مشخصات مقاله | |
ترجمه عنوان مقاله | چگونه شرکت ها هنگام آزمایش با مدل های کسب و کار چرخشی اثرات محیطی را اندازه گیری و پیش بینی می کنند؟ |
عنوان انگلیسی مقاله | How do companies measure and forecast environmental impacts when experimenting with circular business models? |
انتشار | مقاله سال 2022 |
تعداد صفحات مقاله انگلیسی | 13 صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
پایگاه داده | نشریه الزویر |
نوع نگارش مقاله |
مقاله پژوهشی (Research Article) |
مقاله بیس | این مقاله بیس میباشد |
نمایه (index) | Scopus – Master Journals List – JCR |
نوع مقاله | ISI |
فرمت مقاله انگلیسی | |
ایمپکت فاکتور(IF) |
5.335 در سال 2020 |
شاخص H_index | 26 در سال 2021 |
شاخص SJR | 1.019 در سال 2020 |
شناسه ISSN | 2352-5509 |
شاخص Quartile (چارک) | Q1 در سال 2020 |
فرضیه | ندارد |
مدل مفهومی | ندارد |
پرسشنامه | ندارد |
متغیر | ندارد |
رفرنس | دارد |
رشته های مرتبط | مدیریت، اقتصاد |
گرایش های مرتبط | مدیریت کسب و کار، اقتصاد محیط زیست |
نوع ارائه مقاله |
ژورنال |
مجله | تولید و مصرف پایدار – Sustainable Production and Consumption |
دانشگاه | Maastricht Sustainability Institute, School of Business and Economics, Maastricht University, Netherlands |
کلمات کلیدی | اقتصاد چرخشی، مدل های کسب و کار چرخشی، ارزیابی اثرات محیطی، انتقال مدل کسب و کار، آزمایش مدل کسب و کار |
کلمات کلیدی انگلیسی | Circular economy – Circular business models – Environmental impact assessment – Business Model Transition – Business Model Experimentation |
شناسه دیجیتال – doi |
https://doi.org/10.1016/j.spc.2021.10.009 |
کد محصول | E15812 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
فهرست مطالب مقاله: |
Abstract Keywords 1. Introduction 2. Conceptual background 3. Methods 4. Results 5. Discussion 6. Conclusion Declaration of Competing Interest Acknowledgement Appendix. References |
بخشی از متن مقاله: |
Abstract Many companies have innovated their business models in their attempts to transition towards a circular economy. However, the label ‘circular’ does not necessarily mean better for the environment. How do companies measure the environmental performance of their business models? And as they alter them for a circular economy, how do they forecast the potential environmental impacts? These questions are important to better understand the impacts of circular business models. This study sets out to answer these questions through 29 semi-structured interviews and 39 survey responses, with business developers, managers, product designers and consultants from more than 10 industries. The results reveal that while most participants measure the impact of their current business models, they do not forecast the future impacts of their circular business ideas before implementation. The most popular measurement method was rules of thumb, followed by life-cycle assessment (LCA) or LCA-based tools. A lack of data, increased uncertainty during experimentation and a lack of knowledge are the common barriers that keep the participants from measuring environmental impacts. 1. Introduction The need for a more circular economy is increasingly being recognised by both governments and the private sector (European Commission, 2020; Government of Canada, 2019; Government of the Netherlands, 2016; Lewandowski, 2016). Businesses are trying to become more sustainable by trialling circular business models (CBMs) that aim at longer product lifetimes and production of less waste (Bocken and Antikainen, 2018; Geissdoerfer et al., 2020). Companies often do this through business model experimentation (Bocken and Snihur, 2020), which tends to follow the – ‘build, measure, learn’ – Lean Startup approach (Blank, 2013; Ries, 2011). This is an iterative approach, where different business strategies are repeatedly trialled to find the best product-market fit (Chesbrough, 2010). In the circular economy context, business experimentation also focuses on addressing pressing sustainability issues, in particular to narrow, slow, close, and regenerate resource loops (Bocken et al., 2016a,b; Konietzko et al., 2020a). |