From Automation to Augmentation: A Framework for Designing Human-Centric Work Environments in Society 5.0

Cristian Espinal Maya

econ.GNApr 1, 2026

From Automation to Augmentation: A Framework for Designing Human-Centric Work Environments in Society 5.0

Authors: Cristian Espinal Maya

Organizations: Department of Economics, Universidad EAFIT, Medellín, Colombia; Postgraduate Division, ESUMER.

Abstract

Society 5.0 and Industry 5.0 call for human-centric technology integration, yet the concept lacks an operational definition that can be measured, optimized, or evaluated at the firm level. This paper addresses three gaps. First, existing models of human-AI complementarity treat the augmentation function phi(D) as exogenous -- dependent only on the stock of AI deployed -- ignoring that two firms with identical technology investments achieve radically different augmentation outcomes depending on how the workplace is organized around the human-AI interaction. Second, no multi-dimensional instrument exists linking workplace design choices to augmentation productivity. Third, the Society 5.0 literature proposes human-centricity as a normative aspiration but provides no formal criterion for when it is economically optimal. We make four contributions. (1) We endogenize the augmentation function as phi(D, W), where W is a five-dimensional workplace design vector -- AI interface design, decision authority allocation, task orchestration, learning loop architecture, and psychosocial work environment -- and prove that human-centric design is profit-maximizing when the workforce's augmentable cognitive capital exceeds a critical threshold. (2) We conduct a PRISMA-guided systematic review of 120 papers (screened from 6,096 records) to map the evidence base for each dimension. (3) We provide secondary empirical evidence from Colombia's EDIT manufacturing survey (N=6,799 firms) showing that management practice quality amplifies the return to technology investment (interaction coefficient 0.304, p<0.01). (4) We propose the Workplace Augmentation Design Index (WADI), a 36-item theory-grounded instrument for diagnosing human-centricity at the firm level. Decision authority allocation emerges as the binding constraint for Society 5.0 transitions, and task orchestration as the most under-researched dimension

Figures & tables

Literature Strand	What Exists	What This Paper Adds
Society 5.0 / Industry 5.0	Normative concept (human-centricity, sustainability, resilience); 54 conceptual frameworks	Formal definition (Def. 1): human-centricity $\equiv$ $\partial\phi/\partial W>0$ ; WADI as measurement tool
Human-AI augmentation	Micro evidence (Brynjolfsson, Noy, Dell’Acqua): AI increases productivity but effect depends on context	Endogenized $\phi(D,W)$ : workplace design is the context variable; formal model of why same AI yields different outcomes
Organizational economics	Authority theory (Aghion-Tirole); AI delegation models (Athey et al.)	Extension to human-AI authority dyads; W 2 as binding constraint hypothesis; automation trap dynamics
Work design / Psychology	JD-R, SMART, COPSOQ, technostress scales; AI wellbeing evidence	WADI integrates cause (W 1 –W 4 ) and effect (W 5 ); links design choices to augmentation outcomes, not just wellbeing
Sustainability + I5.0	Macro-level roadmaps; CE + I5.0 integration	Firm-level energy constraint on $\phi$ optimization; sustainability-design tradeoff formalized
Task orchestration	Only 4 papers in corpus — genuine gap	W 3 dimension; comparative-advantage task allocation framework for human-AI workflows

Table 1: Gap analysis: What each literature strand contributes and what remains

Dimension	Label	Papers	% of Corpus	Assessment
$W_{5}$	Psychosocial Environment	87	73%	Over-represented
S5.0	Society 5.0 / Industry 5.0	77	64%	Strong foundation
$W_{4}$	Learning Loop Architecture	22	18%	Moderate
$W_{1}$	AI Interface Design	17	14%	Moderate
$W_{2}$	Decision Authority Allocation	14	12%	Thin — binding constraint
INST	Measurement Instruments	12	10%	Thin

Table 2: Evidence density by WADI dimension in the 120-paper corpus

Instrument	Items	Dimensions	Level	AI-specific?	Key limitation vs. WADI
AWWB [ 45 ]	Multi	AI wellbeing	Worker	Yes	Measures $W_{5}$ outcomes only; no design dimensions
AAAW [ 45 ]	25	6 (attitudes)	Worker	Yes	Attitudinal; does not measure organizational design choices
SMART [ 47 ]	21	5 (SMART)	Worker	No	Pre-AI; lacks $W_{1}$ , $W_{2}$ , $W_{4}$
COPSOQ-ISTAS21	44+	6+ (psychosocial)	Worker	No	Generic psychosocial risk; not AI-specific
I4.0 Maturity models	Varies	Technology readiness	Firm	No	Measures technology adoption, not human-centric design
WADI (proposed)	36	5 ( $W_{1}$ – $W_{5}$ )	Firm + Worker	Yes	Integrates design (cause) and outcomes (effect); linked to $\phi(D,W)$

Table 3: Comparison of WADI with existing workplace and AI measurement instruments

Variable	N	Mean	SD	Median	N	Mean	SD	Median
	EDIT Manufacturera 2019–2020				EDITS Services 2020–2021
Management Quality Composite	55	0.504	0.102	0.517	19	0.616	0.109	0.617
KPI Intensity	55	0.645	0.121	0.643	19	0.742	0.160	0.781
Target Intensity	55	0.797	0.109	0.809	19	0.861	0.078	0.896
Bonus Intensity	54	0.351	0.128	0.364	19	0.476	0.157	0.471
Promotion Merit	55	0.223	0.112	0.213	19	0.384	0.147	0.359

Table 4: Summary Statistics by Survey

Panel A: Manufacturing (EDIT 2019–2020, $N=55$ sectors)
	MQC	KPI	Target	Bonus	Promo	$\ln$ TII	HCQ
KPI Intensity	.92
Target Intensity	.84	.75
Bonus Intensity	.83	.64	.53
Promotion Merit	.91	.83	.71	.67
$\ln$ (Tech Inv.)	.55	.51	.36	.53	.52

Table 5: Correlation Matrix: Management Quality, Human Capital, and Innovation

MQC Quartile	N	Mean MQC	Any Innov.	Strict Innov.	HCQ	KPI Int.	Avg. Firms
Panel A: Manufacturing
Q1 (Low)	14	0.373	0.168	nan	0.103	0.505	150
Q2	14	0.472	0.249	0.0056	0.128	0.609	121
Q3	13	0.539	0.299	0.0103	0.148	0.695	142
Q4 (High)	14	0.632	0.369	0.0134	0.214	0.777	83
Panel B: Services

Table 6: Innovation Outcomes by Management Quality Quartile

	(1) Manufacturing	(2) Services
Constant	0.9948***	0.1145
	(0.3747)	(0.9848)
MQC	-1.3720*	-0.4078
	(0.8052)	(1.5109)
log(TII)	-0.1517***	-0.0351
	(0.0483)	(0.1412)

Table 7: OLS: Innovation Share on Management Quality, Technology, and Human Capital

Figure 1: Management Quality Composite vs. Innovation Share by CIIU sector. Bubble size proportional to sector employment; color reflects Human Capital Quality (HCQ). Dashed line: OLS fit. Source: DANE EDIT X (manufacturing, 2019–2020) and EDITS VIII (services, 2020–2021).

Figure 2: Correlation heatmap of management quality sub-indicators, technology investment, human capital, and innovation outcomes. Source: DANE EDIT/EDITS.

(R) = reverse-coded item. Full item bank with bilingual wording available in supplementary materials.
Dimension	#	Sample Items	Theoretical Basis	Source Instruments
$W_{1}$ : AI Interface Design	8	“I can see the reasoning behind AI recommendations” ; “I can easily override or modify AI outputs” ; “AI errors are clearly flagged”	Endsley SA model; Shneiderman HCAI; Parasuraman LOA	HAI Guidelines [ 3 ] ; HCAI [ 48 ] ; NASA-TLX adapted
$W_{2}$ : Decision Authority	8	“Frontline workers can act on AI recommendations without supervisor approval” ; “In practice, I feel I have real authority to use my judgment alongside AI”	Aghion-Tirole formal/real authority; Garicano hierarchies; Bloom management practices	WMS decentralization [ 12 ] ; AT97 authority [ 2 ]
$W_{3}$ : Task Orchestration	7	“Tasks are allocated based on explicit comparative advantage analysis” ; “When working with AI, I can see the end result of my work”	Task-based framework; Hackman-Oldham task identity; Dell’Acqua jagged frontier	JCM [ 34 ] ; Original (from $\phi(D,W)$ model)
$W_{4}$ : Learning Loops	7	“Workers systematically review and correct AI outputs” ; “When I correct an AI error, that correction is incorporated into the system”	Teece dynamic capabilities; Argyris-Sch‘̀on org. learning; HITL/RLHF	Dynamic capabilities [ 51 ] ; MAILS AI literacy scale
$W_{5}$ : Psychosocial Environment	6	“AI increases my freedom to decide how to do my work” ; “AI is used to support me, not to monitor my performance” ; “I worry AI could replace my job” (R)	Karasek JDC; JD-R theory; SMART work design; COPSOQ	COPSOQ-ISTAS21; AWWB [ 45 ] ; AAAW [ 45 ]

Table 8: WADI instrument structure: 36 items across 5 dimensions

Item	Model Mechanism	Review Evidence	Source Instrument
$W_{1}.1$	Interface bandwidth	5 papers (transparency)	Amershi HAI G-13
$W_{1}.2$	Override capability	4 papers (controllability)	Parasuraman LOA-7
$W_{2}.1$	Real authority	3 papers (decision control)	WMS decentralization
$W_{2}.10$	Formal vs. real gap	2 papers (authority discrepancy)	AT97 real authority
$W_{3}.1$	Comparative advantage	4 papers (task allocation)	ALM03 task framework
$W_{3}.6$	Task identity	1 paper (workflow fragmentation)	JCM task identity

Table 9: Item traceability matrix (selected items)

Instrument	What It Measures	What WADI Adds
AWWB Scale	AI-specific workplace wellbeing (worker level)	Adds $W_{1}$ – $W_{4}$ design dimensions; links outcomes to causes; firm-level analysis
AAAW Scale	Attitudes toward AI at work (6 attitudinal dimensions)	Measures organizational design choices, not individual attitudes; prescriptive (what to change), not descriptive
SMART Model	Work characteristics (Stimulating, Mastery, Agency, Relational, Tolerable)	Adds AI-specific dimensions ( $W_{1}$ interface, $W_{2}$ human-AI authority, $W_{4}$ bidirectional learning); connects to formal productivity model
COPSOQ-ISTAS21	Generic psychosocial risk factors	AI-specific; measures design choices that produce psychosocial outcomes, not just the outcomes themselves
I4.0 Maturity Models	Technology adoption readiness	Measures human-centric design quality, not technology quantity; captures the I4.0 $\to$ I5.0 transition
WMS	Generic management practices (monitoring, targets, incentives, talent)	AI-specific; adds interface, task orchestration, learning loop dimensions not in WMS

Table 10: WADI’s incremental contribution over existing instruments

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