Business Model Innovation for Intelligent Automation: Unpacking the Productivity Paradox

过去十年，尽管数字技术盛行，但主要经济体和新兴市场的生产率增长一直在放缓。这种现象被广为人知的是生产率悖论。与其他主要经济体相比，英国的生产率增长放缓尤为严重。此外，信息和通信技术(信通技术)使用量最大的行业似乎是生产率下降的主要原因。生产率下降的主要原因之一可能是，在公司采用新的数字技术后，对业务流程和业务模式的重新设计有限。通过这项研究计划，Velu博士将更好地了解商业模式创新与采用智能自动化技术后生产率提高之间的关系。Velu博士将建立一个管理信息和决策支持系统的数字工具，用于评估商业模式的生产力，以便在采用数字技术后使公司内部的生产力得到快速和持续的提高。在这样做的过程中，Velu博士的目标是为国民收入核算提出一个新的生产率报告框架。VELU将对已经实施智能自动化技术的公司进行历史分析，以学习和制定商业模式生产率衡量的标准。这将包括对公开提供的历史数据的分析，以及对制造、分销和分享经济等一些选定部门的坚定分析。此外，研究将对与历史分析类似行业的公司进行纵向深入分析，以建立一个数字工具，识别采用智能自动化技术后的商业模式创新机会。这将涉及与这些部门选定若干公司的高级管理小组合作，以确定数据要求、起草技术规格、开发软件方案、用数据填充和测试数字工具，并提出将数字生产力工具嵌入现有管理报告系统的方法。这项研究将使企业受益，因为它将为系统评估在实施智能自动化技术后对商业模式创新机会的需求提供基础。这项研究还将通过定义高质量和适当的数据来解决数字经济中衡量生产率的挑战，从而使政策制定者受益。

Productivity growth has been slowing down in the last decade in major economies as well as in emerging markets despite the prevalence of digital technologies. This phenomenon is widely known as the productivity paradox. The productivity growth slowdown is particularly acute in the UK compared to other major economies. Moreover, industries that are the most intensive users of Information and Communication Technologies (ICT) appear to have contributed most to the slowdown in productivity. One of the main reasons for this productivity slowdown could be due to the limited redesign of business processes and business models following the adoption of new digital technologies by firms. Through the research programme Dr. Velu will provide a better understanding the relationship between business model innovation and productivity improvements following the adoption of intelligent automation technologies. Dr. Velu will build a digital tool for management information and decision support systems for assessment of productivity of business models in order to enable rapid and sustained improvements in productivity within firms following the adoption of digital technologies. In doing so, the Dr Velu aims to propose a new framework for productivity reporting for national income accounting.Dr. Velu will conduct historical analysis of firms that have implemented intelligent automation technologies in order to learn and develop the criteria for productivity measurement of business models. This will include analysis from historical publically available data as well as within firm analysis of a number of selected sectors such as manufacturing, distribution and the sharing economy. In addition, the research will conduct longitudinal in depth analysis of firms in similar sectors as the historical analysis in order to build a digital tool that will identify business model innovation opportunities following the adoption of intelligent automation technologies. This will involve working with the senior management team of a selected number of firms in these sectors in order to define the data requirements, draw-up the technology specification, develop the software programme, populate and test the digital tool with data and propose ways to embed the digital productivity tool within existing management reporting systems. The research will benefit firms as it will provide the basis for a systematic evaluation of the need for business model innovation opportunities following the implementation of intelligent automation technologies. The research will also benefit policymakers by defining good quality and appropriate data in addressing the challenges of measuring productivity in the digital economy.