Digitalization of waste management: Insights from German private and public waste management firms.

Policymakers, practitioners, and scholars have long-lauded digital technologies, such as smart waste containers or artificial intelligence for material recognition and robotic automation, as key enablers to more effective and efficient waste management. While these advances promise an increasingly digitalized future for collecting, sorting, and recycling waste material, little is known about the current extent of digitalization by waste management firms. Available studies focus on firms' digitalization intentions, largely neglecting the level of actual adoption of digital technologies, and do not differentiate the level of digitalization alongside different steps of the waste management value chain. Our study reports on a cross-sectional descriptive survey that captures current digitalization efforts and strategies of 130 public and private waste management firms in Germany. We analyze their levels of digitalization along with different steps of the waste management value chain, explore their different objectives, approaches, and transformational measures with regard to digitalization. Our findings reveal that while the perceived importance of digitalization in the waste management sector continues to grow, the actual adoption of advanced digital technologies falls notably behind intentions reported in 2016 and 2017. We explore the reasons for this gap, point out so far largely ignored research opportunities, and derive recommendations for waste management firms and associations.

Development and validation of an instrument to measure and manage organizational process variety.

Organizational processes vary. Practitioners have developed simple frameworks to differentiate them. Surprisingly, the academic literature on process management does not-it typically strives for one method to manage all processes. We draw on organizational information-processing theory to systematically develop a new, theoretically motivated classification model for organizational processes. We validate this model using survey data from 141 process practitioners of a global corporation. We derive three distinct types of processes and demonstrate that an understanding of process variety based on process dimensions can differentiate processes better than existing frameworks used in practice. Our findings can enable process managers to make informed decisions and serve as a basis for contingent process management.