Research Roundup: Team Performance, Demystifying Market Composition and the Reality vs. Hype of Sponsored Search

How do interpersonal relationships affect the performance of individual team members? Why is a shopping mall composed the way it is, and how do different stores affect each other’s business? Do higher-ranked sponsored search listings pay off in terms of a company’s bottom line? Wharton professors Jennifer Mueller, Maria Ana Vitorino and Kartik Hosanagar, respectively, examine these issues — and what they mean for business — in recent research articles.

How Better Interpersonal Relationships Lead to More Effective Team Members

When it comes to teams, less is sometimes more. In a recent paper, Wharton management professor Jennifer Mueller found that while larger teams generally are more productive overall than smaller ones, members of the bigger groups were less fruitful individually than their counterparts on the smaller teams. The research, “Why Individuals in Larger Teams Perform Worse,” was published in the August issue of the journal Organizational Behavior and Human Decision Processes.

“There are costs to collaborating,” says Mueller. “In larger teams, one of those costs is that people may not have the time and energy to form relationships that really help their ability to be productive.” Mueller became interested in the issue of how team size impacted individual performance after reading through material collected from 26 corporate design teams as part of an ongoing research project led by Teresa Amabile, a professor at Harvard Business School. Through the research group, Mueller had access to journals and questionnaires provided by the 238 people on teams tasked with developing a host of products and services, including inventing a new type of dental floss, designing a new airline ticket purchase process and creating a cut-resistant fiber to be worn by factory workers. The content of the journals was eye-opening, Mueller says. “I started to recognize that employees in these larger design teams experienced incredible amounts of stress. People often said, ‘I don’t feel I can get the resources to do what they want me to do.’ One person referred to the experience as a ‘death march.’”

Mueller also began to see a pattern — the stress level seemed higher for members of larger teams. “On a smaller team, people knew what resources were available and felt they could ask questions when things went wrong. The situation was more controllable,” Mueller states. “But in these larger teams, people were lost. They didn’t know who to call for help because they didn’t know the other members well enough. Even if they did reach out, they didn’t feel the other members were as committed to helping or had the time to help. And they couldn’t tell their team leader because [it would look like] they had failed.”

The challenges of larger teams are well studied in academic literature. Mueller says that one meta-analysis showed that larger groups tend to perform better than small groups, but the group performance gains for every additional member are minimal because individuals in these larger groups perform worse than individuals in smaller groups. Previous work has focused on two culprits behind this: motivation and coordination loss. The first stems from the reality that people may not work as hard if their contribution is likely to be lost, or go unrewarded, due to the size of the team. Coordination loss refers to the difficulty getting all the disparate elements of a large team to work well together.

But Mueller suspected that there was a third force at work: relational loss. According to the paper, “Relational losses specifically involve perceptions about the extent to which teammates are likely to provide help, assistance and support in the face of struggle or difficulty.” Mueller’s theory was that this deterioration in connections between team members increased with team size, resulting in weaker performance on average by individual participants.

To assess the impact of relational loss, Mueller gathered questionnaires from the 238 team members from the Harvard study throughout their product or service development effort. The questionnaires included performance evaluations of each individual from both their peers and the team leader. The questionnaires also probed team members on their motivation, the team’s coordination and the degree to which they felt connected to other people in the group. By creating models around that data, Mueller was able to show that the stress caused by a lack of connection to other members of the group was a key driver behind the lower performance of individuals on the larger teams.

“There was some evidence of coordination loss, but I did not see evidence of motivation loss,” Mueller notes. “I saw compelling evidence for relational loss — it loomed larger than you might expect given how much emphasis is given to coordination.” Less-than-optimal relationships make people on a team feel isolated and unsupported, Mueller says, particularly when problems surface. That anxiety can have a direct impact on a team member’s performance. “Stress soaks up your cognitive resources and diminishes the extent to which you can hold information in your memory. That contributes to a decline in performance.”

Mueller’s findings offer important insights on how companies should be approaching team-based initiatives. Given the complexity of product development projects, it may be impossible to gather all of the needed expertise within a small group of people, necessitating the formation of a larger group. But Mueller says finding a way to enhance the connections between members of those large teams is critical to improving their individual effectiveness. “One thing teams could do is to have a person who has the role of troubleshooter — the one who steps in to help when stuff goes wrong.” The troubleshooter knows what skills and resources are available to the team, and can bring the right people together to address problems. “This role could help lubricate these relationships that don’t have the opportunity to form naturally,” Mueller notes. She adds that the “problem solver” position should not be filled by the team leader because team members may be reluctant to go to the boss to discuss problems.

What about trying to foster connections between members of a large team by simply creating opportunities for people to get to know each other better? If a team is likely to be in place for years, that sort of effort — including offsite team-building sessions — makes sense, Mueller says. But for teams that will only operate for a more limited period, those steps can simply take too long to bear fruit.

Building a Better Shopping Mall

No matter the locale, the stores that make up the average American shopping mall are often virtually interchangeable. Given the lack of broad-based, detailed profit data for specific shopping centers and stores, it can be difficult to determine why a market is composed the way it is, and how different stores affect each other’s business.

A recent research paper by Wharton marketing professor Maria Ana Vitorino attempts to demystify this process. In “Empirical Entry Games with Complementarities: An Application to the Shopping Center Industry,” Vitorino outlines a methodology that uses location data for mall anchor stores to show both the negative and positive effects department stores can have on each other’s business when they are situated in the same mall. Given that most mall developers have limited data beyond that of their own properties, Vitorino says her model can help them better determine the impact of losing an anchor store, and can improve their decisions about how to fill vacancies and what types of subsidies to offer retailers in exchange for signing a lease.

Most methods of looking at market composition assume that “the effect of entry of additional stores, or competition, is always negative,” Vitorino says. “It’s not a stupid assumption; it makes sense that in most cases, the entry of additional stores will decrease your profits.” But if that were always true, Vitorino notes, why would car dealerships consent to be part of auto malls, for example? And why would it make business sense for a department store to locate in a mall with other department stores?

There are few studies of the shopping mall market because it is difficult for researchers to obtain data from developers about sales, rent, lease lengths and other factors, Vitorino writes. “To study the determinants of shopping center configuration and measure stores’ strategic effects without any price or quantity data, empirical models of entry and market structure are especially convenient since these are primarily based on the observed number and types of stores in a given market, thus requiring little data.”

Vitorino developed her methodology using a sample of 564 regional shopping centers. She focused on anchor tenants — typically large chain stores that occupy the largest spaces in a shopping center — dividing them into three categories: upscale department stores (such as Dillard’s, Macy’s and Nordstrom), midscale department stores (including J.C. Penney, Mervyn’s and Kohl’s) and discount department stores (like Target, Sears or Wal-Mart.) Based on the way different stores were clustered within the shopping centers, Vitorino was able to form several conclusions about factors contributing to mall profitability. “Not all the effects are the same, and they are not all positive or all negative. Some types of stores seem to benefit from other stores’ presence, and others don’t,” she says.

For example, midscale department stores seem to benefit when more than one is located in a particular mall. Although Vitorino’s model does not test for specific reasons why this is true, she suggests that it may be because consumers in the demographic most likely to frequent J.C. Penney or Kohl’s also tend to be the type who shop around for the best bargains, rather than pledge their loyalty to a specific store. “They’re more likely to go to a mall where they have lots of options for this type of store, meaning those malls are going to attract more people,” Vitorino notes.

The model indicates that upscale stores like Nordstrom are negatively affected if another high-end tenant moves in; those stores benefit from having a midscale neighbor, however. Midscale department stores, though, don’t stand to gain from upscale anchors, Vitorino says. “The profile of a high-end shopper is more loyal, so that shopper is not going to benefit from having a lot of upscale stores around,” she points out. “A shopper from J.C. Penney is more likely to go to Nordstrom to look for bargains than a shopper from Nordstrom is likely to go to J.C. Penney.”

Although Vitorino says mall developers are probably aware of these effects, they usually have limited access to external data. “They can supplement this model with their own data and get finer estimates for these effects,” she notes.

Bursting the Bubble of Assumptions about ‘Sponsored Search’

In theory, it seems like a simple proposition for any advertiser who wants to reach target audiences by placing ads on pages that provide search-engine results: Bid high enough to win the top spot in these “sponsored search” listings, and you will attract more customers — and generate higher profits. No wonder more and more advertisers are paying ever-increasing prices to rank higher in the listings, which appear alongside the results to queries entered in a search engine. According to eMarketer.com, 40% of the projected $34 billion that will be spent on Internet advertising in 2014 will consist of “sponsored search.”

But do higher-ranked sponsored search listings really pay off in terms of a company’s bottom line? When Wharton operations and information management professor Kartik Hosanagar, University of Texas professor Ashish Agarwal and Carnegie Mellon professor Michael D. Smith tested the common assumptions about online ad auctions, they discovered that the reality was quite different from the hype. “We found that the ads that are in the top position get disproportionately higher clicks, but that they are not necessarily maximizing revenues” for those advertisers, notes Hosanagar. In their study, the conversion rate — the percentage of clicks that led to purchases — and revenue were often higher for ads when they were in the second, third or fourth position than when they were at the top of the list. Their study, “Location, Location, Location: An Analysis of Profitability of Position in Online Advertising Markets,” was published in the December 2011 issue of the Journal of Marketing Research.

The researchers discovered two major reasons behind that surprising pattern, according to Hosanagar. For one thing, ads that win the top position in search engine bidding auctions often attract consumers who are simply browsing for information about a particular product, with no real intentions to buy. Second, many serious shoppers will click on more than just the ad positioned first before proceeding with a purchase. And rather than return to the link positioned at the top, the consumer will buy from a lower-ranked site. The authors attribute this to a “recency bias” among consumers. For online shoppers, “the cost of evaluating an extra option is very little,” notes Hosanagar. Consumers will often evaluate product offerings from more than one advertiser and “purchase from the most recently evaluated advertiser if all evaluated options appear reasonable,” adds Smith. What most shoppers don’t realize, however, is the ad positioned at the top of the page may have cost the company significantly more than what firms paid to be positioned second or third.

In conducting their research, Hosanagar, Agarwal and Smith worked with two different advertisers over a period of six weeks. During each week of the study, the advertisers submitted random bids of various values for over 100 keywords, constantly changing the value of their bids so that their ad copy showed up at different positions on the sponsored Google search listings. The researchers observed the number of clicks by page visitors, and the number of purchases (“conversions”) by those who “click through” to the advertiser’s page. They then measured how click-through rates, conversions and revenues changed in relation to ad position.

Beyond bursting the bubble of hype surrounding sponsored search auctions, their research “also shows that different kinds of people behave differently [online], depending on their intentions,” Agarwal points out. Marketers and advertisers, he adds, need to tease out the differences between the ways a “window shopper” searches versus someone who is ready to buy. “Understand your target audience,” he says. “Know what your actual buyers are doing; don’t just go by the number of clicks.” Hosanagar adds that serious buyers are more likely to click through to a website for more information, or to make a purchase, even if a company’s ad is not positioned at the top of the search-engine listings.

What are the implications for search engine companies such as Google? The researchers note that most sponsored search is based on “pay-per-click” calculations: For example, if an advertiser bids $10 per click and there are ultimately 3,000 clicks on the company’s ad, the firm winds up paying close to $30,000. If there is only half that number of clicks, the advertiser pays half that amount. “Google does not actively encourage ‘pay for action’” contracts, says Hosanagar, in part because there is no proven technology for accurately measuring the impact of online ads on actual purchases.

Such a technology would not only have to measure how many consumers immediately made a purchase after clicking on a sponsored search ad, but also how many website visitors attracted by the ad went on to purchase that product days or weeks later — either online or at a local bricks-and-mortar store. Despite the technological challenges involved with such an approach, “we are encouraging the entire search engine ecosystem and its advertisers to take pay-for-action more seriously,” Hosanagar notes. “Technology for measuring pay-for-action is feasible in the long term, but search engines and advertisers will have to work together to create some recognized and transparent metrics for performance.”

For search engines, the study could spell the end of justification for paying the high price for top positioning in sponsored search listings, Hosanagar notes. The research has other implications for advertisers, he adds, depending on what they hope to achieve by advertising on search engines. Companies that are employing sponsored search as a way to create greater brand recognition might be willing to pay for top positioning, he says. On the other hand, advertisers that are focused on generating “transactional revenues” — attracting people who will click through an ad and buy something — “should be cautious about bidding for the top position.” Hosanagar advises companies to “be aware of how much you are paying and what you are getting for it. If you are a transactional advertiser, be careful to ensure that you are not over-paying. If you are not getting enough revenue, you should be lowering your bids.”

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