Post-merger integration, particularly in information system integration, requires specialized expertise that is often lacking among practitioners. Existing decision-support methods, such as AMILI (identification of system groups to be integrated) and AMILP (evaluation and recommendation of integration options), address specific integration tasks but face low adoptions due to steep learning curves and limited motivation. This paper proposes an enterprise modeling approach to implement gamebased learning to enhance motivation and support rapid skill acquisition in post-merger contexts. Building on earlier work that defined requirements for such a framework, this study extends the scope to include the broader enterprise context and transformation processes. The proposed approach integrated instructional and game design principles with enterprise modeling and requirements engineering, resulting in a structured process and data model. These models guide practitioners through analysis, design, development, and evaluation of game-based employee learning in the context of mergers and acquisitions.
and validation. In this paper, the authors expand the scope of their proposed approach by considering the broader enterprise context in which the approach is to be applied, as well as the enterprise transformation processes to which it should contribute. Implementing such a framework in realworld enterprise contexts requires a structured process model that guides practitioners through design, implementation and application of the learning experience. This research is grounded in enterprise architecture and enterprise modeling solutions that have been developed for purposes similar to those of the proposed approach.
Accordingly, the paper presents an extended approach framed as an enterprise model for implementing game-based learning in employee training, particularly in the context of mergers and acquisitions. Building on earlier work, where requirements were defined for both the process and the resulting learning experience [3], this research advances the contribution by proposing a process model and a data model of the transformation process as well as of the resulting learning experience. This paper is structured as follows. After the introduction, the second section reviews relevant learning, instructional, game design, and enterprise modeling theories that form the foundation for the implementation model. The third section outlines the methodology applied to construct the enterprise model. The fourth section presents the proposed model and its implementation logic, linking specific requirements to corresponding model components. In the fifth section the model is validated through a simulation exercise. Finally, the sixth section provides conclusions and identifies directions for future research.
Existing research has produced a variety of frameworks and models for serious game design. Earlier educational game design frameworks [4] combine pedagogy and game design but lack explicit procedural detail for aligning instructional goals with concrete game artefacts. The SCHEMA process [5], which was proposed as a more structured model for serious game creation and evaluation, remains high level, focusing on phases and principles rather than specifying concrete artefacts or detailed guidance for practitioners. Other frameworks emphasize primarily the development of the game itself rather than the learning experience [6], [7]. What is still lacking is a structured and detailed process that specifies concrete artefacts and their relationships. There is also a need for a stronger focus on the design of the learning experience, rather than primarily on game production. Finally, an enterprise implementation perspective should be incorporated, aligning organizational and instructional requirements in the broader context.
Enterprise modeling provides tools and structures for formalizing organizational knowledge, processes, and system component. In the context of this research, enterprise modeling serves as a mechanism that systematically integrates learning design with organizational objectives and contextual requirements. It also facilitates traceability from abstract requirements to implementation components, making it suitable foundation for operationalizing training framework in complex domains such as PMI. In developing an enterprise model to support a game-based learning framework for post-merger information system integration, several existing enterprise architecture and enterprise modeling methodologies were reviewed. TOGAF (The Open Group Architecture Framework) [8] proposed structured architecture development method (ADM) was applied for the model development, as well as model structuring. A recent adaptation of TOGAF for e-learning environments demonstrated its relevance to digital education architecture and showcased how ADM stages can be used in structuring learning system transformations within enterprise environments [9]. In this research ADM process stages and ADDIE [10] process phases were mapped together to define framework phases. The Zachman Framework[11] is focused on ontology and classification of enterprise architecture elements, providing different views of it. Zachman Framework can be adjusted specifically for learning domain, mapping pedagogical intentions to questions that an enterprise is seeking to answer [12]. The same principle was used in this research, using interrogatives to define the developed artifacts and supporting the multidimensional representation of learning design process. While TOGAF and Zachman provide high-level structure and classification mechanisms, the detailed modeling layer of the proposed solution is grounded in three enterprise modeling methods: ArchiMate modeling language [13], MEMO (Multi-Perspective Enterprise Modeling) method [14] and the SOM (Semantic Object Model) method [15]. These methods support traceability between business goals, stakeholder needs, operational alignment, technological infrastructure and the broader context. The ArchiMate modeling language is a formal enterprise architecture modeling standard developed by The Open Group and closely aligned with TOGAF. ArchiMate provides a visual notation and conceptual language to describe the relationships between business processes, applications, and technology infrastructure. ArchiMate served as an inspiration for the visual structuring and viewpoint management of the proposed model. ArchiMate uses behavioral, structural, and motivational perspectives, which was used in the model for separation of process and class viewpoints. Additionally, ArchiMate uses color-coded layers and visual distinction between element types, which was used in the proposed model to distinguish different sources which formed the foundation of the model. MEMO supports the decomposition of enterprise goals into operational processes and system elements, which can be related to aligning high-level learning objectives with instructional tasks and technological components. Additionally, MEMO meta-models were used as validation checklists to ensure that the proposed model is conceptually complete and internally coherent. SOM complements MEMO by focusing on modeling of business objects and relationships in the solution context, which inspired how training PMI specific context elements such as content repositories were defined. MEMO and SOM are generic enterprise modeling approaches and do not address learning processes directly. This research builds upon their general principles to introduce a new enterprise model that represents business process aligned with enterprise goals and aware of the context. The resulting solution specifically supports learning design in the PMI context and enhances the alignment between training solutions and enterprise goals.
The enterprise architecture served as the foundation for structuring the overall model, providing high-level alignment between organizational goals, processes and context. To define specific components of the model in detail, requirements engineering practices were applied. The main approach is based on the International Requirements Engineering Board (IREB) framework [16], with additional guidance from the ISO/IEC/IEEE 29148:2018 standard [17].
The instructional design phases of the proposed model are based on the ADDIE (Analysis-DesignDevelopment-Implementation-Evaluation) framework – linear framework for the sequential process from needs analysis to post-implementation evaluation. The SAM (Successive Approximation Model) model is an iterative framework proposing incremental prototyping based on stakeholder feedback [18]. It extends the model by introducing prototyping, iterative cycles, and feedback from stakeholders. The Ten Steps for Complex Learning model is a framework focused on whole-task learning for complex skill development [19]. Backward Design as a goal focused framework starting with identification of desired learning outcomes and only then designing corresponding instructional components [20], ensures that learning outcomes are defined early and guide learning tasks and content development.
The game design section of the model is based on MDA model (Mechanics, Dynamics, Aesthetics) [21], as one of the foundational frameworks in the game design. It decomposes the game experience into three interconnected layers: mechanics (the formal structures and rules of the game), dynamics (run-time behavior that emerges when players interact with game mechanics), and aesthetics (emotional responses in players evoked by dynamics). The model then is extended by DPE model (Design, Play, Experience) [22], which is grounded in the MDA, perceiving design through mechanics, play as dynamics and experience as aesthetics. But this framework for each of layers provides the design elements to consider for serious games – learning, storytelling, gameplay and technology, which ensures that learning objectives align with player experience. DPE as well expands the process by adding definition of the narrative, user interface, and required technology. DDE model (Design, Development, Evaluation) [23] proposes the iterative design process where each iteration sequentially goes through design, development and evaluation. The LM-GM framework [24] is used to systematically map learning mechanics to game mechanics, ensuring educational and engagement elements reinforce each other. The model is adjusted to the post-merger integration context through integration of AMILI and AMILP methods, as well as post-merger integration specific use cases and their characteristics. These supports role-specific responsibilities and organizational context.
The research follows a design science methodology and is based on the prior requirements definition
phase [3]. The objective of the current research phase is to construct an enterprise implementation
model that translates abstract requirements into an executable game-based learning design
framework. The current research phase was structured as a sequence of interconnected process
activities to progressively refine requirements, construct models and validate solutions (see Fig. 1):
(
This research proposes that training is not an isolated function but part of an enterprise-wide
adaptation following a merger. The “enterprise” being modeled consists of the information system
integration training lifecycle, embedded within a broader merger-related transformation initiative.
The enterprise model reflects the ArchiMate layered viewpoint framework and is represented
through three interlinked artifacts: (
MDA - The framework should require definition of game mechanics, prediction of learning dynamics, and intentional design for aesthetics DPE - The framework should support design across four layers: learning goals, narrative, gameplay mechanics, and enabling technology
Extend the process model with a game design section In the “Design” phase define game mechanics, dynamics, aesthetics In the “Design” phase, before the definition of mechanics and the definition of learning goals, and after the mechanics definition, add the definition of storytelling In the “Develop” phase add activities for the definition of the user interface and technology DDE – The framework should support iterative refinement based on evaluation of learning effectiveness and learner engagement
In the “Design” phase, between the definition of learning goals and mechanics, add an activity for defining micro-learning In the “Develop” phase, after the definition of the user interface, add an activity for integration of behavioral nudges LM-GM - The framework should ensure that learning mechanics are effectively mapped to corresponding game mechanics
In the “Design” phase, after the definition of mechanics, add an activity for mapping learning and gamification In the “Develop” phase, after the integration of behavioral nudges, add an activity for integration of additional gamification
AMILI/AMILP theory and practice - The framework should support accurate transformation of AMILI and AMILP methods descriptions into interactive modules for learner training PMI Stakeholder management – The framework should allow adaptation of training on role-specific responsibilities and knowledge levels of future learners Specific PMI challenge management – The framework should support secure transformation of real-world cases, managing confidentiality and adjusting complexity
Provide the “AMILI and AMILP theory repository” for learning instructional goals and learning experience definition, as well as for game learning goals, micro-learning, mechanics, and gamification definition Provide the “AMILI and AMILP content repository” for learning content and media creation, as well as for game user interface and behavioral nudges definition, and additional gamification integration Provide the “PMI context repository” for learner, stakeholder, and desired results definition Provide the “PMI case study repository” for learning task definition and game storytelling definition
The structure of the process model is grounded in the ADDIE instructional design framework
and mapped to the corresponding phases of the TOGAF ADM, as follows (see Fig. 1): (
In the subsequent step, each phase was operationalized as a detailed set of actions. The default
process flow follows the ADDIE framework, however, based on the formulated requirements in
“Solution Requirements for Instructional Design” and “Solution Requirements for Game-Based
Design”, supplementary activities were incorporated from other instructional and game design
models. These integrations are explicitly highlighted in the diagrams with corresponding
annotations. For instance, to address the functional requirement that “The framework should allow
for iterative prototyping and continuous feedback loops with stakeholders”, the following activities
were added: (
The data model was constructed to represent the data objects that are created, utilized or
transformed throughout the design process. To ensure completeness from both instructional and
game design perspectives, its definition was guide by the Zachman framework interrogatives, which
provide a comprehensive set of architectural viewpoints: (
In addition, the class model was aligned with the MEMO meta-model layers to guarantee
conceptual integrity across abstraction levels: (
The proposed enterprise model was validated through a simulation exercise. The simulation was applied to the case of designing a game-based learning experience for teaching the theory of the AMILI method at first two levels of knowledge acquisition – “Remember” and “Understand” according to Bloom’s taxonomy. At these knowledge levels, a special game design approach was selected, as learners lack the skills to apply concepts practically. The game is designed as a linear interactive storytelling experience. Players face clear, generic challenges that are related to, but not directly the same as, the practical tasks of the method. Each successfully completed challenge unlocks the next part of the story, which describes a case example of a university merger. In this way, engagement and learning are balances even for complete novice learners who cannon yet apply knowledge in the subject matter. The validation results are presented as artefacts that would be created during the process execution (see Table 4). These artefacts are aligned with the phases of the proposed model. Merger of two universities, scope limited to one overlapping business function Repository: list of descriptions for different merger and acquisition case studies to be used as the context for the storyline Content Procedural content
Prototypes in Figma reviewed with learner candidates Contains all supportive and procedural content Repository: predefined supportive and procedural content linked from one side to specific instructional goal, from the other side to specific case study Theory on M&A and PMI Theory on AMILI
AMILI terms and phases Supportive content
Not applicable, as in-house self-organised individual training does not require one Media
Theory presented as intro screens of the game. After theory, game starts automatically Learner guidance
Instructions for learners on how learning is expected to happen Teacher guidance
Not applicable as self-learning User interface
Mobile portrait only. Specific screens to constructed later Behavioural nudge
Motivational intro slides about M&A failure rates and importance of IS integration Additional gamification Technology Gameplay Pilot playtest Virtual learning environment Physical learning environment
Players’s tournament with leaderboard shown at the end of the game
GDevelop platform
Player progresses through story describing AMILI terms and phases via tasks not directly related
to AMILI practice. Three rounds: (
Not applicable as learning will be self-paced through online game Prepared teacher
Not applicable as self-learning Prepared learner
Prepared based on created learner instructions Evaluation criteria
10-uestion MS Forms survey on PMI IS integration and AMILI method Evaluation data
Number of correct survey answers Evaluation
Based on survey data. Expected result = 90% accuracy
Based on the validation results, the model proved its applicability for designing game-based
learning, but several improvement options were identified: (
This paper translates the conceptual requirements for a game-based learning framework in
postmerger information system integration into validated enterprise implementation model. The model
bridges enterprise architecture, instructional theory, game design frameworks, and PMI-specific
needs into a structured design process model and data model, supporting repeatable and adaptable
learning solution design. The validation through simulation demonstrated the model’s applicability
for designing recall-level learning experiences, while also revealing areas for improvement:
introducing optional artefacts, making artefact dependencies explicit, and representing artefact
decomposition. While the model is based on well-established frameworks, its novelty lies in how it
orchestrates them into a layered, constraint-aware design model specifically tailored for the
postmerger integration context. The main innovation of this model is how it combines different
frameworks into one clear structure that works for post-merger training. First, it separates the design
into layers – learning design, game design, and real-world context. Second, it uses enterprise
modeling to link training tasks to business goals, as well as to represent the model visually. Third, it
includes reusable content and tools, so the training can be easily adapted for different use case
scenarios. The main practical value of the model is that it provides a clear and detailed process for
practitioners on how to design training for post-merger IS integration. This goes beyond existing
generic game-based learning design approaches, which are mostly conceptual, by giving
step-bystep guidance and concrete artefacts to work with. Future research will focus on developing and
testing this model for real learning experiences, covering also second part of the requirements
initially stated for the learning experience itself. Based on results this enterprise model can be
extended and adjusted to assure required requirements for the learning experience. It is also planned
to implement toolkits for automated content generation and repository management to streamline
learning experience design. In addition, future work will extend this research in two more directions:
(
The authors have not employed any Generative AI tools.