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This study asks whether working in a R&D intensive industry affects a worker's wage profile. If R&D investment translates into transferable human capital or knowledge, workers' mobility constitutes a negative externality from the point of view of the firm/industry that bears the cost of R&D activities. A steepening of the wage profile would address such externality.

Using PSID data combined with US BEA data on US manufacturing industries' R&D intensities between 1981 and 1992, regression analysis is used to explore the hypothesis that, similarly to general training, industry R&D steepens a worker's wage‐experience profile.

In general the evidence is mixed. The results obtained from biennial wage growth regressions support to some extent the hypothesis that exposure to R&D activities allows a specific group of workers to accumulate general human capital for which they pay a positive price in early stages of their career.

An important caveat applies to the results. Unlike previous research by Møen who uses firm level R&D, the results found in this study are generated by using industry level R&D, which, being possibly affected by severe measurement errors, may bias the estimated coefficients towards zero.

This study complements Møen's evidence based on Norwegian wages with the effects of industry‐specific R&D intensities on the earnings profile in US manufacturing industries. By investigating whether industry R&D affects the return to experience and/or to tenure this study addresses an overlooked issue of which type of skills R&D allows workers to accumulate.

Logistics real estate has been experiencing a recent rebirth led by the growth of retailing and e-commerce. Although these sectors are looking for facilities matching their logistics needs, the identification of the most suitable building becomes a challenging task. To date, from both the practitioner’s and academic perspectives there is a lack of models for assessing the quality of logistics facilities together with functionality (i.e. whether a warehouse is suitable for hosting a given logistics activity). The purpose of this paper is to fill this gap by developing a rating model for assessing the quality and functionality of logistics facilities.

A three-pronged methodology was adopted. First, a Systematic Literature Network Analysis (SLNA) was carried out to identify the relevant features that must be taken into consideration when assessing logistics real estate. Second, a Delphi method involving experts in the field was used to fine-tune the list of features that emerged from the SLNA process and to evaluate the importance of each feature from a company perspective. The rating model was developed and validated through pilot tests on 27 logistics facilities.

The rating model is divided into four sections: location, technical specifications, external spaces and internal areas. As an output, the model determines the building quality and main functionality, together with a gap analysis to detect the weakest emerging elements.

This research fills an identified research gap in the logistics real estate literature. Specifically, it offers a quantitative and shared evaluation method, which can be used to estimate building quality and functionality, thus extending the scope of the previous assessment methods available.

The conventional notion that adopting Artificial Intelligence (AI) positively affects firm performance is often confronted with various examples of failures. In this context, large-scale empirical evidence of the economic performance implications of adopting AI is poor, especially in the context of Small and Medium Sized Enterprises (SMEs). Drawing upon the Resource-Based View and the Digital Complementary Asset literature, we assessed whether the adoption of AI affects SMEs’ revenue growth.

First, we examine the relationship between the adoption of AI and SMEs’ revenue growth. Second, we assess whether AI complements the Internet of Things (IoT) and Big Data Analytics (BDA). We use firm-level data from the European Commission in 2020 on 11,429 European SMEs (Flash Eurobarometer 486).

Among the key findings, we found that ceteris paribus, the adoption of AI positively affects SMEs’ revenue growth and, in conjunction with IoT and BDA, appears to be even more beneficial.

Our results suggest that AI fosters SME growth, especially in combination with IoT and BDA. Thus, SME managers should be aware of the positive impacts of investments in AI and make decisions accordingly. Likewise, policymakers are aware of the positive effects of SMEs’ reliance on AI, so they may design policies and funding schemes to push this digitalization of SMEs further.