Big Data Analytics Capabilities and Eco-Innovation: A Study of Energy Companies
Russell Tatenda Munodawafa () and
Satirenjit Kaur Johl ()
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Russell Tatenda Munodawafa: Department of Management and Humanities, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia
Satirenjit Kaur Johl: Department of Management and Humanities, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia
Sustainability, 2019, vol. 11, issue 15, 1-21
Increased greenhouse gas (GHG) emissions in the past decades have created concerns about the environment. To stymie global warming and the deterioration of the natural environment, global CO 2 emissions need to reach approximately 1.3 tons per capita by 2050. However, in Malaysia, CO 2 output per capita—driven by fossil fuel consumption and energy production—is expected to reach approximately 12.1 tons by the year 2020. GHG mitigation strategies are needed to address these challenges. Cleaner production, through eco-innovation, has the potential to arrest CO 2 emissions and buttress sustainable development. However, the cleaner production process has been hampered by lack of complete data to support decision making. Therefore, using the resource-based view, a preliminary study consisting of energy and utility firms is undertaken to understand the impact of big data analytics towards eco-innovation. Linear regression through SPSS Version 24 reveals that big data analytics could become a strong predictor of eco-innovation. This paper concludes that information and data are key inputs, and big data technology provides firms the opportunity to obtain information, which could influence its production process—and possibly help arrest increasing CO 2 emissions.
Keywords: eco-innovation; cleaner production; sustainable development; CO 2 emission; big data analytics; industry 4.0; resource-based view (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:11:y:2019:i:15:p:4254-:d:255298
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