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State-owned enterprises (SOEs) have increasingly engaged in cross-border acquisitions. However, they face challenges related to regulatory approval in host countries, especially when they aim to acquire companies with rich R&D resources. Regulatory debates around the legitimacy of SOE acquisitions prolong acquisition duration and may also prevent acquisitions from being completed.

AbstractHub network design problems with profits (HNDPPs) extend the classical hub location problems (HLPs), with the profit maximization objective. HNDPPs aim to design a service network with hub nodes and hub links, select the commodities to be served, and determine the transportation routes for the selected commodities. In this article, we allow the transportation route of each commodity to contain more than one hub link, which generalizes existing HNDPPs and is known to be more profitable. A new path‐based formulation is proposed, which is then reformulated in a Benders decomposition fashion, and an exact branch‐and‐cut algorithm is developed to solve the Benders master problem. At the nodes of the enumeration tree, Benders decomposition algorithm is used to solve a linear relaxation of the Benders master problem. When implementing the Benders decomposition algorithm, the special structure of the subproblem is explored, based on which an efficient method is proposed to generate Benders cuts. In addition, refinements and a heuristic strategy are used to speed up the branch‐and‐cut algorithm. Extensive numerical experiments on benchmark instances have been carried out to verify the effectiveness and efficiency of the proposed solution method.

The Behavioral Theory of the Firm suggests that performance below aspirations triggers problemistic search that can lead to risk taking. This prediction has received empirical support from most studies on the topic. However, this literature has typically focused on the internal determinants of firm search and risk-taking behavior and given little attention to the influences of social networks in which firms are embedded. To this end, we incorporate the network embeddedness perspective regarding firms' network positions and their roles in firm decision making. We suggest that a firm's search behavior is jointly directed by its performance feedback and network positions. Specifically, network brokerage and centrality play important yet distinct roles in guiding firm search behavior by differentially shaping the direction of problemistic search: high brokerage directs problemistic search to high-risk solutions, whereas high centrality directs problemistic search to low-risk solutions. Our theoretical predictions receive general empirical support based on analyses using longitudinal data from the Chinese venture capital industry. Our approach incorporates the crucial role of network structures into the problemistic search model and works toward building a problemistic search theory of the embedded firm.