The authors explore the effects of trust at three distinct organizational levels in a marketing collaboration: interorganizational trust between collaborating firms, each firm's agency trust in its own representatives assigned to a collaborative entity (coentity), and intraentity trust among the representatives assigned to the coentity. Dyadic survey and longitudinal objective performance data from 114 international joint ventures indicate that trust at each level has unique effects but similarly influences the collaborating firms' resource investments or the coentity's use of those resources. Interorganizational and agency trust motivate resource investments in the coentity, particularly in the context of a differentiation strategy, whereas intraentity trust promotes coordination within the coentity, and interorganizational trust and a differentiation strategy magnify that effect. Intraentity trust can also undermine coentity responsiveness to environmental change, especially when joined by interorganizational trust between collaborating firms and formalized decision making within the coentity. These findings demonstrate that managing and building trust at multiple levels is critical to the success of interorganizational marketing collaborations.

Context: Conducting experiments is central to research machine learning research to benchmark, evaluate and compare learning algorithms. Consequently it is important we conduct reliable, trustworthy experiments. Objective: We investigate the incidence of errors in a sample of machine learning experiments in the domain of software defect prediction. Our focus is simple arithmetical and statistical errors. Method : We analyse 49 papers describing 2456 individual experimental results from a previously undertaken systematic review comparing supervised and unsupervised defect prediction classifiers. We extract the confusion matrices and test for relevant constraints, e.g., the marginal probabilities must sum to one. We also check for multiple statistical significance testing errors. Results: We find that a total of 22 out of 49 papers contain demonstrable errors. Of these 7 were statistical and 16 related to confusion matrix inconsistency (one paper contained both classes of error). Conclusions: Whilst some errors may be of a relatively trivial nature, e.g., transcription errors their presence does not engender confidence. We strongly urge researchers to follow open science principles so errors can be more easily be detected and corrected, thus as a community reduce this worryingly high error rate with our computational experiments.

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