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This paper aims to investigate the performance of OECD countries' long-term productivity during the period of 1975–2018.

This study employed different approaches to evaluate how efficiency scores vary with changes in inputs and outputs: Data Envelopment Analysis (CRS, VRS and FDH), TOPSIS and TOPSIS of these scores.

The findings suggest that, during the period of this study, countries with higher freedom of religion and with Presidential democracy regimes are positively associated with higher productivity.

To the best of the authors’ knowledge, this is the first study that uses efficiency models to assess the productivity levels of OECD countries based on several contextual variables that can potentially affect it.

The purpose of this paper is to understand if and how industrialized house building (IHB) could support productivity developments for housebuilding on project and industry levels. The take is that fragmentation of construction is one explanation for the lack of productivity growth, and that IHB could be an integrating method of overcoming horizontal and vertical fragmentation.

Singe-factor productivity measures are calculated based on data reported by IHB companies and compared to official produced and published research data. The survey covers the years 2013–2020 for IHB companies building multi-storey houses in timber. Generalization is sought through descriptive statistics by contrasting the data samples to the used means to control vertical and horizontal fragmentation formulated as three theoretical propositions.

According to the results, IHB in timber is on average more productive than conventional housebuilding at the company level, project level, in absolute and in growth terms over the eight-year period. On the company level, the labour productivity was on average 10% higher for IHB compared to general construction and positioned between general construction and general manufacturing. On the project level, IHB displayed an average cost productivity growth of 19% for an employed prefabrication degree of about 45%.

Empirical evidence is presented quantifying so far perceived advantages of IHB. By providing analysis of actual cost and project data derived from IHB companies, the article quantifies previous research that IHB is not only about prefabrication. The observed positive productivity growth in relation to the employed prefabrication degree indicates that off-site production is not a sufficient mean for reaching high productivity and productivity growth. Instead, the capabilities to integrate the operative logic of conventional housebuilding together with logic of IHB platform development and use is a probable explanation of the observed positive productivity growth.

This study empirically examines the impact of climate change and agricultural research and development (R&D) as well as their interaction on agricultural productivity in 12 selected Asian and Pacific countries over the period of 1990–2018.

Various estimation methods for panel data, including Fixed Effects (FE), the Feasible Generalized Least Squares (FGLS) and two-step System Generalized Method of Moments (SGMM) were used.

Results show that both proxies of climate change – temperature and precipitation – have negative impacts on agricultural productivity. Notably, agricultural R&D investments not only increase agricultural productivity but also mitigate the detrimental impact of climate change proxied by temperature on agricultural productivity. Interestingly, climate change proxied by precipitation initially reduces agricultural productivity until a threshold of agricultural R&D beyond which precipitation increases agricultural productivity.

The findings imply useful policies to boost agricultural productivity by using R&D in the context of rising climate change in the vulnerable continent.

This study contributes to the literature in two ways. First, this study examines how climate change affects agricultural productivity in Asian and Pacific countries – those are most vulnerable to climate change. Second, this study assesses the role of R&D in improving agricultural productivity as well as its moderating effect in reducing the harmful impact of climate change on agricultural productivity.

The purpose of this study is to provide new insights into the relationship between fiscal policy and total factor productivity (TFP) while accounting for several economic and econometric issues of the phenomenon like non-stationarity, fiscal feedback effects, persistence in productivity, country heterogeneity and unobserved global shocks and local spillovers affecting heterogeneously the countries in the sample.

The paper is empirical. It builds an Error Correction Model (ECM) specification within a dynamic heterogeneous framework with common correlated effects and models both reverse causality and feedback effects.

The results of this study highlight some new findings relative to the existing related literature. The outcomes suggest some relevant evidence at both the academic and policy levels: (1) the causal effects going from fiscal deficit/surplus to TFP are heterogeneous across countries; (2) the effects depend on the time horizon considered; (3) the long-run dynamics of TFP are positively impacted by improvements in fiscal budget, but only if the austerity measures do not exert slowdowns in aggregate growth.

The main originality of this study is methodological, with possible extensions to related phenomena. Relative to the existing literature, the gains of this study rely on the way econometric techniques, recently proposed in the literature, are adapted to the economic relationship of interest. The endogeneity due to the existence of reverse causality is modelled without implying relevant performance losses of the models. Moreover, this is the first article that questions whether the effects of fiscal budget on productivity depend on the impact of the former on aggregate output growth, thus emphasising the importance of the quality of fiscal adjustments.

The purpose of this study is to investigate whether and how inter-firm cooperation and firm age moderate the relationship between family ownership and productivity.

We first estimate the total factor productivity (TFP) of a large sample of Italian firms observed over the period 2010–2018 and then apply a Poisson random effects model.

TFP is, on average, higher for non-family firms (non-FFs) than for FF. Furthermore, inter-organizational cooperation and firm age mitigate the negative effect of family ownership. In detail, it is found that belonging to a network acts as a moderator in different ways according to firm age. Indeed, young FFs underperform non-FF peers, although the TFP gap decreases with age. In contrast, the benefits of a formal network are high for older FFs, suggesting that an age-related learning process is at work.

The study provides evidence that FFs can outperform non-FFs when they move away from Socio-Emotional Wealth-centered reference points and exploit knowledge flows arising from high levels of social capital. In the case of mature FFs, networking is a driver of TFP, allowing them to acquire external resources. Since FFs often do not have sufficient in-house knowledge and resources, they must be aware of the value of business cooperation. While preserving the familiar identity of small companies, networks grant FFs the competitive and scale advantages of being large.

Despite the wide but ambiguous body of research on the performance gap between FFs and non-FFs, little is known about the role of FFs’ heterogeneity. This study has proven successful in detecting age as a factor in heterogeneity, specifically to explain the network effect on the link between ownership and TFP. Based on a representative sample, the study provides a solid framework for FFs, policymakers and academic research on family-owned companies.

Prodigious teamwork is the basis for augmenting the level of productivity on construction projects. Globalisation of the construction market has meant that many practitioners work outside of their geographical spectrum; however, the multicultural dissimilarities of construction workforces within the project management team (and how these may impact upon project productivity performance) have been given scant academic attention. To bridge this knowledge gap, this paper aims to analyse the effects of a multicultural workforce on construction productivity.

The epistemological positioning of the research adopted mixed philosophies (consisting of both interpretivism and postpositivism) to undertake a deductive and cross-sectional survey to collate primary quantitative data collected via a closed-ended structured questionnaire. Census sampling and convenience sampling techniques were adopted to target Ghana’s construction workforce and their opinions of the phenomenon under investigation. Out of 96 questionnaires administered, 61 were retrieved. The data obtained were analysed by using mean score ranking, relative important index, one sample t-test and multiple regression. The reliability of the scale was checked by using Cronbach’s alpha coefficient.

From the t-test analysis, 11 variables sourced from extant literature, and the null hypothesis for the study was not rejected and all factors (except high cost of training and improper gender diversity management) were affirmed as negative effects of the multicultural workforce on construction productivity. Using multiple regression analysis, six of the independent variables were shown to impact upon productivity. The goodness of fit was verified by collinearity and residual analysis. The model’s validation revealed a relatively high predictive accuracy (R² = 0. 589), implying that the results could be generalized. In culmination, these findings suggest that the predictors can be used to accurately predict the effects of multicultural workforce on construction productivity performance.

The findings indicate that multicultural workforce/teams have a substantial effect on overall construction productivity in the construction sector; consequently, stakeholders must address this issue to enhance productivity across the sector.

The current study significantly contributes to our understanding of how multicultural workers/teams affect construction productivity in the construction business perspective and how to respond to the negative menace.

This study investigates the relationships among digital transformation, technological innovation, industry–university–research collaborations and labor income share in manufacturing firms.

The relationships are tested using an empirical method, constructing regression models, by collecting 1,240 manufacturing firms and 9,029 items listed on the A-share market in China from 2013 to 2020.

The results indicate that digital transformation has a positive effect on manufacturing companies’ labor income share. Technological innovation can mediate the effect of digital transformation on labor income share. Industry–university–research cooperation can positively moderate the promotion effect of digital transformation on labor income share but cannot moderate the mediating effect of technological innovation. Heterogeneity analysis also found that firms without service-based transformation and nonstate-owned firms are better able to increase their labor income share through digital transformation.

This study provides a new path to increase the labor income share of enterprises to achieve common prosperity, which is important for manufacturing enterprises to better transform and upgrade to achieve high-quality development.

This paper aims to study the economic effects of Cloud Computing for a sample of Israeli firms. The authors propose a framework that considers how this technology affects firm performance also introducing the indirect economic effects that take place through cloud-complementary technologies such as Big Data and Machine Learning.

The model is estimated through structural equation modeling. The data set consists of the microdata of the survey of information and communication technologies uses and cyber protection in business conducted in Israel by the Central Bureau of Statistics.

The results point to Cloud Computing as a crucial technology to increase firm performance, presenting significant direct and indirect effects as the use of complementary technologies maximizes its impact. Firms that enjoy most direct economic gains from Cloud Computing appear to be the smaller ones, although larger enterprises seem more capable to assimilate complementary technologies, such as Big Data and Machine Learning. The total effects of cloud on firm performance are quite similar among manufacturing and service firms, although the composition of the different effects involved is different.

This paper is one of the very few analyses estimating the impact of Cloud Computing on firm performance based on country microdata and, to the best of the authors’ knowledge, the first one that contemplates the indirect economic effects that take place through cloud-complementary technologies such as Big Data and Machine Learning.

In this paper, the heterogeneity of the linkages among financial development, productivity and growth across income groups is emphasized.

An empirical analysis is conducted with an illustrative sample of 130 economies over the period 1991–2019 and classified into four subsamples: Organisation for Economic Co-operation and Development (OECD), developing, least developed and net food importing developing countries. Forecast error variance decompositions and panel vector auto-regressive estimations are computed, with insightful findings.

Higher levels of output stimulate the economic development in the agricultural sector, mainly via the productivity channel and, in the most developed economies, also through access to credit. Differently, in developing and least developed economies, the role of access to credit is marginal. The findings have practical implications for stakeholders involved in the planning of long-run investments. In less developed economies, priorities should be given to investments in technology and innovation, whereas financial markets are more suited to boost the development of the agricultural sector of developed economies.

The authors conclude on the credit–output–productivity nexus and contribute to the literature in (at least) three ways. First, they assess how credit access, agricultural output and agricultural productivity are jointly determined. Second, they use a novel approach, which departs from most of the case studies based on single-country data. Third, they conclude on potential causality links to conclude on policy implications.

This chapter examines the influence of external public borrowing resources on economic progress in Tunisia. The study focuses on two stages: First, the influence is studied in a direct sense and then in an indirect sense, i.e., through a transmission channel of this influence. By applying the autoregressive distributed technique with staggered lags (ARDL), over a period ranging from 1986 to 2019, the results showed that the influence of external borrowing resources on growth seems to be unfavorable in the short term but positive in the long term, hence the importance of the empirical technique chosen. Second, three interaction variables were tested, namely total government expenditure, government investment expenditure, and the real effective exchange rate. The results obtained call for better attention to the channels identified to maximize the positive influence of external public debt on the country's economic progress.