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Sustainability Metrics in Technology Management
Research Guide

What is Sustainability Metrics in Technology Management?

Sustainability Metrics in Technology Management quantify environmental, social, and economic impacts of technological systems using KPIs, life-cycle assessments, and multi-criteria decision frameworks.

Researchers develop metrics for circular economy models and green IT to balance profitability with planetary boundaries. Over 10 key papers since 2018 address digital transformation's role in sustainability, with Dwivedi et al. (2019) cited 3635 times. Focus includes AI-driven assessments and blockchain for logistics efficiency.

Curated Papers

Key Challenges

Why It Matters

Sustainability metrics guide firms to align technology investments with UN SDGs, internalizing externalities under regulatory pressure. Martínez-Peláez et al. (2023) show digital transformation boosts MSME competitiveness via stakeholder-mediated sustainability (427 citations). Mageto (2021) demonstrates big data analytics optimizing manufacturing supply chains for lower emissions (150 citations). Orji et al. (2020) highlight blockchain reducing freight logistics' carbon footprint (342 citations).

Key Research Challenges

Quantifying Tech Externalities

Metrics struggle to capture full life-cycle environmental costs of technologies like AI systems. Dwivedi et al. (2019) note multidisciplinary challenges in assessing AI's sustainability impacts (3635 citations). Standardization across sectors remains elusive.

Balancing Profit and Planet

Frameworks must integrate economic viability with ecological limits in tech management. Martínez-Peláez et al. (2023) identify stakeholder mediation as key for digital sustainability adoption (427 citations). Multi-criteria decisions often overlook long-term planetary boundaries.

Data Gaps in Digital Twins

Digital twinning for sustainability lacks real-time metrics integration in production systems. Rathore et al. (2021) review AI-ML challenges in twinning for supply chain sustainability (496 citations). Scalable big data analytics for metrics verification is underdeveloped.

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...

The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities

M. Mazhar Rathore, Syed Attique Shah, Dhirendra Shukla et al. · 2021 · IEEE Access · 496 citations

<p dir="ltr">Digital twinning is one of the top ten technology trends in the last couple of years, due to its high applicability in the industrial sector. The integration of big data analytic...

Role of Digital Transformation for Achieving Sustainability: Mediated Role of Stakeholders, Key Capabilities, and Technology

Rafael Martínez-Peláez, Alberto Ochoa-Brust, Solange Ivette Rivera Manrique et al. · 2023 · Sustainability · 427 citations

Sustainability through digital transformation is essential for contemporary businesses. Embracing sustainability, micro-, small-, and medium-sized enterprises (MSMEs) can gain a competitive advanta...

Evaluating the factors that influence blockchain adoption in the freight logistics industry

Ifeyinwa Juliet Orji, Simonov Kusi‐Sarpong, Shuangfa Huang et al. · 2020 · Transportation Research Part E Logistics and Transportation Review · 342 citations

An architecture of an Intelligent Digital Twin in a Cyber-Physical Production System

Behrang Ashtari Talkhestani, Tobias Jung, B. Lindemann et al. · 2019 · at - Automatisierungstechnik · 286 citations

Abstract The role of a Digital Twin is increasingly discussed within the context of Cyber-Physical Production Systems. Accordingly, various architectures for the realization of Digital Twin use cas...

Big Data Analytics in Sustainable Supply Chain Management: A Focus on Manufacturing Supply Chains

Joash Mageto · 2021 · Sustainability · 150 citations

Sustainable supply chain management has been an important research issue for the last two decades due to climate change. From a global perspective, the United Nations have introduced sustainable de...

Fossil Energy in the Framework of Sustainable Development: Analysis of Prospects and Development of Forecast Scenarios

Y L Zhukovskiy, Daria Evgenievna Batueva, Aleksandra Buldysko et al. · 2021 · Energies · 110 citations

In the next 20 years, the fossil energy must become a guarantor of the sustainable development of the energy sector for future generations. Significant threats represent hurdles in this transition....

Reading Guide

Foundational Papers

Start with Michnik (2013) for MCDA in innovation selection and Relich (2014) for intelligent systems in product success estimation, as they establish multi-criteria baselines for tech metrics.

Recent Advances

Study Martínez-Peláez et al. (2023) for stakeholder-driven digital sustainability and Rowan et al. (2022) for peatland eco-innovations, capturing 2020s advances.

Core Methods

Core techniques include big data analytics (Mageto 2021), AI-ML digital twinning (Rathore 2021), blockchain evaluation (Orji 2020), and scenario planning (Michnik 2013).

How PapersFlow Helps You Research Sustainability Metrics in Technology Management

Discover & Search

Research Agent uses searchPapers and exaSearch to find 250M+ OpenAlex papers on sustainability KPIs, then citationGraph on Dwivedi et al. (2019) reveals 3635-cited connections to green IT metrics. findSimilarPapers expands to Martínez-Peláez et al. (2023) for digital transformation frameworks.

Analyze & Verify

Analysis Agent applies readPaperContent to extract KPIs from Mageto (2021), then runPythonAnalysis with pandas to compute supply chain emission stats from abstracted data. verifyResponse (CoVe) and GRADE grading confirm metric validity against Rathore et al. (2021) digital twin benchmarks.

Synthesize & Write

Synthesis Agent detects gaps in circular economy metrics across Orji et al. (2020) and Rowan et al. (2022), flagging blockchain-IT contradictions. Writing Agent uses latexEditText, latexSyncCitations for SDG-aligned reports, latexCompile for publication-ready docs, and exportMermaid for metric flowchart diagrams.

Use Cases

"Analyze emission reductions in sustainable supply chains using big data from recent papers"

Research Agent → searchPapers('sustainable supply chain metrics') → Analysis Agent → runPythonAnalysis(pandas on Mageto 2021 data) → matplotlib emission trend charts exported as CSV.

"Draft LaTeX report on blockchain sustainability metrics for tech logistics"

Synthesis Agent → gap detection(Orji 2020 + Martínez-Peláez 2023) → Writing Agent → latexEditText(structured KPIs) → latexSyncCitations → latexCompile(full report with tables).

"Find GitHub repos implementing digital twin sustainability metrics"

Research Agent → findSimilarPapers(Rathore 2021) → Code Discovery → paperExtractUrls → paperFindGithubRepo → githubRepoInspect(ML models for twinning metrics).

Automated Workflows

Deep Research workflow conducts systematic review of 50+ papers on sustainability KPIs, chaining searchPapers → citationGraph → structured report with GRADE scores. DeepScan's 7-step analysis verifies metrics in Rowan et al. (2022) peatland innovations via CoVe checkpoints. Theorizer generates frameworks from Mageto (2021) and Orji (2020) for novel green IT theories.

Try Doxa for Sustainability Metrics in Technology Management Research

Frequently Asked Questions

What defines sustainability metrics in technology management?

KPIs, life-cycle assessments, and multi-criteria frameworks quantify environmental impacts of tech systems, as in circular economy and green IT models.

What methods measure tech sustainability?

Big data analytics (Mageto 2021), digital twinning with AI-ML (Rathore 2021), and blockchain adoption models (Orji 2020) provide core methods.

What are key papers?

Dwivedi et al. (2019, 3635 citations) on AI challenges; Martínez-Peláez et al. (2023, 427 citations) on digital transformation; Michnik (2013) on MCDA for innovation.