Managing Payment Flexibility in Rent-to-Own Contracts for Off-Grid Energy Products
Bibliographic record
Abstract
Problem definition: The diffusion of technological innovations in low- and middle-income countries (LMICs) has been facilitated by the use of rent-to-own (RTO) business models, which give flexibility to consumers by allowing them to make incremental payments over time. Understanding how to best manage this flexibility is a fundamental problem for firms in LMICs. Motivated by an application of RTO to the distribution of solar lamps, we examine the drivers and impact of payment flexibility in RTO contracts. Methodology/results: We formulate a dynamic programming model that characterizes an important dimension of payment flexibility (i.e., the ability of consumers to make bundled payments (multiple installments paid at once)). We show that consumers may bundle payments because of uncertainty about budget in future periods, which leads to a nonmonotonic impact of income uncertainty on repayment performance. We prove that bundled payments are more likely to occur closer to the end of the ownership cycle. We further show that the firm’s expected profit objective is perfectly aligned with its social mission of increasing consumer access by reducing the consumers’ expected time to ownership. We examine different flexibility levers that the firm can adjust as part of its contract design (repayment frequency and grace period), accounting for the impact of bundled payments. Our results suggest that an intermediate level of flexibility may benefit both the firm and consumers under certain conditions. Our numerical analysis indicates the robustness of our main results to relaxing several modeling assumptions. Managerial implications: Whereas payment flexibility is a fundamental component of RTO contracts in LMICs, our findings indicate that a moderate level of flexibility can go a long way in helping firms and consumers. Hence, RTO firms may not need to offer extreme degrees of flexibility to achieve desirable outcomes. This is an important insight for RTO firms aiming to balance profits and consumer access. Funding: This work was conducted while E. Rashidinejad was supported by the Ontario Graduate Scholarship. The research of G. Romero was partially supported by the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2025-05848]. The research of G. Romero and H. Zaman was partially supported by the Michael Lee-Chin Family Institute for Corporate Citizenship at the Rotman School of Management, University of Toronto. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.0989 .
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".