A Web-Based Green Innovation Index for Disclosure-Based Environmental Assessment: Evidence from Indonesia’s Manufacturing Sector

Manufacturing remains central to economic development, yet it is also one of the sectors most closely associated with intensive energy use, material throughput, waste generation, and pollutant emissions. As regulatory expectations, stakeholder scrutiny, and market demand for environmentally responsible production continue to increase, manufacturing firms are under growing pressure to demonstrate not only environmental commitment but also measurable environmental improvement [1]. In this context, green innovation has emerged as a strategic pathway through which firms can redesign products, processes, managerial systems, and market practices to reduce ecological burdens while sustaining competitiveness. Recent studies show that green innovation is increasingly linked to environmental upgrading, environmental, social, and governance (ESG) improvement, and long-term corporate performance, particularly when firms embed innovation into broader organizational capabilities and sustainability strategies [2-5].

At the same time, digital transformation is reshaping how firms generate, manage, and use environmental information. Digital technologies such as data integration systems, smart monitoring platforms, artificial intelligence, and advanced analytics can improve the speed, granularity, and consistency of sustainability-related decision-making [6]. Recent evidence suggests that digital transformation does not merely support operational efficiency [7]; it can also act as a catalyst for green innovation and broader green transformation in manufacturing firms [8]. By improving information visibility, coordination, and adaptive capability, digitalization allows firms to identify environmental inefficiencies, monitor performance indicators, and align innovation activity with sustainability objectives more effectively [9-12].

Despite this progress, a major challenge persists at the level of measurement. While firms increasingly publish sustainability reports, annual reports, and ESG-related disclosures, the evaluation of green innovation performance often remains fragmented, subjective, and manually intensive. Existing studies on sustainable manufacturing and corporate sustainability assessment repeatedly note the lack of practical, enterprise-level tools that can integrate multidimensional indicators into a coherent index for organizational assessment. Composite and indicator-based approaches have been proposed for manufacturing sustainability assessment, but many remain methodologically complex, insufficiently digitalized, or not specifically designed to capture green innovation through routinely disclosed corporate information. This leaves an important gap between sustainability disclosure and actionable environmental assessment [13-16].

The challenge is not only technical but also epistemic. Sustainability reports are now a major channel through which companies communicate environmental initiatives, risks, and performance, yet disclosure quality varies substantially and may contain symbolic rather than substantive claims. Recent studies using textual analysis and disclosure-based assessment show that corporate sustainability reporting can provide valuable signals for environmental evaluation, but such signals require systematic extraction, structuring, and interpretation. This is especially relevant when the aim is to move beyond narrative reporting toward comparable firm-level indicators that can inform benchmarking, governance, and strategic planning. In other words, the growing availability of sustainability disclosures has created the data foundation for digital assessment, but robust tools are still needed to convert disclosures into measurable environmental intelligence [17-21].

This issue is particularly salient in emerging economies such as Indonesia, where industrial expansion, sustainability pressures, and digital transformation are unfolding simultaneously. According to WIPO, Indonesia ranked 54th in the Global Innovation Index 2024 and 55th in 2025, indicating continuing innovation progress but also the need to strengthen firm-level innovation systems and assessment capacity. For Indonesian manufacturing firms, the challenge is therefore not only to pursue greener practices, but also to build credible and scalable mechanisms for evaluating those practices in a transparent and decision-relevant manner. A web-based green innovation assessment system can be valuable in this respect because it can reduce reliance on purely manual interpretation, improve consistency across firms, and generate comparative evidence that is useful for managers, investors, and policymakers [22].

From a theoretical perspective, this study is anchored in the Resource-Based View (RBV), which argues that firm-specific resources and capabilities are central to the creation of sustained competitive advantage. In the present context, digital infrastructure, information-processing capability, organizational routines, and green innovation capacity can be understood as strategic resources that enable firms to transform environmental pressure into performance-enhancing innovation [23]. However, the RBV value of these capabilities cannot be fully demonstrated without a measurement architecture capable of capturing how green innovation is manifested across organizational inputs, processes, and outputs. Thus, developing a digital assessment tool is not merely a methodological exercise; it is also a way of operationalizing the resource-based logic of sustainability-oriented competitive advantage [9, 11, 24].

Against this background, the present study develops a web-based Green Innovation Index (GII) to assess the green innovation performance of Indonesian manufacturing companies using information disclosed in sustainability reports and annual reports. The proposed index integrates input, process, and output dimensions with the triple bottom line perspective, economic, environmental, and social, so that green innovation is evaluated not as a single isolated activity, but as a multidimensional organizational capability. This study contributes in three ways. First, it advances the environmental assessment literature by proposing a disclosure-based, firm-level index tailored to manufacturing contexts. Second, it extends research on digital transformation and green innovation by showing how digital tools can support standardized environmental evaluation rather than only operational change. Third, it offers practical relevance for corporate managers and policymakers by providing a structured mechanism for benchmarking green innovation performance and identifying areas requiring strategic improvement [9, 13, 14, 18].

This study adopts a mixed-methods research design with a sequential exploratory approach to develop and validate a web-based GII as a digital environmental assessment tool for manufacturing firms. The methodological approach is structured to ensure both conceptual rigor in indicator development and empirical robustness in measurement implementation. The research integrates qualitative content analysis with quantitative index construction, allowing the transformation of unstructured sustainability disclosures into a structured, comparable, and scalable environmental performance metric.

The research process begins with a qualitative exploratory phase, aimed at identifying and constructing the core indicators of green innovation. Data for this phase are derived from publicly available Sustainability Reports and Annual Reports of companies listed on the Indonesia Stock Exchange (IDX). These documents are selected because they represent the most comprehensive and standardized source of corporate environmental disclosure, widely used in sustainability assessment and ESG evaluation studies. The analysis follows the content analysis framework proposed by Matthew B. Miles and A. Michael Huberman, which consists of data reduction, data display, and conclusion drawing. During this stage, relevant statements, narratives, and quantitative disclosures related to environmental practices and innovation activities are systematically extracted and coded into predefined thematic categories.

To enhance analytical rigor, the content analysis process is guided by a structured coding scheme based on three main dimensions: input, process, and output, which reflect the lifecycle of innovation activities. These dimensions are further integrated with the triple bottom line framework, economic, environmental, and social, ensuring that the resulting index captures the multidimensional nature of sustainability performance. The coding process applies a Content Analysis Method (CAM), combining qualitative interpretation with quantitative scoring, where each indicator is assigned a standardized ordinal value based on the presence, intensity, and quality of disclosure. This approach enables the conversion of qualitative corporate reports into quantifiable data suitable for index construction.

Following the qualitative phase, the study proceeds to the quantitative development and implementation phase, in which the Green Innovation Index is constructed and operationalized through a web-based platform. The index is designed as a composite indicator system, where each dimension (input, process, output) consists of multiple indicators representing specific aspects of green innovation practices. The scoring mechanism is standardized across firms to ensure comparability, using a uniform scale that reflects the level of implementation and disclosure completeness. The aggregated index score is calculated using a weighted or unweighted summation approach, depending on indicator relevance and validation outcomes, allowing the generation of firm-level GII scores.

The empirical dataset consists of manufacturing companies listed on the IDX that publish complete Sustainability Reports, Annual Reports, and financial disclosures for the most recent reporting year. A purposive sampling technique is applied to ensure that selected firms meet the criteria of data availability, reporting consistency, and relevance to environmental assessment. This study is the initial phase of the proposed GII implementation and is not intended to assess the entire population of manufacturing companies listed on the Indonesia Stock Exchange. Instead, the sampling strategy was designed to select the five manufacturing companies with the highest ESG scores for the 2024 reporting period. These companies were selected because they are considered to have better ESG performance, indicating a higher commitment to sustainability and green innovation practices, and therefore suitable for testing the proposed index. We also ensured that all selected companies had prepared annual reports, financial statements, and sustainability reports for 2024. Given that this study does not perform a step-by-step screening of the entire manufacturing population, no exclusion procedures were applied. To strengthen the robustness of the system, the developed GII platform is pilot-tested on a subset of firms with relatively high ESG performance and sustainability reporting quality. This pilot testing serves as a functional validation stage, evaluating the system’s ability to extract, process, and visualize environmental performance data consistently and reliably.

The web-based system is designed to perform three main functions: (1) data structuring, where qualitative disclosures are transformed into coded indicators; (2) index calculation, where scores are automatically generated based on predefined algorithms; and (3) data visualization, where results are presented through comparative dashboards and performance charts. This digital architecture enables real-time or near real-time environmental assessment, significantly improving efficiency compared to manual evaluation methods.

To ensure methodological validity, several strategies are employed. First, construct validity is addressed by grounding the indicators in established literature on green innovation and sustainability assessment. Second, content validity is ensured through iterative refinement of indicators during the qualitative coding process. Third, reliability is enhanced by applying consistent coding rules and standardized scoring procedures across all sampled firms. Disclosure is evaluated on a scale from 0 to 3, depending on the amount of information presented; 0 indicates no disclosure at all, 1 indicates one sentence of disclosure, 2 indicates one paragraph of disclosure, and 3 indicates two or more paragraphs of disclosure. Real-world illustrations are provided for clarity and ease of replication. Furthermore, each marker carries equal value, and the method for summing and adjusting the index is now explained in detail in the text.

All 28 indicators are equally weighted because there is no compelling evidence that one marker should be considered more important than the others. Therefore, each indicator contributes equally to the overall Green Innovation Index score. Where applicable, inter-coder reliability or cross-validation procedures can be incorporated to minimize subjectivity in the interpretation of qualitative disclosures.

Finally, the resulting GII scores are analyzed using descriptive statistical techniques, including mean, median, and distribution analysis, to identify patterns of green innovation performance across firms. The analysis focuses on comparing performance across input, process, and output dimensions, as well as identifying gaps and asymmetries in sustainability practices. This analytical approach supports not only academic interpretation but also practical insights for corporate decision-making and environmental governance.

The findings of this study provide several important insights into the measurement and interpretation of green innovation in manufacturing firms. First, the results show that the web-based GII successfully translates heterogeneous sustainability disclosures into a standardized and comparable firm-level assessment framework. This is important because previous research has repeatedly noted that sustainability assessment in manufacturing is often fragmented, difficult to operationalize, and insufficiently standardized for use in real organizational settings. In this regard, the present study extends earlier indicator-based approaches by demonstrating that disclosure-based information can be digitally structured into a practical assessment architecture, rather than remaining a largely descriptive reporting exercise. This confirms the relevance of recent work arguing that manufacturing organizations need systematic and usable sustainability assessment tools that move beyond static or overly complex frameworks [13].

Second, the descriptive pattern observed in this study, namely strong performance in input-related indicators but weaker performance in output-oriented indicators, suggests that many firms are still positioned in an intermediate phase of green innovation development. The firms in the sample appear to have invested in foundational resources such as employee capability, training, and organizational systems, but these investments have not yet been consistently converted into patents, eco-designed products, or commercialized green innovations. This finding is highly consistent with the Resource-Based View, which emphasizes the strategic value of internal resources, but it also indicates that resource possession alone is insufficient unless firms can orchestrate those resources into tangible innovation outcomes. In that sense, the present study nuances the RBV literature by showing that environmental capability should not be inferred solely from organizational readiness; it must also be evidenced through output realization.

Third, the study’s results are broadly in line with emerging international evidence showing that digital transformation can strengthen green innovation and environmental performance. For example, recent research on listed firms found that digital transformation significantly improves both the quantity and quality of green innovation, while other work in traditional manufacturing sectors shows that digital transformation enhances corporate environmental responsibility through better ESG performance and stronger resource utilization. The present study supports this line of argument, but contributes a different angle: rather than focusing only on digitalization as an operational or strategic driver of green innovation, it shows that digitalization also matters as an assessment infrastructure. In other words, digital transformation is not only a cause of greener behavior; it is also an enabler of more transparent, standardized, and decision-relevant environmental evaluation. This is a meaningful extension of the literature because many prior studies focus on performance effects, whereas this article demonstrates how digital systems can make green innovation measurable at the firm level [31, 32].

Fourth, the findings also speak directly to the sustainability reporting literature. Prior studies show that issuing sustainability reports and using recognized frameworks such as GRI are positively associated with environmental performance, and that greater integration of ESG disclosures can improve internal decision-making and ESG outcomes. However, the same literature also warns that disclosure expansion does not automatically mean disclosure quality, and that reporting may still contain symbolic or impression-management elements. This concern is especially relevant to the present study because the GII relies on disclosed information as its primary data source. The article therefore makes an important methodological contribution: it treats sustainability reports not as unquestioned evidence of environmental excellence, but as structured inputs that require systematic coding and standardized interpretation. In the Indonesian context, this is particularly significant, because recent evidence suggests that while the number of sustainability reports has increased after the transition toward mandatory reporting, disclosure quality has not always improved accordingly. Thus, the GII framework responds to a real governance need: not merely more reporting, but better tools for extracting comparable substance from reported narratives [18, 20, 33-35].

Fifth, one of the most important findings in this study is the gap between compliance-oriented behavior and innovation-oriented outcomes. Firms scored relatively well on indicators related to environmental policy integration, regulation compliance, and participation in environmental programs, yet they performed much more weakly on patenting, open collaboration, and product-level innovation outputs. This suggests that environmental legitimacy is being built primarily through conformity and procedural commitment rather than through breakthrough innovation. Such a pattern is understandable in emerging-economy settings where institutional pressures are strong, but it also reveals an important limitation: compliance may improve environmental governance at a basic level without necessarily generating higher-value innovation. This interpretation is reinforced by recent evidence showing that not all forms of green innovation generate equivalent benefits. Research distinguishing pollution prevention innovation from pollution control innovation finds that prevention-oriented innovation is more strongly associated with future financial and environmental performance, whereas control-oriented innovation tends to produce weaker downstream benefits. Applied to the present study, this implies that the relatively low output scores may reflect not only limited innovation quantity, but also a continuing emphasis on defensive or compliance-based environmental practices rather than transformative innovation [36].

Sixth, the low scores on collaboration with universities, research institutions, and peers indicate that the firms in this sample still rely predominantly on inward-looking innovation models. This is an important finding because contemporary green innovation increasingly depends on open innovation ecosystems, inter-organizational learning, and knowledge recombination across institutional boundaries. Recent reviews on university–industry open innovation emphasize that collaboration mechanisms are central to the creation, diffusion, and scaling of innovation. In contrast, the limited collaboration and patenting activity observed in this study suggest that green innovation in the sampled firms remains only partially embedded in broader innovation networks. This helps explain why internal capabilities do not automatically translate into stronger outputs: without external linkages, firms may struggle to access complementary knowledge, advanced technologies, and co-development opportunities needed for more substantive green innovation. Therefore, the present study adds an ecosystem perspective to the firm-level assessment literature by showing that weak external connectedness is a plausible bottleneck in green capability conversion [37].

Finally, the broader implication of this study is that digital, disclosure-based environmental assessment should be understood as both a managerial tool and a governance mechanism. For managers, the GII can reveal where firms are accumulating capability but failing to convert it into outcomes. For policymakers, it offers a scalable way to benchmark firms and monitor whether sustainability reporting is producing substantive innovation signals rather than symbolic narratives alone. For researchers, the study opens an important pathway for integrating resource-based theory, disclosure studies, and digital governance into one analytical framework. Compared with prior literature, the novelty of this article lies not simply in proposing another sustainability index, but in demonstrating how a web-based system can operationalize green innovation from corporate disclosures in a way that is theoretically grounded, practically scalable, and contextually relevant to emerging manufacturing economies such as Indonesia [13, 31, 35].