While the use of data in business-to-business marketing is not a new phenomenon, the digitization and digitalization of business-to-business firms’ business models have recently attracted a great deal of attention. With the aim of creating an overview and consolidating this stream of research, the present paper offers a brief historical overview of research on digitization and digitalization in business-to-business markets – concluding that this discussion has a long tradition and, thus, is not a new phenomenon. We develop a definition of digitization capability as a basis for discussing how a firm’s digitization capability interacts with its business model to allow for data-enabled growth, i.e. its digitalization, and we highlight promising avenues for future research.

Introduction

Digital technologies have changed the way business-to-business firms act in business markets in terms of what they sell (their value propositions, e.g., Gandhi, Thota, Kuchembuck, & Swartz, 2018) and how they sell it (their value demonstrations, e.g., Syam & Sharma, 2018)—and they also pose new requirements to a firm’s capabilities. Although the topic of digitalization is currently prominent in the minds of many practitioners and academics, digitization and the digitalization of business are not new topics of interest.1 One of the earliest applications of computing power in business was the computerized registration of 26 million US citizen’s employment records by IBM equipment to support the Social Security Act in 1935.2 Similarly, the first conference on artificial intelligence was held at Dartmouth in 1956. Hence, topics like big data (i.e., large amounts of data) and artificial intelligence have been discussed for decades and, therefore, have long been fields of interest for practitioners and academics alike. As such, the current focus on the notion of data-driven disruption is not an issue of “newness.” Instead, it is most likely related to the growth in available data made possible through access to cost-efficient equipment for data collection and access to computing power needed to handle analytics: more data exist today than have done so before in history (Smolan & Erwitt, 2012) – and approximately 5 billion gigabytes were generated from the beginning of recorded history until 2003, whereas 5 billion gigabytes of data were claimed to be generated every 10 seconds in 2015 (Zwitter, 2014). Therefore, growing academic interest into the topic arguably reflects this empirical trend—although it has long been accepted that technology is altering the nature of competition resulting in a ‘new competitive landscape’ (Bettis & Hitt, 1995), albeit the evolutionary nature of competitive change is arguably reflected in different phases (Table 1).