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The increasing demand for high-quality logistics services has forced container shipping firms to decrease logistics service failure to retain the customers. This study thus aims to apply organizational information processing theory (OIPT) to construct a maritime supply chain collaborative decision-making model and examine its impact on logistics service performance.

In total, 142 usable questionnaires were collected from questionnaire survey. A two-step structural equation modeling approach including confirmatory factor analysis was subsequently performed to test the hypotheses.

The results show that internal information integration positively impacts external information integration, that external information integration positively impacts collaborative decision-making, and that collaborative decision-making positively impacts logistics service performance for container shipping firms. However, a relationship between internal information integration and collaborative decision-making was not found in this study.

This study primarily examines collaborative decision-making from the view of container shipping firms. Future research including other supply chain members is needed to generalize the results and could also incorporate other factors such as relationship quality and culture, into the model to address this issue.

To decrease the occurrence of logistics failures and improve service quality in the maritime logistics process, it is suggested that container shipping firms apply information technology for acquiring and assimilating logistics information internally and externally across the supply chain to facilitate decision-making.

This study contributes to the knowledge about the antecedents and impacts of collaborative decision-making for container shipping firms in Taiwan. Particularly, in line with OITP, the findings indicate that container shipping firms can facilitate logistics decision-making and strategy formulation through information integration, which in turn enhances logistics service performance.

Corruption is a major social problem, and scholars have devoted considerable attention to this phenomenon. However, less attention has been paid to how corruption spreads among organizations and what factors can make its spread more likely. This study aims to fill the gap by modelling corruption as an interorganizational contagion.

The authors used social contagion theory to model corruption as an interorganizational contagion, influenced by the susceptibility of organizations and the strength of contagion sources. The study analysed 736 medium and large Italian municipalities over a five-year period, with 3,146 observations (excluding missing data). The authors conducted a longitudinal analysis using panel logistic regression techniques and performed robustness and endogeneity checks through a dynamic panel data model.

The authors found that municipalities with a higher percentage of corrupt neighbouring municipalities were more likely to experience corruption. The probability of experiencing corruption was also significantly higher for municipalities with weaker organizational resistance to corruption contagion.

Previous studies have not clearly explained the organizational mechanisms behind the spread of corruption at the interorganizational level. The study suggests that corruption contagion at the municipal level occurs via reduced uncertainty in decision-makers and is influenced by the prevalence of corruption locally. The spread can be driven by conscious or unconscious mechanisms. This study challenges the idea that corruption contagion is immediate and inevitable. Organizational resistance to corruption can affect the risk of contagion, highlighting the importance of anti-corruption controls and ethical systems in preventing it.

While it is known that digital transformation facilitates data flow in supply chains, its importance on green supply chain management (GSCM) has not been investigated concisely. This paper aims to expand the theory of digital transformation in GSCM by investigating the interconnections between these concepts and providing an integrative view of a smart green supply chain management (Smart GSCM).

This adopts a configurational perspective on digital transformation and supply chain management (SCM) to investigate the different dimensions of Smart GSCM and their contribution to green performance. Therefore, this paper analyzes data from 473 manufacturing companies using regression techniques.

The results show how smart supply chain contributes to green performance through managing green relationships (external GSCM activities) and establishing green operations (internal GSCM activities). Furthermore, this paper finds partial mediating effects for external and internal GSCM activities on green performance. These findings show that smart supply chain (i.e. digital transformation strategy and front-end technologies, supported by several back-end technologies) is directly associated with higher levels of GSCM. It is specifically associated with one of the internal dimensions of green operations, namely, green purchasing activities. Hence, the findings suggest that digital transformation alone is insufficient to achieve green performance, needing a GSCM configuration to mediate this effect.

This study calls attention to how managers should integrate these at least three different perspectives of SCM: digital transformation, external relationships and internal operations to increase green performance.

As the main contribution, this study provides a configurational and holistic understanding of the different dimensions and mechanisms in Smart GSCM.