This month, we will summarize various research results by INSEAD faculty. It includes simple rules for how companies capture long-term value. Another study shows how companies can structure reseller compensation to maximize profits.
Some research explains why some people are trusted more than others within a network. Finally, changes in the environment can influence whether employees act in the interest of their employer or to advance their own career goals.
1. When do employees pursue organizational and career goals?
As companies grow, they must continually adapt to changes in their environment. For example, delegation gives employees better access to important information and allows them to respond more quickly. However, this may also reduce the incentive to use the information for the employer’s benefit. Research by INSEAD’s Victoria Sevcenko and London Business School’s Sendil Ethiraj examines the trade-offs between information and incentives as the environment evolves.
Researchers used data from sell-side securities analysts at U.S. banks to study the impact of external and internal changes on employees’ career concerns. The result is Customer rightization causes employees to focus more on their career goals than on company goals, while positive shocks to company performance have the opposite effect.
Read the working paper
2. What does the price include?
When speculators consider trading personal information, they must consider whether the information is already priced in by the market. INSEAD’s Joel Perez and HEC Paris’ Daniel Schmidt suggest that speculators assess the novelty of information based on recent price movements, and market makers are concerned that speculators may be trading on old news. We developed a model that recognizes certain things.
Using a comprehensive sample of US stocks, this model shows that price increases have a smaller impact on buy volume than sell volume, as they can be attributed to old news trades. After the price goes down, the opposite happens.
Read the full paper
3. How signals in networks explain trust
Trust between people is often built through direct interactions, but little is known about how trust is built within networks. Michael Park and his collaborators* reveal why some people are more trusted than others.
Researchers have shown that networks generate signals of trustworthiness based on an individual’s position in the social structure and their networking behavior. Using data from an online social trading platform with more than 28,000 traders over 38 weeks, we found that traders with higher status in the network and those who expressed positive emotions in their communications were more likely to be trusted by others. It turned out that it was. Moreover, the positive effects of network status and network behavior mutually reinforced and amplified the accumulation of trust.
*Giuseppe Soda of Bocconi University, Aks Zahir and Mani Subramani of the University of Minnesota, and Bill McEvilly of the University of Toronto.
Read the full paper
4. Maximizing revenue when pricing decisions are delegated to agents
In B2B scenarios and B2C environments such as real estate and automobiles, companies tend to delegate pricing to sales agents. These agents can gather signals about customers’ willingness to pay through interactions, which should help them make better pricing decisions.
Atalay Atasu, Florin Ciocan Dragos, and Antoine Desiir studied the fundamental principal-agent problem when agents have the power to set prices. They developed a model that captures how an agent’s pricing decisions depend on the agent’s individual learning efforts and the contracts offered by the firm to induce desired learning and pricing behavior. This effort could allow companies to better manage revenue by optimizing agent compensation structures and maximizing profits.
Read the full paper
5. Simple rules for long-term profit distribution
Febo-Wivens reports the discovery of a simple law governing the allocation of long-term value capture for companies. This applies to different industries, regions, and time periods.
For example, it accurately explains that less than 1 percent of all companies in the dataset generated 73 percent of total long-term profits. Its distributional parameters provide a novel and accurate explanation of cross-environmental differences in economic outcomes, such as the rise of “superstars.” More broadly, the discovery of this law raises serious questions related to competitive strategy, evolutionary path dependence, the structure of technological opportunity, and social inequality.
Read the full paper
