Productivity, efficiency, and production functions in research library technical service operations

The potential for increasing productivity of research library technical processing systems is investigated. This is a difficult question because research libraries are (1) complex organizations comprised of several unique systems, (2) not managed as economic systems; (3) do not measure the productivity of their operations; and (4) have no defined production functions. Methods to improve productivity can be investigated, but productivity cannot be investigated directly. The methodology approaches the question indirectly. Three research questions are asked. First, are there efficiency variations? Four efficiency measures are derived from the production management literature. Second, can the causes of variations be identified? Two scale factors (e.g., labor capacity and workflow volume) and four management factors (e.g., labor adjustment, production sequence, workflow variability system differentiation) are hypothesized to cause variations. Third, might factors that affect efficiency, affect productivity? Regression models test the relationships between factors and two components of productivity measure, (1) products generated, and (2) resources used.;Three productivity improvement methods are considered: (1) scientific advances, (2) technological developments, and (3) operational changes. Only operational changes are financially practical for research libraries. This method is tested on the technical processing system, that resembles a continuous production system with consistent inputs, definable processes, and quantifiable outputs. As an intermediate library system, descriptive technical services statistics are not published. Therefore, simulation models of technical services operations are constructed to provide a data.;The simulation model is a continuous processing system with three stages: materials acquisition, cataloging and physical processing. The parameters of the simulation model are varied to generate data that represent 120 different technical services systems.;A broad range of efficiency values is demonstrated. Some technical services systems are more efficient than others. The regression statistics are strong, but not all six factors are significant. The implications for productivity gains in technical services are not encouraging. The coefficients for the scale factors suggest that diseconomies of scale will occur. The coefficients for the management factors were larger, but the effects were not significant when compared to the annual volume of work.