RDP 2023-10: Adoption of Emerging Digital General-purpose Technologies: Determinants and Effects 4. Determinants of Adoption

In this section, we explore what factors are associated with firms' adoption of these GPT. This can provide insights into the drivers of, or barriers to, adoption. We first focus on firm balance sheet metrics and demographics. We then consider the characteristics of the Board of Directors, including their demographics, educational background and relevant skills.

4.1 Firm-level characteristics

To examine the firm characteristics that are associated with adoption, we estimate a multivariate regression. The regression is a panel regression, where we focus on firms that have not adopted these technologies up to that point. We take this approach as it allows us to abstract from the effects that adoption could have on firm-level variables (e.g. firms may become larger post-adoption and we want to abstract from this). For this reason, we also lag the factor by two periods.^[7]

The regression takes the form:

where Adopt_i,t, takes on the value 1 if firm i adopts in year t, and 0 otherwise. Factors_i,t−2 includes several variables of interest, including balance sheet metrics and firm age, lagged by two years. We also account for industry-by-time fixed effects to abstract from adoption patterns in each industry $\left({\gamma }_{ind,t}\right)$. Essentially, we estimate what characteristics predict adoption among those firms that are yet to adopt these technologies. The regression covers the period from 2013 to 2022, though the results are similar if we focus on more recent adopters (e.g 2017 onwards).

We find that firms are more likely to adopt GPT if they are larger, as measured by (log) assets (Table 3). If a firm were to go from the median size for firms yet to adopt by 2022 ($42.9 million) to that for those adopting in 2022 ($413.9 million), their probability of adoption would rise by around 2.7 percentage points. To put this into context, of the firms that were yet to adopt before 2022, around 7 per cent adopted GPT in 2022 (Table 1). This finding is consistent with a number of papers focusing on AI use (e.g Calvino et al 2022; Calvino and Fontanelli 2023), which have tended to attribute the finding to scale advantages, possibly due to high fixed costs in AI adoption. Liquidity also appears important, with firms with positive cash flow being 1.8 percentage points more likely to adopt, and those with higher cash ratios also being slightly more likely, consistent with findings for AI usage overseas (Alekseeva et al 2021; Babina et al 2024). This suggests that financing constraints could be a relevant barrier to adoption.

That said, it is important to interpret this finding in the context of the firms analysed. We focus on listed firms, who tend to be established. For such firms there may be substantial costs in shifting from existing production technologies to new ones. Such costs will be lower for newly established firms. Moreover, our measure of adoption is more likely to capture substantive investments, rather than small indirect adoption of technologies (e.g. switching to a cloud version of a software product).

While not statistically significant, we find a small negative link between firm age, as measured by years since listing, and adoption. The direction of the relationship is unsurprising as younger firms likely find it easier to adopt newer technologies given the lack of existing ‘vintage’ capital to replace. However, the lack of a significant relationship is consistent with the mixed evidence on AI adoption in Calvino et al (2022) and Calvino and Fontanelli (2023). That said, given our focus is on listed firms, and we measure age based on years since listing, very few firms may be truly young.

Finally, there is some evidence that firms with a higher share of their expenses being labour are more likely to adopt. That said, the magnitude of the coefficient is small (going from no labour costs to all labour costs raises the probability of adoption by just over 1 percentage point).

We run several robustness tests which do not change the results. These include using a probit model and taking a non-parametric approach by replacing the linear explanatory variables with dummy variables showing which decile of the industry-level distribution the firm was in (see Appendix C). The decile model indicates that the largest firms in each industry (i.e. top two deciles) are far more likely to adopt than other firms.

4.2 Board characteristics

Next, we examine the attributes of a firm's Board of Directors. Due to the cross-sectional nature of our board data, we estimate the model as a cross-sectional regression, where adoption is measured as ever having adopted by 2022. Specifically, we run the following regression:

where Adopt by 2022_i is a dummy variable that takes on the value 1 if the firm has adopted a GPT by 2022 (inclusive). Again, we include industry controls to abstract from differences in adoption across industry and just focus of the effect of board characteristics. The variable Board Characteristics_i is a variable that takes on the value 1 if the firm has a director with the characteristic of interest. We include all characteristics together in a multivariate regression. Finally, given the importance of size for adoption, and the possibility that larger firms are more likely to have directors with certain characteristics, we also control for the size of the firm, measured by their (log) assets in 2022.

Focusing again on non-IT firms, we find that having a Board member with prior experience in the IT industry raises the probability of having adopted by around 25 per cent (Table 4). This compares to an unconditional probability of adoption of around 1/3 within the sample (Figure 1, bottom panel). Having a Board member with some experience with GPT is also associated with a higher probability of adoption. One explanation for these findings is that having Board members with these skills contributes to the decision to adopt. Another is that firms bring on Board members with such skills because they intend to or have adopted these technologies, and so these skills facilitate adoption, but do not lead to adoption. Given the lack of longitudinal information we cannot separately test these two explanations. However, both suggest that having directors with technology-related knowledge is important for adoption.

Unconditionally, the presence of a female Board member is linked to around an 8 percentage point increase in the probability of adoption. However, this appears to be driven by the fact that female representation on the Board is highly correlated with firm size. Once we control for firm size, female representation is no longer statistically significantly related to adoption. Similarly, we find no significant relationship between the directors' education and adoption.

It is important to reiterate that these findings should be thought of as correlations, rather than causal. For example, it could be that firms with strong governance have more diverse boards, or boards with more diverse skills, and these same firms may also be more likely to adopt technologies. Still, they provide some evidence that having a mix of skills and backgrounds is associated with greater adoption.