project.

The core of this theory, the notion that successful teams frequently use collective pronouns when they discuss themselves and cognitively conceive of themselves as "we," has been heavily studied. Groups whose members think in terms of "we" act more cohesively and are measurably more committed to collectively reaching their goal.

To enhance the way these attitudes are measured, Dholakia created multiple variations of a new model. These differ from previous models because they include information not just from a "key informant," but from every member of a group. The researcher asks group members questions about themselves, their impressions of others in the group, their impressions about how others in the group think of each member and impressions about the group as a whole. This longer, more elaborate approach offers fresh insights about a group's shared consciousness — which provides a valuable new research outcome.

The professors found that this revision of classic key informant model generally worked the best of the various group-analysis models they tested — even improving on the original key informant approach. Future researchers, Dholakia notes, should consider the context of the team situation to decide which configuration of members is best to analyze.

So the next time you find yourself nonplussed by a chaotic group dynamic at work, remember you are in time-honored company — and that help is out there. By updating the key informant model, Dholakia and his colleagues have added to the analytical toolbox something that can help whip that team into shape. Whether it's an army of accountants or a network of hospital workers, Dholakia writes, the first step to creating a real team is analyzing which intentions they truly share.

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This article originally appeared on Rice Business Wisdom.

Utpal Dholakia is the George R. Brown Professor of Marketing at Jones Graduate School of Business at Rice University.

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Houston researcher builds radar to make self-driving cars safer

eyes on the road

A Rice University researcher is giving autonomous vehicles an “extra set of eyes.”

Current autonomous vehicles (AVs) can have an incomplete view of their surroundings, and challenges like pedestrian movement, low-light conditions and adverse weather only compound these visibility limitations.

Kun Woo Cho, a postdoctoral researcher in the lab of Rice professor of electrical and computer engineering Ashutosh Sabharwal, has developed EyeDAR to help address such issues and enhance the vehicles’ sensing accuracy. Her research was supported in part by the National Science Foundation.

The EyeDAR is an orange-sized, low-power, millimeter-wave radar that could be placed at streetlights and intersections. Its design was inspired by that of the human eye. Researchers envision that the low-cost sensors could help ensure that AVs always pick up on emergent obstacles, even when the vehicles are not within proper range for their onboard sensors and when visibility is limited.

“Current automotive sensor systems like cameras and lidar struggle with poor visibility such as you would encounter due to rain or fog or in low-lighting conditions,” Cho said in a news release. “Radar, on the other hand, operates reliably in all weather and lighting conditions and can even see through obstacles.”

Signals from a typical radar system scatter when they encounter an obstacle. Some of the signal is reflected back to the source, but most of it is often lost. In the case of AVs, this means that "pedestrians emerging from behind large vehicles, cars creeping forward at intersections or cyclists approaching at odd angles can easily go unnoticed," according to Rice.

EyeDAR, however, works to capture lost radar reflections, determine their direction and report them back to the AV in a sequence of 0s and 1s.

“Like blinking Morse code,” Cho added. “EyeDAR is a talking sensor⎯it is a first instance of integrating radar sensing and communication functionality in a single design.”

After testing, EyeDAR was able to resolve target directions 200 times faster than conventional radar designs.

While EyeDAR currently targets risks associated with AVs, particularly in high-traffic urban areas, researchers also believe the technology behind it could complement artificial intelligence efforts and be integrated into robots, drones and wearable platforms.

“EyeDAR is an example of what I like to call ‘analog computing,’” Cho added in the release. “Over the past two decades, people have been focusing on the digital and software side of computation, and the analog, hardware side has been lagging behind. I want to explore this overlooked analog design space.”

12 winners named at CERAWeek clean tech pitch competition in Houston

top teams

Twelve teams from around the country, including several from Houston, took home top honors at this year's Energy Venture Day and Pitch Competition at CERAWeek.

The fast-paced event, held March 25, put on by Rice Alliance, Houston Energy Transition Initiative and TEX-E, invited 36 industry startups and five Texas-based student teams focused on driving efficiency and advancements in the energy transition to present 3.5-minute pitches before investors and industry partners during CERAWeek's Agora program.

The competition is a qualifying event for the Startup World Cup, where teams compete for a $1 million investment prize.

PolyJoule won in the Track C competition and was named the overall winner of the pitch event. The Boston-based company will go on to compete in the Startup World Cup held this fall in San Francisco.

PolyJoule was spun out of MIT and is developing conductive polymer battery technology for energy storage.

Rice University's Resonant Thermal Systems won the second-place prize and $15,000 in the student track, known as TEX-E. The team's STREED solution converts high-salinity water into fresh water while recovering valuable minerals.

Teams from the University of Texas won first and second place in the TEX-E competition, bringing home $25,000 and $10,000, respectively. The student winners were:

Companies that pitched in the three industry tracts competed for non-monetary awards. Here are the companies named "most-promising" by the judges:

Track A | Industrial Efficiency & Decarbonization

Track B | Advanced Manufacturing, Materials, & Other Advanced Technologies

  • First: Licube, based in Houston
  • Second: ZettaJoule, based in Houston and Maryland
  • Third: Oleo

Track C | Innovations for Traditional Energy, Electricity, & the Grid

The teams at this year's Energy Venture Day have collectively raised $707 million in funding, according to Rice. They represent six countries and 12 states. See the full list of companies and investor groups that participated here.

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This article originally appeared on our sister site, EnergyCapitalHTX.com.