Successful exploitation of an entrepreneurial idea calls for that idea to be translated into a viable business model (Amit & Zott, 2001), a challenge that many technology ventures struggle with. While some take up to 7 years to find a viable technology–market combination (Ambos & Birkinshaw, 2010), many others are unable to do so and must cease their activities altogether (Andries & Debackere, 2007). In our 2013 study, we aimed to generate further insights into how the process of business model development could take place in a more efficient and effective way by investigating six technology ventures and their business model development trajectories over time.

The main reason why technology ventures face serious challenges in defining viable business models upfront is that they are confronted with fundamental “Knightian uncertainty” (Knight, 1921). In situations of risk, decision‐making is still a quantifiable process.¹¹ As explained by Knight (1921, p. 20), “the term ‘risk’ as loosely used in everyday speech and in economic discussion, really covers two things which, functionally at least, in their causal relations to the phenomena of economic organization are categorically different …. The essential fact is that ‘risk’ means in some cases a quantity susceptible of measurement, while at other times it is something distinctly not of this character; and there are far‐reaching and crucial differences in the bearings of the phenomenon depending on which of the two is really present and operating …. It will appear that a measurable uncertainty or ‘risk’ proper, as we shall use the term, is so far different from an unmeasurable one that it is not in effect an uncertainty at all.” We follow Knight and use the term uncertainty to describe cases of the nonquantitative type.
Organizations can deal with these situations by developing contingency plans or calculating/balancing risk. In situations of Knightian uncertainty, however, this is no longer the case: Relevant variables and their functional relationships are, to a considerable extent, unknown (De Meyer, Loch, & Pich, 2002; Schrader, Riggs, & Smith, 1993). Differing interpretations of the situation exist, and it is initially unclear to the actors involved what information is needed to solve these differences (Van Looy, Debackere, & Bouwen, 2001). This is typically the case for technology ventures in emerging industries where what the market will become depends on multiple decisions by various stakeholders, and clarity will only arise when entrepreneurial activities result in tangible industry and market developments. Consequently, the set of feasible opportunities and viable business models is simply not predictable in advance (Alvarez & Barney, 2007).

At the time we conducted our study, the dominant view of practitioners, especially investors, was that the development and scaling of an entrepreneurial venture required choosing a specific business model and fully committing to it. Our research challenged this view by pointing to the relevance of a more flexible approach where ventures develop a portfolio of business model experiments; they refine and adjust this portfolio as they go, until a viable business model is found. Informed by our empirical results, we advanced the idea that, in emerging industries, opting for experimentation during the initial phases of the venture's life would be more beneficial than choosing focused commitment.

The recent COVID‐19 pandemic demonstrates that Knightian uncertainty not only applies to the trajectories on which ventures embark during the earlier life cycle phases of technologies (and industries). It seems that large parts of our economy, including companies that are more mature and even those that operate in low‐tech industries, must revise the assumptions and choices concerning their business models and value chain positions—how and where they operate— due to technological and consumer/market evolutions and discontinuities. Therefore, the question becomes: How can our 2013 study inform low‐, medium‐, and high‐tech companies that operate in established markets and are suddenly faced with Knightian uncertainty caused by the recent COVID‐19 situation?

成功地利用企业家的想法要求将该想法转化为可行的商业模式（Amit＆Zott，2001），这是许多技术企业都在努力应对的挑战。虽然有些人可能需要7年的时间才能找到可行的技术与市场的结合（Ambos和Birkinshaw，2010年），但许多其他人却无法做到，必须完全停止活动（Andries和Debackere，2007年）。在我们2013年的研究中，我们旨在通过调查六家技术企业及其随着时间的发展轨迹，以更有效的方式进一步深入了解商业模式开发流程。

技术企业在预先定义可行的业务模型时面临严峻挑战的主要原因是，它们面临根本的“骑士不确定性”（Knight，1921年）。在存在风险的情况下，决策仍然是可量化的过程。^1个1 正如Knight（ 1921，第 20），“风险”一词在日常演讲和经济讨论中都是宽松使用的，实际上涵盖了两件事，至少在功能上，它们与经济组织现象的因果关系是绝对不同的……。一个重要的事实是，“风险”在某些情况下是指易于测量的数量，而在其他时候，则显然不具有此特征。现象的根源存在深远和关键的差异，这取决于两者中的哪一个确实存在并在起作用……。我们将使用术语“可测量的不确定性或'风险'似乎与不可测量的相差甚远，以至于它实际上根本就不是不确定性。” 我们遵循Knight并使用术语不确定性来描述非量化类型的情况。
组织可以通过制定应急计划或计算/平衡风险来应对这些情况。然而，在奈特式不确定性的情况下，情况就不再如此：相关变量及其功能关系在相当程度上是未知的（De Meyer，Loch，＆Pich，2002 ; Schrader，Riggs，＆Smith，1993）。对于这种情况存在不同的解释，并且最初不清楚所涉及的行为者需要哪些信息来解决这些分歧（Van Looy，Debackere和Bouwen，2001年））。对于新兴产业中的技术企业而言，通常就是这种情况，市场的发展取决于各个利益相关者的多项决定，只有当企业家活动导致有形的产业和市场发展时，才会变得清晰。因此，一整套可行的机会和可行的商业模式根本无法预先预测（Alvarez＆Barney，2007）。

在我们进行研究时，从业者（尤其是投资者）的主要观点是，创业企业的发展和规模化需要选择一种特定的商业模式并完全致力于它。我们的研究通过指出一种更灵活的方法的相关性对这一观点提出了挑战，在这种方法中，风险企业开发了一系列商业模型实验；他们会不断完善和调整此投资组合，直到找到可行的业务模型。根据我们的经验结果，我们提出了这样的想法，即在新兴行业中，选择合资企业生命周期初期的实验比选择专注的承诺更为有益。

最近的COVID-19大流行表明，耐特的不确定性不仅适用于风险投资在技术（和行业）生命周期早期阶段所走的轨迹。似乎我们经济的大部分，包括更成熟的公司，甚至是从事低技术行业的公司，都必须由于技术的原因，修改有关其商业模式和价值链地位的假设和选择，即他们的运作方式和地点。以及消费者/市场的演变和不连续性。因此，问题就变成了：我们的2013年研究如何为在成熟市场中运营，突然面临因近期COVID-19局势而导致的Knightian不确定性的低，中，高科技公司提供信息？