Search results

In the pursuit of net-zero, the decarbonization activities of organizations are a critical feature of any sustainability strategy. However, government policy and recent technological innovations do not address the digital carbon footprint of organizations. The paper aims to present the concept of single-use dark data and how knowledge reuse by organizations is a means to digital decarbonization.

Businesses in all sectors must contribute to reducing digital carbon emissions globally, and to the best of the authors’ knowledge, this paper is the first to examine “how” from a knowledge (re)use perspective. Drawing on insights from the knowledge creation process, the paper presents a set of pathways to greater knowledge reuse for the reduction of organizations’ digital carbon footprint.

Businesses continually collect, process and store knowledge but generally fail to reuse these knowledge assets – referred to as dark data. Consequently, this dark data has a huge impact on energy use and global emissions. This model is the first to show explicit pathways that businesses can follow to sustainable knowledge practices.

If businesses are to be proactive in their collective pursuit of net-zero, then it becomes paramount that reducing the digital carbon footprint becomes a key sustainability target. The paper presents how this might be accomplished, offering practical and actionable guidance to businesses for digital decarbonization.

Two critical questions are facing businesses: how can decarbonization be achieved? And can it be achieved at a low-cost? Awareness of the damaging impact digitalization may be having on the environment is in its infancy, yet knowledge reuse is a proactive and cost-effective route to reduce carbon emissions, which is explored in the paper.

People’s tendency to overestimate their ability to control random events, known as illusion of control, can affect financial decisions under uncertainty. This study developed an artifactual field experiment on illusion of control for a farm machinery investment.

In an experiment with two treatments, the individual farmer was either given or not given a sense of control over a random outcome. After each decision, the authors elicited perceived control, and a questionnaire collected additional indirect measures of illusion of control from 78 German farmers and 10 farm advisors.

The results did not support preregistered hypotheses of the presence of illusion of control. This null result was robust over multiple outcomes and model specifications. The findings demonstrate that cognitive biases may be small and difficult to replicate.

The sample is not representative for the German farming population. The authors discuss why the estimated treatment effect may represent a lower bound of the true effect.

Illusion of control is well-studied in laboratory settings, but little is known about the extent to which farmers’ behavior is influenced by illusion of control.