Subtopic Deep Dive
Big Data and Organizational Sustainability
Research Guide
What is Big Data and Organizational Sustainability?
Big Data and Organizational Sustainability examines big data analytics applications for enhancing ESG performance measurement, sustainable supply chains, and circular economy practices in organizations.
Research integrates big data with sustainability metrics and benchmarking frameworks. Over 10 key papers from 2011-2021, including foundational works, explore analytics-driven sustainability. Dubey et al. (2017) demonstrate predictive analytics for social and environmental sustainability with 735 citations.
Why It Matters
Big data analytics enables firms to track ESG metrics amid regulatory pressures, improving sustainable supply chain visibility (Bag et al., 2020, 747 citations). Predictive models from big data enhance circular economy capabilities and operational performance (Dubey et al., 2017; Dubey et al., 2019, 712 citations). Akter et al. (2016) show BDA alignment with business strategy boosts firm performance in sustainable practices (1280 citations).
Key Research Challenges
Institutional Pressures Integration
Firms face regulatory and resource constraints in adopting big data for sustainable practices. Bag et al. (2020) identify pressures driving AI and BDA adoption for circular economy capabilities. Balancing these requires aligned strategies.
Data Quality for Sustainability Metrics
Big data sources often lack reliability for ESG benchmarking. Dubey et al. (2017) highlight challenges in using predictive analytics for environmental sustainability. Verification frameworks are needed for accurate metrics.
Dynamic Environment Adaptation
Environmental dynamism complicates BDA deployment in manufacturing sustainability. Dubey et al. (2019) note entrepreneurial orientation effects on operational performance. Real-time analytics integration remains difficult.
Essential Papers
Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy
Yogesh K. Dwivedi, Laurie Hughes, Elvira Ismagilova et al. · 2019 · International Journal of Information Management · 3.6K citations
<p>As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for d...
Digital Business Strategy: Toward a Next Generation of Insights
Anandhi Bharadwaj, Omar A. El Sawy, Paul A. Pavlou et al. · 2013 · MIS Quarterly · 3.6K citations
Over the last three decades, the prevailing view of information technology strategy has been that it is a functional-level strategy that must be aligned with the firm’s chosen business strategy. Ev...
How to improve firm performance using big data analytics capability and business strategy alignment?
Shahriar Akter, Samuel Fosso Wamba, Angappa Gunasekaran et al. · 2016 · International Journal of Production Economics · 1.3K citations
Digital Transformation: An Overview of the Current State of the Art of Research
Sascha Kraus, Paul Jones, Norbert Kailer et al. · 2021 · SAGE Open · 1.1K citations
The increasing digitalization of economies has highlighted the importance of digital transformation and how it can help businesses stay competitive in the market. However, disruptive changes not on...
Digital Supply Chain Transformation toward Blockchain Integration
Kari Korpela, Jukka Hallikas, Tomi Dahlberg · 2017 · Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences · 839 citations
Digital supply chain integration is becoming \ increasingly dynamic. Access to customer demand \ needs to be shared effectively, and product and service \ deliveries must be tracked to provide visi...
Artificial Intelligence and Business Value: a Literature Review
Ida Merete Enholm, Emmanouil Papagiannidis, Patrick Mikalef et al. · 2021 · Information Systems Frontiers · 767 citations
Abstract Artificial Intelligence (AI) are a wide-ranging set of technologies that promise several advantages for organizations in terms off added business value. Over the past few years, organizati...
Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities
Surajit Bag, J.H.C. Pretorius, Shivam Gupta et al. · 2020 · Technological Forecasting and Social Change · 747 citations
Reading Guide
Foundational Papers
Start with Bharadwaj et al. (2013, 3586 citations) for digital business strategy alignment, then Otto (2011) on data governance for sustainability data management.
Recent Advances
Study Bag et al. (2020, 747 citations) on institutional pressures for circular economy; Dubey et al. (2017, 735 citations) on predictive analytics for ESG sustainability.
Core Methods
Big data analytics capability alignment (Akter et al., 2016); predictive modeling under dynamism (Dubey et al., 2019); institutional resource frameworks (Bag et al., 2020).
How PapersFlow Helps You Research Big Data and Organizational Sustainability
Discover & Search
Research Agent uses searchPapers and exaSearch to find papers like 'Can big data and predictive analytics improve social and environmental sustainability?' by Dubey et al. (2017). citationGraph reveals connections to Bag et al. (2020) on institutional pressures. findSimilarPapers expands to Akter et al. (2016) for BDA strategy alignment.
Analyze & Verify
Analysis Agent applies readPaperContent to extract ESG metrics from Dubey et al. (2017), then verifyResponse with CoVe checks claims against 735-citation impact. runPythonAnalysis with pandas verifies sustainability correlations in extracted data. GRADE grading scores evidence strength for circular economy claims.
Synthesize & Write
Synthesis Agent detects gaps in ESG benchmarking across Dubey et al. (2019) and Bag et al. (2020), flagging contradictions in dynamic environments. Writing Agent uses latexEditText, latexSyncCitations for 10-paper review, and latexCompile for polished reports. exportMermaid visualizes supply chain analytics flows.
Use Cases
"Analyze correlation between big data analytics capability and sustainability performance using Dubey et al. data."
Analysis Agent → readPaperContent (Dubey 2017) → runPythonAnalysis (pandas correlation matrix on ESG metrics) → matplotlib plot of results showing 735-citation validated insights.
"Draft LaTeX review on big data for circular economy capabilities citing Bag et al."
Synthesis Agent → gap detection (across Bag 2020, Dubey 2019) → Writing Agent → latexEditText (add intro) → latexSyncCitations (10 papers) → latexCompile → PDF with sustainability framework diagram.
"Find GitHub repos implementing predictive analytics for sustainable supply chains from recent papers."
Research Agent → paperExtractUrls (Dubey 2017) → paperFindGithubRepo → githubRepoInspect → exportCsv of 5 repos with code for ESG prediction models.
Automated Workflows
Deep Research workflow conducts systematic review of 50+ papers on BDA sustainability: searchPapers → citationGraph → DeepScan 7-step analysis with GRADE checkpoints on Dubey et al. (2017). Theorizer generates theory on institutional pressures from Bag et al. (2020) via gap detection → hypothesis synthesis. DeepScan verifies ESG claims with CoVe across Akter et al. (2016) and Dubey et al. (2019).
Frequently Asked Questions
What defines Big Data and Organizational Sustainability?
It covers big data applications for ESG metrics, sustainable supply chains, and circular economy models, as in Dubey et al. (2017).
What methods dominate this subtopic?
Predictive analytics and BDA alignment with strategy (Akter et al., 2016); institutional pressure models for AI adoption (Bag et al., 2020).
What are key papers?
Dubey et al. (2017, 735 citations) on predictive analytics for sustainability; Bag et al. (2020, 747 citations) on circular economy; Akter et al. (2016, 1280 citations) on BDA capability.
What open problems exist?
Integrating real-time data under dynamism (Dubey et al., 2019); scaling ESG metrics across supply chains amid institutional pressures (Bag et al., 2020).
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