Business Model and Strategy for Sustainable Lending of State-Owned Banks in Indonesia
Bibliographic record
Abstract
Currently, banks are facing challenges in fulfilling the interests of stakeholders, not only from an economic point of view, but also in terms of environmental, social, and governance (ESG) aspects. This is due to the increasing concern for sustainability issues, including lending activities. Lending activities constitute the largest portion of bank assets and are the largest contributor to bank revenues. Thus, banks need certain business models and strategies to encourage sustainable lending growth; otherwise, it will be difficult for banks to fulfill stakeholder’s interests and support sustainable development goals. This study aimed to build a sustainable business model and select sustainable lending strategies in state-owned banks in Indonesia using a value chain approach. The development of a sustainable business model utilizing a triple-layer business model canvas (TLBMC) is based on the results of previous research and sustainability report data of the three state-owned banks. The formulation of strategy selection as the key driver of sustainable lending utilized the analytical hierarchy process (AHP) based on expert respondent data collected through questionnaires. This research showed that the lending distribution business model at state-owned banks in Indonesia, which was built using the TLBMC framework, can realize sustainability goals in the form of a sustainable lending business model. Furthermore, this sustainable business model can be used as a basis for selecting sustainable strategies. In addition, the AHP results yielded alternative strategies in the form of the market development and penetration of green loans and micro, small, and medium enterprises (MSMEs) as the key drivers of sustainable lending growth.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".