Green Cloud? An Empirical Analysis of Cloud Computing and Energy Efficiency

References

• Allwood JM, Ashby MF, Gutowski TG, Worrell E (2011) Material efficiency: A white paper. Resourse Conservation Recycling 55(3):362–381.Crossref, Google Scholar
• Aral S, Brynjolfsson E, Wu L (2012) Three-way complementarities: Performance pay, human resource analytics, and information technology. Management Sci. 58(5):913–931.Link, Google Scholar
• Arellano M, Bond S (1991) Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev. Econom. Stud. 58(2):277–297.Crossref, Google Scholar
• Armbrust M, Stoica I, Zaharia M, Fox A, Griffith R, Joseph AD, Katz R, et al.. (2010) A view of cloud computing. Comm. ACM. 53(4):50.Crossref, Google Scholar
• AWS (2008) Animoto: Scaling through viral growth. Accessed April 3, 2022, https://aws.amazon.com/blogs/aws/animoto-scali.Google Scholar
• AWS (2018) Predictive scaling for EC2, powered by machine learning. Accessed April 3, 2022, https://aws.amazon.com/blogs/aws/new-predictive-scaling-for-ec2-powered-by-machine-learning.Google Scholar
• Baer A, Lee K, Tebrake J (2020) Accounting for cloud computing in the national accounts. IMF Working Paper No. 20/127, International Monetary Fund, Washington, DC.Google Scholar
• Baliga J, Ayre RWA, Hinton K, Tucker RS (2011) Green cloud computing: Balancing energy in processing, storage, and transport. Proc. IEEE 99(1):149–167.Crossref, Google Scholar
• Baptist S, Hepburn C (2013) Intermediate inputs and economic productivity. Philosophical Trans. Roy. Soc. A 371:20110565.Crossref, Google Scholar
• Battese GE, Coelli TJ (1988) Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data. J. Econometrics 38(3):387–399.Crossref, Google Scholar
• Battleson DA, West BC, Kim J, Ramesh B, Robinson PS (2016) Achieving dynamic capabilities with cloud computing: An empirical investigation. Eur. J. Inform. Systems 25(3):209–230.Crossref, Google Scholar
• Bayramusta M, Nasir VA (2016) A fad or future of IT?: A comprehensive literature review on the cloud computing research. Internat. J. Inform. Management 36(4):635–644.Crossref, Google Scholar
• Bloom N, Genakos C, Martin R, Sadun R (2010) Modern management: Good for the environment or just hot air? Econom. J. (London) 120(544):551–572.Google Scholar
• Blundell R, Bond S (1998) Initial conditions and moment restrictions in dynamic panel data models. J. Econometrics 87(1):115–143.Crossref, Google Scholar
• Bose R, Luo X (2011) Integrative framework for assessing firms’ potential to undertake green IT initiatives via virtualization: A theoretical perspective. J. Strategic Inform. Systems 20(1):38–54.Crossref, Google Scholar
• Boyd GA, Curtis EM (2014) Evidence of an ‘energy-management gap’ in U.S. manufacturing: Spillovers from firm management practices to energy efficiency. J. Environment. Econom. Management 68(3):463–479.Crossref, Google Scholar
• Bresnahan TF, Brynjolfsson E, Hitt LM (2002) Information technology, workplace organization, and the demand for skilled labor: Firm-level evidence. Quart. J. Econom. 117(1):339–376.Crossref, Google Scholar
• Brynjolfsson E, Hitt L (1996) Paradox lost? Firm-level evidence on the returns to information systems spending. Management Sci. 42(4):541–558.Link, Google Scholar
• Brynjolfsson E, Milgrom P (2013) Complementarity in organizations. Gibbons R, Roberts J, eds. The Handbook of Organizational Economics (Princeton University Press, Princeton, NJ), 11–55.Crossref, Google Scholar
• Brynjolfsson E, Hofmann P, Jordan J (2010) Cloud computing and electricity: Beyond the utility model. Comm. ACM 53(5):32–34.Crossref, Google Scholar
• Business Advantage (2017) CAD in the Cloud: Market Trends 2017 Report (Business Advantage).Google Scholar
• Chang YB, Gurbaxani V (2012) Information technology outsourcing, knowledge transfer, and firm productivity: An empirical analysis. Management Inform. Systems Quart. 36(4):1043–1063.Crossref, Google Scholar
• Chang YB, Gurbaxani V (2013) An empirical analysis of technical efficiency: The role of IT intensity and competition. Inform. Systems Res. 24(3):561–578.Link, Google Scholar
• Cheng N, Bang Y (2021) A comment on the practice of the Arellano-Bond/Blundell-Bond generalized method of moments estimator in IS research. Comm. Assoc. Inform. Systems 48:38.Google Scholar
• Chou S-W, Chang Y-C (2008) The implementation factors that influence the ERP (enterprise resource planning) benefits. Decision Support Systems 46(1):149–157.Crossref, Google Scholar
• Choudhary V (2007) Comparison of software quality under perpetual licensing and software as a service. J. Management Inform. Systems 24(2):141–165.Crossref, Google Scholar
• Choudhary V, Vithayathil J (2013) The impact of cloud computing: Should the IT department be organized as a cost center or a profit center? J. Management Inform. Systems 30(2):67–100.Crossref, Google Scholar
• Chung S, Animesh A, Han K, Pinsonneault A (2019) Software patents and firm value: A real options perspective on the role of innovation orientation and environmental uncertainty. Inform. Systems Res. 30(3):1073–1097.Link, Google Scholar
• Costanza R (1980) Embodied energy and economic valuation. Science 210(4475):1219–1224.Crossref, Google Scholar
• Dedrick J (2010) Green IS: Concepts and issues for information systems research. Comm. Assoc. Inform. Systems 27(11):173–183.Google Scholar
• Dewan S, Kraemer KL (2000) Information technology and productivity: Evidence from country-level data. Management Sci. 46(4):548–562.Link, Google Scholar
• Dimitropoulos J (2007) Energy productivity improvements and the rebound effect: An overview of the state of knowledge. Energy Policy 35(12):6354–6363.Crossref, Google Scholar
• Engineering.com. (2018) Research report: Identifying the core issues that frustrate product development teams. Engineering.com, https://www.engineering.com/story/research-report-the-core-issues-that-frustrate-product-development-teams-pvjlj.Google Scholar
• Ewens M, Nanda R, Rhodes-Kropf M (2018) Cost of experimentation and the evolution of venture capital. J. Financial Econom. 128(3):422–442.Crossref, Google Scholar
• Fazli A, Sayedi A, Shulman JD (2018) The effects of autoscaling in cloud computing. Management Sci. 64(11):5149–5163.Link, Google Scholar
• Filippini M, Hunt LC (2015) Measurement of energy efficiency based on economic foundations. Energy Econom. 52:S5–S16.Crossref, Google Scholar
• Fisher-Vanden K, Jefferson GH, Liu H, Tao Q (2004) What is driving China’s decline in energy intensity? Resource Energy Econom. 26(1):77–97.Crossref, Google Scholar
• Fortune (2019) The Internet cloud has a dirty secret. Accessed April 3, 2022, https://fortune.com/2019/09/18/internet-cloud-server-data-center-energy-consumption-renewable-coal.Google Scholar
• Gartner (2021) Gartner says four trends are shaping the future of public cloud. Accessed April 3, 2022, https://www.gartner.com/en/newsroom/press-releases/2021-08-02-gartner-says-four-trends-are-shaping-the-future-of-public-cloud.Google Scholar
• Gholami R, Watson RT, Hasan H, Molla A, Bjørn-andersen N (2016) Information systems solutions for environmental sustainability: How can we do more? J. Assoc. Inform. Systems 17(8):521–536.Google Scholar
• Goo J, Kishore R, Rao HR, Nam K (2009) The role of service level agreements in relational management of information technology outsourcing: An empirical study. MIS Quart. 33(1):119–145.Google Scholar
• Google (2012) Google Apps: Energy Efficiency in the Cloud (Google).Google Scholar
• Greene W (2005a) Fixed and random effects in stochastic frontier models. J. Production Anal. 23(1):7–32.Crossref, Google Scholar
• Greene W (2005b) Reconsidering heterogeneity in panel data estimators of the stochastic frontier model. J. Econometrics 126(2):269–303.Crossref, Google Scholar
• Greenpeace (2017) Clicking Clean: Who Is Winning the Race to Build a Green Internet (Greenpeace, Washington, DC).Google Scholar
• Gu W, Wang W (2004) Information technology and productivity growth: Evidence from Canadian Industries. Jorgenson DW, ed. Economic Growth in Canada and the United States in the Information Age, Industry Canada Research Monograph Series (Industry Canada, Ottawa), 57–82.Google Scholar
• Han K, Kauffman R, Nault B (2011) Returns to information technology outsourcing. Inform. Systems Res. 22(4):824–840.Link, Google Scholar
• Han K, Mithas S (2013) Information technology outsourcing and non-IT operating costs: An empirical investigation. Management Inform. Systems Quart. 37(1):315–331.Crossref, Google Scholar
• Hang L, Tu M (2007) The impacts of energy prices on energy intensity: Evidence from China. Energy Policy 35(5):2978–2988.Crossref, Google Scholar
• Hardy Q (2018) How cloud computing is changing management. Harvard Bus. Rev., https://hbr.org/2018/02/how-cloud-computing-is-changing-management.Google Scholar
• Hilty LM, Arnfalk P, Erdmann L, Goodman J, Lehmann M, Wäger Pa (2006) The relevance of information and communication technologies for environmental sustainability: A prospective simulation study. Environ. Modeling Software 21(11):1618–1629.Crossref, Google Scholar
• Horner NC, Shehabi A, Azevedo IL (2016) Known unknowns: Indirect energy effects of information and communication technology. Environ. Res. Lett. 11(10):103001.Crossref, Google Scholar
• Imai K, Keele L, Yamamoto T (2010) Identification, inference and sensitivity analysis for causal mediation effects. Statist. Sci. 25(1):51–71.Crossref, Google Scholar
• Isaksson A (2007) Determinants of total factor productivity: A literature review. Working paper, United Nations Industrial Development Organization, Vienna.Google Scholar
• Iyer B, Henderson JC (2010) Preparing for the future: Understanding the seven capabilities of cloud computing. MIS Quart. Executive 9(2):117–131.Google Scholar
• Iyer B, Henderson JC (2012) Business value from clouds: Learning from users. MIS Quart. Executive 11(1):52–60.Google Scholar
• Jin W, McElheran K (2019) Economies before scale: Survival and performance of young plants in the age of cloud computing. Working paper, Rotman School of Management, University of Toronto, Toronto.Google Scholar
• Jing S-Y, Ali S, She K, Zhong Y (2013) State-of-the-art research study for green cloud computing. J. Supercomput. 65(1):445–468.Crossref, Google Scholar
• Jones N (2018) How to stop data centres from gobbling up the world’s electricity. Nature 561(7722):163–166.Crossref, Google Scholar
• Jorgenson DW, Ho MS, Samuels JD (2011) Information technology and U.S. productivity growth: Evidence from a prototype industry production account. J. Production Anal. 36(2):159–175.Crossref, Google Scholar
• Ju J, Wang Y, Fu J, Wu J, Lin Z (2010) Research on key technology in SaaS. Proc. Internat. Conf. on Intelligent Comput. and Cognitive Inform. (IEEE, New York), 384–387.Google Scholar
• Khuntia J, Saldanha TJV, Mithas S, Sambamurthy V (2018) Information technology and sustainability: Evidence from an emerging economy. Production Oper. Management 27(4):756–773.Crossref, Google Scholar
• Klems M, Nimis J, Tai S (2009) Do clouds compute? A framework for estimating the value of cloud computing. Part of the Lecture Notes in Business Information Processing Book Series, vol. 22 (Springer, Berlin), 110–123.Crossref, Google Scholar
• KPMG (2014) 2014 Cloud Survey Report: Elevating Business in the Cloud (KPMG).Google Scholar
• Kumbhakar S, Lovell K (2000) Stochastic Frontier Analysis (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• Lee B, Barua A (1999) An integrated assessment of productivity and efficiency impacts of information technology investments: Old data, new analysis and evidence. J. Production Anal. 12(1):21–43.Crossref, Google Scholar
• Levina N, Ross JW (2003) From the vendor’s perspective: Exploring the value proposition in information technology outsourcing. MIS Quart. 27(3):331–364.Google Scholar
• Lin B, Du K (2014) Measuring energy efficiency under heterogeneous technologies using a latent class stochastic frontier approach: An application to Chinese energy economy. Energy 76:884–890.Crossref, Google Scholar
• Loukis E, Janssen M, Mintchev I (2019) Determinants of software-as-a-service benefits and impact on firm performance. Decision Support Systems 117:38–47.Crossref, Google Scholar
• Lyubich E, Shapiro JS, Walker R (2018) Regulating mismeasured pollution: Implications of firm heterogeneity for environmental policy. AEA Paper Proc. 108(2):136–142.Crossref, Google Scholar
• Malhotra A, Melville NP, Watson RT (2013) Spurring impactful research on information systems for environmental sustainability. Management Inform. Systems Quart. 37(4):1265–1274.Crossref, Google Scholar
• Marston S, Li Z, Bandyopadhyay S, Zhang J, Ghalsasi A (2011) Cloud computing—The business perspective. Decision Support Systems 51(1):176–189.Crossref, Google Scholar
• Martin R, Muûls M, de Preux LB, Wagner UJ (2012) Anatomy of a paradox: Management practices, organizational structure and energy efficiency. J. Environ. Econom. Management 63(2):208–223.Crossref, Google Scholar
• Masanet E, Shehabi A, Lei N, Smith S, Koomey J (2020) Recalibrating global data center energy-use estimates. Science 367(6481):984–986.Crossref, Google Scholar
• Masanet E, Shehabi A, Ramakrishnan L, Liang J, Ma X, Walker B, Hendrix V, et al. (2013) The Energy Efficiency Potential of Cloud-Based Software: A U.S. Case Study (Lawrence Berkeley National Laboratory, Berkeley, CA).Crossref, Google Scholar
• Mastelic T, Oleksiak A, Claussen H, Brandic I, Pierson J-M, Vasilakos AV (2014) Cloud computing: Survey on energy efficiency. ACM Comput. Survey 47(2):1–36.Crossref, Google Scholar
• Mell P, Grance T (2011) The NIST Definition of Cloud Computing(National Institute of Standards and Technology, Gaithersburg, MD).Crossref, Google Scholar
• Melville NP (2010) Information systems innovation for environmental sustainability. Management Inform. Systems Quart. 34(1):1–21.Crossref, Google Scholar
• Melville NP, Kraemer K, Gurbaxani V (2004) Information technology and organizational performance: An integrative model of IT business value. Management Inform. Systems Quart. 28(2):283–322.Crossref, Google Scholar
• Microsoft (2010) Cloud Computing and Sustainability: The Environmental Benefits of Moving to the Cloud (Accenture, WSP Environment & Energy, Microsoft).Google Scholar
• Murugesan S (2008) Harnessing green IT: Principles and practices. IT Professional 10(1):24–33.Crossref, Google Scholar
• Mytton D (2020) Hiding greenhouse gas emissions in the cloud. Nature Climate Change 10(8):701.Crossref, Google Scholar
• Pang M-S, Tafti A, Krishnan MS (2014) Information technology and administrative efficiency in U.S. state governments: A stochastic frontier approach. Management Inform. Systems Quart. 38(4):1079–1101.Crossref, Google Scholar
• Qian L, Luo Z, Du Y, Guo L (2009) Cloud computing: An overview. Cloud Computing. CloudCom 2009, vol. 5931. Lecture Notes in Computer Science (Springer, Berlin), 626–631.Crossref, Google Scholar
• Qu WG, Pinsoneault A, Oh W (2011) Influence of industry characteristics on information technology outsourcing. J. Management Inform. Systems 27(4):99–128.Crossref, Google Scholar
• Rodrigues J, Ruivo P, Oliveira T (2021) Mediation role of business value and strategy in firm performance of organizations using software-as-a-service enterprise applications. Inform. Management 58(1):103289.Crossref, Google Scholar
• Saunders A, Brynjolfsson E (2016) Valuing information technology related intangible assets. Management Inform. Systems Quart. 40(1):83–110.Crossref, Google Scholar
• Schniederjans DG, Hales DN (2016) Cloud computing and its impact on economic and environmental performance: A transaction cost economics perspective. Decision Support Systems 86:73–82.Crossref, Google Scholar
• Seo H-J, Lee YS (2006) Contribution of information and communication technology to total factor productivity and externalities effects. Inform. Tech. Development 12(2):159–173.Crossref, Google Scholar
• Shao BBM, Lin WT (2001) Measuring the value of information technology in technical efficiency with stochastic production frontiers. Inform. Software Tech. 43(7):447–456.Crossref, Google Scholar
• Shehabi A, Smith SJ, Sartor DA, Brown RE, Herrlin M, Koomey JG, Masanet ER, et al. (2016) United States Data Center Energy Usage Report (Lawrence Berkeley National Laboratory, Berkeley, CA).Crossref, Google Scholar
• Shephard R (1970) Theory of Cost and Production Functions (Princeton University Press, Princeton, NJ).Google Scholar
• Society for Information Management (2020) 2020 SIM IT Trends(Society for Information Management, Mount Laurel, NJ)Google Scholar
• Stiroh KJ (2002) Information technology and the U.S. productivity revival: What do the industry data say? Amer. Econom. Rev. 92(5):1559–1576.Crossref, Google Scholar
• Strassner EH, Medeiros GW, Smith GM (2005) Annual Industry Account: Introducing KLEMS Input Estimates for 1997–2003 Survey of Current Business (U.S. Department of Commerce, Washington, DC).Google Scholar
• Tambe P, Hitt LM (2012) The productivity of information technology investments: New evidence from IT labor data. Inform. Systems Res. 23(3-part 1):599–617.Google Scholar
• Volkswagen Newsroom (2020) Volkswagen Steps Up Development of Industrial Cloud (Volkswagen Newsroom).Google Scholar
• Wauters P, Peijl S, Van Der Cilli V, Bolchi M, Janowski P, Moeremans M, Taylor G, et al. (2016) Measuring the Economic Impact of Cloud Computing in Europe (European Commission/Deloitte).Google Scholar
• Williams DR, Tang Y (2013) Impact of office productivity cloud computing on energy consumption and greenhouse gas emissions. Environ. Sci. Tech. 47(9):4333–4340.Crossref, Google Scholar
• Wurlod J-D, Noailly J (2018) The impact of green innovation on energy intensity: An empirical analysis for 14 industrial sectors in OECD countries. Energy Econom. 71:47–61.Crossref, Google Scholar
• ZDNet (2019) Uber vs. Lyft: How the rivals approach cloud, AI, and machine learning. Accessed April 3, 2022, https://www.zdnet.com/article/uber-vs-lyft-how-the-rivals-approach-cloud-ai-machine-learning/.Google Scholar