Improving natural resource management and human health to ensure sustainable societal development based upon insights gained from working within ‘Big Data Environments’

Song, Malin; Cen, Ling; Zheng, Zhou; Fisher, Ron; Liang, Xi; Wang, Yutao; Huisingh, Donald

doi:10.1016/j.jclepro.2015.02.010

Cited by 19 publications

(12 citation statements)

References 10 publications

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“…The researchers engaged in deep discussion and consultation of literature to verify identification of each resource. (Hart, 1995;Russo & Fouts, 1997;Menguc & Ozanne, 2005)  Assumes an internal focus that over times shifts towards external (Hart, 1995;Menguc & Ozanne, 2005) Product Stewardship  Prioritisation of natural environment throughout entire lifecycle (Hart, 1995;Hart & Dowell, 2011; Shi et al, 2012)  Creation of wholly sustainable products offers opportunities for differentiation (Hart, 1995;Menguc & Ozanne, 2005;Ashby et al, 2012;Golicic & Smith, 2013; Miemczyk et al, 2016  Access to scarce resources via stakeholder integration (Hart, 1995;Hart & Dowell, 2011;Ashby et al, 2012)  Supply chain/ lifecycle focus (Shi et al, 2012;Miemczyk et al, 2016) Clean Technologies  Positive impact operations (Hart & Christensen, 1997;Hart & Milstein, 1999;Song et al, 2015)  Technological innovations as alternatives to non-renewables ( (Hart & Christensen, 1997;Hart & Milstein, 1999Move away from traditional routines to re-create industry in a way which promotes sustainability with products, processes or services that create value or significantly reduce waste (Hart & Christensen, 1997;Hart & Milstein, 1999; Song et al, 2015)  Energy technologies, transport technologies, water technologies and material technologies (Pernick & Wilder, 2007) Base of the Pyramid  Alleviation of social ills on a global scale (Prahalad & Hart, 2002;London & Hart, 2004;Hart et al, 2016)  Stimulation of economic growth/ support of emerging markets at the base of the pyramid (Prahalad & Hart, 2002;London & Hart, 2004;Hart et al, 2016)  Acc...…”

Section: Discussionmentioning

confidence: 99%

“…Sustainable development promotes the consideration of economic, environmental and social issues on a global scale (Hart, 1995;Shrivastava & Hart, 1995). New manufacturing processes in support of environmental advancement and new business markets in support of social advancement promote positive impact operations (Hart, 1997;Song et al, 2015). Opportunities for competitive gain arise in the creation of such processes ahead of competitors and access to new, unsaturated markets of the future (Hart, 1995).…”

Section: Nrbv Resourcesmentioning

confidence: 99%

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An empirical explanation of the natural-resource-based view of the firm

McDougall

Wagner

MacBryde

2019

Production Planning & Control

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To date the natural-resource-based view has been an abstract phenomenon, primarily used by academics to explain competitive sustainable operations. This paper attempts to go beyond this, responding to the need for explanation of the practical existence of the four natural-resource-based view resources in industry. Assuming a critical realist qualitative approach, in-depth interviews with sustainability experts in UK agri-food are undertaken. Findings demonstrate the existence of pollution prevention, product stewardship and clean technologies, and align with Hart's (1995) conceptualisation of sustainability as competitive resources. Whilst the fourth resource, base of the pyramid, cannot be empirically verified, a fifth resource of local philanthropy is uncovered and contributes to the growing body of knowledge surrounding competitive social sustainability. Findings also challenge the hierarchal presentation of the naturalresource-based view to implicate a more cyclical uptake. Thus, in offering the first empirical explanation of the natural-resource-based view, this paper overcomes a theory-practice gap to elucidate the feasibility, orchestration and value of resources in competitive and sustainable operations.

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Section: Discussionmentioning

confidence: 99%

Section: Nrbv Resourcesmentioning

confidence: 99%

An empirical explanation of the natural-resource-based view of the firm

McDougall

Wagner

MacBryde

2019

Production Planning & Control

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“…(Song et al, 2012(Song et al, , 2015b. Big data can hence assist data scientists and policy makers in (i) developing and implementing policies and strategies that protect and manage natural resources in an environmental way; (ii) preventing wastage of resources and degradation of capacities that can provide essential services for human health and sustainable development of society (iii) limiting the production of pollutants by converting them into useful products (Song et al, 2015b); (iv) developing appropriate environmental protection policies and frameworks (Song et al, 2014;2015b); and (v) looking into disaster management and analyzing how people respond to disasters in order to take appropriate measures and devise policies that will enable recovery and restoring back to normality for communities (e.g. Chae et al, 2014;Shelton et al, 2014;Burns, 2015).…”

Section: Big Data Within Supply Chain Network For Sustainabilitymentioning

confidence: 99%

The role of Big Data in explaining disaster resilience in supply chains for sustainability

Παπαδόπουλος

Gunasekaran

Dubey

et al. 2017

Journal of Cleaner Production

513

314

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The purpose of this paper is to propose and test a theoretical framework to explain resilience in supply chain networks for sustainability using unstructured Big Data, based upon 36,422 items gathered in the form of tweets, news, Facebook, WordPress, Instagram, Googleþ, and YouTube, and structured data, via responses from 205 managers involved in disaster relief activities in the aftermath of Nepal earthquake in 2015. The paper uses Big Data analysis, followed by a survey which was analyzed using content analysis and confirmatory factor analysis (CFA). The results of the analysis suggest that swift trust, information sharing and publiceprivate partnership are critical enablers of resilience in supply chain networks. The current study used cross-sectional data. However the hypotheses of the study can be tested using longitudinal data to attempt to establish causality. The article advances the literature on resilience in disaster supply chain networks for sustainability in that (i) it suggests the use of Big Data analysis to propose and test particular frameworks in the context of resilient supply chains that enable sustainability; (ii) it argues that swift trust, public private partnerships, and quality information sharing link to resilience in supply chain networks; and (iii) it uses the context of Nepal, at the moment of the disaster relief activities to provide contemporaneous perceptions of the phenomenon as it takes place.

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“…By the way, energy-related big data have "4V" characteristics (i.e., volume, velocity, variety, and value) (Zhou and Yang, 2016) and "big data approaches may prove to be instrumental in achieving better natural resource allocation and management planning within and among nations" (Song et al, 2015). However, considering the problems of big-data availability, the China Energy Statistical Yearbooks provide the best data available as of now to measure total fossil energy consumption in the 30 provinces studied.…”

Section: Data Selectionmentioning

confidence: 99%