Environmental, Social, and Governance (ESG) concerns are increasingly determining organizational strategies, driven by regulations such as the CSRD (Corporate Sustainability Reporting Directive) and ISSB (International Sustainability Standards Board) as well as investor and societal expectations. While most initiatives treat ESG primarily as a matter of data aggregation reporting and accounting, there is a design-oriented research gap in how ESG concerns can be incorporated into enterprise modeling and knowledge management practices. The aim of this PhD work is to investigate how ESG semantics can be integrated in a knowledge-driven assessment and reporting capability complementary to the current data-driven analytics approaches. This will take the form of an ESG-specific enterprise modeling method and a supporting tool prototype to be used and evaluated as a demonstrator in the spirit of Design Science. Therefore, the intended artifact contribution has two parts: (1) a methodological component, consisting of an enterprise modeling method built around a domain-specific language and reporting mechanisms that semantically leverage ESG concern taxonomies, roles and content flows mapped on traditional BPMN, and (2) a technical component in the form of a prototype implemented on the ADOxx metamodeling platform to support the method deployment and evaluation. In the current stage (1st year of PhD), the work focuses on problem formulation refinement, artifacts requirements specification and an initial structured literature review - which confirms the dominance of data-driven and fragmented approaches across ESG reporting frameworks, and a shortage of design works on ESG integration with enterprise or business process architectures. The paper summarizes such preliminary results and outlines the Design Science research plan tailored to the specificity of modeling method engineering.
Environmental, Social, and Governance (ESG) refers to a set of non-financial criteria that evaluate how organizations manage sustainability and responsibility across the environmental, social, and governance dimensions. Originally a reporting practice driven by regulatory and investor pressure, ESG has evolved into a core element of organizational accountability. However, treating ESG exclusively as a reporting obligation may risk reducing it to a data aggregation task, rather than fostering its role as a knowledge-driven capability. The three ESG dimensions introduce rich semantics and taxonomies in many aspects of an enterprise or business process architecture: new roles, new content (data or knowledge) objects, new task and even types, specific communication or material flows. Established enterprise modeling languages do not traditionally incorporate ESG semantics, while ESG semantics are mostly tacit or implied by conceptual models that manifest in the data structures being collected for ESG data reports - however lacking the traceability and granularity that allows ESG concerns to be mapped on business processes, functions or enterprise architecture layers.
Bringing together enterprise modeling and ESG conceptual models may also contribute to a
Knowledge Management (KM) capability, where ESG-related information is consistently mapped and integrated
into sense-making and decision-making eforts. ESG concerns can be mapped onto the Business Process
Management (BPM) culture that already exists in many organizations, where established modeling
methods (e.g. Business Process Model and Notation (BPMN) [
Initial investigations of this PhD work suggest that ESG concerns are increasingly defining
organizational strategies, under pressure from regulations such as the Corporate Sustainability Reporting
Directive (CSRD) [
Even with this progress, ESG is predominantly addressed through data reporting and disclosure
frameworks, lacking in granular system and process views that would allow mapping ESG concerns
into enterprise models or knowledge management practices [
Enterprise modeling and domain-specific language engineering provides an opportunity to address
this gap. Through well-recognized languages such as BPMN, ArchiMate, or UML, enterprise modeling
enables structured representations of processes, structures, and governance mechanisms. Extending
them with ESG concepts, properties and relationships can facilitate a multi-perspective semantic
integration of sustainability into knowledge-based organizational capabilities [
This doctoral research sets out a Design Science Research (DSR) agenda to address the identified gap
and to propose both technical artifacts and methodological treatments. Guided by the research process
model of Pefers et al. [
We focus on language extension, rather than conceiving new ESG-oriented modeling procedures
for legacy modeling languages, because we aim to enable traceability to semantically distinguishable,
explicit and specialized constructs (e.g. specific types of flows, tasks, events, roles), thus opening
possibilities for machine interpretability through process queries and AI processing of diagrammatic
contents. This is partly motivated by an intention to also contribute to the larger scope of a research
project proposal [
At this early stage (Year 1) preliminary results comprise an initial structured literature review that
confirms the fragmentation of approaches across ESG methods and reporting frameworks, lacking
coherent semantic integration by means of conceptual modeling and drill-down traceability to business
process or enterprise architecture constructs on convenient levels of decomposition, or along semantic
chains of dependencies. Building on this foundation and preliminary work introduced by the doctoral
supervisor [
The PhD project was recently initiated with a literature review following the following strategy: (1) search of academic works through Google Scholar, looking for open access or openly accessible publisher repositories available through university memberships (i.e. from CEUR-WS and MDPI to Springer, IEEE, Elsevier, AIS); (2) consultation of trusted industry and practitioner white papers (e.g., SAP Sustainability Control
Tower [19], LeanIX ESG Capability Modeling [20], PwC reports [
The complete search and screening protocol was detailed in [21] and is briefly summarized in this
section. The initial search looked for general ESG cases and approaches ("ESG methods", "ESG tools",
"ESG accounting"), for connections to enterprise modeling ("conceptual modeling for ESG",
"domainspecific modeling in ESG", "enterprise modeling for ESG") and to knowledge management ("knowledge
management for ESG", "ESG capability", "ESG traceability"). This process initially identified 116 sources
(94 academic, 22 industry). After a first refinement round, an intermediate set of 47 works were retained,
excluding contributions that only mentioned ESG superficially or lacked methodological detail. From
these, a final selection of 15 core contributions - with a focus on ESG methods and tools in relation
to a need for conceptualizations, and not only of data reporting - was consolidated as a conceptual
foundation subsequently grouped into thematic clusters to highlight emerging themes and research
directions:
(1) ESG methods and tools. Contributions in this cluster include works that embed ESG factors
into financial valuation models [ 22], propose AI and natural language processing techniques to
generate ESG ratings [23], and develop decision support systems tailored to domain specificities
such as oil and gas sectors [24]. While these mechanisms improve measurement and comparability,
they remain largely data-driven evaluation tools, with limited integration into enterprise modeling
or capability-based perspectives;
(2) Accounting and reporting. This cluster highlights frameworks such as GRI, SASB, CSRD,
and ISSB, which consolidate ESG disclosure and comparability. Some contributions show how
accountability mechanisms evolve into managerial instruments [
Across these clusters, several conclusions emerge. First, ESG research and practice remain dominated by disclosure frameworks and data collection/analytics tools; secondly, environmental topics receive disproportionate attention compared to social and governance concerns; third, treating ESG as an organizational capability can drive eforts to connect ESG frameworks and indicators to business processes, roles and decisions; capability maps and possibly further propagating across EA layers to inform ESG strategic guidance; finally, conceptual modeling (BPMN/EA/DSML) and semantic techniques can act as a missing bridge between data-centric tools and ESG knowledge work and associated knowledge management capabilities. For this particular PhD project, we employ BPMN as a conceptualization entry point for embedding ESG concerns into business process architecture. This is intended to enable process-centric traceable reporting of ESG at convenient granularities, aligned with the granularity of available process catalogs (e.g. at task level, at subprocess level, at role level).
These insights, together with the doctoral supervisor’s early stage proposal to build around ESG a
knowledge management capability [
Managerial perspectives and accounting practices have begun to integrate ESG indicators, while BPM extensions and semantic technologies show potential for embedding sustainability concerns into organizational processes. However, these approaches remain fragmented and do not converge into a single integrative method. This fragmentation leaves a methodological gap: the absence of an enterprise modeling method that incorporates ESG as first-class modeling constructs of all categories (taxonomies, relationships, data attributes). Considering this problem, this research is structured around a set of objectives:
O1. To conceptualize ESG as a knowledge structure that can semantically enrich business process modeling (possibly extending to other enterprise modeling aspects), in order to establish a conceptual foundation for a process-centric ESG knowledge management capability.
O2. To design an ESG-aware enterprise modeling method (with a focus on business processes and extensions to other layers), consisting of a DSML with ESG-specific constructs, mechanisms for reasoning and reporting and a usage procedure for semantic ESG annotations and model-driven reporting generation.
O3. To implement a supporting tool prototype on the ADOxx metamodeling platform, a technological
choice based on extensive experience and productivity as members of the OMILAB ecosystem [
O4. To empirically evaluate the modeling method and its associated DSML through a multi-criterial approach that adopts prescriptive approaches from method engineering and domain-specific language engineering.
The nature of these objectives determines a Design Science Research (DSR) orientation of the doctoral project, in which knowledge is advanced through the creation of both methodological and technical contributions, balancing engineering and empirical tasks, and aiming to produce prescriptive knowledge regarding the conceptual alignment of ESG concerns with aspects/layers involved in general-purpose enterprise modeling.
The Design Science Research methodology is widely used in Information Systems when the aim is to
design and evaluate artifacts in a pragmatic context, aiming to generalize prescriptive knowledge out of
the artifact engineering efort. DSR provides a structured, iterative approach for addressing complex,
practice-oriented problems while grounding the solutions in a scientific knowledge base and at the
same time enriching that knowledge base. This PhD research is guided by the process outlined in [
The DSR stages are planned as follows:
1. Problem identification and motivation. At this early stage which is currently on-going,
the research focuses on clarifying the problem space. Current literature, as summarized in
Section 2, indicates that the topic of ESG accounting is predominantly shaped by reporting
and disclosure frameworks, while process-centric, conceptual modeling methods or semantic
integration proposals are marginal and fragmented, thus failing to lead to knowledge management
approaches over ESG concerns. The motivation for the doctoral research is to investigate how
ESG can be integrated with enterprise modeling methods for semantically richer and traceable
reporting, in relation to the business processes being impacted by, or having impact on, ESG
concerns and activities.
2. Definition of the objectives. Objectives will consider requirements originating from European
and regional legislation (e.g., CSRD, ISSB), which act both as motivators and constraints for the
design of the method and ESG-specific knowledge structures. Building on the identified problem,
the doctoral research aims to formulate competency questions and model-driven functional
requirements that can guide the design of both the methodological and technical contributions.
Both artifact-oriented and empirically-oriented objectives have been formulated in Section 3.
Each objective will remain traceable to its source of motivation: O1 emerges from the literature
gap, O2 builds on the initial BPMN design decisions introduced in [
The evaluation of the ESG-aware BPMN extension and its supporting prototype must be organized as an iterative and multi-dimensional subprocess, combining established quality frameworks for modeling languages with recent recommendations for method-level assessment.
First, we plan to adopt the SEQUAL framework [39] which diferentiates the syntactic, semantic, and pragmatic quality. This provides a foundation for assessing (1) the syntactic correctness of the metamodel and its associated rules, (2) the semantic adequacy of the ESG constructs in relation to sustainability concepts and reporting requirements, and (3) the pragmatic quality of the method as experienced by the stakeholders. Second, the evaluation plan will be aligned with the Verification, Validation, and Evaluation (VVE) theoretical lens for modeling method quality provided in [37]. According to this framework, verification concerns the internal consistency of the method (e.g., metamodel coherence, tool implementation in ADOxx), validation ensures domain adequacy (e.g., alignment with CSRD and ISSB reporting standards, semantic competency, expert feedback from ESG practitioners), and evaluation addresses practical aspects (e.g., usability tests, case applicability). The dimensions will not be treated sequentially but will be integrated into iterative design-demonstration-evaluation cycles, so that each cycle contributes to the method refinement.
Moreover, the evaluation will also take into account community guidelines for building efective modeling methods [38]- i.e. to consider usability, extensibility, and tool support as desirable qualities of a sustainable method. This implies assessing whether the extensions remain comprehensible to BPMN users, whether they can be adapted to evolving ESG regulations, and whether the supporting prototype efectively enables modeling and reporting tasks.
Envisioned evaluation phases are as follows: Verification Phase to answer “Did we build the method correctly?” cf. [ 37]- the focus is on internal correctness and consistency of the metamodel and tool implementation. Here we will direct attention to: • Metamodel checks: ensure that ESG-specific constructs (tasks, events, roles, flows, data objects) are formally defined and syntactically integrated with BPMN; • Rule enforcement: implement automated constraints in the ADOxx prototype (e.g., every ESG task must be classified under E/S/G dimensions; ESG data objects must be linked to at least one ESG-related task); • Notation consistency: Test whether the new icons, classifications, and flows integrate coherently with BPMN notation, avoiding conflicts or ambiguous representations, and reflecting notational recommendations from the literature, such as Moody’s framework [40]; • Tool verification: Confirm that the prototype correctly enforces rules and generates outputs without errors, i.e. mostly a software testing approach.
Validation Phase to answer “Did we build the correct method?” cf. [37]- the focus is on semantic adequacy, checking if the method reflects ESG requirements and domain needs through: • Regulatory alignment: validate constructs against reporting frameworks (GRI, SASB, ISSB,
CSRD) to ensure coverage of mandatory ESG indicators. • Expert reviews: conduct structured feedback sessions with ESG specialists to check whether the newly incorporated BPMN taxonomies (e.g., social tasks, governance flows) correspond to real ESG practices. • Conceptual mapping: compare ESG-aware BPMN constructs with concepts from ESG capability maps and industry frameworks (e.g., LeanIX ESG capability model, SAP Sustainability Control Tower) to confirm relevance and align with capability management principles [28]. • Traceability tests: verify that ESG constructs can be mapped to reporting needs and relational chains can be followed along semantically rich connections, with the help of the internal ADOxx query language 1 and the ADOxx-to-RDF semantic graph generation [34] to enable a knowledge graph treatment.
Evaluation Phase, to answer “Is the method useful in practice?” cf. [37]- the focus is on pragmatic quality in terms of usability, adoption, and value in real-world scenarios, taking a look at: • Stakeholders groups representing a duality of competences: (a) ability of BPMN practitioners to incorporate ESG extensions into familiar BPMN workflows; (b) the ability of ESG practitioners to align ESG concerns along process-centric viewpoints. • Usability tasks: assign modeling exercises (e.g., model an ESG reporting process) and measure time, errors and ease of understanding. • Case-based applications: apply the method to illustrative scenarios (e.g., an organization’s supply chain ESG reporting) to demonstrate practical relevance. • Feedback for iterative refinement: collect qualitative feedback on clarity, usefulness, and completeness, using evaluation feedback to improve all modeling method building blocks – notation, syntax, semantics, rules, model-driven mechanisms and usage procedure.
At the current stage, the doctoral project is mainly focused on refining the problem formulation and anchoring it in literature gaps on design-oriented research for ESG conceptual modeling. As suggested in the literature review summary of Section 2, we perceive a fragmented state and a weak presence of integrative process-centric methods or knowledge management capabilities, contrasted by a dominance of quantitative data analytics approaches for ESG. The literature survey, with an initial deployment reported in [21], is planned to be expanded as a more comprehensive systematic literature review considering higher quality sources (e.g. no student papers and preprints) and refined inclusion criteria.
In addition to surveying the literature regarding domain-specificity and design proposition gaps, the author is acquiring technical and methodological skills regarding the enablers to be employed: the technological platform (ADOxx) and its possibilities for a knowledge graph treatment, the Agile Modeling Method Engineering approach of OMILAB, the VVE and SEQUAL quality frameworks. 1Available at: https://www.adoxx.org/documentation/aql/
Finally, the research builds on an early proposition formulated by the doctoral supervisor in [
Current metamodeling work focuses on a catalog of new constructs that can extend the competency of BPMN towards the ESG specificity, by reflecting the E/S/G pillars as semantic distinctions overlayed on traditional BPMN; therefore, the initial designs focus on the following categories: • New Task taxonomies – introducing new classifications of BPMN tasks, first distinguished by the three ESG pillars, then further taxonomized into types of activities that ESG management enforces to be added to current operations (e.g. ESG reporting, waste handling tasks etc.); • New Event taxonomies – introducing new classification of BPMN events according to ESG pillars and the types of events expected during ESG activities (requests from ESG authorities, incidents to be handled through reactive ESG strategies etc.); • New Data object taxonomies – introducing new classifications of BPMN content according to ESG pillars and the specific content objects created or used during ESG activities (specific types of reports, information to be collected because of ESG policies etc.); • New Participant roles – introducing new classifications of BPMN pools and lanes according to ESG pillars and the typical roles defined by an organizational ESG strategy (ESG oficer, authority, supply chain partners imposing ESG relations etc.); • New types of Flows and Associations – introducing new classifications of BPMN relationships according to the content, value and material flows generated by ESG activities (ESG information lfows, material waste flows, associations of ESG content objects etc.).
In addition to such BPMN extensions, conceptual extensions originating in ESG practice but not directly related to business processes may also emerge, thus inducing semantic enrichments beyond domainspecific specializations of existing BPMN constructs. This will remain aligned with the requirements originating in ESG reporting obligations (e.g., CSRD, ISSB) at both European and regional levels, which are adopted as possible sources for concept acquisition. This ensures that the proposed method not only tailors BPMN through specialization, but also expands it in response to practical compliance and reporting needs to assure the DSR relevance cycle.
To summarize, this doctoral research aims to advance knowledge and practice in several ways, by following the DSR methodology and evaluation strategies grounded on the VVE theoretical lens and on the SEQUAL quality framework – therefore the contribution is expected to comprise several streamlined artifacts and treatments: • Conceptual contribution. Conceptualizing ESG as a knowledge capability, linking sustainability concerns to enterprise modeling practices towards enabling knowledge management for ESG concerns and their alignment with business processes; • Methodological contribution. Designing an ESG-specific enterprise modeling method, realized through an extension of BPMN with ESG-specific constructs by applying established DSML engineering techniques; • Technical contribution. Developing a prototype tool to deploy the method and its DSML on the ADOxx metamodeling platform; • Practical contribution. Making prescriptive recommendations on how the method should be applied by companies having a Business Process Management and/or a Knowledge Management culture built around enterprise modeling, including empirical insights on how the conceptual, methodological and technical contribution aligns with such practical experiences. Acknowledgement. This doctoral research is conducted under the supervision of Prof. Robert Andrei Buchmann, within the Business Informatics doctoral program at Babeş-Bolyai University, Romania.
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