Project-level Data Research Articles

Capturing Knowledge within and across Firm Boundaries: Evidence from Clinical Development

How do firm boundaries influence employees' acquisition of information? Using detailed project-level data and qualitative evidence, I document that pharmaceutical firms are more likely to outsource the coordination of data-intensive clinical trials, while they are more likely to assign knowledge-intensive trials to internal teams. Managers do not choose between market and hierarchy, but between the hierarchy of the firm—in which subjective performance evaluations are combined with flat explicit incentives—and the hierarchy of its subcontractor—whose virtue stems precisely from the ability to provide high-powered incentives on a narrow set of monitorable tasks.

Integrating Education and Human Service Plans: The Interagency Planning and Support Project

Individual service/support plans from several different agencies were integrated to improve coordination and service delivery, and address difficulties encountered by families whose children are served by multiple agencies. Components of the Interagency Planning and Support model are described, and data on the development, adoption, and utilization of the Collaborative Support Plan (CSP) are presented from a sample of 34 families and 49 providers. Analysis of project level, parent, provider, and administrative data revealed the approach was successful in producing an integrated service plan that was valued by participants and was determined by parents to be better than previous service planning experiences. Factors affecting the success of development, adoption, and utilization of this innovative approach are described.

Strategic management of supplier–manufacturer relations in new product development

This paper discusses the role of contractual and organizational arrangements for the governance of supplier–manufacturer relationships in new product development projects. We present cross-sectional project level data from 50 manufacturer–supplier relations in new product development in the European Major Home Appliance industry using a single dyadic interaction as the unit of analysis, to combine institutional decisions driving the governance of inter-firm relations and their organizational implementation. Our results show that (a) relational outcome depends on the type of joint activities, (b) it can be decomposed into short term (efficiency) and long term (learning) effects, (c) less articulate types of joint development activities increase efficiency, while more articulate joint development activities increase partner’s learning, and (d) inter-organizational structuring decisions are significant explanatory variables to understand relational outcome. Implications for the organization of vertical collaboration in new product development projects are derived focusing on the emerging trade-off between short and long term objectives as a central issue in guiding relational strategies.

Roughness Level Probability Prediction Using Artificial Neural Networks

The accuracy of pavement condition prediction is a major concern associated with a pavement management system (PMS). The PMS used by the Kansas Department of Transportation (KDOT) includes a project-level optimization system that requires models that estimate the probability of a given level of distress occurring. These models are based on historical pavement condition data and specific project-level data concerning pavement structural characteristics, traffic, and climatic conditions. Multiple linear regression and two artificial neural network (ANN) structures are used to predict roughness distress level probability for bituminous pavements as defined by the KDOT PMS. Since the response variable is the probability of being in a given roughness distress level, within the historical database the probability values are binary (1 if the pavement exists in a given roughness distress level or 0 if the pavement is in any other roughness distress level). This produces poorly conditioned data for regression analysis. Therefore, results indicate that ANNs have a superior ability to predict the probability of roughness distress level compared with multiple regression methods.

R&D Costs, Innovative Output and Firm Size in the Pharmaceutical Industry

This study examines the relationships between firm size, R&D costs and output in the pharmaceutical industry. Project–level data from a survey of 12 US–owned pharmaceutical firms on drug development costs, development phase lengths and failure rates are used to determine estimates of the R&D cost of new drug development by firm size. Firms in the sample are grouped into three size categories, according to their pharmaceutical sales at the beginning of the study period. The R&D cost per new drug approved in the US is shown to decrease with firm size, while sales per new drug approved are shown to increase markedly with firm size. Sales distributions are highly skewed and suggest that firms need to search for blockbuster drugs with above–average returns. The results are consistent with substantial economies of scale in pharmaceutical R&D, particularly at the discovery and preclinical development phases.