The idea that socially responsible investments can be viewed in terms of real options is relatively new. We expand on this notion by demonstrating how real option theory, within a Bayesian decision-making framework, can be used by managers to help when making green technology investment decisions. The Bayesian decision framework provides a more flexible approach to investment decision making because it adjusts for new information. Responding to a call for multidisciplinary and multifaceted research in environmental sustainability, this paper integrates ethics, finance, and information technology by viewing investments in environmentally friendly technology as a profit-driven CSR real option. Our model provides managers with the analytic tool needed to make the business case for CSR initiatives providing an opportunity for firms to create economic, social, and ecological solutions that benefit all stakeholders. The practical applicability of our model is demonstrated in an illustrative case scenario based on real data.
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A real option is a contract for the purchase of a physical asset, as opposed to a stock option, such as a put or a call, which trades on a stock exchange.
For simplicity, henceforth a CSR real option will be referred to as a CSR option.
Watson et al. (2010), in defining the term “green IS,” argue that while much of the practitioner literature has devoted attention to “green IT” with exclusive focus on technologies, they prefer “green IS,” which refers to an integrated and cooperating set of people, processes, software, and information technologies to support individual, organizational, or societal goals. This definition includes green IT and allows for a greater variety of possible initiatives to support sustainable business processes. Elliot (2011) argues that the term “green IT” focuses attention on technology rather than IT applications and is often associated with reducing energy use in data centers. He emphasizes that the term “environmental sustainability of IT” is also often used in a narrow sense which ignores its multifaceted nature.
The technical and cost data for the servers are obtained from a publicly available test report prepared by Principled Technologies (2012), available at http://www.principledtechnologies.com/Dell/R720_vs%20_R710_0312.pdf. We label the servers Server A and Server B to avoid favoring one model over the other. Data are scaled to accommodate the computing requirements of a small North American university. Just for estimation purposes, we use the data available from a report available at https://www.trentu.ca/eab/energy_computer.php.
http://researchcomputing.syr.edu/resources/green-data-center/. We wish to thank an anonymous reviewer for pointing out this scenario.
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The authors acknowledge the research funding support from IIIA (Grant No. 336-332-033). Dr. Hemantha Herath acknowledges research funding from the Social Sciences and Humanities Research Council (SSHRC) of Canada (Grant No. 410-2009-1398). The usual disclaimers apply. We would like to thank David Cullum, Andreas Paulisch, and Harry Serabian for their helpful input. We would also like to thank the senior editor, Professor Greg Shailer, and two anonymous reviewers for their helpful comments.
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Herath, H.S.B., Herath, T.C. & Dunn, P. Profit-Driven Corporate Social Responsibility as a Bayesian Real Option in Green Computing. J Bus Ethics 158, 387–402 (2019). https://doi.org/10.1007/s10551-017-3705-1
- Corporate social responsibility
- Green computing
- Investment decisions
- Real options, Bayesian decision framework