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Article

Digital Accounting and Financial Performance of MSMEs in Indonesia: The Mediating Role of Digital Innovation

by
Maryanti
1,
Mediaty
2,*,
Andi Harmoko Arifin
3 and
Anis Anshari Mas’ud
4
1
Faculty of Economics, Andalas University, Padang 25163, Indonesia
2
Faculty of Economics and Business, Hasanuddin University, Makassar 90245, Indonesia
3
Faculty of Economics, Terbuka University, Tanggerang 15437, Indonesia
4
Faculty of Economics, Universitas Sulawesi Barat, Majene 91412, Indonesia
*
Author to whom correspondence should be addressed.
Int. J. Financial Stud. 2026, 14(3), 66; https://doi.org/10.3390/ijfs14030066
Submission received: 10 January 2026 / Revised: 4 February 2026 / Accepted: 24 February 2026 / Published: 4 March 2026
(This article belongs to the Special Issue Accounting and Financial/Non-financial Reporting Developments)
Versions Notes

Abstract

This study investigates the determinants of financial performance among Micro, Small, and Medium Enterprises (MSMEs) in Indonesia, addressing the critical issues of low accountability and limited access to capital. Grounded in the Resource-Based View and Dynamic Capabilities Theory, the research examines the impact of accounting information systems, management knowledge capability, and digital platform capability on financial performance, mediated by digital innovation. A quantitative approach was employed, utilizing a cluster random sampling survey of 403 MSME owners across Indonesia’s major islands. Data were analyzed using Structural Equation Modeling (SEM) with AMOS software. The results reveal that accounting information systems, management knowledge capability, and digital platforms significantly enhance financial performance. Notably, digital platform capability emerged as the most potent driver. Furthermore, digital innovation proved to be a vital mediator, transforming management knowledge and platform capabilities into tangible financial outcomes. The study concludes that while digital tools provide essential infrastructure, innovation serves as the critical mechanism for unlocking value. These findings suggest that MSMEs must transition from passive technology adoption to active digital innovation to achieve sustainable financial success in the digital economy.

1. Introduction

The contemporary global economic landscape is undergoing a fundamental and permanent transformation, shifting from conventional models centered on physical assets toward a digitized environment driven by intangible, knowledge-based value creation. In this era characterized by volatility, uncertainty, complexity, and ambiguity (VUCA), the survival and flexibility of economic actors have become increasingly critical. Particularly within emerging markets, Micro, Small, and Medium Enterprises (MSMEs) function as more than just market participants; they represent the essential pillars of macroeconomic stability and societal integration (Tambunan, 2019). The Indonesian economic landscape is fundamentally defined by the micro, small, and medium enterprise (MSME) sector. Empirical records from 2019, sourced from the Ministry of Cooperatives and SMEs, reveal that approximately 65.4 million units are active nationwide. This signifies that almost the entire business population in Indonesia—roughly 99.9%—is composed of MSME entities (OECD, 2018). Furthermore, these enterprises demonstrate a massive absorption capacity for human capital, employing approximately 119 million workers, which accounts for 96.92% of the total national workforce.
Despite this monumental contribution to employment and macroeconomic activity, the actualized potential of Indonesian MSMEs remains constrained by persistent structural rigidities and information asymmetry (Utomo & Setiyono, 2024). A critical impediment identified in the sector is the accountability gap, a pervasive lack of financial transparency, and administrative discipline that severs these enterprises from formal financial ecosystems. Empirical evidence suggests that approximately 60–70% of Indonesian MSMEs lack access to bank financing, this is attributable to their inability to produce credible financial reports, maintain proper accounting standards, and demonstrate historical solvability to creditors (Idrus & Rastina, 2025). The management of these entities often exhibits a blurring of boundaries between household and business finances, with a scarcity of entities that rigorously record and archive documentation of business activities. Addressing the prevailing accountability deficit necessitates a strategic transition toward robust accounting information systems (AIS). Far from being a simple instrument for historical documentation, AIS represents a fundamental organizational asset that streamlines the acquisition and distribution of critical financial intelligence essential for high-level strategic planning. Scholarship by Mohd Radzi et al. (2024) and Abdullah et al. (2024) reinforces that AIS is instrumental in bolstering firm performance, primarily by narrowing information gaps and refining the accuracy of executive decisions.
In the modern context, AIS is increasingly intertwined with digital technologies, evolving from manual ledgers to sophisticated, computer-based systems that support internal control mechanisms and enterprise resource planning. However, the mere implementation of accounting software is insufficient to guarantee superior performance in a hyper-competitive digital ecosystem. The modern business environment requires organizations to possess dynamic capabilities and the ability to integrate, build, and reconfigure internal and external competencies to address rapidly changing environments. This study posits that management knowledge capability (MKC) and the utilization of digital platforms represent such critical dynamic capabilities. MKC functions as a coordination mechanism that transforms raw resources into competencies, ensuring that the firm can learn from market interactions and optimize its resource allocation. Simultaneously, the proliferation of digital platforms such as e-commerce marketplaces and digital payment gateways has fundamentally altered the topology of market access. These platforms do not just facilitate transactions; they act as catalysts for broader digital innovation, shifting classic business models toward a new era of digitalization (Snell & Morris, 2014).
While the individual impacts of accounting systems, knowledge assets, and digital connectivity on firm outcomes have been studied separately, a profound theoretical and empirical void remains concerning their integrated dynamics. Specifically, it is not yet fully understood how these factors collectively linked to performance through the specific conduit of digital innovation, especially within an emerging archipelagic economy like Indonesia. This study addresses these gaps by proposing a comprehensive structural model that integrates AIS, MKC, and digital platform capabilities. While the term MSME encompasses a broad spectrum of business sizes, this study focuses on the collective digital-dynamic challenges faced by these entities in Indonesia. As shown in our demographic profile (Table 1), over 77% of our respondents are Micro and Small enterprises, which represent the most significant segment experiencing the accountability deficit. Consequently, the findings are particularly tailored to these grassroots actors rather than large-scale corporate structures.
The contemporary global economic landscape is undergoing a fundamental transformation, shifting toward digitized, knowledge-intensive value creation. While MSMEs are the backbone of economic stability, they face an “accountability gap” that restricts their access to formal capital. To address this, our study seeks to answer the following research questions: (1) How do accounting information systems (AIS), management knowledge capability (MKC), and digital platform usage collectively linked to financial performance? (2) Does digital innovation act as a critical mediator in this relationship? Consequently, the general objective of this research is to develop a robust empirical framework explaining the determinants of financial performance for Indonesian MSMEs. Specifically, the study aims to analyze the individual and synergistic effects of AIS, MKC, and digital platforms, while examining the indirect pathways provided by digital innovation. The remainder of this paper is organized as follows: it develops the theoretical framework and hypotheses, describes the research methodology, presents the empirical results, discusses the findings in light of the existing literature, and provides conclusions, implications, and limitations.

2. Theoretical Framework and Literature Review

This study constructs its conceptual framework upon the bedrock of two dominant theories in strategic management: Resource-Based View and Theory of Dynamic Capabilities (DC). These theories provide the necessary lens to understand how internal assets and adaptive processes interact to generate sustainable competitive advantage in the digital era. While financial performance is influenced by diverse determinants such as macroeconomic stability and owner personality, this study focuses specifically on AIS, MKC, and digital platforms. This choice is grounded in the synergy between the Re-source-Based View (RBV) and Dynamic Capabilities (DC) theory. In a digitized economy, AIS serves as the foundational information asset, MKC provides the cognitive ability, and digital platforms offer the connectivity required to scale operations. Together these three represent the core technological and cognitive nexus necessary for survival in a VUCA environment, especially within an emerging archipelagic economy like Indonesia.

2.1. Resource-Based View (RBV)

The Resource-Based View (RBV) framework posits that an organization’s long-term competitive positioning is primarily determined by its internal resource bundle, specifically those that are valuable, rare, inimitable, and non-substitutable. Originating from the works of Wernerfelt (1984) and solidified by Barney (2018), RBV argues that resources are the fundamental correlates of performance. These resources are categorized into tangible assets (e.g., physical infrastructure, financial capital) and intangible assets (e.g., organizational culture, intellectual property, proprietary knowledge). Within this research, accounting information systems (AIS) are regarded as a vital strategic asset. While basic IT hardware may be a commodity available to all, the specific implementation of an SIA—the integration of software with organizational procedures, data integrity protocols, and human capital skills—creates a unique capability. An effective SIA allows MSMEs to process financial transactions with speed and accuracy, providing the “information assets” necessary for strategic planning and control. RBV further posits that resources must be combined effectively; SIA does not operate in a vacuum but must be aligned with the firm’s strategic orientation and human capital competencies to yield performance gains (Kristinae et al., 2023). The failure of many MSMEs to improve performance despite adopting technology often stems from a failure to view SIA as a strategic resource that requires complementary intangible assets, such as managerial expertise.

2.2. Dynamic Capability (DC) Theory

While RBV focuses on the exploitation of existing resource bundles, the Dynamic Capability (DC) theory, championed by Teece (2017), addresses how firms renew these resources in response to shifting market environments. DC is characterized as a firm’s proficiency in synthesizing and restructuring internal and external competencies to navigate turbulent environments. In the context of the digital economy, management knowledge capability (MKC) and digital platform capability serve as critical dynamic tools that enable firms to sense environmental shifts and undergo necessary organizational transformations (Yoshikuni et al., 2024). This study identifies management knowledge capability (MKC) and digital platform capability as dynamic capabilities. KPM as a Dynamic Capability: In the knowledge economy, the static possession of information is less valuable than the dynamic capability to acquire, share, and utilize new knowledge. KPM allows the firm to sense changes in the environment (e.g., shifting customer preferences), seize new opportunities (e.g., through new product development), and transform the organization (e.g., by adopting new business models). Digital Platforms as Enablers of Dynamism: Digital platforms provide the infrastructure for dynamic adaptation. They allow firms to rapidly scale operations, access new markets with low marginal costs, and leverage external ecosystems for innovation. Cenamor et al. (2019) argue that platforms act as triggers that motivate and boost existing dynamism within the firm, facilitating the reconfiguration of resources to meet digital demands.

2.3. Accounting Information Systems and Financial Performance

AIS serves as a structured mechanism for gathering, documenting, and analyzing data to provide actionable intelligence for stakeholders. It comprises six critical components: people, procedures, data, software, IT infrastructure, and internal controls (Spilnyk et al., 2020). For MSMEs, the transition from manual bookkeeping to a computerized AIS represents a leap in operational maturity. Prevailing scholarship reinforces the existence of a positive correlation between the quality of AIS and overall firm success (Surjono, 2021; Vo Van et al., 2024). These systems bolster performance by refining the quality of decisions, strengthening internal governance, and optimizing operational workflows (Lutfi et al., 2022). The lack of effective AIS usage correlates with poor decision-making and low access to capital. Conversely, firms that utilize AIS for strategic planning, budgeting, and performance evaluation exhibit higher profitability and growth. In an emerging market context, AIS is not merely a tool for internal efficiency but a prerequisite for seizing external opportunities via digital platforms. The synergistic effect of digital innovation as a mediator cannot be accurately isolated from the foundational administrative strength of the firm. Therefore, we hypothesize:
H1. 
Accounting information systems have a positive and significant influence on MSME financial performance.

2.4. Management Knowledge Capability and Financial Performance

MKC encompasses the methodical procedures an organization employs to generate, acquire, and utilize knowledge effectively (Belkahla Hakimi et al., 2014). Furthermore, MKC functions as a coordinating mechanism that translates raw organizational resources into tangible competencies. In the MSME sector, where the owner-manager often holds the bulk of critical business knowledge (tacit knowledge), the ability to systematize this into explicit organizational routines is crucial for scaling. Cerchione and Esposito (2017) found that MKC acts as a coordinator that converts resources into competencies. Centobelli et al. (2019) and Mata et al. (2024) provided empirical evidence that ability to acquire knowledge about cheaper raw material sources or more efficient production techniques directly impacts the cost structure and, consequently, the financial bottom line. In the digital era, MKC also involves the capacity to manage digital knowledge understanding data analytics, digital market trends, and various dimensions of financial performance.
H2. 
Management knowledge capability has a positive and significant influence on MSME financial performance.

2.5. Digital Platform Capability and Financial Performance

Digital platforms are defined as online intermediaries that facilitate interactions between different user groups, reducing transaction costs and enabling network effects (Cenamor et al., 2019). Capabilities related to digital platforms involve the firm’s ability to integrate these platforms into their business model, configure them to suit specific needs, and leverage them for market access (Snell & Morris, 2014). For Indonesian MSMEs, platforms like Tokopedia, Shopee, and Gojek have democratized access to national markets. The usage of these platforms allows for scalability that was previously unattainable for small enterprises due to logistical and marketing constraints. Cenamor et al. (2019) argue that platform capability enhances performance by enabling firms to engage in “platform-based competition,” where value is co-created with users and partners. Platforms drastically reduce transaction costs and provide MSMEs with immediate access to a national or even global customer base without the need for heavy investment in physical distribution networks. Studies (Gao et al., 2024) and (Firstian Aldhi et al., 2024) have shown that MSMEs utilizing digital platforms experience faster sales growth and improved profitability compared to offline-only peers.
H3. 
Digital platform capability has a positive and significant influence on MSME financial performance.

2.6. Determinants of Digital Innovation

Digital innovation in this context is strictly defined as the conceptualization and deployment of pioneering digital solutions, such as automated transaction processing or personalized digital customer engagement, that are facilitated through platform-based infrastructures. It is not a generic organizational change but a technology-enabled transformation of the value proposition (Teece, 2018). Management knowledge capability as a driver of innovation is a knowledge-intensive activity (Mata et al., 2024). An organization’s capacity for absorption defined as its proficiency in identifying, internalizing, and monetizing external insights is deeply contingent upon the strength of its management knowledge capability (MKC) MSMEs that actively acquire knowledge about customer needs and competitor moves are better positioned to generate innovative digital solutions. Yoshikuni et al. (2024) and Li et al. (2024) confirm that knowledge management strategies significantly influence the quantity and quality of innovation. Digital platforms provide the technological substrate for innovation, offer modular architectures that allow for generating the ability of uncoordinated audiences to create content or applications that add value to the platform (Nambisan, 2017). By participating in a platform ecosystem, MSMEs gain access to boundary resources (APIs, SDKs) and data that fuel innovation. Cenamor et al. (2019) demonstrate that platforms act as drivers for achieving sustainable digital innovation by facilitating collaboration and reducing the costs of experimentation.
H4. 
Management knowledge capability has a positive and significant influence on digital innovation.
H5. 
Digital platform capability has a positive and significant influence on digital innovation.

2.7. The Role of Mediating Variables in Digital Innovation

The nexus between strategic capabilities, knowledge management and platform integration and organizational success is frequently bridged by innovative output (Teece, 2018). It is not enough to simply know or have access, the firm must innovate to create value. Mediation of management knowledge capability: While MKC provides the potential for improvement, digital innovation is the mechanism through which this potential is realized in the marketplace (Gold et al., 2001). Consequently, digital innovation serves as a vital mechanism for enhancing fiscal transparency and accountability, effectively linking organizational strengths to superior financial results (Al-Hashimy et al., 2025; Hasan et al., 2024). Similarly, presence on a digital platform is a necessary but insufficient condition for superior performance. It is the innovative use of the platform—creating unique customer experiences, leveraging data for personalization, or optimizing digital supply chains—that drives competitive advantage (Rai et al., 2006). Digital platforms drive innovation performance, which in turn drives financial success. Digital innovation enables organizations to promote greater financial accountability and transparency, thereby bridging the gap between upstream capabilities and downstream financial results (Mediaty et al., 2025).
H6. 
Digital innovation mediates the relationship between management knowledge capability and MSME financial performance.
H7. 
Digital innovation mediates the relationship between digital platform capability and MSME financial performance.

3. Methods

3.1. Research Design

This research employs a quantitative explanatory framework specifically designed for hypothesis verification. Utilizing a deductive methodology, the study translates theoretical constructs from the Resource-Based View (RBV) and Dynamic Capabilities (DC) into empirical propositions for statistical validation. The research utilizes a cross-sectional survey method, capturing a snapshot of the MSME landscape in Indonesia at a specific point in time. This design was chosen for its ability to generalize findings across a large population and to statistically model the intricate variable interactions through Structural Equation Modeling (SEM). SEM was selected for its superior ability to analyze complex, simultaneous pathways of dependency and to account for measurement error within the model.

3.2. Population and Sampling

The target population for this investigation includes all Micro, Small, and Medium Enterprises (MSMEs) in Indonesia officially documented within the Bank Indonesia registry. The total population identified for the sampling frame is 11,223 MSMEs across various economic sectors, including manufacturing (5854 units), trade (7373 units), and services. To manage the geographical complexity of the Indonesian archipelago, a Cluster Random Sampling technique was employed. The clustering was based on the major island groupings, which represent distinct economic corridors: Java, Sumatra, Kalimantan, Sulawesi, and Papua. The distribution of respondents is shown in Table 1.
  • Sample Size Determination: Following Hair et al. (2019), the ideal sample size for SEM-based analysis should range from five to ten times the number of indicators used. Consequently, with 49 indicators in the instrument, the minimum sample size was calculated as 245 (49 × 5). To enhance statistical rigor and mitigate the risk of incomplete data, the final target was increased to 490 participants.
  • Sampling Distribution: The sample was distributed proportionally based on the population density of MSMEs in each cluster, targeting approximately 4.4% of the total population in the sampled regions. The target distribution was: Java (115), Kalimantan (121), Sulawesi (97), Papua (24), and Sumatra (133).
  • Inclusion Criteria: To ensure data quality, respondents were required to meet two specific criteria: (1) The MSME must perform some form of financial recording or bookkeeping, and (2) the MSME must utilize digital platforms in its operations.

3.3. Data Collection and Instrument

Primary data acquisition was carried out through digital questionnaires distributed to MSME owners and executives via email and mobile messaging platforms. This digital data collection method was selected to maximize reach across geographically dispersed regions and to facilitate efficient data coding. The study utilized a five-point Likert scale, ranging from Strongly Disagree (1) to Strongly Agree (5). To maintain high construct validity, all measurement items were derived and modified from validated scales found in the existing academic literature. In detail, as follows: (1) To operationalize the accounting information systems (AIS) construct, eight measurement items were derived from the management accounting framework established by Chenhall and Morris (1986). This multi-dimensional scale evaluates information quality across four distinct pillars: integration, aggregation, timeliness, and scope, all of which have been empirically validated within the SME landscape by Hamzah et al. (2023). (2) Management knowledge capability (MKC) was measured using twelve items adapted from the Knowledge Management Process Capability framework developed by Gold et al. (2001) and validated in innovation contexts by Liao and Wu (2010). The scale assesses three dimensions: Knowledge Acquisition, Knowledge Sharing and Knowledge Utilization. (3) Digital platform capability was measured using six items adapted from the IT Infrastructure Flexibility and Integration scales developed by Rai et al. (2006). These items were contextualized for the MSME platform environment following the framework of Cenamor et al. (2019). The measurement captures two key dimensions: (1) Connectivity, which assesses the ease of connecting and exchanging information with partners and customers, and (2) Flexibility, which evaluates the platform’s ability to accommodate changes. (3) Digital innovation, measured using 6 items adapted from Khin and Ho (2019), focusing on the novelty of digital solutions, product superiority, and platform differentiation. (4) MSME financial performance was assessed using a multidimensional construct adapted from Venkatraman and Ramanujam (1986). Subjective measures were employed following the validation by Evinita et al. (2025). The construct comprises five items capturing both financial and operational outcomes. Prior to full distribution, a face validity test was conducted with 20 MSME owners to ensure the questions were clear, unambiguous, and relevant to the Indonesian business context. To ensure clarity and replicability, the operational definitions of the latent variables, their respective indicators, and the sources of measurement scales are summarized in Table 2. All items were measured using a 5-point Likert scale.

3.4. Data Analysis Techniques

Statistical analysis was performed using Structural Equation Modeling (SEM) facilitated by AMOS version 23. This method was selected for its proficiency in examining multiple interdependent relationships concurrently while adjusting for measurement errors within the model (Henseler et al., 2015). The analytical process followed a systematic two-stage procedure: an initial assessment of the measurement model via Confirmatory Factor Analysis (CFA), followed by the evaluation of structural paths.

4. Results

4.1. Demographic Profile of Respondents

A total of 500 questionnaires were distributed, with 467 returned, yielding a response rate of 80.6%. After data cleaning to remove incomplete or invalid responses (e.g., firms not using digital platforms), 403 valid responses was utilized for subsequent analysis. As summarized in Table 3, the demographic characteristics indicate age distribution: the predominant group was aged 41–50 years (37.22%), with those in the 20–30 and 31–40 age brackets each accounting for 25.06%. This pattern demonstrates that digital technology integration is not exclusive to younger entrepreneurs but is also widely adopted by more seasoned business owners. This age distribution suggests that digital adoption is not limited to the youth but is being embraced by experienced business owners. In terms of education, 44.91% held a high school diploma, while a substantial 36.97% held bachelor’s degrees, indicating a relatively high level of human capital which is conducive to knowledge management implementation. Geographically, Java (29.53%) and Sumatra (24.06%) were the most represented, aligning with Indonesia’s economic population distribution. Crucially, 100% of respondents utilized digital platforms, with WhatsApp (76.17%) and Marketplaces/E-commerce (100%) being ubiquitous. In terms of business size, 44.42% had capital between IDR 50 and 500 million (Small Enterprises), while 33.50% were Micro Enterprises (<IDR 50 million), confirming the study’s focus on the grassroots economy.

4.2. Measurement Model Assessment

The psychometric integrity of the research constructs was validated through Confirmatory Factor Analysis (CFA). Standardized factor loadings for all items ranged from 0.685 to 0.984, comfortably surpassing the 0.50 threshold required for convergent validity. Furthermore, the Average Variance Extracted (AVE) values for every latent variable were consistently above 0.50, providing additional evidence of strong convergent validity. Instrument reliability was confirmed as both Cronbach’s Alpha and Composite Reliability (Rho_C) exceeded the 0.70 benchmark. All measurement items displayed external loadings above 0.70, ensuring they meet the acceptable standards for convergent validity as outlined by Hair et al. (2019). Overall, the measurement model demonstrates a high degree of internal consistency and reliability (Detailed in Table 4).

4.3. Multicollinearity Analysis

Harman’s single-factor test was employed to detect potential common method bias. The analysis revealed that the primary factor explained only 33.378% of the total variance. Given that this result is significantly below the 50% critical threshold, it is evident that common method bias does not pose a threat to the findings of this research. Multicollinearity was evaluated using Tolerance and Variance Inflation Factor (VIF) metrics. Following Hair et al. (2019), a model is free from multicollinearity concerns if Tolerance exceeds 0.10 and VIF remains below 10. As shown in Table 5, all exogenous variables displayed VIF values between 1.785 and 3.986, indicating that no problematic multicollinearity exists among the predictors.

4.4. Structural Model

Evaluating the structural model’s congruency within SEM requires a rigorous examination of path coefficients and statistical significance. The fit of the structural framework was determined by analyzing multiple Goodness of Fit (GoF) metrics, categorized into distinct evaluative groups. This study primarily focused on the first two groupings, utilizing their respective fit parameters. Non-significant chi-square results (p > 0.05) and RMSEA values below the 0.8 threshold signify that the estimated model effectively mirrors the population’s covariance structure, thus ensuring the generalizability of the findings. Furthermore, incremental fit indices specifically CFI, NFI, and TLI confirmed that the final model possesses an excellent fit with the empirical data (see Table 6).
Model fit indices, including a low RMSEA (0.036) and a high CFI (0.990), confirm that the theoretical framework aligns exceptionally well with the empirical data. Path analysis results provide strong support for the hypothesized relationships. Specifically, the direct associations of AIS (H1), MKC (H2), and digital platforms (H3) with financial outcomes were found to be positive and statistically significant. Additionally, the findings reveal that both MKC (H4) and digital platforms (H5) act as significant predictors of digital innovation. Most importantly, mediation analysis confirms that digital innovation serves as a vital bridging mechanism, significantly mediating the pathways from MKC (H6) and digital platforms (H7) to enhanced financial performance. The detailed results regarding the direct and indirect effects of the structural model analysis can be seen in Table 7.
The structural model assessment demonstrates the correlation among variables through path analysis. The proposed model indicates direct, indirect, and total paths, suggesting significant mediating effects of digital innovation. Table 6 presents the path coefficients (β) for direct effects, along with their significance levels. Specifically, System Information Accounting (SIA) has a positive and significant direct effect on financial performance (β = 0.22, p < 0.001), supporting H1. Knowledge Management Capability (KPM) and digital platform (DP) also significantly enhance financial performance with coefficients of 0.21 (p < 0.001) and 0.67 (p < 0.001), respectively, confirming H2 and H3. Furthermore, the results validate the predictors of innovation; KPM (β = 0.11, p < 0.01) and digital platform (β = 0.12, p < 0.001) positively influence digital innovation (DI), thereby accepting H4 and H5. Finally, the mediation analysis using the Sobel test confirms that digital innovation significantly mediates the relationship between KPM and financial performance (Sobel test = 4.336, p < 0.001) meaning that H6 is accepted, as well as the relationship between digital platform and financial performance (Sobel test = 10.869, p < 0.001) meaning that H7 is accepted.

5. Discussion

This exploration successfully integrates the traditional operational backbone—specifically accounting information systems (AIS)—with contemporary dynamic capabilities, namely management knowledge capability (MKC), digital platform capability (DP), and digital innovation (DI). The derived empirical results provide compelling evidence that these constructs are not merely additive, but synergistic in their influence on MSME financial performance. A primary contribution of this study is the empirical validation of the Digital-RBV framework, where the definition of strategic resources is expanded from owned assets to access-based ecosystem resources. An unexpected finding is the sheer magnitude of the digital platform capability’s influence, which is nearly three times more potent than the impact of internal accounting information systems. This suggests that for MSMEs in emerging markets, market connectivity via external platforms may temporarily compensate for internal administrative weaknesses, although it introduces a boundary condition of platform dependency. Furthermore, the mediation analysis confirms that while access to knowledge and platforms is critical, the transformation of these assets into innovative products, services, or processes serves as a primary mechanism linked to higher economic returns.
This research empirically validates that digital innovation serves as a significant intervening mechanism, facilitating the link between MKC and financial performance, as well as between digital platform capability and firm success. This offers a profound analytical interpretation of the findings, delineating the theoretical extensions necessary to comprehend MSME survival in the digital economy. Contrasting the productivity paradox often cited in the IT investment literature, this study observes positive outcomes, suggesting that the IT business value is realized when technology is embedded in organizational capabilities and analyzing the potential of digital platform (Barney, 2018; Yoshikuni et al., 2024). These findings align with the paradigm shift in dynamic capabilities theory, suggesting that the ability to sense and capitalize on opportunities within a digital ecosystem is a more potent predictor of success than traditional internal efficiencies (Kringelum et al., 2025; Teece, 2018).
Addressing the first research objective regarding the data confirms that well-implemented accounting information systems (AIS) significantly enhance MSME financial results. As a foundational information asset, AIS provides the detailed data necessary for informed strategic planning (Evinita et al., 2024; Johri, 2025). In the context of MSMEs, the formalization afforded by AIS reduces information asymmetry. This corroborates (Rita & Nastiti, 2024), who found that AIS usage in SMEs is significantly associated with performance improvement through better financial monitoring. Furthermore, Akande et al. (2024) argue that digitalized management accounting systems enhance the information processing capability of the firm, enabling precise strategic adjustments. While some scholars note that sophisticated systems can lead to information overload (Maryanto et al., 2025), for MSMEs in this study, digital accounting serves as a critical signal of legitimacy to access formal financing, a vital mechanism in developing economies. The underlying mechanism driving this positive association is likely the reduction in information asymmetry, both internally and externally, by enhancing the quality, timeliness, and reliability of financial reporting. This enables MSMEs to reduce capital costs, and the improved accuracy and transparency provided by AIS increases investor confidence and facilitates informed risk management. The results resonate with contemporary RBV scholarship, indicating that digital integration can substantially elevate the quality of AIS particularly in service and system dimensions, thereby driving performance through refined strategic maneuvering (Al-Hashimy et al., 2025). Ultimately, this study confirms that the adoption of digital accounting solutions is likely to facilitate access to formal financing, as well as serve as a crucial mediation mechanism for financial performance in developing economies.
The second finding substantiates that management knowledge capability (MKC) is a key determinant of financial outcomes. This aligns with RBV principles, which categorize knowledge as a valuable and unique organizational resource (Barney, 1991). The positive trajectory indicates that MSMEs capable of acquiring and utilizing market knowledge are better positioned to adapt to environmental turbulence. This aligns with Cerchione and Esposito (2017), who highlight that despite the resource constraints of SMEs effective knowledge management systems significantly boost operational efficiency. The prior literature further indicates that MKC enhances the velocity and quality of decision-making, directly feeding into operational success (Hasan et al., 2024; Wahyono & Hutahayan, 2021). While MKC is fundamental for adaptation, Centobelli et al. (2019) emphasize that a digital divide persists for MSMEs lacking tools for knowledge codification. Without digital systems to formalize and share insights, investments in knowledge management often result in a performance gap where internal expertise fails to translate into financial outcomes. Previous studies have argued that the relationship between management capabilities and financial performance becomes insignificant without a strong mediating variable (Culebro-Martínez et al., 2024). Thus, these results suggest that without digital tools to codify knowledge, knowledge in the MSME sector remains informal and fragmented, hindering scalable performance improvement. Traditionally, knowledge management in MSMEs is prone to failure because the departure of key employees can lead to significant knowledge loss. However, when MKC is combined with a digital platform, this inherent vulnerability is overcome. In this study, the integration of MKC with digital platforms mitigates the fragility of SME knowledge, often caused by high staff turnover, by embedding knowledge into digital routines, acting as a form of organizational memory (Baig et al., 2025). Therefore, these results suggest that digital maturity is a prerequisite for effective knowledge management and they also reinforce the theoretical view that knowledge is merely potential energy that must be transformed into innovation to drive substantial economic returns.
The most salient finding of this study is the influence of digital platform capability on MSME financial performance, signaling that for Indonesian MSMEs, connectivity stands as a paramount determinant of success. This aligns with Cenamor et al. (2019), who observes that platform participation grants instant access to expansive markets, logistical infrastructure, and payment gateways capabilities that MSMEs could rarely establish independently. This further corroborates the work of Liu et al. (2024), who posit that digital platforms enable resource-constrained firms to tap into logistics, marketing, and trust mechanisms which were traditionally the exclusive privilege of large multinational corporations. Effectively, digital platforms outsource the complexities of scaling, thereby allowing MSMEs to concentrate on their core products and value propositions. While statistical results identify the adoption of digital platforms—such as digital accounting tools—as a primary driver of financial success, the findings of Gao et al. (2024) and Papić-Blagojević and Kuzman (2025) issue a caveat regarding platform dependency. Although platforms bolster financial performance in the short term, business actors like MSMEs become reliant on platform algorithms for visibility and operational profitability. Any alteration in platform fee structures or ranking algorithms could potentially obliterate MSME revenues. However, this connectivity introduces the risks of platform capitalism, where MSMEs are subject to algorithmic opacity and potential rent extraction by platform owners. Conceptually, these results elucidate that as dependency grows and the platform’s power to extract rents increases, MSME profitability faces the potential for stagnation. Thus, the findings extend the Resource-Based View (RBV) by suggesting that competitive advantage may stem from accessing non-scarce resources, such as digital accounting software rather than possessing rare assets (Herdinata et al., 2025). This study implies that digital platform capability is not merely about account ownership, but rather pertains to the dynamic capability to reconfigure platform features to align with firm-specific needs.
The final set of findings concerns the mediation analysis, which confirms that digital innovation plays a pivotal role in translating both MKC and digital platform capabilities into tangible performance. This finding helps resolve a prevalent conflict in the prior literature regarding why IT investments occasionally fail to yield returns (Nambisan, 2017). Concurrently, our results align with Khin and Ho (2019), who argue that digital assets are merely latent resources that generate economic rents only when activated through innovation—such as creating new digital product lines, novel delivery methods, or personalized customer experiences. The mediation path demonstrates that distinct from mere access, the active utilization of tools to launch flash-sale campaigns or product bundling proves that the mechanism of value creation follows a sequential logic: resources, innovation, and ultimately, financial performance. This provides robust evidence nuancing the assumption of a direct link between technology adoption and performance; it demonstrates that while a direct relationship exists, the mediated path elucidates the variance in financial performance among firms that share identical technological baselines. These results highlight that digital innovation functions as an absorptive capacity, enabling firms to metabolize the “raw potential” of platforms and knowledge into digestible market value.
Empirically, the capability mechanism within MSMEs validates the micro-foundations of dynamic capabilities, which is adapted from the foundational framework of Teece (2017) and contextualized for the digital domain by Matarazzo et al. (2021): (1) Sensing is represented by MKC through the acquisition of knowledge from stakeholders; (2) Seizing is enacted through digital platforms to access markets; and (3) Transforming is realized via digital innovation to create new value propositions. This provides a concrete mechanism for Teece (2017) theory within the context of emerging market MSMEs, transitioning dynamic capabilities from abstract concepts into measurable pathways. By establishing digital innovation as a mediator, this study offers a resolution to the technology productivity paradox in the MSME sector. The generalizability of these findings is bounded by the geographic and economic context of Indonesia, where high logistical costs make digital platform connectivity an absolute necessity. Furthermore, as a cross-sectional study, these results represent a snapshot of digital maturity, and the identified relationships should be viewed as associative rather than strictly causal over the long term.

6. Conclusions

The primary objective of this study was to examine factors related to financial performance in Indonesian MSMEs by developing a structural model that integrates accounting information systems (AIS), management knowledge capability (MKC), and digital platform capability, with digital innovation serving as a crucial mediator. By validating this model, the research successfully establishes that MSME financial sustainability in the digital era is not merely a result of isolated variables, but is driven by a synergistic ecosystem comprising internal accountability, cognitive sensing (MKC), and external connectivity. The empirical confirmation of hypothesis results suggests that the financial success of Indonesian MSMEs is not a direct byproduct of merely adopting digital tools. Consequently, owners must shift from passive technology adoption—where digital platforms are used solely as transactional storefronts—to active technology-based innovation. This involves leveraging the information quality provided by AIS and MKC to execute data-driven product bundles or personalized customer engagement strategies, which our model identifies as the actual unlocking mechanism for superior returns.
These findings suggest that MSME owners must shift their mindset from technology adoption to technology-enabled innovation. Investing in digital accounting or joining a marketplace is only the first step, the competitive advantage lies in using the data from platforms to create unique customer value. Furthermore, while digital platform capability is the strongest predictor of performance, the study identifies a critical strategic vulnerability. The reliance on external ecosystems introduces risks of platform capitalism, characterized by algorithmic opacity and potential rent extraction by platform providers. To manage this, MSME managers should utilize digital platforms not as a permanent dependency, but as a catalyst to build firm-specific dynamic capabilities, ensuring that unique digital innovation remains an internal asset that can survive shifts in platform policies. Despite its contributions, this study has several limitations that suggest directions for future inquiry. First, the cross-sectional design provides only a snapshot of the MSME landscape, which may not capture the long-term evolution of digital maturity. Second, the study utilizes subjective performance metrics reported by owners. Although validated, future research should integrate objective financial data to strengthen the findings. Finally, while connectivity through platforms yields high returns, it introduces a strategic risk of platform dependency that warrants further longitudinal investigation.

Author Contributions

Conceptualization, M. (Maryanti), M. (Mediaty), and A.A.M.; methodology, M. (Maryanti), M. (Mediaty), A.H.A. and A.A.M.; software, A.H.A.; validation, M. (Maryanti), M. (Mediaty) and A.A.M.; formal analysis, A.H.A. and A.A.M.; investigation, M. (Mediaty) and A.A.M.; resources, M. (Maryanti); data curation, M. (Maryanti), M. (Mediaty) and A.A.M.; writing—original draft preparation, M. (Mediaty) and A.H.A.; writing—review and editing, M. (Maryanti), M. (Mediaty) and A.A.M.; visualization, M. (Maryanti); supervision, M. (Maryanti) and M. (Mediaty); project administration, M. (Maryanti), M. (Mediaty) and A.A.M.; funding acquisition, Maryanti. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This research was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Andalas University, decision Indonesian Research Collaboration Program (Number 13/UN16.19/PT.01.03/RKI/2025, approved in March 2025).

Informed Consent Statement

Informed consent for publication was obtained from all identifiable human participants.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

The authors are grateful to the Indonesian Legal Entity State University (PTNBH) for supporting this research through the 2024 Indonesia Collaborative Research Scheme. Andalas University, Hasanuddin University, and Terbuka University provided invaluable institutional support. The authors would like to express their sincere appreciation to all Indonesian MSMEs registered under Bank Indonesia’s mentorship program for their willingness to participate. Bank Indonesia Headquarters played a crucial role in facilitating data collection.

Conflicts of Interest

No potential conflict of interest was reported by the authors.

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Table 1. Distribution of respondents.
Table 1. Distribution of respondents.
RegionProvinceNumber of MSMEsSample Size (4.4%)
JavaWest Java714115
Central Java1.089
East Java837
KalimantanSouth Kalimantan976121
Central Kalimantan150
East Kalimantan1.645
SulawesiSouth Sulawesi1.04897
Central Sulawesi138
Southeast Sulawesi530
North Sulawesi501
PapuaPapua9424
West Papua447
SumatraWest Sumatra596133
South Sumatra676
North Sumatra1.782
Total1.062490
Source: primary data processed, 2025.
Table 2. Variables and measurement items.
Table 2. Variables and measurement items.
ConstructDescriptionMeasurement Items
Accounting information systems (AIS)An integrated framework for the systematic gathering and reporting of financial data to assist in executive governance.(1) Reporting frequency; (2) Organization report summary; (3) Direct reporting; (4) Automatic receipts; (5) Targets of budgetary control and variance analysis for short-term financial forecasting; (6) Frequency of information exchange to align financial targets with market reality; (7) Speed of information delivery into the company’s decision-making process; and (8) Digital market trends that influence long-term profitability.
Management knowledge capability (MKC)The organizational proficiency in generating and utilizing knowledge assets to foster a sustainable competitive edge.(1) Knowing customer needs; (2) Supplier knowledge; (3) Generating new knowledge; (4) Frequency of MSMEs conducting benchmarking of business partners in the digital ecosystem; (5) Trying new ideas; (6) Periodic meetings; (7) Division of labor mechanisms; (8) Team grouping; (9) Effectiveness of the regular meeting feedback system; (10) The company’s accuracy in transforming market trend information; (11) Product development usage; and (12) Acquired insights for innovative product development.
Digital platform capability (DPC)The capacity to leverage virtual marketplaces and online communication to facilitate scalable, cost-efficient growth.(1) Easy connection with partners; (2) Connection with customers; (3) Utilizing the platform’s analytics features to track consumer behavior; (4) Inclusion of new partners; (5) Adjustment to new customers; and (6) Ease of API integration with third-party partners.
Digital innovation (DI)The process of engineering pioneering solutions or services through the utilization of digital technological substrates.(1) Superior digital quality; (2) Technical ease with which MSMEs can integrate new platform features; (3) Different digital application; (4) Incremental improvement; (5) Platform differentiation; and (6) Development of novel digital customer interfaces.
MSME financial performance (FP)The ability of the organization to achieve set goals using resources efficiently and effectively.(1) Sales return average; (2) Profit growth; (3) Cash flow turnover; (4) Customer satisfaction (financial impact); and (5) Cash flow efficiency and financial impacts.
Table 3. Demographic profile of respondents.
Table 3. Demographic profile of respondents.
CharacteristicCategoryFrequencyPercentage (%)
Age (Years)<2092.23
20–3010125.06
31–4010125.06
41–5015037.22
>504210.42
RegionJava11929.53
Sumatra9724.06
Kalimantan9022.33
Sulawesi8220.35
Papua153.72
EducationSD-SMP (Basic)389.43
SMA (High School)18144.91
S1 (Bachelor)14936.97
S2/S3 (Postgrad)307.45
PositionOwner20951.86
Manager8721.59
Employee10726.55
Capital (IDR)<50 Million13533.50
50–500 Million17944.42
>500 Million8922.08
Source: primary data processed, 2025.
Table 4. Validity and reliability test.
Table 4. Validity and reliability test.
ConstructValidityReliability
Loading FactorAVECronbach’s AlphaRho_C
Accounting Information Systems0.756–0.9390.6960.9430.941
Management Knowledge Capability0.685–0.9320.6890.6300.905
Digital Platform Capability0.848–0.9840.8930.9800.980
Digital Innovation0.734–0.9630.7770.9500.954
Financial Performance0.832–0.9480.8150.9560.956
Source: processed by researchers, 2025.
Table 5. Diagnostics for multicollinearity.
Table 5. Diagnostics for multicollinearity.
ConstructCollinearity StatisticsVIF
AIS0.3141.785
MKC0.1933.986
DPC0.2821.938
DI0.3013.027
Source: processed by researchers, 2025.
Table 6. Goodness of fit indices.
Table 6. Goodness of fit indices.
ConstructThresholdResultDecision
RMSEA≤0.080.036Good Fit
CFI≥0.900.990Good Fit
TLI≥0.900.988Good Fit
NFI≥0.900.972Good Fit
CMIN/DF≤2.01.523Good Fit
Source: processed by researchers, 2025.
Table 7. Summary of hypothesis testing.
Table 7. Summary of hypothesis testing.
Hypotheses PathCoefficient (Beta)Critical Ratio (t)Level of Sig. (p)Decision
Direct Effect
H1AIS → FP0.224.0450.001 *Supported
H2MKC → FP0.214.6700.001 *Supported
H3DP → FP0.6712.550.001 *Supported
H4MKC → DI0.112.6100.009 *Supported
H5DP → DI0.123.0130.003 *Supported
Indirect Effect
H6MKC → DI → FP0.444.3360.000 *Supported
H7DP → DI → FP0.4810.8690.000 *Supported
Note: * Significant at p < 0.05 level. Significant at <0.05 level, AIS (X1) = accounting information systems; MKC (X2) = management knowledge capability; DP (Y1) = digital platform capability; DI (Y2) = digital innovation. Source: (Processed by researchers, 2025).
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Maryanti; Mediaty; Arifin, A.H.; Mas’ud, A.A. Digital Accounting and Financial Performance of MSMEs in Indonesia: The Mediating Role of Digital Innovation. Int. J. Financial Stud. 2026, 14, 66. https://doi.org/10.3390/ijfs14030066

AMA Style

Maryanti, Mediaty, Arifin AH, Mas’ud AA. Digital Accounting and Financial Performance of MSMEs in Indonesia: The Mediating Role of Digital Innovation. International Journal of Financial Studies. 2026; 14(3):66. https://doi.org/10.3390/ijfs14030066

Chicago/Turabian Style

Maryanti, Mediaty, Andi Harmoko Arifin, and Anis Anshari Mas’ud. 2026. "Digital Accounting and Financial Performance of MSMEs in Indonesia: The Mediating Role of Digital Innovation" International Journal of Financial Studies 14, no. 3: 66. https://doi.org/10.3390/ijfs14030066

APA Style

Maryanti, Mediaty, Arifin, A. H., & Mas’ud, A. A. (2026). Digital Accounting and Financial Performance of MSMEs in Indonesia: The Mediating Role of Digital Innovation. International Journal of Financial Studies, 14(3), 66. https://doi.org/10.3390/ijfs14030066

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