Publication records

Using a longitudinal dataset of research collaborations over 15 years at Stanford University, we build a theory of intraorganizational task relationships that distinguishes the different factors associated with the formation and persistence of network ties. We highlight six factors: shared organizational foci, shared traits and interests, tie advantages from popularity, tie reinforcement from third parties, tie strength and multiplexity, and the instrumental returns from the products of ties. Findings suggest that ties form when unfamiliar people identify desirable and matching traits in potential partners. By contrast, ties persist when familiar people reflect on the quality of their relationship and shared experiences. The former calls for shallow, short-term strategies for assessing a broad array of potential ties; the latter calls for long-term strategies and substantive assessments of a relationship’s worth so as to draw extended rewards from the association. This suggests that organizational activities geared toward sustaining persistent intraorganizational task relationships need to be different from activities aimed at forging new ones.

With permission of Sage

Our objective is to understand how a firm’s product development capability (PDC) affects the launch strategy for a durable product that is sequentially improved over time in a market where consumers have heterogeneous valuations for quality. We show that the launch strategy of firms is affected by the degree to which consumers think ahead. However, only the strategy of firms with high PDC is affected by the observability of quality. When consumers are myopic and quality is observable, both high and low PDC firms use price skimming and restrict sales of the first generation to consumers with high willingness to pay (WTP). A high PDC firm, however, sells the second generation broadly while a low PDC firm only sells the second generation to consumers with low WTP. When consumers are myopic and quality is unobservable, a firm with high PDC signals its quality by offering a low price for the first generation, which results in broad selling. The price of the second generation is set such that only high WTP consumers buy. A firm with low PDC will not mimic this strategy. If a low PDC firm sells the first generation broadly, it cannot discriminate between the high and low WTP consumers. When consumers are forward looking, a firm with high PDC sells the first generation broadly. This mitigates the “Coase problem” created by consumers thinking ahead. It then sells the second generation product only to the high WTP consumers. In contrast, a firm with low PDC does the opposite. It only sells the first generation to high WTP consumers and the second generation broadly.

 

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Customer decision-making uncertainty (DMU) is a persistent phenomenon in business-to-business markets. However, there is substantial variation in the degree customers perceive DMU and hence suppliers should react to it. Based on existing industrial buying typologies, this paper proposes a new classification scheme to explain variance in customer DMU. To this end, market offering complexity and co-creation are used as defining dimensions and four ideal types of industrial market offerings are constructed. We show theoretically that DMU is especially prevalent for complex solutions. The paper closes with guidance for suppliers of industrial market offerings and an outlook for future research.

With permission of Emerald

In diagnostic services, agents typically need to weigh the benefit of running an additional test and improving the accuracy of diagnosis against the cost of delaying the provision of services to others. Our paper analyzes how to dynamically manage this accuracy/congestion trade-off. To that end, we study an elementary congested system facing an arriving stream of customers. The diagnostic process consists of a search problem in which the service provider conducts a sequence of imperfect tests to determine the customer's type. We find that the agent should continue to perform the diagnosis as long as her current belief that the customer is of a given type falls into an interval that depends on the congestion level as well as the number of performed tests thus far. This search interval should shrink as congestion intensifies and as the number of performed tests increases if additional conditions hold. Our study reveals that, contrary to diagnostic services without congestion, the base rate (i.e., the prior probability of the customer type) has an effect on the agent's search strategy. In particular, the optimal search interval shrinks when customer types are more ambiguous a priori, i.e., as the base rate approaches the value at which the agent is indifferent between types. Finally, because of congestion effects, the agent should sometimes diagnose the customer as being of a given type, even if test results indicate otherwise. All these insights disappear in the absence of congestion.

© 2013 INFORMS
This publication was a finalist of the 2016 Service SIG Best Paper Competition.

We present results from two experiments that reveal significant gender differences in ordering behavior in the newsvendor problem. In high margin settings, males tend to order more than females, and they also tend to achieve higher profits. There are no gender differences in low margin settings. We show that the observed gender differences are partially driven by (or mediated by) gender differences in risk appetite. Males tend to prefer taking greater risk than women, and this leads them to order more in the newsvendor problem (in high margin settings). We show that the risk-ordering relationship is related to financial risk attitudes but not to social risk attitudes, and also that the effect is not driven by gender differences in affect, a likely alternative explanation for the results.

With permission of Elsevier

Argues that, despite their large investments in data scientists, and IT tools, many companies are struggling to capitalize on the massive amounts of data now available to them. Suggests that most managers are employing the conventional approach to designing and installing IT systems and this approach may not be appropriate for exploiting the tremendous amounts of data now available from internal and external sources. Notes that a big data or analytics project is likely to be smaller and shorter than a conventional IT initiative, such as installing an enterprise resource planning (ERP) system. Identifies five guidelines for taking an alternative approach to IT systems development: place people who will create meaning from the information at the heart of the initiative; unlock value from IT by asking second-order questions and giving teams the freedom to reframe business problems; equip teams with cognitive and behavioural scientists, who understand how people perceive problems and analyse data.; focus on learning by facilitating information sharing, examining assumptions, and striving to understand cause and effect; and worry more about solving business problems than about deploying technology.

In July 2010, André Glardon, who is the regional sales director of a major health care products provider in Spain, returns from an unpleasant customer visit. The head of administration to the Hospital de Gandía, one of his important customers, just delayed the order of two MRIs, making it almost impossible for André to reach his personal annual sales targets. External factors such as the health care reforms, the exploding costs of the health care system, a shift in the buying behavior of hospitals and increased economic pressures on hospitals, might be causing a fundamental shift in the industry. Through the implementation of new business models that address these issues. André and two friends developed a business idea and were wondering whether they should push this idea within their company or leave their company and launch the business on their own.