AbstractIn marketing channels, distributor whistleblowing can deter unethical behaviors, though little academic research investigates this tactic. Drawing on whistleblowing literature in business ethics and organizational theory, as well as field interviews with channel managers, this article identifies and elucidates the notion of distributor whistleblowing in marketing channels. Specifically, this study investigates how a manufacturer’s control modes (monitoring and incentives) encourage or discourage distributor whistleblowing. This study also considers the impact of distributor whistleblowing on relationship quality and the moderating effects of (...) exchange hazards (i.e., transaction-specific investments and market uncertainty). The empirical results from a sample of 211 Chinese distributors indicate that manufacturers’ monitoring inhibits, whereas incentives boost distributor whistleblowing. Further, a positive link arises between distributor whistleblowing and relationship quality, which is weakened by distributor transaction-specific investments and strengthened by market uncertainty. (shrink)

Network embedding aims to learn the low-dimensional representations of nodes in networks. It preserves the structure and internal attributes of the networks while representing nodes as low-dimensional dense real-valued vectors. These vectors are used as inputs of machine learning algorithms for network analysis tasks such as node clustering, classification, link prediction, and network visualization. The network embedding algorithms, which considered the community structure, impose a higher level of constraint on the similarity of nodes, and they make the learned node embedding (...) results more discriminative. However, the existing network representation learning algorithms are mostly unsupervised models; the pairwise constraint information, which represents community membership, is not effectively utilized to obtain node embedding results that are more consistent with prior knowledge. This paper proposes a semisupervised modularized nonnegative matrix factorization model, SMNMF, while preserving the community structure for network embedding; the pairwise constraints information are effectively fused with the adjacency matrix and node similarity matrix of the network so that the node representations learned by the model are more interpretable. Experimental results on eight real network datasets show that, comparing with the representative network embedding methods, the node representations learned after incorporating the pairwise constraints can obtain higher accuracy in node clustering task and the results of link prediction, and network visualization tasks indicate that the semisupervised model SMNMF is more discriminative than unsupervised ones. (shrink)