There is a rarity of research about technology management in the solar energy industry in Rwanda and very little analysis is available on the effectiveness of the technology used in solar home systems (SHSs). Using a binary logistic regression to primary data collected through Kobo Collect, this study assesses the factors that are involved in SHS business in Rwanda. The findings reveal a difference in successfully collecting money from customers who purchase SHSs on an installment payment plan concerning the type of SHS (i.e. pay-as-you-go (PAYG) and non-pay-as-you-go (non-PAYG)). In terms of the odds ratio concept, a statistical interpretation was provided. If the customer has a non-PAYG SHS, getting a reminder from the solar energy company’s agent will increase by 9.52 times. That means, it is 9.52 times higher for the non-PAYG SHS as compared to a customer with PAYG SHS. The results also show that the multicollinearity assumptions for the economic, social, technological, and management models are not violated and are optimal because each of the variance inflation factors (VIFs) was close to unity. However, only the socialization predictors were not significant enhancements in the goodness-of-fit relative to the intercept-only model. The Nagelkerke R2 indicates only an average (49.5%) relationship between the prediction and grouping of the modified economic pricing model. The Hosmer–Lemeshow measures indicate that the models in this research are optimal and good fits for the data studied. Furthermore, the predicted success overall performance for the economic, social, technological, and managerial models was 73.2% on average. The Wald criterion equally demonstrated that only the monthly installment amount (with and without outliers), type of SHS, and payment channel made significant contributions to the prediction optimization problem. Therefore, the solar energy companies in Rwanda need to rely on the findings of studies like this to successfully manage customers’ accounts optimally.

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卢旺达太阳能技术管理优化及影响评估

关于卢旺达太阳能行业技术管理的研究很少，而且对家用太阳能系统（SHS）所使用技术的有效性的分析也很少。本研究对通过 Kobo Collect 收集的主要数据进行二元逻辑回归，评估了卢旺达 SHS 业务所涉及的因素。调查结果显示，通过分期付款计划购买 SHS 的客户成功收款的方式存在差异，涉及 SHS 类型（即现收现付 (PAYG) 和非现收现付 (non-PAYG)） ））。根据比值比的概念，提供了统计解释。如果客户拥有非PAYG SHS，收到太阳能公司代理商的提醒将增加9.52倍。这意味着，非 PAYG SHS 客户的费用是 PAYG SHS 客户的 9.52 倍。结果还表明，经济、社会、技术和管理模型的多重共线性假设并未被违反，并且是最优的，因为每个方差膨胀因子 (VIF) 都接近于 1。然而，相对于仅截距模型，只有社会化预测变量的拟合优度没有显着增强。内格尔克 R2仅表示修改后的经济定价模型的预测和分组之间的平均关系（49.5%）。Hosmer-Lemeshow 测量表明本研究中的模型是最优的并且非常适合所研究的数据。此外，经济、社会、技术和管理模型的预测成功总体绩效平均为 73.2%。Wald 准则同样证明，只有每月分期付款金额（有或没有异常值）、SHS 类型和支付渠道对预测优化问题有显着贡献。因此，卢旺达的太阳能公司需要依靠此类研究的结果来成功地以最佳方式管理客户的账户。