Greener Operations Amid a Complex Customer Base: The Role of Environmental Management Systems and Innovation
Chengyong Xiao et al.
Abstract
Firms currently operate in increasingly complex business environments, often facing increasingly diverse environmental requirements from their customers. This challenge is reflected in the concept of “customer base complexity,” which encompasses three dimensions: horizontal complexity (i.e., number of customers), vertical complexity (i.e., depth of customer relationships), and spatial complexity (i.e., customers being located in diverse geographical locations). These complexities can negatively affect a firm's environmental performance because the number and diversity of customer‐specific environmental requirements make it difficult to efficiently deploy resources to minimize environmental impacts. This study applied the absorptive capacity perspective to investigate how two environmental routines—environmental management system (EMS) and environmental innovation (EI)—can mitigate the adverse effects of customer base complexity on environmental performance. Secondary data from four databases (Thomson Reuters ESG, KLD, Bloomberg SPLC, and Compustat) were used to construct a panel dataset with 940 firm‐year observations across 236 US‐listed manufacturing firms. Fixed effects regression analyses reveal that three dimensions of customer base complexity are associated with poorer environmental performance, as indicated by increased energy use and/or CO 2 emissions per unit of sales. Moreover, EMS and EI, as absorptive capacity routines, are found to be effective for mitigating the negative effects of different complexity dimensions. These findings suggest that firms can combine these two absorptive routines to mitigate the negative influences of customer base complexity in the context of environmental management.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.