Research

This paper examines the impact brought by robot use on manufacturing firms’ innovation performance. The analysis uses a rich panel dataset of Spanish manufacturing firms over 27 years (1990-2016). Our findings document, the first time in the literature, that robot use has a negative effect on firms’ process innovation. However, we do not observe a similar effect on firms’ product innovation. We also explore mechanisms by which robot use may affect process innovation. We find that the negative effect of robot use on process innovation is only salient for complex manufacturing, rather than light manufacturing or heavy manufacturing. In addition, we find that the negative effects brought by robots on process innovation are smaller for older firms. These results point to a potential mechanism whereby robots may impede process innovation through reducing human involvement. Our findings highlight possible disadvantages brought by robots in manufacturing firms, a notion neglected by the previous literature.

This project investigates the early adoption and diffusion of various AI-related technologies, such as computer vision, machine learning and natural language processing, within manufacturing firms. Using a rich firm-level data from Spanish manufacturers, I examine the relationship between AI adoption and key characteristics, including firm size, firm age, industry sector, output  and R&D expenditure. This research provides insights into how AI technologies are integrated into manufacturing operations and the factors driving their adoption. I found that AI adoption in Spain remains limited in 2018, with only 14.26% of manufacturing firms utilizing these technologies. Specifically, 11.18% adopted computer vision, 5.04% used machine learning, 1.48% employed automatically guided drivers, 1.09% used augmented and virtual reality, and just 0.59% implemented natural language processing. Also, adoption rates vary significantly across industries, with 36.84% of firms in computer products, electronics and optical sectors adopting AI, compared to only 6.65% in the textile and clothing sector. The analysis also reveals that larger and younger firms are more likely to adopt AI technologies within their industries and cohorts. Additionally, firms with higher output and greater R&D expenditures show a higher likelihood of adopting AI. Furthermore, firms that adopt AI tend to rely more heavily on both industrial robots and cloud computing. In the next step, we plan to utilize newly available panel data on firm-level AI use to provide robust empirical evidence on how AI affects firms' operations and management practices. 

This project examines the investment patterns of venture capitalists (VCs) related to AI in British startup firms. Using detailed data from 4,371 startups in the software-as-a-service (SaaS) industry in UK—covering startup firms’ descriptions of the type and purpose of the technology they use, as well as their venture histories (investment timing and amounts)—combined with private firm-level panel data of financial statement from FAME, this study addresses several key questions. First, do AI ventures attract more funding? We investigate this by comparing the amount of VC funding received by AI versus non-AI ventures. Second, using text mining based on firms’ description of the type and purpose of the technology they use within the AI-focused ventures, We analyze whether the novelty of AI affects the amount of VC funding. Finally, We explore whether these investments contribute to firm success, based on their financial statement.