Conceptual arguments favouring a relational rather than a transactional approach to the study of buyer‐seller relationships are now well understood. However, attempts to quantify the factors contributing towards relationship quality have been held back by the complexity of the underlying factors and their interrelatedness. Traditional regression techniques are not effective in analysing data with high levels of multi‐collinearity and missing information, typical in many studies of buyer behaviour. Makes use of a relatively new technique – neural network analysis – to try to quantify the factors contributing to buyer‐seller relationship quality. The technique uses a statistically‐based learning procedure modelled on the workings of the human brain which quantifies the relationship between input and output variables through an intermediate “hidden” variable level analogous to the brain. For this study, a neural network was developed with two outcome components of relationship quality (relationship satisfaction and trust), and five input antecedents (the salesperson′s sales orientation, customer orientation, expertise, ethics and the relationship′s duration). In a comparison of multiple regression and neural network techniques, the latter was found to give statistically more significant outcomes. New applications within marketing for neural network analysis are being found. Contributes towards the development of the technique and suggests a number of further possible applications.

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Purpose -The purpose of this paper is to develop and test a model of the relative performance of open source software (OSS) projects. Design/methodology/approach -This paper evaluates the relative performance of OSS projects by evaluating multiple project inputs and multiple project outputs by using a data envelopment analysis (DEA) model. The DEA model produces an efficiency score for each project based on project inputs and outputs. The method of producing an efficiency score is based on the convex envelopment technology structure. The efficiency measure quantifies a "distance" to an efficient frontier. Findings -The DEA model produced an index of corresponding intensities linking an inefficient project to its benchmark efficient project(s). The inefficiency measures produced an ordering of inefficient projects. Eight projects were found to be "efficient" and used as benchmarking projects.Research limitations/implications -This research is limited to only security-based OSS projects. Future research on other areas of OSS projects is warranted. Practical implications -The result of this research is a practical model that can be used by OSS project developers to evaluate the relative performance of their projects and make resource decisions. Originality/value -This research extends the work of previous studies that have examined the relative performance of software development projects in a traditional development environment. As a result of this research, OSS projects can now be adequately benchmarked and evaluated according to project performance. An OSS project manger can effectively use these results to critically evaluate resources for their project and judge the relative efficiency of the resources.

Determining the number of circulating kanban cards is important in order effectively to operate a just‐in‐time with kanban production system. While a number of techniques exist for setting the number of kanbans, artificial neural networks (ANNs) and classification and regression trees (CARTs) represent two practical approaches with special capabilities for operationalizing the kanban setting problem. This paper provides a comparison of ANNs with CART for setting the number of kanbans in a dynamically varying production environment. Our results show that both methods are comparable in terms of accuracy and response speed, but that CARTs have advantages in terms of explainability and development speed. The paper concludes with a discussion of the implications of using these techniques in an operational setting.

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