Demand side management (DSM) programs across the United States commonly approach barriers to energy efficiency through technical/economic means and evaluate their impact through technical/economic analysis. To the extent that non-technical barriers exist and influence decision making, they complicate the expected capture of savings. Two utility DSM projects -- Pacific Gas and Electric's Advanced Customer Technology Test for Maximum Energy Efficiency (ACT2) and Bonneville Power Administration's Energy Edge -- serve as case studies to illustrate how non-technical barriers to specific energy-efficiency measures (EEMs) can limit technical conservation potential. An analysis of rejected EEMs suggest that lessons about non-technical barriers to specific energy-efficiency measures (EEMs) can limit technical conservation potential. An analysis of rejected EEMs suggests that lessons about non-technical barriers may be lost or obscured because of the predominant focus on technical/economic criteria over social, institutional, or cultural constraints. These findings support the need for different evaluation methodologies and further social science research devoted to understanding the non-technical barriers confronted by DSM project participants.

Evidence-Based Decision-Making: How to Leverage Available Data and Avoid Cognitive Biases examines how a wide range of factual evidence, primarily derived from a variety of data available to organizations, can be used to improve the quality of business decision-making, by helping decision makers circumvent the various cognitive biases that adversely impact how we all think. The book is built on the following premise: During the past decade, the new ‘data world’ emerged, in which the rush to develop competencies around business analytics and data science can be characterized as nothing less than the new commercial arms race. The ever-expanding volume and variety of data are well known, as are the great advances in data processing/analytics, data visualization, and related information production-focused capabilities. Yet, comparatively little effort has been devoted to how the informational products of business analytics and data science are ‘consumed’ or used in the organizational decision-making processes, as the available evidence shows that only some of that information is used to drive some business decisions some of the time. Evidence-Based Decision-Making details an explicit process describing how the universe of available and applicable evidence, which includes organizational and other data, industry benchmarks, scientific studies, and professional experience, can be assessed, amalgamated, and funneled into an objective driver of key business decisions. Introducing key concepts in relation to data and evidence, and the history of evidence-based management, this new and extremely topical book will be essential reading for researchers and students of data analytics as well as those working in the private and public sectors, and in the voluntary sector.

This comprehensive Handbook provides an overview of state-of-the-art research on quantitative models for inventory management. Despite over half a century’s progress, inventory management remains a challenge, as evidenced by the recent Covid-19 pandemic. With an expanse of world-renowned inventory scholars from major international research universities, this Handbook explores key areas including mathematical modelling, the interplay of inventory decisions and other business decisions and the unique challenges posed to multiple industries.

Behavioural studies have shown that while humans may be the best decision makers on the planet, we are not quite as good as we think we are. We are regularly subject to biases, inconsistencies and irrationalities in our decision making. Decision Behaviour, Analysis and Support explores perspectives from many different disciplines to show how we can help decision makers to deliberate and make better decisions. It considers both the use of computers and databases to support decisions as well as human aids to building analyses and some fast and frugal tricks to aid more consistent decision making. In its exploration of decision support it draws together results and observations from decision theory, behavioural and psychological studies, artificial intelligence and information systems, philosophy, operational research and organisational studies. This provides a valuable resource for managers with decision-making responsibilities and students from a range of disciplines, including management, engineering and information systems.