The power of prediction: predictive analytics, workplace complements, and business performance

Abstract

Anecdotes abound suggesting that the use of predictive analytics boosts firm performance. However, large-scale representative data on this phenomenon have been lacking. Working with the Census Bureau, we surveyed over 30,000 American manufacturing establishments on their use of predictive analytics and detailed workplace characteristics. We find that productivity is significantly higher among plants that use predictive analytics-up to $918,000 higher sales compared to similar competitors. Furthermore, both instrumental variables estimates and timing of gains suggest a causal relationship. However, we find that the productivity pay-off only occurs when predictive analytics are combined with at least one of three workplace complements: significant accumulation of IT capital, educated workers, or workplaces designed for high flowefficiency production. Our findings support claims that predictive analytics can substantially boost performance, while also explaining why some firms see no benefits at all.

A portrait of AI adopters across countries

“… Unreported preliminary results confirm this also in the case of Ireland 34. The only exception is Israel, where the coefficients of size do not monotonically increase.…”

Business Analytics and Firm Performance: A Literature Review

“…Several of the reviewed articles develop and empirically test research models that combine business analytics capability with different valuable, rare, and difficult to imitate resources and capabilities, which have theoretically significant roles in translating business analytics into improved firm performance. For instance, recent research highlights the complementary roles of dynamic capabilities (e.g., Wamba et al, 2017;Mikalef et al, 2020), business strategy alignment (Akter et al, 2016;Wamba et al, 2019), organizational culture (Behl, 2020;Upadhyay and Kumar, 2020), information technology capital, educated workers, high flow-efficiency production workplaces (Brynjolfsson et al, 2021), knowledge management (Ferraris et al, 2019, Shabbir andGardezi, 2020), ambidexterity (Božič and Dimovski, 2019;Rialti et al, 2019;Aljumah et al, 2021), big data analytics management capabilities (Razaghi and Shokouhyar, 2021), and resource orchestration and circular economy implementation (Kristoffersen et al, 2021). In addition, business analytics positively influences firm performance through mediating outputs, such as informational benefits (Asadi Someh and Shanks, 2015), information quality (Ashrafi et al, 2019), technological innovation (Saleem et al, 2019), product and process innovation (Saleem et al, 2020), dual innovations (Su et al, 2020), sustainable product development (Ali et al, 2020), business process performance (Aydiner et al, 2019), business decision making (Chatterjee et al, 2021), and functional performance (Torres et al, 2018).…”

The Data Governance Act: – Promoting or Restricting Data Intermediaries?

“…In particular, Big Data analyses can uncover great amounts of new information from data which would be otherwise inaccessible. Said information can then be used to improve decision-making in order to innovate (Niebel et al, 2019) or to increase productivity (Brynjolfsson et al, 2021).…”