Designing a Location-Based Digital Waste Bank System Model: A Design Science Research Approach for Urban Waste Service Integration

1.1 Background

Urban waste management has shifted from a technical issue of collection to a complex matter of public service governance. Population growth, changing consumption patterns and the expansion of urban economic activities are increasing the volume of waste whilst simultaneously heightening the need for systems capable of reducing waste at source. Recent literature positions the sustainability of waste management as the result of the interaction between technical, environmental, social, economic, institutional and governance dimensions [1]. Consequently, an effective waste management system cannot rely solely on end-of-life facilities, but must strengthen waste sorting, collection, record-keeping, material recovery and coordination amongst service providers.

Waste banks are a form of local institution that is relatively well-suited to the nature of community-based waste management in Indonesia. Through mechanisms for depositing waste and recording its economic value, waste banks can encourage changes in household behavior whilst broadening community participation in waste reduction. Recent research indicates that the practice of waste banks contributes to local environmental resilience [2], but their uptake and effectiveness are still influenced by organizational readiness, administrative regularity, and operational system support [3]. The problems that frequently arise are not merely the absence of waste banks, but rather a lack of readily accessible information regarding their locations, services, timetables, transactions, and collection arrangements.

In Makassar, the operational need for an integrated digital waste bank model is supported by recent local evidence. A comparative study of waste bank policy implementation in Makassar and Bantaeng [4] reported that implementation had not been optimal, particularly because stakeholder communication, community socialization, and public-private participation were still limited. A study of community-based waste banks in Makassar further showed that programme challenges were associated with sustainability and resource mobilization, including the need to strengthen stakeholder roles, strategic partnerships, and user-friendly technology [5]. A recent multi-stakeholder study in Makassar identified limited community involvement, inadequate resources and infrastructure, market instability, knowledge gaps, geographical-logistical constraints, fragmented authority, and communication breakdowns as major constraints in waste bank management [6]. These academic findings are consistent with broader Indonesian evidence showing that digitalization can strengthen waste-recycling platforms, improve transaction visibility, and support circular-economy practices [7], while the official waste bank portal listed 323 waste bank units and 7,252 customers in 2026, indicating the need for updated, integrated location and service data [8]. Evidence from community-based waste bank governance in Indonesian cities also shows that internal human-resource capacity, stakeholder support, and the ability to build a waste-management economic chain are important conditions for sustaining waste bank contributions [9].

The role of waste banks is becoming increasingly important as waste management is linked to the circular economy. Waste is no longer viewed as a final by-product, but rather as a material that can be returned to the value chain through sorting, collection, processing, and reuse. The integration of the circular economy into waste management in Indonesia is considered relevant in responding to urbanization and environmental pressures [10]. The waste hierarchy principle also emphasizes prevention, reuse, recycling, and resource recovery as the basis for more sustainable management [11]. Within this framework, waste banks can act as the initial hub for restoring material value, provided that service flows, user data, transactions, and collection are managed in an orderly and interconnected manner.

1.2 Literature review on waste banks and the circular economy

Studies on waste banks indicate that service continuity depends on the relationship between community participation, managerial capacity, and routine administrative control. Waste banks provide a channel for residents to sort and deposit recyclable materials [2], while national evidence shows that their effectiveness varies according to organizational readiness, operational discipline, and information-system support [3]. Evidence from Makassar strengthens this argument: collaborative policy implementation remains constrained by limited communication, socialization, and public-private participation [4], while community-based waste bank development requires stronger resource mobilization, clearer stakeholder roles, strategic partnerships, and user-friendly technology [5]. Accordingly, the operational issue is not only whether waste banks exist, but whether their customer data, service schedules, waste categories, deposit records, transaction values, and reports can be managed as a connected service process.

The circular economy literature clarifies why this operational integration matters. Circular waste management in developing economies requires policy coordination, community participation, economic incentives, and productive resource use [10]. Charging and incentive schemes must also consider user behavior and the financial logic of participation [11], while transition studies emphasize that institutional arrangements, infrastructure, and operational procedures must change together [12]. Recent evidence from Makassar adds a local operational layer to this argument by identifying limited community involvement, inadequate resources and infrastructure, market instability, knowledge gaps, logistical barriers, fragmented authority, and communication breakdowns as constraints in waste bank management [6]. Thus, waste banks should not be treated only as community savings units; they should be designed as traceable service nodes that connect material flows, actor responsibilities, transaction records, and monitoring data.

The concept of waste as a recoverable resource provides the conceptual basis for this integration. Regenerative business models position municipal waste within an economic cycle [13]. Resource-recovery, waste-to-energy, and plastic-upcycling studies also show that material value depends on reliable collection and supporting data [14, 15]. However, these works mainly address downstream technologies or broader economic models. For waste bank services, the immediate design requirement is a model that records deposits, collection points, materials, calculated values, and verification.

1.3 Literature review on digitalization and smart waste management

Digitalization offers a practical response to weaknesses in waste bank services, particularly access to information, coordination, transparency, and monitoring. Internet of Things (IoT)-based smart waste management studies show the value of data connectivity, service-condition monitoring, and timely operational response [16-18]. Smart city research further demonstrates that digital technology can support the management of urban environmental services, including waste, water, air, and transport [19], and can improve coordination in municipal service delivery [20]. For waste banks, however, the relevant contribution is not the adoption of advanced sensing alone. The immediate need is an information system that connects location information, service requests, collection schedules, transaction records, balances, and reports in one workflow.

Digital transformation studies also indicate that information systems contribute to sustainability through data collection, decision support, organizational coordination, and technology adoption strategies [21-24]. Evidence from the use of a waste bank management application in Bandung shows a similar pattern: perceived usefulness and ease of use can be high, but actual use may remain limited when regulation is not mandatory, training is insufficient, digital literacy varies, technical assistance is weak, and operational data such as waste prices require manual updating [25]. This finding is directly relevant to the proposed model because a waste bank application cannot be evaluated only as an interface. It must also support reliable data entry, role-based responsibilities, updated service information, transaction accuracy, and sustained use by administrators and customers.

Several technical developments in smart waste management are useful as design references, but their relevance must be positioned carefully. Digital transformation literature shows that digitalization can support sustainability by improving data visibility, coordination, and decision-making across organizational and service processes [26]. In waste management, AI- and IoT-oriented architecture reviews emphasise the value of status data, notification mechanisms, and route-related information for municipal service coordination [22], while artificial intelligence studies show potential for classification, prediction, and optimization in urban waste management [27]. Nevertheless, the present study does not implement IoT sensors, artificial intelligence, or digital twin technology. These studies are therefore used to derive design requirements related to data availability, service status, route information, and future extensibility, not to claim that those advanced technologies have already been deployed. Within this narrower scope, smart waste management frameworks support the integration of managerial processes and digital functions [28], which is aligned with the search for the nearest waste bank, collection-request allocation, status updates, transaction recording, and monitoring.

Traceability and coordination remain central to the proposed system model. Blockchain and multi-agent simulation studies show that smart waste systems require mechanisms for trust, traceability, and service evaluation [29, 30]. In this article, those mechanisms are translated into conventional information-system functions rather than blockchain implementation: verified service requests, actor-specific access, transaction histories, and auditable records. The path towards sustainable waste management in smart cities also depends on the alignment of policy, infrastructure, technology, and public participation [31]. Consequently, the proposed digital waste bank model must clarify data flows, actor roles, verification procedures, and transaction recording before broader performance, security, or optimization modules can be tested.

1.4 Research gaps

The reviewed literature indicates three streams of knowledge, but it also reveals a specific design gap. Studies on waste banks mainly emphasize community participation, organizational readiness, resource mobilization, and local operational constraints [2-6]. Circular economy studies explain policy coordination, incentive mechanisms, resource recovery, and the economic logic of returning materials to productive use [10-15]. Digital and smart waste management studies discuss IoT, artificial intelligence, simulation, traceability, mobile applications, and service optimization [18, 19, 25-29, 32]. However, these streams do not yet sufficiently explain how a community-based waste bank can be modelled as an integrated urban service that combines location search, pickup scheduling, transaction recording, balance management, administrator verification, driver assignment, and supervisory monitoring within a single artefact.

The gap is therefore practical, methodological, and conceptual. Practically, users need reliable information on nearby waste banks, available services, collection schedules, and transaction values, while managers need tools for request verification, waste-category recording, transaction validation, and service history monitoring. Existing application-oriented studies show the usefulness of location-based services, crowdsourcing, and digital transaction support for waste bank participation [32, 33]. Nevertheless, many of these studies focus on a particular application feature, user interface, or implementation case rather than a generalized system model that explains actor responsibilities, data movement, service verification, and monitoring logic. Methodologically, the present study uses Design Science Research (DSR) to produce a design artefact whose contribution is the explicit modelling of service processes, architecture, database relationships, and prototype functions. Conceptually, the study positions the waste bank not only as a community unit, but as a data-driven service node that links urban waste collection with circular-economy transaction records and future decision support [34].

1.5 Aims and contributions

This research aims to design and evaluate a DSR-based model for a location-based digital waste bank system. The model integrates nearest-waste-bank search, service recommendation, collection requests, scheduling, administrator verification, driver task assignment, transaction recording, balance management, and service monitoring. The design is tailored to urban waste bank services in Makassar and defines four main actors: customers, waste bank administrators, drivers, and superadmins. The study does not claim to measure city-wide operational effectiveness, recycling-rate improvement, route optimization performance, or long-term economic impact. Instead, it focuses on the development and preliminary evaluation of a system model and functional prototype that can be used as a basis for subsequent implementation and field testing.

The contribution of this research is threefold. First, it provides an artefact-oriented information system model that translates waste bank operations into linked use cases, activity flows, sequence interactions, class structures, database entities, architecture layers, and interface prototypes. Second, it clarifies the difference between a digital waste bank catalogue and an integrated service model by connecting location information, pickup coordination, transaction validation, account balance records, and monitoring dashboards. Third, it refines the application of smart waste management concepts to the institutional characteristics of waste banks, where community participation, administrator verification, and traceable transaction data are more central than sensor deployment or fully automated optimization. These contributions are intended to support future empirical testing of service efficiency, data accuracy, user adoption, system security, and scalability.

3.1 Results of stakeholder mapping and service requirements

The stakeholder mapping identified four actor groups and translated their roles into service requirements. Customers require access to nearby waste bank information, service availability, pickup request submission, transaction history, and balance visibility. Waste bank administrators require functions for updating waste bank data, verifying pickup requests, validating waste categories and weights, recording transaction values, and correcting service status. Drivers require assigned pickup lists, customer location information, schedule details, and status update functions. Superadmins require aggregate monitoring, activity summaries, reporting, and system-level control. This mapping responds to the operational fragmentation identified in Makassar waste bank services, where location data, service schedules, collection activities, and transaction records are not yet consistently connected [4-8].

The resulting requirements show that the system should not be treated as a simple waste bank directory. The core service requirement is an integrated workflow that connects actors, service requests, location data, verification decisions, collection execution, transaction recording, balance updates, and monitoring outputs. This finding is consistent with previous studies that emphasize the need for institutional readiness, operational support, and information-system strengthening in waste bank management [2, 3, 33]. The stakeholder mapping also clarifies role boundaries: customers initiate and monitor services, administrators validate operational and transaction data, drivers update field execution, and superadmins evaluate aggregated service performance.

3.2 Results of the service flow and system interaction design

The service flow design produced a sequential model that begins with customer location activation and nearest waste bank search. The customer then reviews available services, submits a pickup request, and provides address, waste type, estimated weight, and preferred schedule. The waste bank administrator verifies the request before assigning or confirming the driver task. The driver updates pickup status during field execution, while the administrator records the final waste weight, transaction value, and balance update after collection. These activities produce service data that are forwarded to the monitoring dashboard. Figure 3 presents this workflow as an activity model and shows the control points where requests, status changes, and transactions are verified.

The system interaction design in Figure 4 shows that the location service functions as the entry point for several subsequent processes rather than as a stand-alone map feature. Customer coordinates and registered waste bank coordinates are used to support nearest-waste-bank recommendations; the selected waste bank then becomes the reference for pickup scheduling, administrator verification, driver assignment, and transaction recording. This interaction pattern strengthens reproducibility because it clarifies which actor initiates each process, which component receives the request, which data are stored, and when service status is updated. It also addresses the reviewer's concern that location-based services should explain the connection between distance calculation, service selection, and operational follow-up.

Figure 3. Activity diagram of the proposed waste bank pickup service

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Figure 4. Sequence diagram of the pickup request process

3.3 Results of the data structure and system architecture design

The data structure design resulted in a set of related entities that support actor identity, service location, pickup coordination, transaction validation, and monitoring. The class diagram in Figure 5 represents users, customers, drivers, waste banks, pickup requests, and transactions as connected objects. The pickup request class acts as the operational link between customer demand, waste bank verification, driver assignment, and transaction recording. This relationship is important because one pickup request can generate service-status data, actual waste-weight data, transaction value, and balance updates. The model therefore supports traceability from the initial customer request to the final transaction record.

The database design in Figure 6 translates the class model into storage entities for users, customers, drivers, waste_bank, pickup_request, waste_category, transactions, balances, and activity history. The waste_bank entity stores identity, address, coordinates, service availability, and active status. The pickup_request entity stores customer identity, selected waste bank, pickup address, estimated waste type and weight, schedule, verification status, driver assignment, and completion status. The transaction entity stores verified weight, unit price, total value, balance update, and transaction date. These relationships allow administrators to check whether a transaction is linked to a valid customer, an active waste bank, and a completed pickup request.

The architecture result is a three-layer model consisting of the client layer, application layer, and data layer, as illustrated in Figure 2. The client layer separates access for customers, waste bank administrators, drivers, and superadmins. The application layer contains authentication, role management, location processing, recommendation ordering, pickup-request management, verification, driver-task management, transaction processing, balance calculation, reporting, and monitoring. The data layer stores operational records and provides the basis for dashboards and reports. The security boundary is designed through authenticated access, role-based function separation, administrator verification, and auditable activity history. At this stage, the architecture remains a design artefact; production-level encryption, penetration testing, scalability testing, and deployment infrastructure are reserved for later development.

3.5 Results of functional testing and user evaluation

Functional testing was conducted using black-box scenarios for the main functions of the prototype. The testing verified whether each function produced the expected output when accessed by the appropriate actor. The tested modules included authentication, waste bank search, pickup request submission, administrator verification, driver task updating, transaction and balance recording, and monitoring dashboards. The results in Table 1 show that these functions were valid in terms of input-output conformity. However, the test was limited to functional behavior and did not evaluate response time, system load, security vulnerability, scalability, route optimization, data loss, or failure recovery.

The user perception evaluation involved 32 early evaluators and used five indicators derived from the system requirements. The overall acceptance score was 89.50%, which indicates a positive preliminary perception of the proposed design. The highest scores were recorded for usefulness of the pickup service and support for community participation, each at 91.25%. These results suggest that respondents considered pickup support and participatory service access to be relevant features for waste bank use. The lowest score was interface ease of use at 86.25%, indicating that the collection form, administrator verification flow, and driver status update process still require simplification before broader field testing. The detailed results are presented in Table 2.

These evaluation results should be interpreted cautiously. The 32 respondents were early evaluators rather than a statistically representative sample of Makassar residents or all waste bank users. The questionnaire measured perceived usefulness, usability, information clarity, service relevance, and initial feasibility, but it did not measure operational performance. Therefore, the results support the feasibility of continuing system development, while further empirical testing is still required to measure transaction accuracy, service response time, route efficiency, security, scalability, and long-term economic or environmental impact.

Table 1. Summary of black-box functional testing

Table 2. User perception evaluation of the proposed system

3.6 Discussion

The findings indicate that the proposed model addresses a practical service gap in waste bank operations by linking location search, pickup requests, administrator verification, driver tasks, transactions, balances, and monitoring. This result extends previous waste bank studies that emphasize community participation, institutional readiness, resource mobilization, and operational support [2-6]. The contribution of the present study is not the claim that a full digital service has already improved urban waste performance, but the modelling of an integrated service artefact that clarifies how actors, data, and service decisions can be connected before large-scale implementation.

From the perspective of the circular economy, the model supports the early information layer needed for material value recovery. Circular economy studies emphasize policy coordination, incentives, resource recovery, and the need to return materials to productive use [10-15]. The proposed model complements these studies by specifying how waste weight, transaction value, balance updates, and pickup history can be recorded as traceable service data. Nevertheless, the present study does not measure actual recycling rates, recovered material quantity, household income change, or landfill reduction. The circular-economy implication is therefore positioned as a design contribution that prepares measurable data for future evaluation, not as evidence of realized environmental impact.

The results are also consistent with smart waste management literature, but the scope of technological adoption is deliberately narrower. Studies on smart waste systems discuss IoT connectivity, artificial intelligence, simulation, route optimization, and decision support [18, 19, 30, 31, 40-42]. In this study, those references are used to inform the need for data connectivity, service status, traceability, and future extensibility. The prototype does not implement IoT sensors, artificial intelligence, blockchain, digital twins, or automated route optimization. This clarification is important because the proposed model is designed for the institutional characteristics of waste banks, where administrator verification, community participation, location data, and transaction validity are more immediate design needs than advanced automation.

The user evaluation provides preliminary support for the relevance of the model, especially because pickup services and community participation received the highest scores. This result is aligned with technology acceptance evidence from waste bank services, where perceived usefulness and ease of use influence the adoption of digital waste management applications [33]. At the same time, the lower score for interface ease of use indicates that acceptance cannot be assumed from feature availability alone. The prototype must be refined through task-based usability testing, clearer form design, simplified verification steps, and broader field evaluation with active waste bank operators and residents.

Overall, the study contributes to information systems research by demonstrating how DSR can structure a waste bank service problem into requirements, models, architecture, prototype modules, and preliminary evaluation. The discussion also defines the boundary of the contribution. The model provides a foundation for integrated digital waste bank services, but further studies are required to test performance, security, scalability, transaction accuracy, field adoption, and measurable circular-economy outcomes. This staged interpretation keeps the findings consistent with the evidence produced in the present study and avoids implying practical effectiveness that has not yet been experimentally verified.

The authors declare no conflict of interest regarding the publication of this manuscript.