Zamani Efpraxia D, Griva Anastasia, Conboy Kieran
Information School, The University of Sheffield, Sheffield, UK.
Lero - The Science Foundation Ireland Research Centre for Software, School of Business & Economics, National University of Ireland Galway, Galway, Ireland.
Inf Syst Front. 2022;24(4):1145-1166. doi: 10.1007/s10796-022-10255-8. Epub 2022 Mar 2.
The COVID-19 pandemic has had an unprecedented impact on many industry sectors, forcing many companies and particularly Small Medium Enterprises (SMEs) to fundamentally change their business models under extreme time pressure. While there are claims that technologies such as analytics can help such rapid transitions, little empirical research exists that shows if or how Business Analytics (BA) supports the adaptation or innovation of SMEs' business models, let alone within the context of extreme time pressure and turbulence. This study addresses this gap through an exemplar case, where the SME actively used location-based business analytics for rapid business model adaptation and innovation during the Covid-19 crisis. The paper contributes to existing theory by providing a set of propositions, an agenda for future research and a guide for SMEs to assess and implement their own use of analytics for business model transformation.
新冠疫情对许多行业部门产生了前所未有的影响，迫使许多公司，尤其是中小企业（SME）在极端的时间压力下从根本上改变其商业模式。虽然有人声称分析等技术有助于这种快速转型，但几乎没有实证研究表明商业分析（BA）是否以及如何支持中小企业商业模式的适应或创新，更不用说在极端时间压力和动荡的背景下了。本研究通过一个典型案例填补了这一空白，该中小企业在新冠疫情危机期间积极使用基于位置的商业分析来快速进行商业模式的适应和创新。本文通过提出一系列命题、未来研究议程以及为中小企业评估和实施自身对分析技术的使用以实现商业模式转型提供指南，为现有理论做出了贡献。
