INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume X Issue III March 2026

From Instinct to Intelligence: People Analytics as a Framework for

Human-Centred HRM in Nigerian Manufacturing Organizations

Sunday Onuminya Yusuf., Christopher Chinedu Okpara., Dr. Ibrahim Aminu Mohammed., Florence

Nkonye Akinrinlola., Paul Shaibu Akogwu., Mohammed Aliyu Haladu

Business Administration, University of Jos, Jos, Plateau, Nigeria

DOI: https://doi.org/10.47772/IJRISS.2026.100300556

Received: 26 March 2026; Accepted: 01 April 2026; Published: 17 April 2026

ABSTRACT

Purpose — Nigerian manufacturing HRM remains intuition-driven, applying uniform motivational strategies to

an occupationally diverse workforce lacking data infrastructure. This article argues that people analytics applied

to motivation and satisfaction data provides the methodological foundation for human-centred HRM consistent

with Society 5.0's vision of technology serving individual flourishing.

Aims — The article maps motivation–satisfaction evidence onto the four-level people analytics maturity model,

develops a phased implementation roadmap for Nigerian industrial contexts, and constructs an ethical risk matrix

ensuring analytics serves worker flourishing rather than surveillance.

Design/methodology/approach — A cross-sectional survey of 144 employees across Lagos, Kano, and Port

Harcourt employed validated instruments (Cronbach α = .76–.93), hierarchical regression, and moderation

analysis (PROCESS macro), mapped as a proof-of-concept across descriptive, diagnostic, predictive, and

prescriptive analytics levels. The study demonstrates how conventional survey methodology, when designed

with occupational granularity, can populate each tier of the people analytics maturity model without requiring

longitudinal or big-data infrastructure.

Findings — Mean job satisfaction was M = 3.14 (SD = 0.86), concealing substantial heterogeneity. Working

conditions and recognition were primary drivers (β = .19; β = .15); 34.7% of workers were educationally

underemployed (d = 0.58); technical staff (M = 3.42) reported markedly higher satisfaction than non-skilled

workers (M = 3.02).

Originality/value — The first empirically grounded people analytics framework for Nigerian manufacturing

HRM, with an ethical risk matrix calibrated to structural inequalities, occupational hazards, and union relations.

Keywords: people analytics, human-centred HRM, Nigerian manufacturing, Society 5.0, workforce

segmentation, ethical analytics

Paper type: Research paper

INTRODUCTION

The promise of people analytics—using data and evidence to make HR decisions that improve both

organizational performance and employee wellbeing—has been extensively theorized in Western organizational

contexts. A systematic review of 122 research papers identified people analytics as an evidence-based approach

that uses technology to analyse employee data to improve HRM and overall business performance (Belizón et

al., 2024). Yet both the scholarly literature and industry practice have concentrated predominantly in high-

income, high-technology environments, leaving a critical question unanswered: what does people analytics look

like—and what can it deliver—in the resource-constrained, labour-intensive industrial organizations that

constitute the backbone of Nigeria’s manufacturing sector?

Page 7660

www.rsisinternational.org

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume X Issue III March 2026

Nigerian manufacturing is both among the most consequential and least analytically served of the country’s

formal sectors. With a workforce operating under physically demanding conditions, compressed wages relative

to financial and telecommunications sectors, and occupational structures that concentrate non-skilled production

workers at the base of steep positional hierarchies, manufacturing organizations face HRM challenges that are

qualitatively distinct from service sector equivalents. Yet HR practice in most Nigerian manufacturing

organizations remains what Ellmer and Reichel (2021) call the ‘one-size-fits-all approach’—uniform

motivational strategies applied to a heterogeneous industrial workforce without the data infrastructure to

differentiate production workers from technical staff, new hires from long-tenure machine operators, or

underemployed graduates from well-matched tradespeople.

This article argues that people analytics provides exactly the methodological foundation needed to transition

Nigerian manufacturing HRM from instinct-driven to intelligence-driven practice—and that this transition

constitutes the organizational expression of the Society 5.0 vision within industrial contexts. Society 5.0

envisions a human-centred society in which advanced technology serves individual human flourishing rather

than organizational efficiency optimization alone (Breque et al., 2021; Maddikunta et al., 2022). Industry 5.0

extends this to the workplace, emphasizing that technological capability should amplify human strengths, protect

human dignity, and produce equitable outcomes across diverse workforce segments (Gamberini et al., 2024). In

manufacturing environments characterized by physical risk, skill stratification, and often adversarial industrial

relations, human-centred HRM is not a luxury but an operational and ethical imperative.

A survey of 144 Nigerian manufacturing employees reveals a satisfaction deficit that uniform HRM approaches

both produce and perpetuate: manufacturing workers reported a mean satisfaction of M = 3.14, driven by acute

deficiencies in working conditions, compensation equity, and organizational policy transparency. People

analytics reframes what this finding contributes. Standard organizational behaviour research asks: what predicts

manufacturing worker satisfaction? People analytics asks: how can that predictive knowledge be systematically

collected, interpreted, and applied to improve HR decisions that address the specific motivational architecture

of this sector?

The article makes three contributions. First, it provides the first empirically grounded people analytics

implementation framework for Nigerian manufacturing organizations. Second, it introduces an ethical risk

matrix calibrated to the power asymmetries, physical hazard contexts, and industrial relations dynamics specific

to manufacturing—a contribution to the transparency and fairness discourse central to Society 5.0. Third, it

demonstrates that classical motivation research can be reinterpreted through a people analytics lens to generate

data-driven HRM intelligence without requiring the expensive technology infrastructure that current people

analytics discourse implicitly assumes.

Theoretical Background

People Analytics: From Metrics to Intelligence

People analytics has evolved from rudimentary HR reporting—headcount, absenteeism, turnover rates—toward

a sophisticated capability for predictive and prescriptive workforce intelligence. Belizón et al. (2024), in their

systematic review, identify a knowledge discovery process comprising four progressively sophisticated

analytical levels: descriptive (what is happening), diagnostic (why it is happening), predictive (what will

happen), and prescriptive (what should be done). Most organizations, particularly in developing economies,

operate at the descriptive level—producing dashboards of workforce metrics without the diagnostic or predictive

capability to convert data into actionable intelligence (Habtamu et al., 2025).

In Nigerian manufacturing, the analytics maturity gap is particularly consequential. A production manager

knowing that average satisfaction is ‘moderate’ across the plant floor cannot design targeted recognition or

working conditions programs, allocate intervention resources by occupational segment, or anticipate turnover in

high-skill technical roles. A diagnostic analytics capability reveals which motivational factors drive satisfaction

for which sub-groups—production workers versus supervisors, long-tenure versus new entrants, well-matched

versus overqualified employees. A predictive capability anticipates where satisfaction deterioration is emerging

Page 7661

www.rsisinternational.org

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume X Issue III March 2026

before it becomes a turnover or productivity event. The difference between descriptive and predictive capability

is the difference between knowing a problem exists and knowing where it is forming and how to prevent it.

HR analytics research demonstrates that data-driven decision-making substantially outperforms instinct-based

HR practice on key outcomes including turnover prediction, engagement improvement, and performance

management effectiveness (McCartney & Fu, 2022; Di Prima et al., 2024). The empirical contribution of

motivation–satisfaction research—typically treated as an end in itself within organizational behaviour

scholarship—becomes instrumental input to the analytics pipeline when viewed through this lens.

Society 5.0 and Human-Centred HRM in Manufacturing Contexts

Society 5.0’s vision of a human-centred society that harnesses technology to solve social challenges and improve

quality of life has direct implications for how manufacturing organizations manage their human resources. As

Gamberini et al. (2024) document, Industry 5.0 within the Society 5.0 framework positions the worker not as a

production input to be optimized but as a human being whose flourishing is an end in itself. This represents a

substantive departure from Industry 4.0’s efficiency-first automation logic, and it aligns with calls in the HRM

literature for a human-centred approach that gives priority to practices designed to enhance employee wellbeing

(Cooke et al., 2022).

The human-centred HRM agenda is particularly consequential for manufacturing contexts. Production workers

in Nigerian factories face concentrated occupational risks—physical hazard exposure, shift work disruption,

noise and environmental stress, limited agency over work pace—that make both their wellbeing vulnerability

and their motivational needs structurally distinct from office-based workers. A Society 5.0-aligned

manufacturing HRM does not merely extend generic analytics frameworks to a new sector; it requires analytics

capability specifically calibrated to the motivational architecture of physically demanding, hierarchically

stratified industrial work.

The connection between people analytics and Society 5.0 values is not automatic. When demographic and

position-level segmentation data is used to identify which manufacturing workers most need organizational

investment—which production sections have the worst working conditions ratings, which tenure cohorts are

most at risk of disengagement, which qualified employees are underutilized—it serves human-centred goals.

When the same data is used to identify which workers to retain and which to replace based on predicted

performance trajectories, it risks reproducing structural inequalities through algorithmic means. Establishing

ethical guardrails is therefore a central challenge for Society 5.0-aligned manufacturing HRM.

The Equity, Transparency, and Fairness Imperative in Manufacturing

The ethical dimension of people analytics has received increasing scholarly attention as deployment has

expanded. Belizón et al. (2024) identify ethics, data privacy, and algorithmic transparency as the most rapidly

growing research theme in people analytics scholarship. In Nigerian manufacturing contexts, where structural

inequalities along occupational grade, educational attainment, and seniority lines are empirically documented,

the ethical stakes of demographic analytics are amplified by three sector-specific dynamics.

First, the physical power asymmetry between management and production workers in manufacturing

environments creates conditions in which data collection can readily become surveillance. Workers on factory

floors have less capacity to resist invasive monitoring than office workers, and the precedent of quality control

data collection (production speed, error rates, attendance) means that the conceptual boundary between

performance monitoring and personal data collection is already blurred. Analytics governance in manufacturing

must actively protect this boundary.

Second, industrial relations dynamics in Nigerian manufacturing—including union presence in larger

establishments and informal collective action in smaller ones—mean that analytics perceived as serving

management’s interests at workers’ expense will encounter resistance that undermines the organizational trust

analytics itself depends on. Transparency protocols must be designed for workforce populations with legitimate

scepticism about management data initiatives.

Page 7662

www.rsisinternational.org

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume X Issue III March 2026

Third, the qualification–fit problem in Nigerian manufacturing is acute and structurally produced. Graduate

underemployment across the Nigerian economy means that a substantial proportion of manufacturing workers—

estimated at 34.7% of the manufacturing sample—are educationally overqualified for their current roles, often

as a result of economic necessity rather than vocational choice. Analytics that identifies overqualification without

a corresponding organizational commitment to role enrichment risks producing labelling effects that further

demotivate rather than address the underlying structural condition.

The Empirical Evidence Base

Study Overview

The empirical evidence informing this article derives from a cross-sectional survey of 144 permanent employees

drawn from Nigerian manufacturing facilities in Lagos, Kano, and Port Harcourt—Nigeria’s three principal

manufacturing hubs—conducted between December 2025 and February 2026. Purposive sampling was

employed to ensure representation across occupational grades; facilities were selected from food processing,

textile, and metalwork sub-sectors. All participants were permanent, full-time employees with a minimum of six

months’ tenure, ensuring sufficient organizational experience to provide meaningful motivation and satisfaction

assessments. Informed consent was obtained from all participants, and data were collected through structured

questionnaires administered by trained research assistants during non-production periods to minimize response

disruption.

Motivation was measured using adapted subscales from Herzberg et al.’s (1959) two-factor theory instrument

and Hackman and Oldham’s (1975) Job Diagnostic Survey, capturing intrinsic factors (recognition, meaningful

work, autonomy, development opportunity) and extrinsic factors (working conditions, compensation equity,

promotion opportunity, organizational policy). Job satisfaction was measured using the Minnesota Satisfaction

Questionnaire short form (Weiss et al., 1967) and the Job Descriptive Index (Smith et al., 1969), yielding a

composite five-facet satisfaction profile. All instruments were adapted for Nigerian manufacturing contexts,

piloted with 20 employees, and refined for clarity. Internal consistency was satisfactory across all scales

(Cronbach α = .76–.93).

Hierarchical multiple regression examined the overall motivation–satisfaction relationship, with demographic

variables entered at Block 1 and motivational factor subscales at Block 2. Relative weight decomposition

(Johnson, 2000) was applied to partition the unique and shared variance contributions of each motivational

predictor, producing a rank-ordered importance profile independent of multicollinearity constraints. Moderation

analysis using the PROCESS macro (Hayes, 2018, Model 1) tested whether demographic variables—age cohort,

tenure group, and gender—significantly moderated the overall motivation–satisfaction slope, with simple slopes

probed at high (+1 SD) and low (−1 SD) values of each moderator. Qualification–fit was assessed by comparing

respondents’ highest educational qualification against the minimum qualification specified for their current role;

employees whose qualification exceeded the role requirement by one NQF level or more were classified as

educationally underemployed. Group differences in satisfaction between well-matched and underemployed

employees were tested using independent samples t-tests, and the practical effect size was computed as Cohen’s

d.

The manufacturing sample is occupationally structured into non-skilled production workers (42%), semi-skilled

operatives (28%), technical and artisan staff (18%), and supervisory/managerial staff (12%)—a stratification

that closely mirrors the occupational composition of Nigeria’s formal manufacturing sector (National Bureau of

Statistics, 2023). From a people analytics perspective, this occupational heterogeneity within a single sector is

exactly the workforce segmentation challenge that analytics is designed to address.

Descriptive Analytics: The Manufacturing Satisfaction Landscape

Descriptive analytics establishes the satisfaction baseline and identifies where gaps exist. Manufacturing

employees reported a mean job satisfaction of M = 3.14 (SD = 0.86)—a moderate score that aggregate reporting

would treat as unremarkable, but which conceals acute within-workforce heterogeneity. Position level generated

the largest satisfaction gap: technical and supervisory staff reported M = 3.42 while non-skilled production

workers reported M = 3.02—a 0.40-point differential representing the systematic accumulation of motivational

advantages at higher positions in the production hierarchy.

Page 7663

www.rsisinternational.org

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume X Issue III March 2026

The compensation satisfaction dimension (M = 2.76, SD = 1.18) was the lowest-rated facet in manufacturing,

reflecting the wage compression and below-market pay structures that characterize much of Nigeria’s industrial

sector. Promotion satisfaction (M = 2.94, SD = 1.09) was the second lowest-rated dimension. An organization

relying on aggregate satisfaction scores would report ‘moderate’ workforce satisfaction and conclude that no

urgent action is required. People analytics reveals that this aggregate conceals acute deficits among production

workers in compensation equity and advancement opportunity that are both measurable and addressable.

Sector-level descriptive analytics produces an additional actionable finding: the high standard deviations on

compensation (1.18) and promotion (1.09) satisfaction indicate that these are not universal experiences across

manufacturing facilities. Some plants or organizational units have addressed these concerns more effectively

than others. Identifying those units and replicating their practices is a diagnostic question that descriptive data

alone cannot answer—but descriptive data is the necessary prerequisite that makes the diagnostic question

visible.

Diagnostic Analytics: Why Satisfaction Varies and for Whom

Diagnostic analytics identifies the causal architecture driving satisfaction variation in manufacturing. The

hierarchical regression reveals working conditions as the dominant satisfaction predictor (β = .19, RW = 11.3%),

followed by recognition (β = .15, RW = 9.2%) and compensation equity (β = .14, RW = 8.1%). This diagnostic

profile reflects the structural realities of manufacturing work: the physical environment of production directly

shapes worker wellbeing in concrete, measurable ways, while shift-level acknowledgement and pay fairness

constitute the two motivational levers that manufacturing HR functions can most directly influence within

existing resource constraints.

Overall, employee motivation explained 51.8% of job satisfaction variance (R² = .518, F = 39.87, p < .001).

Manufacturing-specific moderation analysis reveals that motivation–satisfaction associations vary significantly

by career stage (B = 0.44 at age 20–29 to B = 0.68 at age 50+), tenure (B = 0.42 for < 1 year to B = 0.61 for >

10 years), and gender (B = 0.45 for female vs. B = 0.57 for male). These differential slopes mean that equal

motivational investment produces unequal satisfaction returns across workforce segments—a finding with direct

resource allocation implications that only analytics can surface.

The qualification–job fit analysis is particularly diagnostic for manufacturing. Employees identified as

educationally underemployed— 34.7% of the manufacturing sample—reported satisfaction 0.58 standard

deviations below well-matched employees (d = 0.58, p < .001). Importantly, underemployed manufacturing

workers showed heightened sensitivity to development opportunities (r = .64 vs. .49 for well-matched

employees) and autonomy (r = .56 vs. .42). The diagnostic implication is clear: underemployed graduates on

factory floors do not primarily need higher wages to close their satisfaction gap—they need role enrichment,

skill deployment pathways, and developmental progression that existing manufacturing job designs frequently

fail to provide.

Predictive and Prescriptive Analytics: From Diagnosis to Decision

A critical methodological note governs this section: the present study is cross-sectional, and cross-sectional

moderation slopes cannot themselves constitute predictive analytics findings in the longitudinal sense the

maturity model implies. What the study contributes at the predictive level is more precisely described as

empirically grounded parameters for future predictive use—a proof-of-concept demonstration that motivation–

satisfaction survey data, when designed with occupational granularity and moderation analysis, can populate the

predictive tier once replicated longitudinally. The distinction matters for scientific integrity: the slopes reported

here are demonstrated cross-sectional differentials, not forecasts validated against future outcomes.

With that framing established, the cross-sectional moderation results provide the most empirically credible

starting parameters currently available for Nigerian manufacturing predictive analytics. The motivation–

satisfaction slope varies from B = 0.42 among employees with less than one year’s tenure to B = 0.61 among

those with more than ten years—a demonstrated differential indicating that the same unit improvement in

working conditions is associated with meaningfully larger satisfaction gains for long-tenure workers than for

new entrants. These slopes are directly usable as provisional predictive parameters: a manufacturing HR function

Page 7664

www.rsisinternational.org

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume X Issue III March 2026

that knows the tenure distribution of each facility can estimate the expected satisfaction impact of a planned

working conditions intervention, subject to longitudinal validation. Similarly, the career stage moderation (B =

0.44 at age 20–29, rising to B = 0.68 at age 50+) suggests differential cohort responsiveness to motivational

investment that, once replicated in longitudinal designs, would enable targeted sequencing of recognition and

working conditions programs across facilities with different workforce age profiles.

Prescriptive analytics translates diagnostic and predictive findings into evidence-grounded recommended actions

for manufacturing HR. The relative weight decomposition findings provide a natural prescriptive priority

ranking, ordered by each factor’s demonstrated contribution to satisfaction variance. Working conditions

improvement is the highest-priority structural intervention: its relative weight (RW = 11.3%) reflects its greatest

unique variance contribution among all motivational predictors, and its direct organizational controllability

makes it the most consequential lever available within existing resource constraints. Recognition—shift-level

acknowledgement, safety performance commendation, peer-nomination schemes—ranks second (RW = 9.2%):

empirically demonstrable as strongly salient among production-grade employees, low-cost, and scalable across

facilities without capital expenditure. Compensation equity review (RW = 8.1%) ranks third: the high within-

facility standard deviation on compensation satisfaction (SD = 1.18) indicates that compensation concerns are

addressable through policy consistency and transparency rather than necessarily through wage increases alone.

Prescriptive analytics thus converts the diagnostic evidence directly into a priority-ordered intervention sequence

calibrated to both effect size and resource feasibility.

Mapping The Evidence to People Analytics Maturity Levels

Table 1 maps the empirical evidence from the Nigerian manufacturing motivation–satisfaction study to each

level of the people analytics maturity model, demonstrating the analytical intelligence each level unlocks and

how conventional survey-based research—when designed with analytics application in mind—can populate the

entire maturity ladder without requiring big data infrastructure.

Table 1 People Analytics Maturity Mapping: Evidence from Nigerian Manufacturing Motivation–Satisfaction

Study (n = 144)

Analytics

Level

Conventional

Approach

PA Manufacturing Evidence Base

Headcount,

absenteeism,

dashboards

Satisfaction means by occupational grade and tenure cohort (M = 2.76–

turnover 3.42); compensation and promotion dimensions lowest-rated; non-skilled

production workers M = 3.02 vs. technical/supervisory staff M = 3.42

Descriptive

Exit

engagement

identifying

interviews; Hierarchical regression identifying working conditions (β = .19) and

surveys recognition (β = .15) as primary satisfaction drivers; 34.7%

why underemployment with d = 0.58 satisfaction penalty; moderation slopes by

Diagnostic

satisfaction varies

career stage (B = 0.44–0.68)

Turnover risk scoring; Motivation–satisfaction slopes by tenure/career segment enabling

Predictive

flight risk identification prediction of satisfaction change per unit motivational improvement;

by segment

working conditions sensitivity especially high for long-tenure workers (B

= 0.61)

Recommended

interventions

identified

Evidence-ranked intervention sequence: working conditions improvement

for (RW = 11.3%) as highest-priority structural intervention; recognition

risk programs (RW = 9.2%) as highest-priority intrinsic intervention;

targeted compensation equity review (RW = 8.1%); role enrichment for

Prescriptive

segments;

resource allocation

underemployed graduates; equity audit before budget allocation

Note. PA = people analytics. Statistics derived from hierarchical regression with relative weight decomposition,

moderation analysis, and qualification–fit comparison reported in Section 3. The four maturity levels follow

Belizón et al.’s (2024) knowledge discovery process framework.

Page 7665

www.rsisinternational.org

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume X Issue III March 2026

Three observations emerge from Table 1. First, the progression from descriptive to prescriptive analytics in

manufacturing does not require technology upgrades at early stages—it requires analytical capability applied to

existing survey data. Nigerian manufacturing organizations already conducting annual engagement surveys have

the raw material for descriptive and diagnostic analytics; what they typically lack is the statistical expertise to

convert those surveys into prescriptive intelligence.

Second, the prescriptive level requires the ethical framework discussed in Section 5. Prescribing differentiated

interventions based on occupational grade data creates ethical exposure—particularly in manufacturing

environments where grade boundaries intersect with ethnicity, gender, and educational background in ways that

can reproduce structural inequalities if analytics outputs are used carelessly.

Third, the manufacturing analytics journey has a natural Nigerian sequencing. Descriptive analytics is achievable

within six months using existing survey tools and basic SPSS or Excel. Diagnostic capability requires

moderation analysis—achievable with standard statistical training and the PROCESS macro. Predictive

capability is grounded in the present study’s moderation slopes, which provide cross-sectionally demonstrated

parameters that serve as empirically informed starting points for forecasting segment-level satisfaction responses

to motivational investment; longitudinal replication is required to validate these parameters as genuine predictive

instruments. Prescriptive capability follows directly from relative weight decomposition, translating effect sizes

into an evidence-ordered intervention sequence. Section 6 operationalizes this progression.

Ethical Risk Matrix: People Analytics In Society 5.0 Manufacturing Contexts

Society 5.0’s human-centred values require that analytics capability serves worker flourishing rather than

organizational efficiency at workers’ expense. Table 2 presents an ethical risk matrix identifying the specific

risks each people analytics application creates in Nigerian manufacturing contexts, the severity of those risks,

and the human-centred safeguards required.

Table 2 Ethical Risk Matrix for Demographic People Analytics in Nigerian Manufacturing OrganizationsSociety

5.0 Human-Centred Safeguards

PA Application

Ethical Risk

Risk

Human-Centred Safeguard

Level

Occupational

segmentation

satisfaction data

grade Stereotyping — grade- Moderate Grade data informs strategy design, not

of level averages treated as

individual prescriptions

individual decisions; individual consent and

anonymity preserved; results shared in aggregate

only

Qualification–fit

identification

(overqualification

flagging

Labelling

overqualified workers

face adverse treatment

— High

Overqualification data used exclusively to trigger

role enrichment and internal mobility pathways;

never used

in disciplinary, selection, or

among or exclusion

termination decisions; results shared only in

aggregate

production workers)

Predictive

satisfaction Algorithmic

High

Human review mandatory for all prescriptive

outputs; demographic variables prohibited from

compensation or promotion algorithms; quarterly

transparency reports to workforce representatives

scoring by demographic discrimination

group

—

decisions

automated

disadvantaging

protected groups

Production

sentiment monitoring

floor Surveillance — erosion High

of psychological safety;

Participation strictly voluntary; data aggregated

to section/shift minimum; no linkage to

production performance metrics; union or worker

conflation

with

performance

monitoring

representative

implementation

consultation required

before

Page 7666

www.rsisinternational.org

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume X Issue III March 2026

Evidence-informed

Distributional inequity Moderate Intervention

priority

sequence

(working

budget allocation using —

relative weight rankings concentrated at higher-

grade segments despite

investment

conditions, recognition, compensation equity)

explicitly directs resources toward segments with

largest demonstrated motivational gaps; equity

evidence pointing to

audit

required

before

budget

allocation;

production

deficits

worker

minimum investment floor for non-skilled grade

mandated

Note. Risk levels: High = potential for direct individual harm or systemic discrimination; Moderate = potential

for indirect harm if safeguards are absent. All applications require explicit employee communication, informed

consent for data collection, and—for High-risk applications in manufacturing—worker representative

consultation prior to deployment.

Four principles govern the ethical framework underlying Table 2, drawn from the intersection of Society 5.0

values and Nigerian industrial justice research.

The transparency principle requires that manufacturing workers understand what data is collected, how it is

analysed, and how analytics outputs influence HR decisions affecting them. In Nigerian manufacturing

environments where organizational policy scored the lowest motivational factor (M = 2.76), transparency is not

merely an ethical aspiration but an organizational trust requirement. Analytics processes perceived as opaque or

management-serving will erode the workforce confidence on which the entire analytics value proposition

depends, producing a counterproductive feedback loop in which the intervention designed to improve conditions

becomes itself a source of dissatisfaction.

The fairness principle requires that analytics outputs be used to reduce rather than reproduce existing workforce

inequalities. The empirical evidence shows that non-skilled production workers, early-career employees, and

educationally underemployed graduates carry disproportionate satisfaction deficits. People analytics should

direct organizational investment toward these groups—not toward protecting high performers at senior grades

while deprioritizing those with the greatest motivational need.

The individuality principle requires that grade-level analytics findings never substitute for individual-level

human judgment in personnel decisions. The statistical patterns described in this study describe tendencies across

occupational groups; they describe no individual worker’s experience. Grade variables may inform strategy

design but must not be used as automated inputs to individual employees’ compensation, promotion, or

employment continuity.

The proportionality principle requires that the invasiveness of data collection be proportionate to the benefit

it enables. This principle is especially critical in manufacturing environments where the existing infrastructure

of quality control, time-and-motion, and attendance monitoring already subjects workers to data collection that

is often experienced as disciplinary rather than supportive. Survey-based people analytics represents a low-

invasiveness, high-intelligence alternative that avoids the surveillance dynamic.

Implementation Roadmap For Nigerian Manufacturing Organizations

Table 3 presents a phased implementation roadmap translating the people analytics maturity model into specific,

sequenced actions for Nigerian manufacturing HR functions. The roadmap is calibrated to the resource realities

of Nigeria’s industrial sector—acknowledging that most manufacturing HR departments lack data science

capability, advanced analytics software, or dedicated people analytics teams—while maintaining fidelity to the

human-centred principles that distinguish Society 5.0-aligned HRM from efficiency-first surveillance.

Page 7667

www.rsisinternational.org

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume X Issue III March 2026

Table 3 Phased People Analytics Implementation Roadmap for Nigerian Manufacturing HRM with Society 5.0

Scope Alignment

Phase

PA Capability

HRM Action

Human-Centred Outcome

Scope Points

Descriptive

analytics:

occupational grade survey

profiling;

Administer

motivation–satisfaction

segmented

validated Plant

understands where satisfaction

by gaps exist across the

grade, tenure, gender, and occupational hierarchy and

satisfaction baseline qualification fit; establish which conditions are worst-

management 4, 8

Phase 1

(0–6

months)

by section and shift

working conditions and rated at production worker

compensation benchmarks grade

by facility

Diagnostic

analytics:

Use

relative

weight HRM moves from uniform to 4, 5, 8, 9

Phase 2

(6–18

rankings and moderation differentiated

strategy;

moderation

slopes to identify priority highest-effect-size

months)

analysis;

factor interventions by segment; interventions

deployed

to

sensitivity mapping deploy working conditions highest-gap

by grade and tenure improvement, recognition (production

segments

workers)

first,

segment

schemes,

and ordered

by

demonstrated

compensation

equity relative weight

review in sequence ordered

by demonstrated

size; initiate

enrichment

effect

role

for

educationally

underemployed graduates

Predictive analytics: Integrate satisfaction data HR becomes data-informed 5, 9, 10

Phase 3

(18–36

months)

satisfaction

trajectory

into talent management; strategic partner; Society 5.0

create

early-warning human-centred

in

values

workforce

modelling; turnover dashboards by shift and embedded

risk scoring by section;

intervention effectiveness audit

evaluate decisions with full ethical

trail and worker

occupational

segment

by segment; incorporate representative transparency

into HR budgeting

Note. Scope Points refer to journal scope categories most directly addressed: 4 = Current workforce trends; 5

= HR in Society 5.0; 8 = New methodologies; 9 = Expected HR transformations; 10 = Data science and ethics.

All phases require parallel development of the ethical governance framework in Table 2 and, for Phase 3, worker

representative consultation.

Phase 1 — Descriptive Foundation (0–6 Months)

The starting point for Nigerian manufacturing organizations’ people analytics journey is systematic

measurement calibrated to the occupational granularity of industrial work. Most Nigerian manufacturing HR

surveys—where they exist at all—ask aggregate satisfaction questions that collapse the substantial heterogeneity

between production workers, semi-skilled operatives, technical staff, and management into a single meaningless

mean. The validated instruments deployed in this study (adapted MSQ, JDI, and motivation subscales capturing

working conditions, recognition, and compensation equity dimensions) provide the measurement template that

Phase 1 requires. Organizations can implement this instrument with minimal adaptation: it requires no specialist

software, is administrable during shift changeovers, and produces the occupationally segmented satisfaction

profile that descriptive analytics demands.

Page 7668

www.rsisinternational.org

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume X Issue III March 2026

Phase 1’s analytical outputs—satisfaction means by occupational grade and tenure cohort, motivational factor

rankings within the manufacturing workforce—are achievable with SPSS, Excel, or Google Sheets and standard

descriptive statistics. What Phase 1 requires is HR leadership commitment to making data collection systematic,

representative across production shifts and sections (not merely among administrative staff), and ethically

governed. The single most common failure mode in Nigerian manufacturing HR survey practices is surveying

office-based employees while treating factory floor workers’ satisfaction as either unknowable or irrelevant—

which produces descriptive analytics that reflects the least analytically important segment of the workforce.

Phase 2 — Diagnostic Intelligence (6–18 Months)

Phase 2 advances from knowing what the satisfaction landscape looks like to understanding why it looks that

way and for whom motivational investments produce the greatest returns. This requires moderation analysis

capability—testing whether the relationship between working conditions improvements, recognition programs,

or compensation equity interventions and satisfaction outcomes differs across occupational segments. Both are

achievable with SPSS PROCESS macro (Hayes, 2018) or R.

The specific diagnostic outputs Phase 2 produces include: a ranked motivational factor profile for each major

occupational segment (by grade, career stage, qualification–fit status); relative weight comparisons revealing

which factors have the strongest demonstrated influence on satisfaction for which segments; moderation slopes

identifying where motivational investment produces the largest returns; and the identification of segments

carrying disproportionate satisfaction deficits—particularly non-skilled production workers, early-career

employees, and educationally underemployed graduates—whose motivational needs most urgently require

organizational response. Phase 2 is where people analytics transitions from a reporting function to a strategic

manufacturing HR capability.

Phase 3 — Predictive and Strategic Integration (18–36 Months)

Phase 3 applies the provisional predictive parameters established in Phase 2 to real-time workforce monitoring.

The moderation slopes documented in this study—demonstrating, for example, that long-tenure employees’

satisfaction is more sensitive to working conditions deterioration (B = 0.61) than new entrants’ (B = 0.42)—

provide the best empirically grounded starting parameters currently available for Nigerian manufacturing

analytics. These slopes are cross-sectional and require longitudinal replication to function as validated predictive

instruments; Phase 3 is precisely when that replication occurs. Extending to longitudinal data collection at regular

intervals using the Phase 1 instrument allows within-person and within-section change analysis that converts

cross-sectional differential parameters into tracked trajectory models—moving from proof-of-concept to

genuine predictive analytics capability.

Phase 3 is also where people analytics becomes genuinely integrated into strategic manufacturing HR decision-

making: linking satisfaction data to quality and safety performance metrics, connecting motivational investment

tracking to outcome measurement, and incorporating analytics outputs into HR budgeting processes. At this

stage, the manufacturing HR function has transformed from an administrative unit that manages personnel

processes to a strategic partner that produces workforce intelligence—the Society 5.0 vision of human-centred

organizational capability applied to the industrial context that arguably needs it most.

DISCUSSION AND THEORETICAL IMPLICATIONS

Repositioning Classical Research Within the Analytics Pipeline

The core argument of this article—that motivation–satisfaction research in manufacturing is most valuable when

understood as input to a people analytics pipeline rather than as an end in itself—has implications for how the

organizational behaviour field understands its practical relevance to industrial contexts. A definitional

clarification is warranted here. People analytics in its mature, enterprise form involves large-N administrative

datasets, proprietary HR system integration, and continuously updated predictive models—capabilities

substantially beyond what the present study deploys. What this article demonstrates is a methodologically

accessible proof-of-concept: that cross-sectional survey data, designed with occupational granularity and

Page 7669

www.rsisinternational.org

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume X Issue III March 2026

analysed with moderation techniques available in standard statistical packages, can be mapped onto each level

of the analytics maturity model. The study does not claim to constitute predictive analytics in the full longitudinal

sense; it claims to generate the empirically grounded parameters—differential slopes, relative weights, segment-

level satisfaction profiles—that a longitudinal predictive analytics capability would require as its starting point.

This distinction is important for both scientific integrity and practical uptake: Nigerian manufacturing HR

functions should understand the present framework as a Phase 1–2 foundation, not as a substitute for the

longitudinal data collection that genuine predictive capability demands.

Future motivation–satisfaction research oriented toward manufacturing people analytics application should

prioritize: occupational granularity sufficient to enable grade-level analysis; longitudinal measurement enabling

trajectory modelling by shift and section; and factor sensitivity analysis by tenure and career stage rather than

merely main effect reporting. The present study’s moderation analysis is directly relevant to the predictive

analytics level in a way that main effect studies are not, because differential slopes enable differential prediction

rather than merely differential description.

People Analytics in Nigerian Manufacturing: Developing Economy Context

The HR analytics literature has concentrated almost exclusively in Western organizational contexts where data

infrastructure, analytical capability, and privacy regulation are substantially more developed than in sub-Saharan

Africa. The Ethiopian HR analytics study (Habtamu et al., 2025) finding that HR analytics significantly enhances

organizational performance through strategic alignment confirms that the analytics value proposition holds in

developing economy contexts. The present article extends this by specifying what analytics capability looks like

at each maturity level for Nigerian manufacturing, how it can be implemented with existing resources, and what

ethical constraints are specifically salient in high-power-distance, physically hazardous industrial environments.

The Nigerian manufacturing context differs from both Western manufacturing and Nigerian service sector

contexts in ways that shape implementation priorities and ethical requirements. Factory floor data collection

faces the challenge of shift patterns and literacy variation among production workers that are irrelevant in office

contexts. Power asymmetries between HR functions and production management are acute, meaning analytics

outputs must be presented in forms that non-technical plant management can understand and act on. And the

existing infrastructure of production monitoring creates a surveillance backdrop against which any new data

initiative will be interpreted—making the transparency and consent framework not a procedural formality but a

substantive precondition for analytics legitimacy.

Contribution to Society 5.0 HRM Theory

This article’s theoretical contribution to Society 5.0 HRM in manufacturing is the specification of what human-

centred analytics means in practice within an industrial context—not as an abstract value but as a concrete set of

design choices about what data is collected, how it is analysed, for what purposes it is used, and what governance

structures ensure it serves worker flourishing rather than organizational surveillance. The four principles

articulated in Section 5 (transparency, fairness, individuality, proportionality) constitute the beginning of a

Society 5.0-aligned analytics governance framework calibrated to Nigerian manufacturing’s specific power

dynamics and structural inequalities.

The fairness principle deserves particular emphasis in the manufacturing context. The finding that non-skilled

production workers carry a satisfaction deficit of 0.40 points relative to supervisory staff—while receiving fewer

organizational motivational investments in recognition, development, and advancement—creates a distributional

equity challenge that Society 5.0 values demand be addressed explicitly. People analytics that directs investment

toward highest-gap segments rather than highest-performance or most-visible segments operationalizes the

equity commitment in HRM resource allocation terms. This constitutes a novel prescriptive contribution

specifically salient for Nigerian manufacturing: the specification of how analytics-informed resource allocation

can deliver equity for the workers who most need it and least have the organizational power to advocate for it

themselves.

Page 7670

www.rsisinternational.org

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume X Issue III March 2026

CONCLUSION

Nigerian manufacturing HRM stands at a methodological inflection point. The instinct-driven, uniform-strategy

approaches that characterize most current practice are not merely inefficient—they are inequitable,

systematically under-serving production workers, early-career employees, and educationally underemployed

graduates who carry the greatest motivational deficits while receiving the least organizational motivational

investment. People analytics provides the intelligence infrastructure to move from this intuition-based uniformity

to evidence-based differentiation: knowing that production workers need working conditions improvement and

meaningful recognition most urgently, that underemployed graduates need role enrichment rather than higher

pay, that long-tenure employees carry the strongest motivation–satisfaction coupling—and deploying

organizational resources accordingly.

The transition from instinct to intelligence in Nigerian manufacturing does not require advanced AI, big data

infrastructure, or dedicated analytics teams at its early stages. It requires measurement rigor (including factory

floor workers, not just administrative staff), analytical capability (moderation analysis applied to existing survey

data), and the organizational commitment to let evidence inform resource allocation decisions that tradition and

hierarchy would prefer to make on intuitive grounds. The phased implementation roadmap presented here

provides Nigerian manufacturing HR functions with a realistic path from where most currently stand—collecting

little or no motivational data from production workers—to where Society 5.0’s human-centred HRM vision

requires them to reach: deploying workforce intelligence in service of every employee’s flourishing, including

those at the most physically demanding and least analytically visible positions on the factory floor.

The ethical risk matrix is not a caveat to be appended to the analytics enthusiasm—it is a constitutive part of the

people analytics proposition for manufacturing. Analytics capability not governed by transparency, fairness,

individuality, and proportionality principles will reproduce in data form the structural inequalities it was

supposed to address, and will do so with the additional legitimacy that algorithmic outputs are often accorded

over human judgment. In Nigerian manufacturing contexts where those inequalities are empirically documented

and their motivational consequences measurable, the ethical framework is not optional. It is what distinguishes

Society 5.0-aligned industrial HRM from production-efficiency surveillance dressed in HR language.

Future research should evaluate the effectiveness of the phased implementation roadmap through longitudinal

case study designs tracking Nigerian manufacturing organizations through analytics maturity stages, assess

whether people analytics-informed HRM produces measurable satisfaction improvements relative to uniform-

strategy comparators, and examine how worker representative consultation models in unionised manufacturing

environments can be incorporated into analytics governance frameworks to strengthen the legitimacy—and

therefore the impact—of people analytics as a tool for worker flourishing rather than organizational control.

REFERENCES

1. AIHR. (2025). People analytics: An essential guide for 2026. Academy to Innovate HR. Retrieved from

https://www.aihr.com/blog/people-analytics/

2. Belizón, M. J., Majarín, D., & Aguado, D. (2024). Human resources analytics in practice: A knowledge

discovery process. European Management Review, 21(3), 659–677. https://doi.org/10.1111/emre.12605

3. Breque, M., De Nul, L., & Petridis, A. (2021). Industry 5.0: Towards a sustainable, human-centric and

resilient European industry. European Commission, Directorate-General for Research and Innovation.

4. Cooke, F. L., Dickmann, M., & Parry, E. (2022). Building sustainable societies through human-centred

HRM: Emerging issues and research opportunities. International Journal of Human Resource

Management, 33(1), 1–15. https://doi.org/10.1080/09585192.2021.2021732

5. Deloitte. (2026). 2026 Global Human Capital Trends. Deloitte Insights. Retrieved from

https://www.deloitte.com/us/en/insights/topics/talent/human-capital-trends.html

6. Di Prima, V., Galati, F., & Maisano, D. A. (2024). Help me help you: How HR analytics forecasts foster

organizational

creativity.

Technological

Forecasting

and

Social

Change,

203,

123540.

https://doi.org/10.1016/j.techfore.2024.123540

Page 7671

www.rsisinternational.org

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume X Issue III March 2026

7. Ellmer, M., & Reichel, A. (2021). Staying close to business: The role of epistemic alignment in rendering

HR analytics outputs relevant to decision-makers. International Journal of Human Resource

Management, 32(12), 2622–2642. https://doi.org/10.1080/09585192.2021.1886148

8. Gamberini, L., & Pluchino, P. (2024). Industry 5.0: A comprehensive insight into the future of work,

social sustainability, sustainable development, and career. Australian Journal of Career Development,

33(1), 5–14. https://doi.org/10.1177/10384162241231118

9. Habtamu, Y., Mihret, D. G., & Yigzaw, E. (2025). The effect of human resource analytics on

organizational

performance:

Insights

from

Ethiopia.

Systems,

13(2),

134.

https://doi.org/10.3390/systems13020134

10. Hackman, J. R., & Oldham, G. R. (1975). Development of the job diagnostic survey. Journal of Applied

Psychology, 60(2), 159–170.

11. Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis (2nd ed.).

Guilford Press.

12. Herzberg, F., Mausner, B., & Snyderman, B. B. (1959). The motivation to work (2nd ed.). John Wiley

& Sons.

13. Johnson, J. W. (2000). A heuristic method for estimating the relative weight of predictor variables in

multiple regression. Multivariate Behavioral Research, 35(1), 1–19.

14. Maddikunta, P. K. R., Pham, Q. V., B, P., Deepa, N., Dev, K., Gadekallu, T. R., Ruby, R., & Liyanage,

M. (2022). Industry 5.0: A survey on enabling technologies and potential applications. Journal of

Industrial Information Integration, 26, 100257.

15. McCartney, S., & Fu, N. (2022). Bridging the gap: Why, how and when HR analytics can impact

organizational performance. Management Decision, 60(13), 25–47.

16. National Bureau of Statistics. (2023). Labour force statistics: Unemployment and underemployment

report Q2 2023. National Bureau of Statistics, Nigeria.

17. Onuoha, U. C., & Idemudia, E. S. (2021). The influence of organizational culture on employee job

satisfaction in selected Nigerian organizations. SA Journal of Human Resource Management, 19, a1423.

18. Osibanjo, A. O., Adeniji, A. A., Falola, H. O., & Heirsmac, P. T. (2014). Compensation packages: A

strategic tool for employees’ performance and retention. Leonardo Journal of Sciences, 25, 65–84.

19. Smith, P. C., Kendall, L., & Hulin, C. L. (1969). The measurement of satisfaction in work and retirement:

A strategy for the study of attitudes. Rand McNally.

20. Weiss, D. J., Dawis, R. V., England, G. W., & Lofquist, L. H. (1967). Manual for the Minnesota

Satisfaction Questionnaire. University of Minnesota, Industrial Relations Center.

Contact address:

Yusuf Sunday Onuminya, Faculty of Management Science,

Ahmadu Bello University, Zaria, Nigeria,

Dr. Mohammed Ibrahim Aminu, Faculty of Management

Science, Ahmadu Bello University, Zaria, Nigeria, email:

Okpara Christopher Chinedu, Faculty of Management Science, University of Jos, Nigeria,

Akinrinlola Florence Nkonye, Faculty of Management Science, University of Jos, Nigeria,

Akogwu Paul Shaibu, Ahmadu Bello University, Zaria, Nigeria

Haladu Mohammed Aliyu, Ahmadu Bello University, Zaria, Nigeria

Page 7672

www.rsisinternational.org

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume X Issue III March 2026

Declaration of AI and AI-assisted technologies in the writing process

The author(s) used AI-assisted tools (specifically, a large language model) to support manuscript editing, prose

refinement, and structural review during the preparation of this work. The underlying empirical data, analytical

decisions, interpretations, theoretical framing, and conclusions are the sole responsibility of the author(s). AI

assistance was limited to language and editing support and did not extend to data analysis or the generation of

novel intellectual content.

Page 7673

www.rsisinternational.org