RDP 2023-10: Adoption of Emerging Digital General-purpose Technologies: Determinants and Effects 1. Introduction

Productivity growth is the main driver of living standards over the medium term. However, productivity growth has slowed significantly over recent decades, both in Australia and in other advanced economies, leading to lower economic growth and incomes.

One factor contributing to slower productivity growth in Australia appears to be slower diffusion and adoption of technologies. Adoption and adaptation of technologies and approaches pioneered by other businesses is a key driver of productivity growth. This is particularly the case in Australia, where only a very small share of firms tends to create new-to-world products, technologies or approaches in a given year, whereas 40 to 50 per cent adopt or adapt an existing one to improve their business (DISER 2021; Productivity Commission 2023). However, Australian firms appear to be falling further behind the global productivity frontier over time, suggesting slower diffusion (Andrews et al 2022).

Against this background, it is important to understand in more detail how technologies diffuse, what barriers exist, and what determines whether a firm can profitably use the technology. There is, however, relatively little evidence on these topics, particularly for Australia. For example, several survey-based studies have documented that while adoption of cloud computing technology can be associated with higher profitability, it can also be associated with losses and ultimately abandonment of the technology (Makhlouf 2020; McMillan et al 2022; West 2022). But there is little evidence on what factors could contribute to profitable adoption of technologies. Similarly, several papers have explored the correlates of artificial intelligence/machine learning (AI/ML) adoption or its effects (e.g. Alekseeva et al 2021; Calvino et al 2022; Babina et al 2023, 2024), but there is little evidence on what facilitates profitable adoption.

To provide more evidence on the nature and effects of technology adoption in Australia we construct a new database capturing adoption of cloud computing and AI/ML, two emerging digital general-purpose technologies (GPT), using text from earnings calls and annual reports of listed firms. In particular, we define adoption as the first reference to these technologies in the calls and reports, using a word list developed in earlier work by Bloom et al (2021) to identify references to the technologies.

We focus on these two technologies for several reasons. First, both represent emerging GPT, in that they are ‘generic’ technologies that have the potential to become pervasive and transform business processes and that it will take time from initial introduction for these technologies to mature and for complementary investments to be effectively utilised (Etro 2010; Bayrak, Conley and Wilkie 2011; Crafts 2021). Second, these technologies also have potentially important implications for labour markets. On the one hand, use of these technologies tends to require highly skilled and educated workers, which has the potential to affect demand for skilled labour and could have implications for inequality (Acemoglu and Restrepo 2018; Burgess and Connell 2020; Ellis 2021). On the other hand, AI/ML could provide a complement to lower-skilled workers augmenting their abilities rather than automating them (Agrawal, Gans and Goldfarb 2023b). Third, these technologies are inherently interlinked, with increased computing power provided by cloud computing helping to fuel AI/ML, and many cloud technologies incorporating AI/ML (Loucks 2018).

Using our adoption metric, we find evidence that larger and more liquid firms are more likely to adopt GPT, suggesting that financing frictions and returns to scale could be important barriers to adoption. In part, this could reflect fixed costs in shifting from existing technologies to new technologies. These may be more important for mature, listed companies such as those in our sample, than for smaller or emerging firms. To the extent that our adoption metric is more likely to capture sizeable investments in such technologies, rather than, for example, shifting to a cloud-based version of a software program, this could also help to explain the importance of liquidity.

We also document a surge in adoption of cloud computing technologies across all sectors during the COVID-19 pandemic. However, this appears to have been a one-off spike in adoption with the rate of new firms adopting cloud computing quickly reverting to pre-pandemic levels, despite the fact that a very large share of firms still appear not to have adopted such technologies by the end of our sample in 2022.

We then consider the roles of worker and Board skills in the profitable adoption of these GPT. We find that firms with directors with strong technical backgrounds (experience with relevant technologies) are far more likely to adopt the technologies and to increase their profitability post-adoption. Adopting firms are also more likely to try to hire workers with GPT-related skills, indicating adoption is associated with increased demand for relevant skills. This is consistent with Bahar and Lane's (2022) finding that demand for skills related to cloud computing and AI/ML have increased significantly over the past decade as adoption has increased. The increased demand for skilled workers is particularly evident for firms that have directors with technology experience, which are also more likely to make a profit post-adoption. While it is hard to draw strong causal conclusions, these findings suggest that having sufficiently skilled workers and managers facilitates adoption, and therefore highlights the importance of increasing the availability of relevant skills, for example via education or training.

We also consider whether other director characteristics are associated with a higher probability of profitable adoption. Firms with female representation on the Board are more likely to increase their profitability post-adoption, adding to existing evidence on the benefits of within-firm diversity for firm performance (Gordini and Rancati 2017; EmadEldeen et al 2021) and dynamic capabilities (Wilson et al 2023). However, the magnitude of the effect is smaller, compared to the effect of having Board members with relevant technology experience. Other factors that we were able to explore with the data, such as the educational background of the directors, are not associated with more profitable adoption. That said, given limitations in our data on directors, which are a snapshot as at March 2023, further work exploring these other factors with other data on management could be fruitful.

Finally, we consider whether the nature of adoption may have changed over time. There is a large literature showing that GPT often diffuse slowly over time as complementary inventions or organisational changes facilitate profitable adoption of the GPT (e.g. Agrawal, Gans and Goldfarb 2023a), or as relevant skills become more widely available. Consistent with this, we notice a significant downturn in profitability around the time of adoption for early adopters (2013–2016). Conversely, profitability of late adopters (2017 onwards) is broadly unchanged on average.

This final finding suggests that profitable adoption may have become easier over time, which might mean more scope for effective adoption of these technologies going forward and therefore improved productivity. That said, given the other results in this paper, increasing adoption is still likely to be somewhat dependent on the availability of relevant skills and experience. Moreover, given existing evidence that much of the decline in productivity growth in Australia reflected factors such as declining competitive pressures and dynamism, these issues would also likely still need to be addressed to incentivise adoption (e.g. Andrews et al 2022; Hambur 2023; Hambur and Andrews 2023).