Beyond words: Testing alignment among inter-organizational performance measures

Purpose: This paper seeks to measure in a quantitative way the degree of alignment among a set of performance measures between two organizations. Design/methodology/approach: This paper extends Venkatraman's test of coalignment to assess the alignment of a set of performance measures governing a contractual inter-organizational relationship. The authors applied the test and present coefficients of misalignment across three sets of measures: those used by a service provider involved in the research, those used by customers contracting the services, and those documented in 11 contracts studied. Findings: Results confirmed a high degree of alignment between target and actual operational performance in the contracts. The alignment of customers' financial objectives and contracts' operational metrics was low. Calculations show poor alignment between the objectives of the provider and the contribution received from the contracts. Research limitations/implications: Some limitations of the conclusions include the small sample of contracts used in the calculations. Further research should include not only actual contracts, but also failed ones. Practical implications: It is possible that misaligned goals, represented in misaligned performance measures, lead to tensions in intra-firm relationships. If these tensions are not addressed properly the relationship could be unstable or terminated prematurely. This method of measuring alignment could detect early potential dangers in intra-firm relationships. Originality/value: This paper extends Venkatraman's test of coalignment to assess the alignment of a set of performance measures governing a contractual inter-organizational relationship. Management researchers and business professionals may use this methodology when exploring degrees of alignment of performance measures in intra-functional and inter-firm relationships. © Emerald Group Publishing Limited.

Emergency department crowding: prioritising quantified crowding measures using a Delphi study.

AimsEmergency department (ED) crowding has been associated with a number of negative health outcomes, including unnecessary deaths, increased waiting times and a decrease in care quality. Despite the seriousness of this issue, there is little agreement on appropriate crowding measures to assess crowding effects on ED operations. The objective of this study was to prioritise a list of quantified crowding measures that would assess the current state of a department.MethodsA three round Delphi study was conducted via email and an Internet based survey tool. The panel consisted of 40 professionals who had exposure to and expertise in crowding. Participants submitted quantified crowding measures which, through three rounds, were evaluated and ranked to assess participant agreement for inclusion.ResultsThe panel identified 27 measures of which eight (29.6%) reached consensus at the end of the study. These measures comprised: (1) ability of ambulances to offload; (2) patients who leave without being seen or treated; (3) time until triage; (4) ED occupancy rate; (5) patients' total length of stay in the ED; (6) time to see a physician; (7) ED boarding time; and (8) number of patients boarding in the ED.ConclusionsThis study resulted in the identification of eight quantified crowding measures, which present a comprehensive view of how crowding is affecting ED operations, and highlighted areas of concern. These quantified measures have the potential to make a considerable contribution to decision making by ED management and to provide a basis for learning across different departments.

Copula-based Kernel Dependency Measures

The paper presents a new copula based method for measuring dependence between random variables. Our approach extends the Maximum Mean Discrepancy to the copula of the joint distribution. We prove that this approach has several advantageous properties. Similarly to Shannon mutual information, the proposed dependence measure is invariant to any strictly increasing transformation of the marginal variables. This is important in many applications, for example in feature selection. The estimator is consistent, robust to outliers, and uses rank statistics only. We derive upper bounds on the convergence rate and propose independence tests too. We illustrate the theoretical contributions through a series of experiments in feature selection and low-dimensional embedding of distributions.