Sustainability by Default? Nudging Carbon Offsetting Behavior in E-Commerce

The continuous rise of e-commerce and the resulting global transportation activities lead to an increased environmental load, specifically in the form of carbon emissions. While carbon offset donations offer the potential to mitigate the ecological harm, these voluntary options are not yet prevalent among e-commerce customers. Prior research has shown that information systems (IS) can be utilized to encourage more sustainable behavior by digitally nudging people into offsetting their carbon emissions. Therefore, this study intends to examine the influence of defaults on carbon offsetting in e-commerce checkout processes. A digital experiment with 125 participants revealed that higher default donation values significantly increase people’s carbon offset contributions in an e-commerce checkout process. Participants in the treatment group (high default) donated, on average, 33 percent more for carbon offsetting compared to the control group (low default). As a result, this research contributes to the fields of behavioral economics in IS, digital nudging as well as green IS and has valuable implications for IS practitioners and designers

STUDYING DYNAMICS AND CHANGE WITH DIGITAL TRACE DATA: A SYSTEMATIC LITERATURE REVIEW

Digital trace data offer promising opportunities to study dynamics and change of various socio-technical phenomena over time. While we see a surge of empirical and conceptual articles, we lack a systematic understanding of why, how, and when digital trace data are or can be used to study dynamics and change. In this article, we present the findings of a systematic literature review to uncover common approaches, motivations, findings, and general themes in the existing literature. We systematically reviewed 40 studies that were published in premium outlets in the information systems field. Our review sheds light on (1) underlying purposes of such studies, (2) utilized data sources, (3) research contexts, (4) socio-technical phenomena of interest, (5) applied analytical methods, and (6) measures that are being used. Building on our findings, we point to several implications for research and shed light on avenues to advance this field in the future

Explaining Change with Digital Trace Data: A Framework for Temporal Bracketing

Digital trace data, along with computational techniques to analyze them, provide novel means to study how organizational phenomena change over time. Yet, as digital traces typically lack context, it is challenging to explain why and how such changes take place. In this paper, we discuss temporal bracketing as an approach to integrate context into digital trace data-based research. We conceptualize a framework to apply temporal bracketing in the analysis of digital trace data. We showcase our framework on the grounds of data from an onboarding process of a financial institution in Central Europe. We point to several implications for computationally intensive theory development around change with digital trace data