No abstract

No abstract

Purpose Researchers have long sought to understand how risks in supply chains (SCs) affect firm performance. Yet, they have not fully subjected claims of how SC risks affect firm financial performance to theoretical and empirical scrutiny. The purpose of this paper is to investigate the links between SC risks and firm financial performance. Design/methodology/approach The author analyzes how SC risks affect firm financial performance from the perspective of marginal financial performance (MFP) using survey and financial statement data. The author employs structural equation modeling to examine the hypotheses using 106 Taiwanese listed companies across 20 industries. Findings The findings regarding the importance of industry-specific risk, organizational risk, internal business process risk, and demand risk are consistent with prior studies. The author finds that demand risk has an MFP of −0.20, the highest negative effect among the risk variables. The findings also show that industry-specific risk possesses an MFP of −0.16, the second-highest negative effect, despite having no direct effect on financial performance. Research limitations/implications This paper examines how SC risks affect MFP via combining survey and financial statement data. It does not assume the reported MFP estimates apply to all businesses in other countries. However, future research could triangulate our findings. Originality/value This study combines survey and financial data to analyze how SC risks affect firm financial performance. Specifically, it provides a methodology for estimating quantitative cause-effect relationships between SC risk and firm financial performance, an important topic that receives less research interest in the field of supply chain management.

No abstract

Abstract. Identifying the critical determinants of innovation performance is crucial. However, few studies explore and quantify systematically the relationships between innovation factors and the performance of capital projects. This study of 121 capital projects shows that the relationships among project innovation stimulants, innovation capacity, and project performance are indeed significant. Hierarchical robust regression analyses using a maximum R-square improvement procedure show that technology management has the highest effect on the variation in our project performance data. Validating out-of-sample data demonstrates that our optimal model explains 34.42% of the variation in the performance of capital projects. Ultimately, our findings suggest that project human factors are essential stimulants in innovation performance, which in turn affect the performance of capital projects. Our findings also reveal that the stimulant factors do not have a direct impact on capital project performance, but rather have an indirect impact via project innovation capacity.

No abstract

No abstract

No abstract