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An ever-increasing share of people is using online shopping to satisfy their consumer needs. This has led to a vivid discussion regarding the environmental sustainability of e-commerce that also emphasized the role that consumer's decisions can play in mitigating its negative impacts. However, while many individuals state that they are willing to act more sustainably, they often struggle to follow through with their 'green' intentions. We propose digital nudging as an approach to encourage environmentally sustainable online shopping decisions and empower consumers to act in line with their intentions. In an online experiment with 323 participants, we evaluate the effectiveness of three different nudging interventions (defaults, active choice, and self-nudging) to promote environmentally sustainable shipping options in an online store and assess the consumers' ethics and empowerment perceptions of the nudges. We find that all nudges are effective in changing decisions, but default nudges lead to negative perceptions among consumers.

Automated checkout systems promise greater sales due to an improved customer experience and cost savings because less store personnel is needed. The present design-oriented IS research study is concerned with an automated checkout solution in fashion retail stores. The implementation of such a cyberphysical system in established retail environments is challenging as architectural constraints, well-established customer processes, and customer expectations regarding privacy and convenience impose limits on system design. To overcome these challenges, the authors design an IT artifact that leverages an RFID sensor infrastructure and software components (data processing and prediction routines) to jointly address the central problems of detecting purchases in a reliable and timely fashion and assigning these purchases to individual shopping baskets. The system is implemented and evaluated in a research laboratory under real-world conditions. The evaluation indicates that shopping baskets can indeed be detected reliably (precision and recall rates greater than 99%) and in an expeditious manner (median detection time of 1.03 s). Moreover, purchase assignment reliability is 100% for most standard scenarios but falls to 42% in the most challenging scenario. Keywords Design-oriented IS research Á Digital innovation Á Internet of things Á Cyberphysical systems Á Retail industry Á Radio frequency identification Á Machine learning Á Automated checkout systems Accepted after two revisions by the editors of the special issue.

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