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PurposeThis study is based on knowledge-based view to examine the relationships among buyer–supplier interaction, ambidextrous innovation and business performance. It includes competitive intensity and dysfunctional competition to clarify boundary conditions.Design/methodology/approachThe ordinary least squares regression was conducted to test hypotheses. The survey data were collected from 182 Hong Kong manufacturing firms.FindingsBuyer–supplier interaction facilitates ambidextrous innovation, namely exploitative innovation and exploratory innovation. In turn, exploitative innovation enhances business performance, whereas exploratory innovation has no influence on business performance. Competitive intensity strengthens while dysfunctional competition weakens the impact of buyer–supplier interaction on ambidextrous innovation.Research limitations/implicationsThis study is based on the knowing processes of knowledge-based view. It contends that business performance is derived from ambidextrous innovation, which depends on the utilization of acquired supplier knowledge and the influence of external competitive environment. The test of relationships is constrained by the single-source and cross-sectional data.Practical implicationsFirms should engage in buyer–supplier interaction to acquire and utilize supplier knowledge. Meanwhile, they should monitor competitive environment to seize opportunities and avoid threats.Originality/valueThis study builds a holistic framework for buyer–supplier interaction, which reconciles the mixed arguments by distinguishing its effects on ambidextrous innovation, and by clarifying boundary conditions in terms of competitive intensity and dysfunctional competition.

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We investigate an integrated inventory routing problem (IRP) in which one supplier with limited production capacity distributes a single item to a set of retailers using homogeneous vehicles. In the objective function we consider a loading cost which is often neglected in previous research. Considering the deterioration in the products, we set a soft time window during the transportation stage and a hard time window during the sales stage, and to prevent jams and waiting cost, the time interval of two successive vehicles returning to the supplier's facilities is required not to be overly short. Combining all of these factors, a two-echelon supply chain mixed integer programming model under discrete time is proposed, and a two-phase algorithm is developed. The first phase uses tabu search to obtain the retailers' ordering matrix. The second phase is to generate production scheduling and distribution routing, adopting a saving algorithm and a neighbourhood search, respectively. Computational experiments are conducted to illustrate the effectiveness of the proposed model and algorithm.

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