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This research aims to explore the fine-grained relationships between nurse staffing and hospital operational performance with respect to care quality and operating costs. The authors also investigate the moderation effect of competition in local hospital markets on these relationships.

A six-year panel data is assembled from five separate sources to obtain information of 2,524 USA hospitals. Fixed-effect (FE) models are used to test the proposed hypotheses.

First, nurse staffing is initially associated with improved care quality until nurse staffing reaches a turning point, beyond which nurse staffing is associated with worse care quality. Second, a similar pattern applies to the relationship between nurse staffing and operating costs, although the turning point is at a much lower nurse staffing level. Third, market competition moderates the relationship between nurse staffing and care quality so that the turning point of nurse staffing will be higher when the degree of competition is higher. This shift of turning point is also observed in the relationship between nurse staffing and operating costs.

The study identifies three ranges of nurse staffing in which hospitals will likely experience simultaneous improvements, a tradeoff or simultaneous decline of care quality and operating costs when investing in more nursing capacity. Hospitals should adjust nurse staffing levels to the right directions to achieve better care or reduce operating costs.

Nurses constitute the largest provider group in hospitals and profoundly impact care quality and operating costs among all health care professionals. Optimizing the level of nurse staffing, therefore, can significantly impact the care quality and operating costs of hospitals.

Drawing on transaction cost economics (TCE) theory and organizational information processing theory (OIPT), this study investigates how the alignments between the characteristics of service (i.e. task complexity and measurement ambiguity) and governance mechanisms (i.e. contract specificity and monitoring) can affect service performance.

The paper uses a rigorously designed survey to collect data from professionals who manage service outsourcing contracts in various industries. The respondent pool consists of randomly selected members of the Institute of Supply Management (ISM). The authors’ research question is analyzed using 261 completed and useable responses. Structural equation modeling is adopted to examine the data and test the proposed hypotheses.

The authors find that both contract specificity and monitoring have a positive impact on supplier performance. Further, for high task complexity services, contract specificity is more effective than monitoring, and for high measurement ambiguity services, the opposite is true. Moreover, the effect of contract specificity is mediated by monitoring.

Service outsourcers should use both contract specificity and monitoring in governing outsourced services and know that the former depends on the latter during execution. Facing resource constraints, they can prioritize crafting detailed contract provisions over implementing monitoring for highly complex services but consider monitoring as the primary governance tool in services whose outcomes are difficult to measure.

This study is the first to couple TCE with OPIT and consider the nature of outsourced services in the choice of governance mechanisms and empirically test the simultaneous effects of contract specificity and monitoring in the context of service outsourcing.

The purpose of this paper is to examine the strategic contingency of plant improvement capability and innovation capability.

Two forms of fit between the two capabilities and competitive priorities were empirically tested. Data collected from a sample of 238 manufacturing plants were used to test the hypotheses using regression.

The results provide partial support for fit as mediation. However, there was no evidence supporting fit as moderation. It was found that improvement capability and innovation capability are associated with different competitive priorities and also have varying impact on different operational performance dimensions.

There are two limitations to this research: only three operations management (OM) practices are included in each capability examined; and there are somewhat limited measures of competitive priorities and operational performance.

This study examines multiple forms of fit between competitive priorities and operations capabilities. The findings can inform managers to selectively implement OM practices for developing the needed operations capabilities given the chosen competitive priorities.

The impact of information technology (IT) on mass customization (MC) capability has been implied in the literature but seldom subjected to empirical examination. This study seeks to theoretically relate four types of IT applications with MC capability and empirically examines these relationships.

This study identifies four types of IT that potentially support MC capability, including product configurator IT, new product development IT, manufacturing IT, and supplier collaboration IT. Drawing on organizational information processing theory, this study associates the four IT types with a manufacturer's MC capability. A structural equation model is tested using survey data collected from a sample of manufacturing plants that focus on product customization.

The empirical results indicate that two of the four IT types strongly support a manufacturer's MC capability.

No strong relationship between configurator IT and MC was observed, which calls for further investigation. Data used are cross‐sectional in nature. A set of refined IT measures should be developed in future studies. In addition, future studies could control for the effects of more variables that may impact IT use by mass customizers.

The paper identifies managerial opportunities for investing in IT to support or enhance MC capability.

This study provides a theoretical foundation for the IT‐MC relationship and develops a classification framework of IT applications in manufacturing plants. The study is one of the first efforts that empirically examines the impact of multiple types of IT applications on MC.