From Assessment to Action: A Decision-Support Methodology for Digital Government Transformation

Abstract

Digital technologies are reshaping the public sector, yet meaningful transformation requires more than technology adoption alone. A persistent gap in the literature is the absence of holistic approaches for assessing the digital government environment and translating assessment results into context-sensitive strategic action. Existing studies often examine isolated dimensions without fully considering the interdependence of human, organizational, governance, technical, financial, and legal factors. Moreover, many planning strategies are driven by digital trends rather than by evidence of governments’ actual readiness. This study addresses this gap by asking: how can governments holistically assess their digital environment and use that assessment to develop context-sensitive digital transformation strategies? In response, the study proposes an integrated decision-support methodology that combines the Digital Government Strategic Assessment (DGSA) and the Digital Government Planning Strategy (DGPS). DGSA evaluates six dimensions of the digital environment and measures readiness levels and the maturity of strategic objectives, while DGPS translates assessment results into targeted strategic actions. A digital platform supports implementation by digitizing assessment and planning and providing analytical capabilities for weighing key pillars. The study offers a holistic approach for evidence-based digital transformation and illustrates its application through a literature-based use case focused on government as a platform in Indonesia.

1. Introduction

Digital transformation has become a major priority for governments seeking to improve public services, administrative efficiency, transparency, and public value. Although the literature offers many approaches for assessing digital readiness, maturity, and performance, these approaches often focus on individual dimensions rather than on the digital government environment as a whole. Moreover, assessment and planning are frequently treated as separate activities, leaving governments without a clear way to translate diagnostic results into context-sensitive strategic action.

This article addresses that gap by proposing a decision-support methodology for digital government transformation. The methodology is designed to help governments first assess the preparedness of their digital environment and then identify targeted actions to improve their capacity to achieve selected strategic objectives. Rather than treating assessment as an end in itself, the proposed approach links multidimensional diagnosis to structured planning.

The methodology combines two complementary components. The first—the Digital Government Strategic Assessment (DGSA)—supports evaluation of the digital government environment through six key dimensions: human, organizational, governance, technical, financial, and legal. It is used to assess preparedness for digital transformation and to estimate the maturity of strategic sub-objectives. The second—the Digital Government Planning Strategy (DGPS)—builds on those assessment results to identify, prioritize, and structure strategic actions aimed at improving readiness and supporting digital transformation.

To operationalize this methodology, the study also presents a supporting digital platform that digitizes the assessment and planning process and provides analytical support for weighting and prioritizing key pillars.

The article makes three main contributions. First, it proposes a holistic assessment methodology that conceptualizes digital government transformation as an interdependent system shaped by human, organizational, governance, technical, financial, and legal conditions. Second, it introduces a planning methodology that translates assessment outputs into targeted strategic actions through incremental improvement, reinforcement, and strengthening mechanisms. Third, it illustrates the operational application of the methodology through a literature-based use case focused on the strategic objective of government as a platform in Indonesia. This use case is intended as a methodological illustration rather than as an empirical evaluation of actual government performance.

By integrating strategic evaluation, planning, and dedicated digital platform, the proposed approach provides a comprehensive methodology for assessing current capabilities, identifying priorities, and planning actionable steps, offering governments a robust toolset for achieving their digital transformation goals.

2. Strategic Approaches in Digital Government

This section explores the foundational strategic approaches in digital transformation, focusing on the development of digital government strategies, the evaluation of governmental initiatives, and the planning processes that guide the successful implementation of these strategies.

2.1. Digital Government Strategy

Strategy generally refers to a long-term framework that defines an organization’s direction, priorities, and resource allocation to achieve its objectives [1,2]. It typically involves a vision, a set of strategic goals, implementation priorities, and mechanisms for monitoring progress [1]. In the public sector, these core elements remain essential, but strategy is also shaped by broader public responsibilities, including accountability, transparency, service delivery, coordination across institutions, and the creation of public value [3].

Digital transformation refers to the integration of digital technologies into organizational processes, decision-making, service delivery, and interactions with stakeholders [4,5]. For governments, it has become increasingly important because it can strengthen administrative efficiency, expand access to services, improve transparency, and foster citizen engagement [6,7]. Yet digital transformation is not simply a matter of adopting new technologies; it also requires a clear strategic direction to ensure that technological change is aligned with institutional priorities and public needs [6].

A digital government strategy provides this direction by framing how digital technologies can be used to support public sector modernization and improve governance and service delivery [7]. To translate this strategy into practice, broad priorities are expressed through strategic objectives (SOs) [8], which may then be further specified into sub-strategic objectives (SSOs) to enable more precise implementation and assessment [9].

The implementation of such a strategy also depends on strategic evaluation and strategic planning [10]. Strategic evaluation examines the extent to which the strategy is being implemented, and its objectives are being achieved [11]. The evidence generated through this process then informs strategic planning by supporting priority setting, resource allocation, and the identification of future actions.

2.2. Government Strategic Evaluation

Strategic evaluation is essential to understanding whether government strategies are being implemented effectively and whether they are achieving their intended objectives. In the public sector, it refers to the measurement and testing of strategic decisions efficiency as well as the effective implementation of business strategy to achieve desired business objectives [12]. The evaluation can take several forms, including audits, performance assessments, policy analysis, and citizen feedback mechanisms [13,14,15,16]. Unlike audits, which usually focus on compliance, procedural regularity, and financial control, or performance evaluations, which often examine the efficiency and outputs of specific programs, strategic evaluation is broader in scope. It is concerned with whether strategic priorities are coherent, implementation processes are effective, and long-term objectives are being achieved. As such, it supports accountability, identifies implementation gaps, and generates evidence for strategic adjustment in public administration. In the context of digital government, strategic evaluation is especially important because transformation depends not only on technological adoption but also on institutional readiness, organizational capacity, governance arrangements, and user engagement.

Accordingly, the evaluation of digital government should not be limited to measuring technological deployment alone. It should also examine how digital technologies are embedded in public sector processes and how they contribute to improved service delivery, efficiency, transparency, and citizen satisfaction. Existing studies have evaluated digital government through indicators such as service quality [17], digital infrastructure robustness [18], cybersecurity readiness [19], digital literacy and accessibility [20], and citizen satisfaction [16].

Several frameworks and benchmarking tools have been developed to evaluate digital government across different contexts and levels of analysis. Examples include the EU eGovernment Benchmark Framework 2023 [21], the United Nations E-Government Survey covering 193 member states [22], and the World Bank GovTech Maturity Index [23]. These initiatives illustrate the diversity of existing evaluation approaches and their value in measuring progress. However, they also show that digital government evaluation is often oriented toward comparative performance measurement rather than toward providing a holistic understanding of the strategic environment that shapes governments’ capacity for digital transformation.

2.3. Government Strategic Planning

Strategic planning refers to a process and framework for relating an organization to its environment, defining its scope and direction, and deciding actions needed to achieve specific goals [24]. It includes the broader political, institutional, economic, social, technological, and legal conditions that shape decision-making and action [1]. Strategic planning is consistently ranked among the five most popular managerial approaches worldwide [2]. It contrasts with operational planning, budget planning, and development planning because it focuses on long-term direction, major goals, and policy priorities, while these other forms of government planning concentrate more on short-term implementation, departmental actions, financial allocation, or sector-specific development.

To fully benefit from the digital revolution, governments need both a digital strategy and a system for evaluating digital transformation. It provides a clear framework for setting priorities, optimizing resource allocation, aligning initiatives with long-term goals, and improving overall efficiency, accountability, and responsiveness to citizens’ needs.

Governments worldwide have adopted various strategic planning approaches to guide their policies and achieve long-term goals effectively; for instance, Ontario’s 2021 “Digital and Data Strategy” (#ON06) and 2017 “Putting Justice Within Reach” (#ON05) that outline several specific strategic objectives for implementing AI and other digital technologies within Ontario’s public sector and justice system [25]. In addition, the Urban Redevelopment Authority (URA) in Singapore, launched the Master Plan 2019 to guide sustainable urban development with strategies translated into detailed plans for execution [26]. On another hand, different research works were led with the aim to support strategic planning in governmental context, such as these dedicated to Smart City Strategy Development and Governance [27,28], e-Health policies [29]. Indeed, smart cities represent a place-based and urban expression of these agendas, integrating digital transformation and digital government with urban infrastructure, governance, and citizen services to address city-level challenges [4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]. In this sense, smart cities can be seen as a practical domain where digital transformation and digital government are implemented at the city level.

2.4. Research Gap in Digital Government Assessment and Planning

Recent frameworks for evaluating digital government have broadened the analytical scope of the field, but they continue to reflect a fragmented understanding of the conditions that shape digital transformation. Valdés et al. [31] developed an e-government maturity model that allows public agencies to assess their level of readiness integrating the assessment of technological, organizational, operational, and human capital capabilities, but its focus remains primarily on maturity progression within agencies rather than on the full strategic environment surrounding digital government. Malodia et al. [32] conceptualize e-government as a multidimensional construct structured around empowered citizenship, hyper-integrated networks, and evolutionary system architecture, but their model is primarily explanatory rather than designed as a comprehensive assessment instrument.

Darusalam et al. [33] assess the impact of digitalization on the quality of governance, whereas Zhu et al. [34] evaluate digital government through service quantity and quality performance measures; both frameworks provide valuable insights, although they emphasize results more than the institutional and structural conditions that make such results possible. In addition, some influential stage-based frameworks, such as Janowski’s [30] Digital Government Evolution model, project progression toward advanced forms of transformation, engagement, and contextualization that may be difficult to operationalize in many developing-countries where infrastructure, institutional capacity, and resources remain constrained.

Taken together, these studies show that the literature has made important progress in evaluating digital government, but two gaps remain. First, there is still a lack of sufficiently holistic approaches that assess the digital government environment as an interdependent system shaped by human, organizational, governance, technical, financial, and legal factors.

Second, existing methods rarely provide a structured mechanism for translating assessment findings into targeted and context-sensitive strategic interventions. This study positions an integrated decision-support methodology that combines the DGSA and DGPS components in response to these gaps by combining a multidimensional assessment of the digital government environment with a planning process that links diagnostic results to strategic action.

3. Digital Government Strategic Assessment

The Digital Government Strategic Assessment (DGSA) is a comprehensive approach designed to help governments evaluate and enhance their digital capabilities.

3.1. Digital Government Environment Dimensions

Digital Government Environment embraces regulatory, financial, and technical dimensions, real forces that are shaping any digital transformation strategy. Their materialization is also influenced by governance, organizational and cultural environments specific to each government and its bodies. The analysis and assessment of this ecosystem’s components can strongly help to identify and to deal with all barriers encountered when developing d-government initiatives. The six dimensions and their factors are:

• Human: Refers to the emphasis on the human factor in the implementation of digital technologies within government systems, focusing on the impact on individuals and their role in the adoption and use of these technologies. It targets three main objectives: (i) to improve the society digital readiness level [35]; (ii) to incorporate cultural considerations into the deployment of digital services [36]; and (iii) to enable a digitally savvy and innovative public sector workforce [37];
• Organizational: focuses on enhancing governmental processes to improve productivity, reduce costs, and meet citizens’ expectations for personalized public services. It emphasizes modernizing public administrations while targeting [38,39]: (i) to simplify and harmonize administrative procedures; (ii) define clearly internal roles and responsibilities; and (iii) empower public Institutions with Autonomous IT Acquisition Policies;
• Governance: Refers to a government’s capacity to establish clear decision-making arrangements, assign roles and responsibilities, ensure accountability, and foster collaboration among stakeholders to achieve defined strategic objectives. It aims to: (i) establish a Clear Governance Structure [40]; (ii) enhance collaboration among Stakeholders [41]; and (iii) Strengthen the political leadership [42];
• Legal: focuses on developing and ensuring the consistency of laws and decrees to align with real needs, enabling the provision of online public services that are legal, secure, and compliant. The pillars necessary for this dimension are [43,44]: (i) ensure citizen protection; (ii) establish a legal Framework for Coverage; (iii) enhance IT Regulation Quality; and (iv) strengthen intellectual Property protection;
• Technical: encompasses the technical requirements tied to the IT maturity of public entities, particularly their capacity to utilize foundational and emerging technologies effectively. Its goals are to: (i) address the persistence of a heavy information systems legacy [45]; (ii) improve infrastructure Access sharing capacities; (iii) support converged networks and services support; and (iv) embrace disruptive IT adoption (SMAC, IA);
• Financial: pertains to the budgets needed to acquire or develop digital government systems and the administration’s ability to ensure long-term sustainability. It focuses on four key objectives [46,47,48]: (i) Manage digital Government initiatives funding; (ii) enhance Investment Promotion; (iii) Establish awards schemes; (iv) Manage financial aspects of Public Procurement.

3.2. Digital Government Key Pillars

Based on recent digital trends and technological recommendations issued by international organizations, several key technologies have been identified that governments should adopt to successfully achieve digital transformation [49].

Beyond these digital trends, the selection of key pillars must be aligned with the government’s strategic objectives. Digital transformation should not be driven solely by technological innovation, but by clearly defined public policy priorities.

Examples of such pillars include interoperability for cross-agency coordination [50], digital identity for secure service access and trusted digital transactions [51], emerging technologies for innovative public services and technological modernization [52], data management for data quality assurance, evidence-based policymaking [53], multi-channel digital services for broad service accessibility and digital engagement [54], and one-stop-shop portals for centralized service delivery and simplified user access [55].

3.3. Proposed Methodological Approach for Digital Government Assessment

The methodological approach for the Digital Government Assessment is grounded in established scientific methods commonly used in public sector digital transformation research. It draws on three complementary methodological pillars: (i) the definition of strategic objectives and the identification of key pillars based on recognized approaches in digital government assessment, such as Aristovnik et al. [56], Gong et al. [57], Janowski [30], and Gil-Garcia [58]; (ii) the evaluation of maturity levels using the maturity model developed by Valdés et al. [31]; and (iii) a hybrid qualitative–quantitative approach aligned with methodologies used in major digital government assessment tools and indices, including the OECD Digital Government Policy Framework [59], the United Nations [22] E-Government Survey, and the World Bank’s [60] digital economy for Africa.

The methodological approach proposed to define digital government assessment strategy is based on the following steps:

• SOs and SSOs Identification, based on needs identified following the environment assessment. An SO can respond to the administration and/or the customer needs. Each SO is broken down into several SSOs that represent measurable steps necessary for achieving the overall SO;
• Dimensions and Key pillars setting, Definition of the six dimensions along with their corresponding factors, followed by the selection of key pillars based on IT trends and recommendations;
• KP importance Evaluation with Weights Fixation, consisting in defining how much a KP contributes to reach a specific SO. These weights can be identified based on quantitative analytical research of specialized and proven reports and best practices in the Government digital transformation domain, and calculated according to the importance of each KP and the benefits that are derived from its use;
• Environment Readiness Level Evaluation, achieved through the study of the digital environment based on the six key dimensions (governance, technical, legal, organizational, human and financial). Each dimension is broken down into key factors, whose readiness level indicates each factor capacity to support the KPs implementation. The readiness levels are defined in Appendix A.

As shown below in Equations (1) and (2), the calculation of each KP Preparedness Level (PL) relies on the public agencies’ maturity model described in [31,61,62]. The readiness level reflects the current condition of the assessed environment, while the preparedness level indicates its ability to support and leverage a specific Key Pillar.

Value(dim_jk) = (∑ RL_Current(Factor_jkp)/NF_k)

(1)

where

NF_k: Number of Factors composing the dimension_k

RL_Current(Factor_jkp): Readiness Level of the Factor_p, composing the dimension_jk, to make the KP_j better leveraged.

RL_jkp ∈{1,to 5}, and it is estimated through a specific questionnaire.

PL_Current(KP_j) = (∑ Value(dim_jk))/ND_DGE

(2)

where

PL_Current(KP_j): Preparedness Level to leverage a KP

ND_DGE: Number of DGE dimension

• Maturity Levels Identification, describing the maturity of the environment in achieving SSO, providing valuable insight into the progress of digital transformation in government. Its calculation, shown in (Equation (3)) is based on both the KP weight and the environment Readiness Level Evaluation [31].

AI_Current(SSO_i) = ∑ (Weight_ij × PL_Current(KP_j))

(3)

where

AI_Current(SSO_i): The current Achievement Indicator of an SSO indicates the degree of KPs’ environment preparedness to contribute to achieve SSO’s achievement. The definition of the five maturity levels is listed in

Table 1. Maturity levels for sub-strategic objectives definition.

Maturity Level	AI (SSO)	Significance: The Environment Necessary to Achieve the SSO Is
0—Initial	[0,1[	limited, and its development process is unpredictable
1—Developing	[1,2[	implemented by dedicated projects, and its development process is frequently reactive
2—Defined	[2,3[	implemented through planned projects, and its development process is more proactive
3—Managed	[3,4[	implemented by proactive and regularly measured strategic action plans and policies
4—Integrated	[4,5[	implemented by dedicated measured and continually improved

inspired by [31].

Σ Weight_ij = 1 and: Achievements thus evaluated depict the maturity degree of each SSO.

3.4. Evaluation of KP Importance and Weight Assignment

Within the DGSA component, the assignment of Key Pillar (KP) weights is a critical step for calculating the achievement indicators of the selected Sub-Strategic Objectives (SSOs). To improve methodological rigor, the weighting process was based on a structured qualitative content analysis of the scientific literature and selected institutional reports using the Dedoose 10.0.59 data analysis platform. The purpose of this analysis was to identify, in a systematic and transparent manner, how frequently and in what contexts specific KPs were associated with particular SSOs in the literature.

The document corpus was established through a purposeful selection strategy. Only documents directly relevant to digital government transformation, government as a platform, interoperability, digital identity, data management, emerging technologies, multi-channel digital services, and one-stop-shop service delivery were retained. Priority was given to peer-reviewed journal articles, complemented where necessary by high-quality reports from recognized international organizations in order to capture both conceptual and policy-oriented perspectives. Documents were excluded if they were not substantially related to the selected strategic objective or if they did not provide meaningful evidence on the relationship between KPs and SSOs.

After the corpus had been finalized, a coding protocol was developed in Dedoose 10.0.59 A hierarchical code tree was created containing the predefined categories of Key Pillars and Sub-Strategic Objectives. The coding process was theory-driven, as the codes were derived from the conceptual structure of the DGSA component rather than generated inductively. Relevant excerpts were then coded when they explicitly or implicitly described a contribution of a given KP to one or more SSOs. To improve coding consistency, the coding followed predefined decision rules specifying when a passage should be linked to a code, how overlapping meanings should be handled, and how ambiguous excerpts should be treated.

The weights were subsequently derived from the code co-occurrence matrix generated in Dedoose 10.0.59. This matrix records the frequency with which a KP code and an SSO code were assigned to the same excerpt. The assumption underlying this approach is that repeated co-occurrence in the literature reflects a stronger analytical relationship between the corresponding KP and SSO. The raw co-occurrence frequencies were then normalized within each SSO so that the sum of all KP weights for a given SSO equaled 1. This normalization step ensured comparability across SSOs and enabled the resulting coefficients to be used directly in the achievement indicator calculation.

To strengthen reliability, the coding procedure was conducted using a standardized codebook defining each code and its scope of application. In addition, coded excerpts were reviewed iteratively to verify internal consistency and reduce interpretive drift across documents. Where excerpts appeared ambiguous or potentially associated with multiple codes, coding decisions were refined through repeated comparison with the code definitions and with previously coded passages. Thus, Dedoose 10.0.59 was used not as a subjective scoring tool, but as a systematic evidence-consolidation instrument supporting transparent, traceable, and reproducible weight generation from the literature.

4. Digital Government Planning Strategy (DGPS) and the Global Conceptual Model

Digital Government Planning Strategy (DGPS) builds directly upon the insights gained from the Digital government Strategic Assessment component, serving as a strategic roadmap for future actions within the digital government framework.

4.1. Conceptual Structure of DGSA and DGPS

The class diagram presented in Figure 1 illustrates the overall structure of the DGSA and DGPS components.

Within the DGSA, the main interconnected classes include Strategy, Strategic Objectives and Strategic Sub-Objectives, Dimensions with their associated Factors and Assessments, and finally Key Pillars. These elements collectively form a structured architecture for strategic evaluation.

Within the DGPS, the core interconnected classes are Actions and Programs. Actions are directly linked to the Assessment class, where the RL_Target is defined as an evolution of the RL_Current, through improvement, reinforcement, or strengthening initiatives.

The multiplicities in the class diagram define the number of instances that can participate in each relationship. The notation 1..* indicates that one instance of a class must be associated with one or more instances of another class, while 0..* indicates an optional relationship in which an instance may be associated with zero or more instances. For example, a Program is linked to one or more Actions, and a Strategic Sub-Objective may be related to zero or more Key Pillars. Similarly, the notation 1..n indicates that a relationship must include at least one instance and can extend up to a defined maximum number n. These multiplicities clarify the structural constraints between the DGSA and DGPS components and ensure consistency in the model.

Each Dimension may be decomposed into one or more Factors, which are evaluated using two primary attributes:

• RL_Current represents the current readiness level, determined through an assessment of the existing institutional environment;
• RL_Target represents the desired readiness level that the government aims to achieve. Within the DGPS component, RL_Target is determined by increasing the current readiness level (RL_Current) by +1 (Improve), +2 (Reinforce), or +3 (Strengthen).

From these readiness levels, preparedness indicators are derived using Equations (1) and (2):

• PL_Current reflects the current preparedness level and is calculated based on RL_Current;
• PL_Target reflects the targeted preparedness level and is calculated based on RL_Target.

Finally, performance is captured through achievement indicators using Equation (3):

• AI_Current represents the current achievement indicator, computed from PL_Current and RL_Current;
• AI_Target represents the intended achievement indicator, derived from PL_Target and RL_Target.

4.2. Digital Government Planning Strategy (DGPS)

The DGPS involves the formulation of a comprehensive program comprising specific actions tailored to meet the identified needs of the government. Strategic actions in the digital government context are carefully designed interventions intended to improve the readiness of key factors essential for the successful implementation and adoption of foundational technologies.

Within the DGPS, each action is systematically categorized and defined to enhance, reinforce, or strengthen the existing readiness levels of a dimension’s factors. The component is organized as follows:

• Improve (+1): Actions designated to ‘improve’ are focused on making incremental enhancements that address immediate inefficiencies or shortcomings. These are typically quick wins that slightly boost the readiness level, making the environment more conducive to technological adoption without extensive overhauls;
• Reinforce (+2): The ‘reinforce’ actions go a step further by building on the existing strengths or addressing deeper issues that require more substantial changes. These actions are designed to make more significant modifications that fortify the readiness of the factors, ensuring that they are more robust and capable of supporting digital initiatives;
• Strengthen (+3): At the highest level, ‘strengthen’ actions are transformative, aiming to solidify and expand the capabilities of the factors extensively. This category involves strategic, long-term initiatives that fundamentally enhance the overall structure, functionality, and resilience of the digital government framework.

By classifying strategic actions into these three levels, governments can systematically build a program that addresses the readiness of each factor according to its current state and the strategic objectives of the digital transformation agenda.

The effectiveness of the government program’s implementation is assessed by evaluating the achievement of its target indicators, its subprograms’ implementation, and the various activities undertaken within the program during the reporting year [63].

4.3. Digital Platform for DGSA and DGPS Components

An experimental platform has been developed as a proof-of-concept to operationalize the DGSA and DGPS components. Implemented using Node.js (JavaScript) and a MySQL database, the platform serves a dual purpose. First, it enables users to assess the digital government environment by automatically generating the achievement indicator for each sub-strategic objective within the digital strategy. This is calculated based on the weighted average of the preparedness levels for each Key Pillar (KP). Second, the platform empowers users to define future actions by leveraging the readiness levels assessed through the DGSA, facilitating the creation of a comprehensive digital transformation program.

The platform includes several modules that users must populate to evaluate the current maturity level and outline future actions effectively. These modules are designed to support systematic data entry, analysis, and strategic planning, ensuring a robust and user-friendly experience for digital government transformation.

5. DGSA and DGPS Applied to Government as a Platform Strategic Objective in Indonesia

This use case is presented for the purpose of illustrating how the DGSA and DGPS may be applied in the context of a particular country. The findings do not constitute an empirical evaluation of the country’s actual digital government performance. Rather, the values and assessments presented are based on evidence drawn from the scientific literature and are used here as a methodological illustration, not as a direct representation of real-world conditions.

5.1. DGSA Applied to Government as a Platform Strategic Objective in Indonesia

The first step of the application consists of using the DGSA to assess Indonesia’s current level of preparedness for achieving the strategic objective of Government as a Platform, by identifying the relevant sub-strategic objectives, weighting the key pillars, and evaluating the readiness of the enabling factors across the six dimensions.

5.1.1. Government as a Platform as DGSA Strategic Objective

The strategic objective of government as a platform can be divided into three sub-strategic objectives as detailed in

Table 2. Sub-Strategic Objectives of Government as a Platform.

Strategic Objective	Strategic Sub-Objective	Definition
Government as a platform	Shared infrastructure	The development of common digital assets and reusable components that support standardized and scalable service delivery across government.
	Cross-agency service integration	The ability of public institutions to connect systems, exchange data, and integrate workflows across organizational boundaries.
	Collaborative government	The involvement of citizens, businesses, civil society, and other stakeholders in the design, delivery, and improvement of digital public services

: (i) developing shared digital building blocks and reusable components, (ii) enabling interoperability and cross-agency service integration, and (iii) fostering co-creation and multi-actor participation in public service delivery. This division is consistent with the recent scientific literature that conceptualizes digital government as an integrated system built on common technological architecture, connected organizational networks, and collaborative value creation. In particular, the platform perspective emphasizes the role of shared infrastructures and intergovernmental participation, while broader digital government research highlights hyper-integrated networks and evolutionary system architectures as key enablers of transformation [32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64]. Likewise, the interoperability literature shows that cross-agency coordination and data exchange are foundational conditions for effective digital public services, and co-production research demonstrates that the participation of citizens and external stakeholders contributes directly to public value creation in digital transformation processes [50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65].

The table structure above is grounded in peer-reviewed work on integrated digital government, interoperability, and co-production in public administration. Malodia et al. [32] identify “hyper-integrated network” and “evolutionary system architecture” as core dimensions of future e-government; Campmas et al. [50] show the importance of interoperability frameworks for digital public services; Scupola and Mergel [65] demonstrate how co-production supports digital transformation and public value creation; and Styrin et al. [64] analyze government as a platform through intergovernmental participation on a national digital service platform.

5.1.2. KP Importance Evaluation with Weights Fixation

The weights attributed to the key pillars (KPs) indicate their relative contribution to the implementation of the sub-strategic objectives (SSOs) of government as a platform. This weighting outlined in

Table 3. Literature-informed weights of key pillars for achieving the sub-strategic objectives of Government as a Platform using Dedoose 10.0.59.

Strategic Objective	Strategic Sub-Objective	Interoperability	Digital Identity	Emerging Technologies	Data Management	Multi-Channel Digital Services	One-Stop-Shop Portals
Government as a platform	Shared infrastructure	0.247	0.213	0.114	0.131	0.152	0.143
	Cross-agency service integration	0.315	0.182	0.106	0.205	0.074	0.118
	Collaborative government	0.127	0.124	0.133	0.136	0.242	0.238

was informed by a review of scientific studies addressing the relationships between platform-based government and the key pillars identified in Section 3.2: interoperability, digital identity, emerging technologies, data management, multi-channel digital services, and one-stop-shop portals. The reviewed literature emphasizes interoperability and service architecture as critical enablers of integrated public service delivery [50,66,67], identifies data governance and digital identity as foundational elements of trusted and accessible digital government [68,69], and highlights one-stop-shop models, co-production, and platform governance as central mechanisms for collaboration and citizen-oriented service provision [64,65,70,71]. Furthermore, the inclusion of emerging technologies is supported by broader studies on digital government transformation and innovation in the public sector [32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72]. The weights shown in the table below were produced using the Dedoose 10.0.59 data analytics tool and are based on the corresponding co-occurrence matrix, which records the frequency with which each pair of codes (KP and SSO) was assigned to the same excerpt.

5.1.3. Indonesia Environment Readiness and Maturity Level Identification Using DGSA

The readiness scores presented in

Table 4. Literature-informed Indonesian current preparedness levels calculation.

Environment Dimension	Factors	Interoperability	Digital Identity	Emerging Technologies	Data Management	Multi-Channel Digital Services	One-Stop-Shop Portals
Organizational	Institutional autonomy in IT acquisition	3	3	3	3	3	3
	Role and responsibility clarity	3	3	3	4	3	3
	Administrative simplification	3	3	3	3	4	4
Organizational Value		3	3	3	3.33	3.33	3.33
Human	Innovation-oriented workforce	3	3	4	3	3	3
	Cultural alignment and acceptance	3	3	3	3	3	3
	Citizen digital readiness	3	3	3	3	4	4
Human Value		3	3	3.33	3	3.33	3.33
Financial	Funding for digital government initiatives	4	4	3	4	4	4
	Investment promotion capacity	3	3	4	3	3	3
	Incentive and award schemes	3	3	3	3	3	3
	Public procurement financial management	3	3	3	3	3	3
Financial Value		3.25	3.25	3.25	3.25	3.25	3.25
Governance	Governance structure clarity	4	4	3	4	4	4
	Stakeholder collaboration	4	3	3	4	4	4
	Accountability and coordination mechanisms	3	3	3	4	3	3
Governance Value		3.67	3.33	3	4	3.67	3.67
Technical	Legacy systems management	3	3	3	3	3	3
	Infrastructure sharing capacity	4	3	3	4	4	4
	Converged networks and services	3	3	3	4	4	4
	Adoption of disruptive technologies	3	3	4	3	3	3
Technical Value		3.25	3	3.25	3.5	3.5	3.5
Legal	Citizen protection and rights	3	3	3	3	4	3
	Coverage and adequacy of the legal framework	4	4	3	4	4	4
	Quality of IT regulation	3	3	3	3	3	3
	Intellectual property protection	3	3	4	3	3	3
Legal Value		3.25	3.25	3.25	3.25	3.5	3.25
PL_Current (KP)		3.24	3.14	3.18	3.39	3.43	3.39

are literature-informed analytical assessments based on studies of Indonesia’s digital government development [33,73,74,75,76,77]. They reflect the relative maturity of enabling factors across key pillars and should be interpreted as qualitative judgments rather than audited measurements. The predominance of Levels 3 and 4 is consistent with Indonesia’s intermediate stage of digital government development, characterized by active reform efforts alongside persistent coordination, capability, and integration challenges. The value of each dimension is calculated based on Equation (1) and the preparedness level of each KP is calculated based on Equation (2).

Indonesia currently occupies an intermediate readiness position for the implementation of a government-as-a-platform model. This assessment is consistent with recent studies showing that, although national digital government reforms have substantially progressed, important challenges remain in interoperability, application integration, data governance, institutional coordination, and local implementation capacity. In practical terms, these reforms have been translated into major national initiatives such as the Electronic-Based Government System (SPBE), which is used as the main framework for digital government transformation and is evaluated annually across central and local institutions, as well as One Data Indonesia, which was introduced to improve data standards, interoperability, and cross-government data sharing. More recently, the government has also launched INA Digital/GovTech Indonesia to integrate digital public services and reduce fragmentation across thousands of public-sector applications, while MPP Digital (Public Service Mall Digital) has been developed to expand integrated and multi-channel access to public services. These initiatives show that Indonesia has moved beyond isolated pilots and has established strategic actions in several areas, but the literature and official evaluations also indicate that such actions are not yet uniformly measured, updated, and continuously improved across all dimensions and institutions [33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73]. Research on citizen adoption and regional digital inequalities further supports moderate readiness levels in the human dimension, particularly regarding digital readiness and socio-cultural inclusion [75,76]. At the same time, studies on integrated public service delivery indicate stronger progress in multi-channel services and one-stop-shop arrangements, especially through the expansion of public service malls and their digitalization, although these remain under consolidation rather than at full maturity [74]. Overall, the literature supports the conclusion that Indonesia is characterized by active digital transformation with persistent structural and implementation gaps, which justifies the concentration of readiness scores in the medium-to-high range rather than at the highest level.

5.1.4. Government as a Platform SSOs Maturity Levels Identification in Indonesia

Table 2 presents the weights generated via Dedoose 10.0.59, while Table 3 provides the Preparedness Levels values. Based on these inputs and using Equation (3), the achievement indicator of each SSO has been calculated. The resulting maturity levels of Government as a Platform SSOs in Indonesia are presented in

Table 5. Literature-informed Indonesian current Maturity levels calculation.

Strategic Objective	Strategic Sub-Objective	AI_Current	Maturity Level
Government as a platform	Shared infrastructure	3.28	3—Managed
	Cross-agency service integration	3.28	3—Managed
	Collaborative government	3.32	3—Managed

.

In the Indonesian context, these results suggest that Government as a Platform is being implemented at a managed, but not yet optimized, level of maturity. It also indicates that Indonesia has made comparable progress in building the basic institutional and technical foundations of platform government and in connecting services across agencies. This is visible in national initiatives such as SPBE as the overarching digital-government framework, One Data Indonesia for data standards and interoperability, and INA Digital as an integrated GovTech platform intended to reduce fragmentation and support unified public services. At the same time, the fact that these scores remain in the managed range rather than a higher maturity level reflects the continuing challenges reported in the literature, including fragmented applications, uneven local implementation capacity, and incomplete integration across institutions. Overall, the findings portray Indonesia as a country that has moved beyond isolated digital projects and established structured reforms, but is still in a consolidation phase in which shared infrastructure, service integration, and collaboration are being actively managed rather than continuously optimized and improved.

5.2. DGPS Applied to Government as a Platform in Indonesia

In the case of Indonesia, and based on the findings reported in Section 5.1, current readiness levels can be subject to three types of intervention: improvement (+1), reinforcement (+2), or strengthening (+3). These interventions may be applied iteratively until the recalculated preparedness levels and achievement indicators attain the targeted maturity levels. Accordingly, the DGPS uses the outputs of the DGSA as an operational basis for identifying the areas of the Indonesian environment that require priority action. The actions derived from this process form a strategic program of intervention aimed at enhancing digital government readiness and supporting progress toward a higher maturity stage.

Table 6. Example of Indonesia’s targeted preparedness levels calculation.

Environment Dimension	Action	Interoperability	Digital Identity	Emerging Technologies	Data Management	Multi-Channel Digital Services	One-Stop-Shop Portals
Organizational	Reinforce Institutional autonomy in IT acquisition	5	5	5	5	5	5
	Improve Role and responsibility clarity	4	4	4	5	4	4
	Improve Administrative simplification	4	4	4	4	5	5
Organizational Target Value		4.33	4.33	4.33	4.67	4.67	4.67
Human	Improve Innovation-oriented workforce	4	4	5	4	4	4
	Reinforce Cultural alignment and acceptance	5	5	5	5	5	5
	Improve Citizen digital readiness	4	4	4	4	5	5
Human Target Value		4.33	4.33	4.67	4.33	4.67	4.67
Financial	Improve Funding for digital government initiatives	5	5	4	5	5	5
	Improve Investment promotion capacity	4	4	5	4	4	4
	Reinforce Incentive and award schemes	5	5	5	5	5	5
	Reinforce Public procurement financial management	5	5	5	5	5	5
Financial Target Value		4.75	4.75	4.75	4.75	4.75	4.75
Technical	Reinforce Legacy systems management	5	5	5	5	5	5
	Improve Infrastructure sharing capacity	5	4	4	5	5	5
	Improve Converged networks and services	4	4	4	5	5	5
	Improve Adoption of disruptive technologies	4	4	5	4	4	4
Technical Target Value		4.5	4.25	4.5	4.75	4.75	4.75
Governance	Governance Value	3.67	3.33	3	4	3.67	3.67
Legal	Legal Value	3.25	3.25	3.25	3.25	3.5	3.25
PL_Target (KP)		4.14	4.14	4.14	4.14	4.14	4.14

provides an illustrative example of improved readiness levels that may contribute to the attainment of a higher maturity level. In this case, target readiness levels were defined for the technical, financial, human, and governance dimensions.

By way of illustration, within the Human dimension, factors with a current readiness level of 3 may be selected for reinforcement (+2), while those already at level 4 may be selected for improvement (+1). Under this logic, the DGPS would recommend the following actions: improving the innovation-oriented workforce, reinforcing cultural alignment and acceptance, and improving citizen digital readiness.

Based on the recalculated targeted preparedness levels, the corresponding target achievement indicators are also derived using Equation (3), which are presented in

Table 7. The targeted Maturity Levels Of Government as a platform SSOs In Indonesia.

Strategic Objective	Strategic Sub-Objective	AI_Target	Maturity Level
Government as a platform	Shared infrastructure	4.18	4—Integrated
	Cross-agency service integration	4.18	4—Integrated
	Collaborative government	4.22	4—Integrated

. This allows the government to more directly identify the actions required to attain the desired maturity level.

6. Discussion

The findings of this study provide two main insights into the current state of digital government assessment and planning.

First, this study shows how a holistic approach is capable of assessing the digital government environment as an interdependent system shaped by human, organizational, governance, technical, financial, and legal factors. Second, the main contribution of this study lies in demonstrating how the proposed methodological approach integrates assessment and planning into a coherent decision-support. Rather than treating digital maturity evaluation as an end in itself, the study shows that the Digital Government Strategic Assessment (DGSA) can generate actionable evidence that directly informs the Digital Government Planning Strategy (DGPS). This is an important result because it addresses a common weakness in digital government practice, where assessments are often conducted without a clear mechanism for translating findings into implementable strategies and programs.

The first result is particularly significant because it reinforces the view that digital government transformation cannot be adequately understood through narrow or isolated assessment dimensions. In many existing approaches, emphasis is placed on technological infrastructure or service digitization, while other enabling or constraining conditions receive less systematic attention. However, the results of this study suggest that digital government environments are inherently multidimensional and that the effectiveness of transformation depends on the interaction among institutional capacity, governance arrangements, regulatory conditions, financial readiness, technical infrastructure, and human capabilities. This means that assessment models examining these factors separately can fail to capture the actual complexity of digital transformation processes. Therefore, this study contributes to the digital government literature by emphasizing the interdependence of these dimensions rather than treating them as independent variables. Furthermore, the proposed approach is not limited to the descriptive examination of these dimensions, but uses the maturity model of Valdés et al. [31] to calculate their preparedness and maturity levels. Indeed, its originality lies in its integration of needs-based strategic objectives, weighted key pillars, multidimensional readiness assessment, and maturity-level calculation into a single integrated decision-support methodology.

The second main result of the study is equally important. The findings show that even when assessment tools identify relevant weaknesses or priority areas, they often stop at diagnosis and do not provide a clear method for converting those findings into strategic action. This is a critical limitation in the existing literature on digital assessment and planning, because assessment without a structured planning pathway reduces its practical value. In practice, governments need more than a description of their current condition; they need guidance on how to respond in ways that are targeted, feasible, and adapted to their specific context.

The Indonesia use case helps to illustrate this contribution. Although it is not presented as an empirical evaluation of actual government performance, it shows how the methodology can be applied to a specific strategic objective and how literature-based evidence can be translated into a structured assessment and planning process. The case also suggests that digital transformation may be progressing in a meaningful way while still remaining uneven across dimensions and key pillars. This highlights the practical relevance of combining assessment with planning, especially in contexts where reforms are advancing but institutional, organizational, and human-capacity constraints continue to limit overall maturity.

Future research can build on this study in several directions. Comparative applications of the DGSA and DGPS across different countries, sectors, or administrative levels would help to evaluate their transferability and contextual adaptability. Longitudinal studies could examine whether the integration of holistic assessment and strategic planning leads to better implementation outcomes, stronger institutional alignment, or improved digital public service performance over time. Future research could also use the weights obtained through Dedoose 10.0.59 as training data for an AI-based component, using deep learning techniques to model relationships between sub-strategic objectives and their associated key pillars. Such a component could support the dynamic recalculation of weights, the addition of new SSOs, and the identification of structural links between related objectives. By learning recurring patterns in weight distributions, this module could improve the efficiency and adaptability of the methodology, particularly in contexts involving a large number of strategic objectives and evolving planning requirements.

7. Conclusions

This study proposed an integrated decision-support methodology for digital government transformation that combines the Digital Government Strategic Assessment (DGSA) and the Digital Government Planning Strategy (DGPS). The proposed methodology responds to an important need in the digital government literature by offering both a more holistic way of assessing the digital government environment and a structured way of translating assessment results into strategic action. A class diagram was also presented to clarify the relationships among the main components of the methodology, and a supporting digital platform was introduced to facilitate its operational use.

The value of this contribution lies in its ability to connect multidimensional assessment with a planning logic oriented toward action. By combining readiness assessment, preparedness-level calculation, maturity evaluation, and targeted intervention design, the methodology provides a coherent basis for supporting digital transformation in government. In this respect, it moves beyond descriptive diagnosis by showing how assessment outputs can be used to guide strategic choices and prioritize reform efforts.

The Indonesia use case illustrated how the proposed methodology can be applied to a specific strategic objective—namely, government as a platform—using evidence drawn from the scientific literature. The case does not constitute an empirical validation of Indonesia’s actual digital government performance; rather, it serves as a methodological demonstration of how the decision-support approach can be operationalized in practice. This distinction is important, as the study’s primary contribution lies in the design and illustration of the methodology rather than in the production of audited country-level findings.

Overall, this research highlights that digital government transformation requires not only an understanding of the current environment, but also a structured mechanism for converting that understanding into context-sensitive action. The proposed decision-support methodology offers such a mechanism and may therefore serve as a useful foundation for more systematic, evidence-based, and actionable digital government development. Future research should further strengthen this contribution through empirical validation, expert-based assessment, and comparative applications across countries, sectors, or administrative levels.