Green Decision Making by Organizations: Understanding Strategic Energy Choices
Notice bibliographique
Résumé
There is a growing need to better understand environmental decision making in the context of climate change and limited renewable resources. This dissertation deepens our understanding of such decision making by focusing on strategic green decisions, which can be defined as the individual and collaborative green decisions within or between organizations that help organizations improve their operating position, adapt to changes in their external institutional environments, and simultaneously generate environmental benefits. The particular focus is on decisions related to energy in the North American context. \n \nThe research draws on and contributes to organizational theory with the aim of better understanding those factors that motivate and/or facilitate green decisions by organizations, especially social economy organizations—an area of only limited research to date. Two complementary empirical studies address the overarching research goal. \n \nThe first study focuses on understanding the nature and extent of the association between organizational attributes and those factors that motivate and/or facilitate a green energy decision. Insights are based on a bi-national survey of 212 organizations that voluntarily began to purchase green electricity between 1999 and 2008. Findings indicate that important influences are similar across organizational types. Survey results highlight the importance of organizational culture and internal champions—both individually and in combination—in making the initial decision to purchase green electricity, despite its relatively higher price. These two factors, as well as strategic benefits, emerge as the dominant explanations for why organizations expand their green energy purchases. The relative importance and particular roles of these factors vary across organizational and decision types. \n \nThe second empirical study extends our understanding of how organizations adapt to external changes while maintaining the capacity to innovate in order to address their core objectives. The focus is on the residential energy services market, and is based on 12 interviews with the executive directors of non-profit environmental service organizations (ESOs) that are part of a national network called Green Communities Canada. These organizations survived a funding shock by creating new services and diversifying funding sources with actions that collectively can be referred to as ‘green collaborative entrepreneurship’; collaborative because \nit was facilitated by strategic partnerships with businesses and local governments, as well as the cross-national social capital network connecting the ESOs. The important motivating factors of green collaborative entrepreneurship were the green values and objectives that drive these organizations. The facilitating factors of green collaborative entrepreneurship included human capital, social capital and strategic partnerships, which acted as dynamic capabilities because of their flexibility to help increase the level of entrepreneurship when necessary for organizational survival, and yet, scale-up and deliver core programs during stable funding periods. \n \nThe dissertation provides important insights into broad questions related to green decisions, especially for organizations that are affected by political policy cycles. The findings highlight that organizations are able to be more environmentally sustainable while also improving their own strategic performance by making green decisions that either provide the capacity to adapt to exogenous change for survival, or to create endogenous change for competitive advantage. The research contributes to our understanding of societal transitions to sustainable development by highlighting two green decisions that are occurring in the social economy. The dissertation contributes to organizational theory and in particular the traditional corporate literature by including multiple organizational types. Sustainability researchers should focus on green decisions that both enhance organizational stability and ecological sustainability if they wish to better understand creative green solutions from organizations.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,006 |
| Communication savante | 0,009 | 0,011 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».