The Defensive Equilibrium - Balancing between Algorithmic Efficiency and Human-Centric Sustainability
(2026) BUSN09 20261Department of Business Administration
- Abstract
- The integration of Artificial Intelligence (AI) into corporate sustainability promises to alleviate the cognitive boundaries of the Strategic Decision-Making Process (SDMP) by synthesising massive, complex datasets. However, the inherent friction between the reductionist, instrumental logic of algorithmic models and the pluralistic, value-laden logic of sustainability creates profound operational and ethical paradoxes. Employing a qualitative, multi-case study of the Swedish manufacturing sector and utilising the Gioia methodology, this thesis investigates how corporate decision-makers navigate the tension between algorithmic efficiency and human control.
The findings reveal a stark literature-practice gap: rather than experiencing... (More) - The integration of Artificial Intelligence (AI) into corporate sustainability promises to alleviate the cognitive boundaries of the Strategic Decision-Making Process (SDMP) by synthesising massive, complex datasets. However, the inherent friction between the reductionist, instrumental logic of algorithmic models and the pluralistic, value-laden logic of sustainability creates profound operational and ethical paradoxes. Employing a qualitative, multi-case study of the Swedish manufacturing sector and utilising the Gioia methodology, this thesis investigates how corporate decision-makers navigate the tension between algorithmic efficiency and human control.
The findings reveal a stark literature-practice gap: rather than experiencing seamless "agentic delegation," decision-makers face severe socio-technical friction driven by fragmented sustainability data architectures. Consequently, managers experience extreme epistemic uncertainty and actively engage in "strategic non-use." They assign AI to administrative synthesis while retaining strict, defensive human authorisation over high-stakes sustainability choices. This study conceptualises this phenomenon as the Defensive Equilibrium, a path-dependent state where locally rational risk-aversion, combined with domain-neutral IT governance, collectively traps the firm in a state of systemic algorithmic under-use.
To dismantle this equilibrium, the thesis proposes a Human-Centric AI Governance Approach that maps domain-specific ethical guardrails and continuous "algorithmic interrogation" protocols directly onto the phase-gates of the SDMP. Ultimately, this research argues that the strategic value of AI in sustainability is entirely contingent upon resolving foundational data constraints and formally institutionalising human moral accountability. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/student-papers/record/9232083
- author
- Ngo, Quang Tuan LU and Wamsler, Emilia LU
- supervisor
- organization
- course
- BUSN09 20261
- year
- 2026
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- Artificial Intelligence, Strategic Decision-Making, Corporate Sustainability, Algorithmic Governance, Algorithm Aversion, Bounded Rationality, Socio-Technical Systems.
- language
- English
- id
- 9232083
- date added to LUP
- 2026-06-29 14:05:37
- date last changed
- 2026-06-29 14:05:37
@misc{9232083,
abstract = {{The integration of Artificial Intelligence (AI) into corporate sustainability promises to alleviate the cognitive boundaries of the Strategic Decision-Making Process (SDMP) by synthesising massive, complex datasets. However, the inherent friction between the reductionist, instrumental logic of algorithmic models and the pluralistic, value-laden logic of sustainability creates profound operational and ethical paradoxes. Employing a qualitative, multi-case study of the Swedish manufacturing sector and utilising the Gioia methodology, this thesis investigates how corporate decision-makers navigate the tension between algorithmic efficiency and human control.
The findings reveal a stark literature-practice gap: rather than experiencing seamless "agentic delegation," decision-makers face severe socio-technical friction driven by fragmented sustainability data architectures. Consequently, managers experience extreme epistemic uncertainty and actively engage in "strategic non-use." They assign AI to administrative synthesis while retaining strict, defensive human authorisation over high-stakes sustainability choices. This study conceptualises this phenomenon as the Defensive Equilibrium, a path-dependent state where locally rational risk-aversion, combined with domain-neutral IT governance, collectively traps the firm in a state of systemic algorithmic under-use.
To dismantle this equilibrium, the thesis proposes a Human-Centric AI Governance Approach that maps domain-specific ethical guardrails and continuous "algorithmic interrogation" protocols directly onto the phase-gates of the SDMP. Ultimately, this research argues that the strategic value of AI in sustainability is entirely contingent upon resolving foundational data constraints and formally institutionalising human moral accountability.}},
author = {{Ngo, Quang Tuan and Wamsler, Emilia}},
language = {{eng}},
note = {{Student Paper}},
title = {{The Defensive Equilibrium - Balancing between Algorithmic Efficiency and Human-Centric Sustainability}},
year = {{2026}},
}