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In investigating the performance of multidivisional organizations, ability to account for each division's importance and contribution enhances the deftness of resource allocation and targeting desired outcomes. With this motivation, the author aims to introduce network data envelopment analysis (NDEA) from operations research in this conceptual article and discuss how two articles from this journal can be extended using this approach.

NDEA was first developed to deliver a more in-depth understanding of underlying sources of operational inefficiency. Thus, NDEA can be viewed as a peer benchmarking method useful in comparing performance of organizations and identifying divisional inefficiencies that may detract from overall performance. NDEA's ability to capture interactions among multiple variables in an objective manner based on actual observed data rather than sample averages is one of its key advantages.

The author discusses how NDEA can be applied in organisational analysis by examining two articles from this journal. Briefly commenting on one of the cases here, the author shows that a network can be defined as the interacting divisions of cultural norms and structural forms. The potential improvements (i.e. horizontal re-alignment) indicated by NDEA can guide management on the extent organisational alignment that could be changed in reaching strategic aims. The author's theoretical model is conducive to assessing the amount and direction of change from the proposed alignment model in a multi-criteria framework – characteristics embraced by NDEA.

Given the hierarchical nature of organizations where employees are nested in work groups or teams, groups nested in departments or divisions, and divisions nested in organizations, application of NDEA at various levels of analysis is feasible.

NDEA's ability to account for each division's importance or assign desired weights in what-if analyses adds to flexibility in managerial decision-making regarding allocation of resources, or re-alignment of processes and targeting of desired outcomes. Such a method that does not assume independence among multiple performance measures provides additional assurance to those concerned about shortcomings of additive scales in complex organizations.

A 17‐item scale emerges following the study to develop an instrument for measuring customer service quality at trading bank branches, with a focus on retail banking. The conceptual framework addresses the psychometric shortcomings of the existing work in service quality research. A robust research design takes the study through multiple stages of development where the construct is pretested and piloted; in the main survey stage, data collection methods are triangulated, returning 791 completed questionnaires. Analysis of instrument reliability, dimensionality and validity present gratifying results; for example, scale alpha is recorded at 0.9249. The instrument can be applied as part of branch performance measurement, as well as help diagnose problems in delivery of service, and segment the bank′s customer base for healthier decision making in marketing.

This paper studies the profit efficiency of a sample of large U.S. commercial banks and explores how this performance varies with selected measures of bank risk reflecting aspects of credit risk, liquidity risk, and insolvency risk. We use a standard profit function and the stochastic frontier approach, and compare two standard functional forms – Cobb‐Douglas and translog – to assess the tradeoff between precision and parsimony. We find that profit efficiency is sensitive to credit risk and insolvency risk but not to liquidity risk or to the mix of loan products.