Assessing Value Co-Creation in Blockchain Enabled Learning

Certificates: A Knowledge Management Perspective

Nathalya Guruge

and Jyri Vilko

Department of Industrial Engineering and Management, LUT University, kauppalankatu 13, 45100 Kouvola, Finland

Keywords: Blockchain Credentials, Knowledge Conversion, Resource Integration, Stakeholder Engagement, Value

Co-Creation.

Abstract: Blockchain enabled learning certificates promise immutable, transparent proof of skills and achievements, yet

their potential for sustained value co - creation remains underexplored. Grounded in Nonaka and Takeuchi’s

SECI model, Service-Dominant Logic, and the Co-Creation Triad, this position paper advances an integrative

analytical model to evaluate how blockchain credentials instantiate knowledge‐conversion processes, operant

resource integration, and stakeholder engagement structures. A dual‐stream methodology first maps construct

from 50 prior studies to these lenses - revealing a research landscape heavily focused on technical

architectures but largely neglectful of on-chain Socialization, Internalization, and ongoing co-creation

incentives. We then apply our model to four illustrative platforms (LearnCoin, Blockcerts, Badgr, and the

Learning Economy Foundation), systematically coding each system’s support for explicit knowledge

externalization/combination, smart-contract-driven workflows, and dialogic customization. Our cross-case

analysis confirms universal strengths in artifact codification and protocol automation but identifies persistent

gaps in reflective learning cycles and sustained co-creation mechanisms. We conclude by calling for next‐

generation credential designs that embed on-chain communities of practice, adaptive operant resources, and

multi-phase token economies, thereby charting a research and design roadmap for transforming blockchain

certificates into living ecosystems of shared learning value.

1 INTRODUCTION

Blockchain technology has emerged as a promising

solution for enhancing trust and transparency in

credentialing systems (Alkhudary & Gardiner, 2024;

Pham et al., 2024; Zhou et al., 2024). By

decentralizing record-keeping, blockchains can

reshape how institutions, learners, and employers

verify and exchange credentials (Zhou et al., 2024;

Jin et al., 2023; Pokhrel & Shrestha, 2021). However,

immutability and shared governance do not

automatically improve how knowledge is created,

shared, or leveraged.

From a knowledge management perspective, it is

critical to examine how stakeholders collaborate in

these ecosystems. Value co-creation, the joint

production of mutual benefits depends on more than

reliable technology (Pham et al., 2024; Xie & Zhang,

2023; Tlili et al., 2021). It requires supportive

https://orcid.org/0009-0003-2052-1911

https://orcid.org/0000-0002-9906-0470

structures, incentives, and open channels for

exchange. Without alignment, blockchain-based

certificates risk becoming technical artifacts rather

than catalysts for learning communities (Pham et al.,

2024; Xie & Zhang, 2023; Markopoulos et al., 2022).

We argue that effective blockchain credentialing

must integrate robust KM strategies. Embedding

principles such as stakeholder engagement,

transparent communication, and collective

sensemaking enables genuine value co-creation.

Moreover, we contend that Future research and

practice should therefore focus on the interplay

between technological design and KM processes to

ensure blockchain-enabled certificates succeed.

Highlighting this intersection reveals both

opportunities and challenges in embedding

blockchain into educational ecosystems from a

knowledge management standpoint.

Guruge, N. and Vilko, J.

Assessing Value Co-Creation in Blockchain Enabled Learning Certiﬁcates: A Knowledge Management Perspective.

DOI: 10.5220/0013743000004000

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2025) - Volume 2: KEOD and KMIS, pages

267-277

ISBN: 978-989-758-769-6; ISSN: 2184-3228

267

1.1 Background of the Study

Blockchain’s decentralization, immutability, and

transparency tackle key credentialing issues;

verification delays, data breaches, and fraud

(Tripathi, Ahad & Casalino, 2023; Vipie, Afumatu &

Caramihai, 2023) by letting learners securely control

tamper-proof records and enabling instant,

intermediary-free validation (Pu & Lam, 2023).

When paired with robust KM infrastructures; such as

the Blockchain of Learning Logs for seamless cross-

institutional record sharing (Ocheja et al., 2019), it

supports secure knowledge capture, storage, and

reuse, allowing institutions, learners, and employers

to co-create value via shared understanding,

trustworthy credentials, and efficient information

flows (Zamiri & Esmaeili, 2024).

2 THEORETICAL FOUNDATION

We draw on two KM theories and two service-science

frameworks to build our analytical model. Nonaka

and Takeuchi’s SECI model identifies four

knowledge conversion modes; Socialization,

Externalization, Combination, and Internalization

and highlights how blockchain’s immutable ledger

enhances Externalization and Combination by

encoding credentials as explicit records, while peer-

verified exchanges can support Socialization and

Internalization (Nonaka & Takeuchi, 1995; Pham et

al., 2024). The Knowledge-Based View (KBV)

frames certificates as portable artifacts: smart

contracts standardize data, lower verification costs,

and enable seamless knowledge flows across

institutions, learners, and employers (Grant, 1996;

Tlili et al., 2021).

Service-Dominant (S-D) Logic views value as

emerging from service-for-service exchanges rather

than being embedded in products (Vargo & Lusch,

2004). In credentialing ecosystems, blockchain

platforms act as operant resources, with smart

contracts and decentralized networks enabling

learners, issuers, and verifiers to integrate

competencies and co-produce value (Xie & Zhang,

2023).

The stakeholder co-creation triad emphasizes

engagement, transparency, and mutual customization

(Prahalad & Ramaswamy, 2004). Blockchain’s real-

time visibility supports this dialogue: learners set

goals, institutions validate achievements, and

employers endorse competencies, tailoring and

legitimizing credentials collaboratively

(Markopoulos et al., 2022).

Figure 1: Integrative Socio-Technical Model of Value

Co-Creation in Blockchain-Enabled Learning Certificates.

Synthesizing SECI, KBV, S-D Logic, and the co-

creation triad, our integrated model (Figure 1) treats

blockchain-enabled certificates as socio-technical

artifacts whose affordances must align with KM

processes and co-creation mechanisms to generate

sustainable, mutual value. The model spans three

dimensions: (1) Knowledge Conversion (SECI), (2)

Resource Integration (S-D Logic), and (3)

Stakeholder Engagement (Co-Creation Triad). It

offers a foundation for evaluating certificate

platforms; subsequent sections apply these lenses to

real-world cases.

2.1 Main Thesis and Position

We posit that blockchain-enabled learning

certificates, when embedded within robust knowledge

management (KM) practices, can facilitate value co-

creation among educational institutions, learners,

and employers.

While blockchain’s decentralization and

transparency foster trust in credentialing systems

(Sharples & Domingue, 2016; Grech & Camilleri,

2017), genuine collaboration and knowledge

exchange hinge on frameworks that align

stakeholders and cultivate open sharing cultures

(Nonaka & Takeuchi, 1995; Wenger, 1998).

Consequently, blockchain’s technical affordances

must be reinforced by KM centric processes, such as

communities of practice, incentive mechanisms, and

shared goal setting, to transform credentials from

static proofs into catalysts for continuous, co -

creative learning ecosystems (Vargo & Lusch, 2004;

Prahalad & Ramaswamy, 2004).

3 METHODOLOGY

This section outlines our dual stream, theory driven

approach, combining a structured synthesis of KM

and value co creation literature with an illustrative

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268

multiple case analysis of real-world blockchain

credential platforms. We then detail how we selected

cases, gathered data, and applied our integrative

analytical model in three systematic steps.

3.1 Research Approach

We adopt a qualitative, multiple-case, theory-driven

approach to illustrate how blockchain certificate

platforms instantiate our KM and value co-creation

model. This aligns with the goal of a position paper: to

argue a conceptual stance through real-world examples.

3.2 Literature Search and Synthesis

A literature review was conducted to synthesize

existing knowledge in three areas: blockchain in

education, value co-creation theory, and knowledge

management frameworks. Using the Web of Science

(WoS) database, we applied a structured Boolean

search: ("blockchain" OR "distributed ledger") AND

("learning certificate*" OR "digital credential*" OR

"digital certificate*" OR "educational credential*")

AND ("value co-creation" OR "value cocreation" OR

"co-creation of value" OR "service-dominant logic"

OR "knowledge management" OR "knowledge

creation" OR "knowledge sharing" OR "communities

of practice"). The search was limited to peer-

reviewed journal articles, conference proceedings,

and reviews from the last five years to ensure

contemporary relevance. Articles were screened for

direct relevance, and an analytical framework (Figure

2) was developed by integrating Service-Dominant

Logic (Vargo & Lusch), Nonaka’s SECI model, and

Wenger’s communities of practice. This framework

provided a structured lens to examine how blockchain

technologies can facilitate value co-creation in

education.

3.3 Case Selections

We purposively selected four blockchain credential

initiatives; LearnCoin, Blockcerts, Badgr on

Ethereum, and Learning Economy based on:

1) Blockchain type: public vs. consortium,

2) Maturity: pilot vs. production, 3) Stakeholder

diversity: learners, issuers, verifiers.

3.4 Limitations

As a position paper, our cases are illustrative rather

than representative; and while we grounded our

argument in peer-reviewed theory, we did not

conduct full-scale empirical testing.

3.5 Analytical Procedure

Figure 2: Analytical procedure for dual stream analysis.

4 FINDINGS

In this section, we present the results of our dual‐

stream analytical process. First, we map the

“Findings” from fifty prior studies onto our three

theoretical lenses to reveal how extant literature

aligns with (or diverges from) our integrative model

(See figure 1). Next, we apply these lenses to four

illustrative blockchain credential platforms

(LearnCoin, Blockcerts, Badgr, and the Learning

Economy Foundation), systematically coding each

system’s support for explicit knowledge flows,

operant resource integration, and stakeholder

engagement mechanisms. Finally, we synthesize

cross‐case patterns to identify common strengths,

shared gaps, and unique practices, setting the stage

for the broader theoretical and practical implications

discussed in Section 5.

Step 1: Map Literature Constructs

• Organize constructs into three lenses: Knowledge Conversion,

Resource Integration, Stakeholder Engagement

Step 2: Code Cases Against Lenses

• Facilitate SECI modes

• Leverage smart contracts as operant resources

• Enable transparency and dialogue

Step 3: Synthesize Cross-Stream Insights

• Contrast literature-derived expectations with case observations

to surface alignments and gaps

Assessing Value Co-Creation in Blockchain Enabled Learning Certiﬁcates: A Knowledge Management Perspective

269

Figure 3: Article Distribution 2017- 2024.

Research on blockchain‐enabled learning credentials

surged from 2020, peaking in 2024, driven by rapid

blockchain adoption, evolving digital credential

practices, and COVID-19–induced digital

transformation. While this growth signals strong

interest, it also demands careful vetting to distinguish

trend-driven studies from robust scholarship. The

work appears across diverse outlets, from Journal of

Knowledge Management and Information Sciences

to IEEE Internet of Things Journal and Sustainability,

reflecting an interdisciplinary convergence of KM,

educational technology, blockchain, and digital trust.

Leading conferences (ECKM, AIS, ICCE, iMeta)

underscore themes like speculative system

architectures, decentralized learning models, and

trust-centered innovation. References to the

metaverse, XR, and sustainability further point to a

vision of blockchain credentials as integral

components of inclusive, transparent, and future-

ready learning ecosystems.

4.1 Mapping Literature Constructs to

Analytical Lenses

To understand how existing blockchain, KM research

aligns with our theoretical model (See figure 1), we

mapped the “Findings” from all 50 studies in our

dataset to the three analytical lenses introduced in

Section 3; Figure 2. The table below summarizes the

distribution (Table 1). For the first three categories we

provide all available citations; the full list of

uncategorized papers, citations are available in

Appendix A.

1. Service-Dominant Logic (Resource

Integration)

Ten studies foreground blockchain elements smart

contracts, protocols, or token economies as operant

resources that structure knowledge workflows. For

example, Alkhudary and Gardiner (2024)

demonstrate that embedding smart contracts in

project management information systems streamlines

credential verification, while Wang and Li (2024)

describe a blockchain-based orchestration layer that

binds federated-learning assets into coherent service

offerings. These works confirm that protocols and

smart contracts are central to how stakeholders

integrate and exchange knowledge in credentialing

ecosystems.

Table 1: Count of studies mapped to each analytical lens.

Analytical

Lenses

Number

of studies

Key References

Service-

Dominant

logic

(Resource

Integration)

10 Alkhudary & Gardiner

(2024); Wang & Li

(2024); Zhou et al.

(2024); Pham et al.

(2024); Hu et al. (2028);

Alagha et al. (2024);

Bestas et al. (2023); nan

(2019); Pfeiffer et al.

(2020), Wu et al.,

(

2024

)

Co-creation

Triad

(Stakeholder

Engagement)

8 Alkhudary & Gardiner

(2024); Xie & Zhang

(2023); Zhou et al.

(2024); Chai et al.

(2020); Fu et al. (2023);

Hu et al. (2018); Pham

et al.

(

2024

)

; nan

(

2023

)

SECI

(Knowledge

creation)

3 Chai et al. (2020); Zhou

et al. (2024);

Markopoulos et al.

(

2022

)

Uncate

orize

33 -

2. Co-Creation Triad (Stakeholder

Engagement)

Eight studies examine incentive, transparency, and

dialogic mechanisms that drive stakeholder

participation. Xie and Zhang (2023) show how token-

based incentives on chain dramatically increase

community contributions, and Zhou et al. (2024)

highlight how on-chain “distilled” knowledge

artifacts circulate among peers, fostering

collaborative engagement. Such research illustrates

blockchain’s capacity to enable mutual customization

and open dialogue among learners, issuers, and

verifiers.

3. SECI (Knowledge Conversion)

Only two studies explicitly address SECI’ s

knowledge conversion processes. Chai et al. (2020)

externalize tacit vehicular insights into immutable

blockchain records enabling both Externalization and

Combination while Zhou et al. (2024) also point to

on-chain artifact sharing as a form of Socialization.

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The relative scarcity of SECI focused work suggests

an important gap: few researchers have framed

blockchain credentials in terms of broader KM

cycles.

4. Gaps and Opportunities

A majority of studies (33) did not map clearly onto

our three lenses, typically emphasizing security,

performance, or technical architecture instead. This

landscape reveals two key opportunities for our case

analyses: 1) Deepening KM Cycles - Investigate how

real-world platforms support the underexplored SECI

modes (especially Internalization), 2) Enriching Co-

Creation - Examine whether case platforms extend

beyond simple incentive schemes to foster sustained

dialog and mutual customization. These insights

establish a clear baseline against which to evaluate

our four illustrative blockchain certificate initiatives

in Section 4.2.

4.2 Case-by-Case Lens Application

In this section, we demonstrate how our integrative

analytical model (SECI, Service - Dominant Logic,

Co - Creation Triad) manifests in practice by applying

it to four prominent blockchain credential platforms.

For each case, we systematically map key features

and mechanisms onto our three theoretical lenses,

illustrate with concrete examples, and assess the

degree to which each platform supports knowledge

conversion, resource integration, and stakeholder

engagement. This detailed comparison will surface

both best practices and persistent gaps, setting the

stage for our cross-case synthesis in Section 5.

To ensure consistency across all four case studies,

we apply a simple three-point support rubric; High,

Medium, Low, when mapping platform features onto

our three lenses. A High rating denotes that a

capability is natively built into the on-chain protocol

or core smart-contract logic, fully addresses most

sub-dimensions of the lens (e.g., both Externalization

and Combination for SECI), and is central to

everyday issuance or verification workflows. A

Medium rating indicates the feature exists - often via

optional smart-contract hooks or dashboard support -

but only covers one sub-dimension well (e.g., one-off

token incentives) or plays a secondary role. A Low

rating means the feature is absent or only supported

off-chain, forcing stakeholders to rely on external

tools or manual processes (e.g., no on-chain dialog for

Socialization). We will use this rubric to assess

LearnCoin and then apply the same criteria to the

three other platforms in turn.

4.2.1 Case Study: Learncoin

LearnCoin is a unified “currency for learning”

platform that issues, verifies, and transfers

educational credentials on a public blockchain.

Below, we map LearnCoin’s core features onto our

three lenses, illustrate with concrete examples, and

assess support levels. All feature details are drawn

from the LearnCoin website (LearnCoin, n.d.).

Table 2: Mapping LearnCoin Features to SECI, Service-

Dominant Logic, and Co-Creation Triad Lenses.

Lens Observations Support level

SECI

(Knowledge

Conversion)

Externalization: Every issued

credential is hashed and

recorded on-chain, turning

tacit learner achievements into

explicit, immutable artifacts

(LearnCoin, n.d.).

Combination: Dashboard and

explorer aggregate on-chain

data into comprehensive

learner portfolios (LearnCoin,

n.d.).

Socialization/Internalization:

No on-chain forum or narrative

annotations; learners must

export credentials off-chain for

reflection (LearnCoin, n.d.).

Externalization:

High

Combination:

High

Socialization/In

ternalization:

Low

S-D Logic

(Resource

Integration)

Operant Resources: Smart

contracts automate issuance,

revocation, and credit-transfer

workflows between

institutions (LearnCoin, n.d.).

Protocol as Backbone: The

LearnCoin token mediates

value exchange - micro-

credentials, upskilling credits -

across diverse providers,

acting as the platform’s service

“currency” (LearnCoin, n.d.).

High

Co-Creation

Triad

(Engagement)

Transparency: All credential

transactions are publicly

verifiable, enabling any

stakeholder to audit issuance

histories (LearnCoin, n.d.).

Dialog & Customization:

LearnCoin offers off-chain

FAQs and blog posts but lacks

on-chain messaging channels

for learner–issuer dialogue or

credential co-design

(LearnCoin, n.d.).

Incentives: Token rewards for

early adopters and validators

spur initial participation but do

not support ongoing co-

creation (LearnCoin, n.d.).

Transparency:

High

Dialog/Custom

ization: Low

Incentives:

Medium

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271

4.2.2 Case Study: Blockcerts

Blockcerts is an open-source standard and toolkit -

comprising developer libraries, a mobile wallet, and a

universal verifier - that enables the creation, issuance,

viewing, and on-chain verification of blockchain

credentials (Blockcerts, n.d.). Table 3 maps its core

features onto our SECI, Service-Dominant Logic, and

Co-Creation Triad lenses, using the same

High/Medium/Low rubric introduced in Section 4.2.

Table 3: Mapping BlockCerts Features to SECI, Service-

Dominant Logic, and Co-Creation Triad Lenses.

Lens Observations Support Level

SECI

(Knowledge

Conversion)

Externalization: Issuers hash

credential assertions (batch

Merkle roots) into Bitcoin

transactions (OP_RETURN),

converting tacit proof of

achievement into immutable,

explicit records (Blockcerts,

n.d.).

Combination: The universal

verifier and mobile wallet

assemble on-chain proofs into

human-readable certificates

(Blockcerts, n.d.).

Socialization/Internalization: A

public community forum invites

discussion, but there is no on-

chain peer-to-peer annotation or

reflective storytelling.

Externalization:

High

Combination:

High

Socialization/In

ternalization:

Medium

S-D Logic

(Resource

Integration)

Operant Resources: Smart-

contract-style workflows (batch

issuance, revocation lists) and

Merkle-proof verification

functions are provided as

reusable libraries (Blockcerts,

n.d.).

Protocol as Backbone: The

OP_RETURN protocol and

open JSON schemas

standardize credential format,

enabling any compliant

application to integrate issuance

and verification services.

High

Co-Creation

Triad

(Engagement)

Transparency: All credential

transactions and revocation

events are publicly verifiable on

Bitcoin, offering full

auditability (Blockcerts, n.d.).

Dialog & Customization: The

community forum supports off-

chain technical dialogue and co-

development of the standard;

however, credential attribute

customization occurs in off-

chain issuer tooling rather than

via on-chain mechanisms

(Blockcerts, n.d.).

Incentives: No native token or

reward mechanism;

participation is driven by open-

source collaboration.

Transparency:

High

Dialog/Custom

ization:

Medium

Incentives:

Low

Illustrative Examples:

Externalization: An academic institution issues a

cohort diploma by hashing its Merkle root into

Bitcoin’s OP_RETURN, making proof of issuance

tamper-proof (Blockcerts, n.d.).

Operant Resource: A third-party verifier imports the

JSON credential into the universal verifier web app,

which runs Merkle-proof checks against the

blockchain to confirm authenticity (Blockcerts, n.d.).

Engagement: Developers propose schema extensions

in the public forum, enabling incremental co-design

of credential types - though these discussions and

customizations occur off-chain.

Blockcerts thus provides robust on-chain knowledge

conversion and resource-integration capabilities, and

a transparent - but primarily off-chain - environment

for co-creation. Its lack of native incentive tokens and

absence of on-chain socialization tools highlight

areas for future enhancement.

4.2.3 Case Study: Badgr

Badgr implements the Open Badges standard on

Ethereum, enabling issuers - from K–12 schools to

professional training providers - to mint, share, and

verify micro-credentials (Badgr, n.d.). Table 4. maps

Badgr’s core capabilities onto our SECI, Service-

Dominant Logic, and Co-Creation Triad lenses, using

the High/Medium/Low rubric.

Illustrative Examples

Externalization: A coding bootcamp issues a

“JavaScript Essentials” badge by embedding a hash

of the badge assertion into an Ethereum transaction,

ensuring tamper-proof proof of skill (Badgr, n.d.).

Combination: A learner’s Badgr Backpack

automatically groups all “Web Dev” badges into a

single “Full Stack” collection for easy sharing.

Engagement: Within a cohort’s pathway, peers’

comment on one another’s project badges, offering

feedback that learners internalize to improve

subsequent badge applications.

Badgr thus excels not only at on-chain knowledge

conversion and resource integration but also fosters

richer engagement through gamified incentives and

off-chain co-creation tools - addressing some of the

gaps observed in LearnCoin and Blockcerts.

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Table 4: Mapping Badgr Features to SECI, Service-

Dominant Logic, and Co-Creation Triad Lenses.

Lens Observations Support Level

SECI

(Knowledge

Conversion)

Externalization: Issuers define

badge criteria and metadata in

JSON, then anchor badge

assertions on-chain

converting tacit learner

achievements into explicit,

verifiable records (Badgr,

n.d.).

Combination: The Badgr

Backpack and dashboard

aggregate badges into learner

portfolios, enabling new

badge “collections” (Badgr,

n.d.).

Socialization/Internalization:

Learners can comment on and

endorse peers’ badges within

group cohorts, fostering

Socialization and reflection.

Externalization:

High

Combination:

High

Socialization/

Internalization:

Medium

S-D Logic

(Resource

Integration)

Operant Resources: Ethereum

smart contracts mint, transfer,

and revoke badges; Badgr’s

RESTful APIs expose these

functions for integration into

LMSs and corporate HR

systems (Badgr, n.d.).

- Protocol as Backbone: The

Open Badges JSON schema

and Ethereum token flows

standardize credential

exchange, allowing any

compliant system to integrate

micro-credential services.

High

Co-Creation

Triad

(Engagement)

Transparency: Badge

metadata and issuance events

are viewable on-chain and via

the Badgr.org public gallery

(Badgr, n.d.).

- Dialog & Customization:

Educators co-design badge

criteria with learners using

off-chain authoring tools;

built-in cohort forums let

recipients discuss criteria and

provide feedback (Badgr,

n.d.).

- Incentives: Gamified badge

“pathways” and social

leaderboards incentivize

sustained participation

beyond one-off issuance.

Transparency:

High

Dialog/

Customization:

Medium

Incentives:

High

4.2.4 Case Study: Learning Economy

Foundation

The Learning Economy Foundation (LEF) stewards a

Web3 “Internet of Education,” providing open, API-

driven platforms; LearnCard, LearnCloud,

ScoutPass, Metaversity - for issuing, storing, and

sharing verifiable credentials and skills portfolios

(Learning Economy Foundation, n.d.). Using our

High/Medium/Low rubric, Table 5. maps LEF’s core

capabilities onto the SECI, Service-Dominant Logic,

and Co-Creation Triad lenses.

Table 5: Mapping Learning Economy Foundation Features

to SECI, Service-Dominant Logic, and Co-Creation Triad

Lenses.

Lens Observations Support Level

SECI

(Knowledge

Conversion)

Externalization: LearnCard

mints “Skills Passports” (VCs)

on-chain via Merkle proofs,

converting tacit learner

achievements into explicit,

immutable tokens (Learning

Economy Foundation, n.d.).

Combination: LearnCloud’s

dashboard and portfolio APIs

aggregate credentials and

pathway data into cohesive

learner profiles (Learning

Economy Foundation, n.d.).

Socialization/Internalization:

Community “ScoutPass”

cohorts and forum discussions

exist off-chain; no native on-

chain annotation or narrative

tools for reflection.

Externalization:

High

Combination:

High

Socialization/In

ternalization:

Medium

S-D Logic

(Resource

Integration)

Operant Resources: LEF

provides SDKs and RESTful

APIs (LearnCard, LearnCloud)

that encapsulate issuance,

revocation, and credential–

wallet interactions as reusable

operant resources (Learning

Economy Foundation, n.d.).

Protocol as Backbone: The

Internet of Education protocols

(Open Skills, LER, W3C

universal wallet) standardize

credential formats and enable

interoperability across any

compliant system.

High

Co-Creation

Triad

(Engagement)

Transparency: All credential

issuance and revocation events

are publicly verifiable via

blockchain explorers and LEF

dashboards (Learning

Economy Foundation, n.d.).

Dialog & Customization: Off-

chain community forums,

“Position Paper” working

groups, and roadmap feedback

channels enable co-design of

protocol extensions; credential

metadata schemas are

customizable in issuer tooling.

Incentives: “Earn-to-Learn”

pilots (e.g., ScoutPass token

rewards) and “Advance

Colorado” partnerships offer

localized token incentives,

though these remain

experimental.

Transparency:

High

Dialog/

Customization:

Medium

Incentives:

Medium

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273

Illustrative Examples:

Externalization: A learner’s completion of a

SuperSkills LEGO quest is hashed into a ScoutPass

NFT, ensuring tamper-proof proof of play-based skill

acquisition (Learning Economy Foundation, n.d.).

Operant Resource: An employer’s HR system calls

LearnCloud’s credential-verification API to pull and

verify a candidate’s LearnCard portfolio without

manual intervention (Learning Economy Foundation,

n.d.).

Engagement: In LEF’s “Advance Colorado” pilot,

learners earn tokens for completing statewide

workforce credentials - an experimental incentive

mechanism designed to co-create curriculum via

stakeholder feedback (Learning Economy

Foundation, n.d.).

LEF’s ecosystem demonstrates robust on-chain

knowledge conversion and protocol-driven resource

integration, with evolving but still maturing

approaches to on-chain socialization and incentive

design - highlighting both its leadership in credential

interoperability and areas for deeper co-creation

practice.

4.3 Cross–Case Patterns

Bringing together our four case studies LearnCoin,

Blockcerts, Badgr, and the Learning Economy

Foundation reveals clear commonalities, shared gaps,

and distinctive approaches across the three lenses:

Table 6: Cross-case pattern summary.

Pattern

Type

SECI

(Knowledge

Conversion)

S-D Logic

(Resource

Integration)

Co-Creation Triad

(Stakeholder

Engagement)

Common

Strengths

•Externalization

& Combination

(4/4)

•Smart

contracts &

protocols as

operant

resources

(4/4)

•Full transparency

of credential

transactions (4/4)

Shared

Weaknesses

•On-chain

Socialization/

Internalization

absent (0/4)

• N/A (all

cover

resource

integration

fully)

•Ongoing incentive

mechanisms weak

or one-off (1/4)

Variant

Practices

•Badgr offers

peer

endorsement (1)

•LEF forums &

working groups

(1)

• LEF’s Open

Skills

protocols

enable cross-

platform

interoperabi-

lity

•Badgr gamified

pathways (High)

• LEF pilot tokens

(Medium)

•LearnCoin one-off

rewards (Medium)

•Blockcerts open-

source

collaboration

(Low)

Externalization & Combination: Every platform

writes hashes on-chain and provides dashboards/APIs

to recombine records into learner portfolios,

confirming the centrality of explicit KM artifacts in

blockchain credentials.

Operant Resources: All four systems treat smart

contracts, token protocols, or standards

(OP_RETURN, Open Badges JSON, Internet of

Education) as reusable operant resources that

automate issuance, revocation, and verification.

Transparency: Public blockchains guarantee

auditability across all cases, underscoring

blockchain’s unique value for verifiable credentials.

Lack of On-chain Socialization/Internalization: None

of the platforms natively support on-chain dialogue,

annotations, or reflective storytelling - learners must

export to off-chain forums or social media to

internalize and share insights.

Incentive Design: Only Badgr’s gamified

leaderboards and LEF’s experimental “Earn-to-

Learn” pilots approach sustained co-creation

incentives; the others rely on institutional mandates

or one-off token grants.

Dialog & Customization Variations: Badgr’s cohort

forums enable richer peer-to-peer feedback; LEF’s

working groups and roadmap channels invite

reviewer co-design of protocol extensions;

Blockcerts and LearnCoin leave customization in

issuer UIs.

Interoperability & Ecosystem Scope: LEF stands out

for its cross-platform protocols (Open Skills, W3C

wallet) that foster multi-actor credential exchanges,

while the others focus on point-to-point issuance.

5 DISCUSSION

Our cross case analysis of LearnCoin, Blockcerts,

Badgr, and the Learning Economy Foundation shows

that blockchain platforms uniformly excel at

codifying and recombining explicit knowledge

supporting SECI’s Externalization and Combination

modes (Nonaka & Takeuchi, 1995; Grant, 1996) and

at embedding routine workflows via smart contracts,

confirming their role as operant resources under

Service‐Dominant Logic (Vargo & Lusch, 2004).

However, they consistently under support deeper KM

cycles: on-chain Socialization and Internalization

remain neglected, and smart contracts seldom enable

dynamic pathway reconfiguration.

KMIS 2025 - 17th International Conference on Knowledge Management and Information Systems

274

While all four systems offer High transparency

through public auditability, true co-creation requires

dialogic engagement and mutual customization

capabilities, only Badgr and LEF partially address

off-chain (Prahalad & Ramaswamy, 2004). Without

on-chain channels for annotation, peer review, or

iterative feedback, credential ecosystems risk

remaining transactional proof repositories rather than

transformational learning communities.

These patterns highlight both theoretical and

practical imperatives. KM scholars must explore

embedding communities of practice and verifiable

learning narratives into smart contracts to close the

full SECI loop on-chain (Wenger, 1998). Designers

should prototype AI-augmented credentialing agents

that adapt pathways in real time, extending Service

Dominant Logic into fluid ecosystems. Finally,

protocol innovations such as multi-phase token

economies rewarding issuance, peer annotation,

mentoring, and co-design are needed to sustain

ongoing collaboration. By mapping both literature

and cases to our integrative model, we fill a critical

gap,66 % of prior studies overlook these KM and

service-science dimensions and provide a roadmap

for next-generation, value-co-creative credentialing

systems.

6 CONCLUSION AND FUTURE

WORK

Our dual stream review of 50 studies and four

platforms (LearnCoin, Blockcerts, Badgr, Learning

Economy Foundation) shows that current blockchain

credential systems excel at explicit knowledge

Externalization and protocol driven resource

integration, yet consistently under support, on- chain

Socialization, Internalization, and sustained co -

creation incentives (Blockcerts, n.d.; Ocheja et al.,

2019). By synthesizing SECI (Nonaka & Takeuchi,

1995), Service-Dominant Logic (Vargo & Lusch,

2004), and the co-creation triad (Prahalad &

Ramaswamy, 2004), we demonstrate that embedding

KM processes such as on-chain communities of

practice, narrative annotation layers, and multi-phase

token economies, directly into smart contracts is

essential to close knowledge - conversion loops and

foster dynamic stakeholder engagement.

Building on these insights, future research should:

1) Validate tacit exchanges by empirically

examining how on-chain reflective and

social interactions support Internalization

and Socialization.

2) Augment operant resources through AI-

driven credentialing agents and adaptive

smart-contract modules that enable real-time

resource reconfiguration.

3) Design dialogic governance mechanisms -

such as token incentives and schema co-

design workflows, which sustain multi-

stakeholder collaboration beyond one-off

transactions (Markopoulos et al., 2022).

Addressing these issues will deepen theoretical

rigor and inform sustainable platform design.

Pursuing this agenda will move blockchain - enabled

certificates from static proofs of achievement toward

living ecosystems of continuous, co - creative

learning. Moreover, our model (Figure 1) invites

evaluation of socio-technical trade-offs, such as

privacy versus transparency, and calls for

longitudinal studies to track how on-chain KM

processes evolve over time.

REFERENCES

Akram, S. V., Malik, P. K., Singh, R., Gehlot, A., Juyal, A.,

Ghafoor, K. Z., & Shrestha, S. (2022). Implementation of

Digitalized Technologies for Fashion Industry 4.0:

Opportunities and Challenges. Scientific Programming,

2022(1), 1–17.

Alagha, A., Otrok, H., Singh, S., Mizouni, R., & Bentahar, J.

(2024). Blockchain-based crowdsourced deep

reinforcement learning as a service. Information

Sciences, 679, 121107. https://doi.org/10.1016/j.ins.2

024.121107

Alexeis Garcia-Perez, & Symkin, L. (2020). Proceedings of

the 21st European Conference on Knowledge

Management: a Virtual Conference hosted by Coventry

University, UK, 2–4 December 2020. The Use of

Blockchain-Supported Reward Systems for Knowledge

Transfer between Generations.

Anastasiia Gurzhii, Haque, B., Naqvi, B., Jaakko Vuolasto,

Janne Parkkila, & Islam, N. (2025). Exploring

Perceptions of Blockchain in Cross-Border Workforce

Mobility. Lecture Notes in Business Information

Processing, 186–201. https://doi.org/10.1007/978-3-

031-85849-9_16

Asharaf, S., & Adarsh, S. (2017). Decentralized Computing

Using Blockchain Technologies and Smart Contracts.

Advances in Information Security, Privacy, and Ethics.

https://doi.org/10.4018/978-1-5225-2193-8

Beştaş, M., Taş, R., Akin, E., Ozkan-Okay, M., Aslan, Ö., &

Aktug, S. S. (2023). A Novel Blockchain-Based

Scientific Publishing System. Sustainability, 15(4), 3354.

https://doi.org/10.3390/su15043354

Assessing Value Co-Creation in Blockchain Enabled Learning Certiﬁcates: A Knowledge Management Perspective

275

Blockcerts. (n.d.). (2025, May). About Blockcerts. Retrieved

May 2025, From. https://www.blockcerts.org/overview.

html

Badgr. (n.d.). (2025, May). Open Badges & Badgr.

https://badgr.com/overview.html

Chai, H., Leng, S., Chen, Y., & Zhang, K. (2020). A

Hierarchical Blockchain-Enabled Federated Learning

Algorithm for Knowledge Sharing in Internet of

Vehicles. IEEE Transactions on Intelligent

Transportation Systems, 1–12. https://doi.org/10.1109/ti

ts.2020.3002712

Chai, H., Leng, S., Wu, F., & He, J. (2022). Secure and

Efficient Blockchain-Based Knowledge Sharing for

Intelligent Connected Vehicles. IEEE Transactions on

Intelligent Transportation Systems, 23(9), 14620–14631.

https://doi.org/10.1109/tits.2021.3131240

Chen, X., Meng, W., & Huang, H. (2025). Blockchain-

Driven Distributed Edge Intelligence for Enhanced

Internet of Vehicles. IEEE Internet of Things Journal,

12(5), 4773–4782. https://doi.org/10.1109/jiot.2024.349

2074

Cristina-Maria Vipie, Alexandra-Diana Afumatu, & Mihai

Caramihai. (2023). Blockchain-Based Educational

Certificates: A Proposal. IntechOpen EBooks.

https://doi.org/10.5772/intechopen.109392

Ding, Q., Yue, X., Zhang, Q., Xiong, Z., Chang, J., & Zheng,

H. (2024). Bc2FL: Double-Layer Blockchain-Driven

Federated Learning Framework for Agricultural IoT.

IEEE Internet of Things Journal, 12(4), 1–1.

https://doi.org/10.1109/jiot.2024.3485208

Fekete, D. L., & Kiss, A. (2023). Toward Building Smart

Contract-Based Higher Education Systems Using Zero-

Knowledge Ethereum Virtual Machine. Electronics,

12(3), 664. https://doi.org/10.3390/electronics12030664

Fu, Y., Li, C., F. Richard Yu, Luan, T. H., & Zhao, P. (2023).

An Incentive Mechanism of Incorporating Supervision

Game for Federated Learning in Autonomous Driving.

IEEE Transactions on Intelligent Transportation

Systems, 24(12), 14800–14812. https://doi.org/10.1109/

tits.2023.3297996

Gokul Yenduri, Dasaradharami Reddy K, Srivastava, G.,

Supriya Y, Ramalingam M, Thippa Reddy Gadekallu, &

Awaysheh, F. M. (2023). Federated Learning for the

Metaverse: A Short Survey. https://doi.org/10.1109/ime

ta59369.2023.10294796

Grant, R. M. (1996). Toward a knowledge-based theory of

the firm. Strategic Management Journal, 17(S2), 109–

122. https://doi.org/10.1002/smj.4250171110

Grech, A., & Camilleri, A. F. (2017). Blockchain in

education. Publications Office of the European Union.

https://doi.org/10.2760/60649

Hafeez, S., Mohjazi, L., Imran, M. A., & Sun, Y. (2023).

Blockchain-enabled Clustered and Scalable Federated

Learning (BCS-FL) Framework in UAV Networks. 2023

IEEE 28th International Workshop on Computer Aided

Modeling and Design of Communication Links and

Networks (CAMAD), 68–73. https://doi.org/10.1109/

camad59638.2023.10478423

Hewa, T., Ylianttila, M., & Liyanage, M. (2020). Survey on

blockchain based smart contracts: Applications,

opportunities, and challenges. Journal of Network and

Computer Applications, 177, 102857. https://doi.org/

10.1016/j.jnca.2020.102857

Hireche, O., Benzaïd, C., & Taleb, T. (2022). Deep data plane

programming and AI for zero-trust self-driven

networking in beyond 5G. Computer Networks, 203,

108668. https://doi.org/10.1016/j.comnet.2021.108668

Hu, S., Lin, H., Chen, G., Weng, J., & Li, J. (2018).

Reputation-based Distributed Knowledge Sharing

System in Blockchain. https://doi.org/10.1145/3286978

.3286981

Jin, W., Xu, Y., Dai, Y., & Xu, Y. (2023). Blockchain-Based

Continuous Knowledge Transfer in Decentralized Edge

Computing Architecture. Electronics, 12(5), 1154.

https://doi.org/10.3390/electronics12051154

Keller, T., Gämperli, M., & Brucker-Kley, E. (2024). Proof-

of-Work Explained in VR: A Case Study. Lecture Notes

in Computer Science, 15027, 114–129.

https://doi.org/10.1007/978-3-031-71707-9_7

Khalid, M. (2024). Energy 4.0: AI-enabled digital

transformation for sustainable power networks.

Computers & Industrial Engineering, 110253–110253.

https://doi.org/10.1016/j.cie.2024.110253

Kuleto, V., Bucea-Manea-Țoniş, R., Bucea-Manea-Țoniş, R.,

Ilić, M. P., Martins, O. M. D., Ranković, M., & Coelho,

A. S. (2022). The Potential of Blockchain Technology in

Higher Education as Perceived by Students in Serbia,

Romania, and Portugal. Sustainability, 14(2), 749.

https://doi.org/10.3390/su14020749

Lei, C., & Eric W.T. Ngai. (2023). Blockchain from the

information systems perspective: Literature review,

synthesis, and directions for future research. Information

& Management, 60(7), 103856–103856.

https://doi.org/10.1016/j.im.2023.103856

Lin, X., Wu, J., Bashir, A. K., Li, J., Yang, W., & Piran, J.

(2020). Blockchain-Based Incentive Energy-Knowledge

Trading in IoT: Joint Power Transfer and AI Design.

IEEE Internet of Things Journal, 9(16), 1–1.

https://doi.org/10.1109/jiot.2020.3024246

Learning Economy Foundation - A Steward of the Internet of

Education, Web3, Blockchain, Digital Wallets, Crypto,

DOAs, and Verifiable Credentials for Learning. (2024).

Learningeconomy.io.

LearnCoin. (2024). Learncoin.me. https://learncoin.me/

learncoin#feature

Markopoulos, E., Kirane, I. S., Balaj, D., & Vanharanta, H.

(2019). Artificial Intelligence and Blockchain

Technology Adaptation for Human Resources

Democratic Ergonomization on Team Management.

Human Systems Engineering and Design II, 1026, 445–

455. https://doi.org/10.1007/978-3-030-27928-8_68

Mehmet Ozgen Ozdogan, Levent Çarkacioğlu, & Berk

Canberk. (2022). Digital Twin Driven Blockchain Based

Reliable and Efficient 6G Edge Network.

https://doi.org/10.1109/dcoss54816.2022.00062

Molopa, S. T., & Cronje, J. (2024). Research on Blockchain

Adoption in Higher Education: A Systematic Review and

Conceptual Model. Lecture Notes in Networks and

Systems, 110–130. https://doi.org/10.1007/978-3-031-

53963-3_10

KMIS 2025 - 17th International Conference on Knowledge Management and Information Systems

276

Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating

company: How Japanese companies create the dynamics

of innovation. Oxford University Press.

Ocheja, P., Flanagan, B., Ueda, H., & Ogata, H. (2019).

Managing lifelong learning records through blockchain.

Research and Practice in Technology Enhanced

Learning, 14(1). https://doi.org/10.1186/s41039-019-

0097-0

Pham, C. M., Lokuge, S., Nguyen, T., & Adamopoulos, A.

(2023). Exploring knowledge management enablers for

blockchain-enabled food supply chain implementations.

Journal of Knowledge Management, 28(1).

https://doi.org/10.1108/jkm-07-2022-0586

Pokhrel, S. R. (2021). Blockchain Brings Trust to

Collaborative Drones and LEO Satellites: An Intelligent

Decentralized Learning in the Space. IEEE Sensors

Journal, 21(22), 1–1. https://doi.org/10.1109/jsen.20

21.3060185

Prahalad, C. K., & Ramaswamy, V. (2004). Co-creation

experiences: The next practice in value creation. Journal

of Interactive Marketing, 18(3), 5–14.

https://doi.org/10.1002/dir.20015

Pu, S., & Lam, J. S. L. (2023). The benefits of blockchain for

digital certificates: A multiple case study analysis.

Technology in Society, 72, 102176.

https://doi.org/10.1016/j.techsoc.2022.102176

Rami Alkhudary, & Gardiner, P. (2024). Utilizing blockchain

to enhance project management information systems:

insights into project portfolio success, knowledge

management and learning capabilities. International

Journal of Managing Projects in Business.

https://doi.org/10.1108/ijmpb-01-2024-0021

Seddon, I. A., Rosenberg, E., & Houston, S. K. (2023).

Future of virtual education and telementoring. 34(3),

255–260. https://doi.org/10.1097/icu.00000000000009

Sottilare, R. A. (2024). Examining the Role of Knowledge

Management in Adaptive Military Training Systems.

Lecture Notes in Computer Science, 14727, 300–313.

https://doi.org/10.1007/978-3-031-60609-0_22

Sharples, M., & Domingue, J. (2016). The blockchain and

kudos: A distributed system for educational record,

reputation and reward. In K. Verbert, M. Sharples, & T.

Klobučar (Eds.), Adaptive and adaptable learning. EC-

TEL 2016. Lecture Notes in computer science (pp. 490–

496). Springer. https://doi.org/10.1007/978-3-319-

45153-4_48

Sychov, S., Mikushev, V., Kurashova, S., Motorin, D.,

Seravin, A., & Radikov, I. (2019). Knowledge Erosion

and Knowledge Protection in the Age of Increasing Data

Flow. 2019 5th International Conference on Information

Management (ICIM), 178–182.

https://doi.org/10.1109/infoman.2019.8714655

Tlili, A., Zhang, J., Papamitsiou, Z., Manske, S., Huang, R.,

Kinshuk, & Hoppe, H. U. (2021). Towards utilising

emerging technologies to address the challenges of using

Open Educational Resources: a vision of the future.

Educational Technology Research and Development,

69(2), 515–532. https://doi.org/10.1007/s11423-021-

09993-4

Tripathi, G., Ahad, M. A., & Casalino, G. (2023). A

Comprehensive Review of Blockchain technology:

Underlying Principles and Historical Background with

Future Challenges. Decision Analytics Journal, 9(1),

100344. https://doi.org/10.1016/j.dajour.2023.100344

Vargo, S. L., & Lusch, R. F. (2004). Evolving to a new

dominant logic for marketing. Journal of Marketing,

68(1), 1–17. https://doi.org/10.1509/jmkg.68.1.1.24036

Wang, J., & Li, J. (2024). Blockchain and Access Control

Encryption-Empowered IoT Knowledge Sharing for

Cloud-Edge Orchestrated Personalized Privacy-

Preserving Federated Learning. Applied Sciences, 14(5),

1743–1743. https://doi.org/10.3390/app14051743

Wenger, E. (1998). Communities of practice: Learning,

meaning, and identity. Cambridge University Press.

Wu, H., Zhu, X., & Hu, W. (2024). A Blockchain System for

Clustered Federated Learning with Peer-to-Peer

Knowledge Transfer A Blockchain System for Clustered

Federated Learning with P2P Knowledge Transfer.

17(5), 966–979. https://doi.org/10.14778/3641204.3641

208

Xie, J., Tang, H., Huang, T., Yu, F. R., Xie, R., Liu, J., & Liu,

Y. (2019). A Survey of Blockchain Technology Applied

to Smart Cities: Research Issues and Challenges. IEEE

Communications Surveys & Tutorials, 21(3), 2794–

2830. https://doi.org/10.1109/comst.2019.2899617

Xie, R., & Zhang, W. (2023). Online knowledge sharing

in blockchains: towards increasing participation.

Management Decision. https://doi.org/10.1108/md-06-

2022-0767

Yves Demazeau, Gao, J., & Xu, G. (2018). 2018 5th

International Conference on Behavioral, Economic, and

Socio-Cultural Computing (BESC). A Blockchain Model

for Word-Learning Systems. https://doi.org/10.1109/bes

c46168.2018

Zamiri, M., & Esmaeili, A. (2024). Strategies, methods, and

supports for developing skills within learning

communities: A systematic review of the literature.

Administrative Sciences, 14(9), Article 231.

https://doi.org/10.3390/admsci14090231

Zhou, X., Huang, W., Liang, W., Yan, Z., Ma, J., Pan, Y., &

Kevin I-Kai Wang. (2024). Federateddistillation and

blockchain empowered secure knowledge sharing for

Internet of medical Things. Information Sciences, 662,

120217–120217. https://doi.org/10.1016/j.ins.2024.1202

Zhou, Y., & Huang, F. (2024). Navigating Knowledge

Dynamics: Algorithmic Music Recombination, Deep

Learning, Blockchain, Economic Knowledge, and

Economy. https://doi.org/10.1007/s13132-023-01700-3

Assessing Value Co-Creation in Blockchain Enabled Learning Certiﬁcates: A Knowledge Management Perspective

277