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Digitalization brings complexity and challenges to the relationship governance between logistics outsourcers and their providers. Drawn on resource dependence theory (RDT) and resource-based view (RBV), this study aims to examine the role of relationship commitment in simultaneously managing dependence and improving operational performance in logistics outsourcing in the digital economy, as well as the contingent factors (i.e. communication, relationship length, and company size) that affect the effectiveness of relationship commitment.

Based on data collected from 130 third-party logistics (3PL) users in China, our model was tested with the partial least squares (PLS) approach.

First, relationship commitment is necessary for 3PL users to manage dependence on 3PL providers and improve operational performance in the digital economy. Second, communication helps 3PL users to develop higher relationship commitment but weakens the motivating effect of dependence on relationship commitment. Third, a long relationship history develops inertia to diminish the effectiveness of dependence on driving relationship commitment while it boosts the impact of relationship commitment on operational performance. Last, company size is an important signal to amplify the effectiveness of relationship commitment for operational performance enhancement.

This study contributes to the logistics outsourcing literature by integrating RDT and RBV to explain the twofold roles of relationship commitment, simultaneously tackling dependence and enhancing operational performance in the digital economy. Additionally, it expands the understanding of the boundary conditions (e.g. communication, relationship length, and company size) on these twofold roles.

As the application of crowdsourcing contests grows, leveraging the participation of superstars (i.e. solvers who have outstanding performance records in a crowdsourcing platform) becomes an emergent approach for managers to solve crowdsourced problems. Although much is known about superstars’ performance implications, it remains unclear whether and how their participation affects the size of a contest crowd for a crowdsourcing contest. Based on social contagion theory, this paper aims to examine the impact of superstars’ participation on the crowd size and studies how this impact varies across solvers with different heterogeneity in terms of skills, exposure and cultural proximity with superstars in crowdsourcing contests.

This paper uses secondary data from one crowdsourcing platform that includes 6,587 innovation contests to examine superstars’ main and contextual effects on the crowd size of a contest.

Our results reveal that superstars’ participation positively affects the crowd size of a contest in general. This finding suggests that social contagion is a fundamental mechanism underlying crowd formation in crowdsourcing contests. Our results also indicate that in contests that involve multiple superstars, superstars’ effect on crowd size becomes negative when we simultaneously consider other solvers’ heterogeneity in terms of skills, exposure and cultural background, and this negative effect will be intensified by increases in the skill gap, extent of exposure and cultural proximity between superstars and other solvers in the same contest.

Our research enhances the understanding of the influence of superstars and the mechanism underlying the emergence of contest crowds in crowdsourcing contests and contributes knowledge to better understand social contagion in a competitive setting. The results are meaningful for sourcing managers and platform supervisors to design contests and supervise crowd size in crowdsourcing contests.

Based on signaling theory, this paper aims to explore the impact of supply chain transparency (SCT) on firms' bank loan (BL) and supply chain financing (SCF) in the context of voluntary disclosure of supplier and customer lists.

Based on panel data collected from Chinese-listed firms between 2012 and 2021, fixed-effect models and a series of robustness checks are used to test the predictions.

First, improving SCT by disclosing major suppliers and customers promotes BL but inhibits SCF. Specifically, customer transparency (CT) is more influential in SCF than supplier transparency (ST). Second, supplier concentration (SC) weakens SCT’s positive impact on BL while reducing its negative impact on SCF. Third, customer concentration (CC) strengthens the positive impact of SCT on BL but intensifies its negative impact on SCF. Last, these findings are basically more pronounced in highly competitive industries.

This study contributes to the SCT literature by investigating the under-explored practice of supply chain list disclosure and revealing its dual impact on firms' access to financing offerings (i.e. BL and SCF) based on signaling theory. Additionally, it expands the understanding of the boundary conditions affecting the relationship between SCT and firm financing, focusing on supply chain concentration. Moreover, it advances signaling theory by exploring how financing providers interpret the SCT signal and enriches the understanding of BL and SCF antecedents from a supply chain perspective.