The thought‐provoking paper by Cooper (2021) provides a beacon for researchers across disciplines contributing to the domain of innovation management. Cooper (2021) identifies five developments originating from the need for accelerating product development in the COVID‐19 pandemic, but he also highlights a root cause which lies at a deeper level of change, viz. digital transformation. Digitalization has increasingly reduced the innovation cycles and accelerated new product development. The current pandemic is acting as a virtual magnifying glass and shows in a micro snapshot what the future has in store for innovation management. Cooper (2021) rightly points out that digital product development will increase the efficiency by 19% and reduce the time‐to‐market by 17% on average. These statistics are impressive, but even more impressive is the performance of the top 10% digital product champions (PWC, 2019): they increase their efficiency by 31%, reduce their time‐to‐market by 28%, and their production cost by 20% (13% on average). Digitalization has fundamentally changed the way we innovate by introducing novel digital business models, new smart products and services, emerging digital technologies, and the evolution of the experience economy. How can academic research support this paradigm shift to digital innovation management?

First of all, and maybe most importantly, we need to learn across disciplines. The COVID‐19 pandemic has clearly shown us that we need a truly interdisciplinary approach to find solutions to the challenges it has posed to the global community. Numerous disciplines have explored the potential impact of digital transformation on innovation management, but we still lack an interdisciplinary approach to study phenomena from an integrative perspective to avoid white spots in our field of study. Digital transformation requires the revision of our theories as well as our methods. Our classical methods based on surveys, experiments, and qualitative techniques might only show us part of the phenomena we are studying. Digital transformation has resulted in the availability of large quantities of unstructured data, such as text, images, video, audio, eye‐tracking, facial expressions and neuro data, and is growing at an ever‐increasing pace (Balducci & Marinova, 2018). It is estimated that unstructured data constitute about 80%–90% of all data worldwide. How can the potential of unstructured data be harnessed for innovation management? Unstructured data requires novel data analytical approaches to extract features for further analysis. For instance, Khanday et al. (2020) show that machine learning can be used to diagnose COVID‐19 using clinical text data. However, most of this research still takes place in disciplinary silos and ultimately we will need to integrate these conceptual and empirical insights in order to stimulate truly interdisciplinary research in innovation management.

One of the more popular tools in the digital innovation toolbox is crowdsourcing. More in particular innovation contests, which operate using digital platforms allowing participants to contribute by generating, developing, and discussing ideas. For example, Vermicelli et al. (2021) provide evidence from 16 projects that crowdsourcing can provide novel and effective solutions for COVID‐19 challenges. In order to study how innovation processes evolve during innovation contests we need to take an interdisciplinary perspective. We need to learn how users communicate with each other during innovation contests, typically using textual data, and with whom they communicate at which point in time. In order to realize the potential of this data and these new approaches and theoretical lenses we need to learn from other disciplines, such as information systems, computer science, linguistics, education, and communication. This involves using computational linguistics, natural language processing, and social network analysis to provide additional insights over the use of focus groups and surveys (cf. Ludwig et al., 2014).

Innovation hardly ever takes place in splendid isolation anymore. Quite the contrary, the innovation process involves co‐creation by a network of different parties in the innovation process. For example, BioNTech and Pfizer development decided to cooperate early in the development process of their COVID‐19 vaccine, the first mRNA vaccine to be authorized for humans. Information can also be obtained from the communication with customers, such as by virtual and human agents. These insights can be complemented by data generated by sensors in smart products and services on the Internet‐of‐Things. Smart products and services create value to customers as a bundle of cyber‐physical arrangements (Raff et al., 2020). Customers need to co‐create the data that is necessary to personalize their customer experience over time. In order to provide a personalized customer experience during the touchpoints of the customer journey firms will need data from their customers. Design thinking concepts and tools can be helpful to integrate all this information in a meaningful way (Micheli et al., 2019). For instance, the mobile apps experience fellow (https://www.experiencefellow.com) or indeemo (https://indeemo.com) allow customers to record the touchpoints across their customer journey. This information is then compiled into dashboard and provides valuable insights for digital innovation management. However, we need to be aware of privacy issues, as customers give up their data for a more personalized customer experience. A key example for the importance of privacy concerns is the development of various COVID‐19 mobile tracking apps based on Bluetooth technology.

Teams have been considered the panacea for new product development. However, the underlying mechanisms of teamwork remain under‐researched. A team is more than the sum of its members, but the interaction process leading to team performance is not well understood. Video and audio recording of interaction patterns in teams can provide more granular insights by automatic coding of gestures and facial expressions, voice analysis, and transcription of the audio sequences. Using wearables with multiple sensors, such as sociometric badges (Pentland, 2008) might provide complementary insights, as they focus on body movement, speech features, proximity, and face‐to‐face communication among team members. Virtual innovation teams, and increasingly most teams are virtual in the COVID‐19 pandemic, are faced by even more critical challenges. Virtualness in teams has an almost universally negative impact on team performance and satisfaction. However, for longer‐term teams these effects are considerably weaker or even disappear (De Guinea et al., 2012). Especially, dedicated teams have proven invaluable in the development of the COVID‐19 vaccine by BioNTech and Pfizer. We need more research at the individual and team level to determine how virtual teams should be composed at different stages of the product development process.

Recently, Rindfleisch et al. (2020) highlighted the importance of new digital technologies for innovation management. However, a content analysis by the same authors showed that actually less than a dozen papers have focused on these new technologies, predominantly covering social media and big data. Digital technologies, such augmented and virtual reality (AR/VR), artificial intelligence (AI), and blockchain have not received adequate attention in the innovation management community to date. Augmented and virtual reality (AR/VR) shows great promise to provide customers with a virtual prototype of products and services at a relatively low cost and within a short timeframe. Different version of products and services can easily be created and used for testing. For example, 42dp Labs build a virtual store for the German retailer Metro for their new stores in France for testing purposes (https://www.42dp.com/projects/metro‐vr‐store/). In addition, 3D‐printing may provide haptic feedback from new products prototypes, while audio and scents could be employed to simulate a multisensory customer experience. Artificial intelligence is developing fast to impact the idea generation and development stages of the innovation process. Artificial intelligence and natural language processing have also been employed to find drug candidates for COVID‐19 (Keshavarzi Arshadiet et al., 2020). Blockchain technology is an under‐researched digital technology in innovation management. Its main applications are currently in finance, for example, cryptocurrencies. Some researchers compare the potential impact of blockchain technology in the upcoming decennium to the advent of the Internet in 1990s (cf., Morabito, 2017). Blockchain might also provide a solution to privacy concerns on social media. For example, Diaspora is a social media platform built on the blockchain principles, which allows users to own their data without revealing their identity. Some blockchain social networks, such as Steemit and Minds, even use cryptocurrency to pay users for contributing to the platform.

The current COVD‐19 pandemic has highlighted the ongoing paradigm shift in innovation management. The real challenge for innovation researchers and practitioners alike is to learn from the current pandemic situation for the future of digital innovation management. The key message is: Digital innovation is the future and there is no way back. Interdisciplinary research will no longer be a “nice to have,” but rather a “must have.” Unstructured data, big data, and digital tools, such as crowdsourcing, will show us the road to new insights. Co‐creation with customers and other network parties will open new doors for innovation in the emerging experience economy. Emerging digital technologies will provide new approaches to study and understand phenomena in innovation management.

Cooper（2021）发人深思的论文为跨学科的研究人员提供了灯塔，为创新管理领域做出了贡献。Cooper（2021）确定了五项发展，这些发展源于在COVID-19大流行中加速产品开发的需要，但他也强调了更深层次变化的根本原因，即。数字化转型。数字化日益减少了创新周期，并加快了新产品的开发。当前的流行病就像一个虚拟的放大镜，并在微观快照中显示了创新管理的未来前景。库珀（2021）正确地指出，数字产品开发将平均提高效率19％，缩短上市时间17％。这些统计数据令人印象深刻，但排名前10％的数字产品冠军的表现则更令人印象深刻（PWC，2019）：他们将效率提高了31％，将产品上市时间缩短了28％，并将生产成本降低了30％。 20％（平均13％）。数字化通过引入新颖的数字业务模型，新的智能产品和服务，新兴的数字技术以及体验经济的发展，从根本上改变了我们的创新方式。学术研究如何支持这种范式向数字创新管理的转变？

首先，也许是最重要的是，我们需要跨学科学习。COVID-19大流行清楚地向我们表明，我们需要一种真正的跨学科方法来寻找解决方案，以应对全球社会所面临的挑战。许多学科都探索了数字化转型对创新管理的潜在影响，但是我们仍然缺乏跨学科的方法来从整体角度研究现象，以避免在我们的研究领域出现白点。数字化转型需要修订我们的理论和方法。我们基于调查，实验和定性技术的经典方法可能只会向我们展示我们正在研究的现象的一部分。数字转换导致大量非结构化数据的可用性，例如文本，图像，视频，音频，2018）。据估计，非结构化数据约占全球所有数据的80％–90％。如何利用非结构化数据的潜力进行创新管理？非结构化数据需要新颖的数据分析方法来提取特征以进行进一步分析。例如，Khanday等。（2020年）表明，可以使用临床文本数据将机器学习用于诊断COVID-19。但是，大多数研究仍在学科孤岛上进行，最终我们将需要整合这些概念和实证见解，以激发创新管理中真正的跨学科研究。

数字创新工具箱中最受欢迎的工具之一是众包。尤其是创新竞赛，竞赛使用数字平台进行，允许参与者通过产生，发展和讨论想法做出贡献。例如，Vermicelli等。（2021年）从16个项目中提供的证据表明，众包可以为COVID-19挑战提供新颖有效的解决方案。为了研究创新竞赛中创新过程的演变方式，我们需要采取跨学科的观点。我们需要学习创新竞赛期间用户之间如何进行交流（通常使用文本数据）以及与用户在何时进行交流。为了实现这些数据以及这些新方法和新理论的潜力，我们需要向其他学科学习，例如信息系统，计算机科学，语言学，教育和传播。这涉及使用计算语言学，自然语言处理和社交网络分析，以提供有关使用焦点小组和调查的更多见解（参见Ludwig等，2014）。

创新几乎不再是孤立的。恰恰相反，创新过程涉及在创新过程中由不同方的网络共同创造。例如，BioNTech和辉瑞的开发人员决定在其COVID-19疫苗的开发过程中尽早进行合作，这是第一种获准用于人类的mRNA疫苗。信息也可以从与客户的交流中获得，例如通过虚拟和人工代理。物联网智能产品和服务中的传感器生成的数据可以补充这些见解。智能产品和服务通过一系列网络物理安排为客户创造价值（Raff et al。，2020）。客户需要共同创建必要的数据，以随着时间的推移个性化其客户体验。为了在客户旅程的接触点期间提供个性化的客户体验，公司将需要来自其客户的数据。设计思维概念和工具可能有助于以有意义的方式集成所有这些信息（Micheli等人，2019）。例如，移动应用程序体验伙伴（https://www.experiencefellow.com）或indeemo（https://indeemo.com）允许客户记录其整个客户旅程中的接触点。然后，此信息将被编译到仪表板中，并为数字创新管理提供有价值的见解。但是，我们需要注意隐私问题，因为客户为了获得更加个性化的客户体验而放弃了他们的数据。隐私问题重要性的一个关键示例是基于蓝牙技术的各种COVID-19移动跟踪应用程序的开发。

团队被认为是开发新产品的灵丹妙药。但是，团队合作的基本机制仍未得到充分研究。团队不仅仅是其成员的总和，但是导致团队绩效的互动过程并没有被很好地理解。通过自动对手势和面部表情进行编码，语音分析以及音频序列的转录，团队中交互模式的视频和音频记录可以提供更详尽的见解。将可穿戴设备与多个传感器配合使用，例如社会计量学徽章（Pentland，2008年））可能会提供补充的见解，因为它们专注于团队成员之间的身体动作，言语特征，亲近度和面对面的交流。虚拟创新团队以及越来越多的大多数团队在COVID-19大流行中处于虚拟状态，面临着更为严峻的挑战。团队中的虚拟性对团队绩效和满意度几乎普遍具有负面影响。但是，对于长期的团队来说，这些影响要弱得多，甚至消失（De Guinea et al。，2012）。特别是，BioNTech和Pfizer证明了专门的团队对于COVID-19疫苗的开发具有不可估量的价值。我们需要在个人和团队级别进行更多研究，以确定在产品开发过程的不同阶段应如何组成虚拟团队。

最近，Rindfleisch等人。（2020年）强调了新数字技术对创新管理的重要性。但是，同一作者的内容分析表明，实际上只有不到十篇论文关注这些新技术，主要涉及社交媒体和大数据。迄今为止，诸如增强和虚拟现实（AR / VR），人工智能（AI）和区块链之类的数字技术尚未在创新管理界得到足够的重视。增强和虚拟现实（AR / VR）展示了以较低的成本在较短的时间内为客户提供产品和服务的虚拟原型的巨大前景。可以轻松创建不同版本的产品和服务，并将其用于测试。例如，42dp Labs为德国零售商Metro为其在法国的新商店建立了虚拟商店，以进行测试（https://www.42dp.com/projects/metro-vr-store/）。此外，3D打印可能会提供新产品原型的触觉反馈，而音频和气味可能会被用来模拟多感官客户体验。人工智能正在快速发展，以影响创新过程的思想产生和发展阶段。人工智能和自然语言处理也已被用于寻找COVID-19的候选药物（Keshavarzi Arshadiet等人，人工智能正在快速发展，以影响创新过程的思想产生和发展阶段。人工智能和自然语言处理也已被用于寻找COVID-19的候选药物（Keshavarzi Arshadiet等人，人工智能正在快速发展，以影响创新过程的思想产生和发展阶段。人工智能和自然语言处理也已被用于寻找COVID-19的候选药物（Keshavarzi Arshadiet等人，2020年）。区块链技术是创新管理中研究不足的数字技术。它的主要应用程序目前在金融领域，例如，加密货币。一些研究人员将区块链技术在即将来临的十年中的潜在影响与1990年代互联网的出现进行了比较（参见Morabito，2017年）。区块链还可以为社交媒体上的隐私问题提供解决方案。例如，Diaspora是基于区块链原理构建的社交媒体平台，该平台允许用户拥有自己的数据而无需透露其身份。一些区块链社交网络，例如Steemit和Minds，甚至使用加密货币来支付用户对该平台的贡献。

当前的COVD‐19大流行着重说明了创新管理中正在进行的范式转变。对于创新研究人员和从业人员而言，真正的挑战是从当前的大流行情况中汲取教训，以了解数字创新管理的未来。关键信息是：数字创新是未来，没有回头路。跨学科研究将不再是“必不可少的”，而是“必不可少的”。非结构化数据，大数据和数字工具（例如众包）将为我们展示获得新见解的道路。与客户和其他网络方的共同创造将为新兴体验经济中的创新打开新的大门。新兴的数字技术将为研究和理解创新管理中的现象提供新的方法。