Recently, Digital Financial Advice Solutions (i.e., “Robo Advice” or “Robo Advisory”) are emerging rapidly within the financial services sectors, which can be outlined by the respective Assets under Managements’ CAGR of 255.9 % from 2016 to 2018 in Germany (Kaya, 2019). However, these developments imply both opportunities and threats for traditional financial institutions: On the one hand, potential customer out-migrations, the loss of cross-selling potentials and potential yields as well as challenged competitiveness pose significant risks. On the other hand, if traditional banks manage to implement appropriate measures timely, the recent developments also offer great market potentials. Thus, it is inevitable to identify, understand and discuss factors that drive the customers’ future usage intention of Digital Financial Advice Solutions. As a result, we derive, from the traditional financial institutions’ point of view, strategic and managerial implications on how to deal with the currently emerging trends of Digital Financial Advice Solutions. For this purpose, we conducted a questionnaire-based online survey, which ultimately led to 600 evaluable observations. Finally, according to the two strands of literature this study bases on, i.e., the Net Valence Framework and Unified Theory of Acceptance and Use of Technology 2, we built a partial least squares (PLS)-based structural equation model (SEM)¹ that incorporates a comprehensive set of variables. In doing so, we contribute to not only the general understanding of Digital Financial Advice Solutions and two different strands of literature but also to the solution of issues that are of great relevance for practitioners, too. Subsequently, this study concludes by the derivation of future research requirements regarding these, both theoretically and practically, important matters.

近期，数字金融咨询解决方案（即“Robo Advice”或“Robo Advisory”）在金融服务领域迅速兴起，这可以用德国各管理资产2016年至2018年的复合年增长率为255.9%来概括（卡亚，2019 年）。然而，这些发展对传统金融机构来说既意味着机遇，也意味着威胁：一方面，潜在的客户外流、交叉销售潜力和潜在收益的丧失以及竞争力的挑战构成了重大风险。另一方面，如果传统银行能够及时采取适当措施，近期的发展也提供了巨大的市场潜力。因此，识别、理解和讨论驱动客户未来使用数字金融建议解决方案的因素是不可避免的。因此，我们从传统金融机构的角度得出了如何应对当前数字金融建议解决方案新兴趋势的战略和管理影响。为此，我们进行了一项基于问卷的在线调查，最终产生了 600 个可评估的观察结果。最后，根据本研究所基于的两股文献，即净价框架和技术接受和使用的统一理论 2，我们建立了基于偏最小二乘法 (PLS) 的结构方程模型 (SEM) 这最终导致了 600 个可评估的观察结果。最后，根据本研究所基于的两股文献，即净价框架和技术接受和使用的统一理论 2，我们建立了基于偏最小二乘法 (PLS) 的结构方程模型 (SEM) 这最终导致了 600 个可评估的观察结果。最后，根据本研究所基于的两股文献，即净价框架和技术接受和使用的统一理论 2，我们建立了基于偏最小二乘法 (PLS) 的结构方程模型 (SEM)¹包含一组全面的变量。这样做，我们不仅有助于对数字金融建议解决方案和两种不同的文献的一般理解，而且有助于解决与从业者非常相关的问题。随后，本研究通过推导关于这些理论和实践重要问题的未来研究要求得出结论。