Search results

This study aims to examine the influence of the blue economy factors on the economic growth of the South Asian Association for Regional Cooperation (SAARC) countries.

Secondary data from 1995 to 2018 have been used for the analysis of eight countries. The contributing factors that measure the fishing production are total aquaculture production, total fisheries production and agriculture, forestry and fishing. Trade and the rate of inflation are used as control variables. Using the feasible generalized least square technique.

It was found that the blue economy factors play a statistically significant role in the economic growth of SAARC countries and contribute to the achievement of Goal 14 of the United Nations’ sustainable development goals: to conserve and sustainably use the oceans, seas and marine resources for sustainable development.

This study highlights the fact that proper management and utilization of water resources may assist the stimulation of economic growth and meet the challenges of food insecurity by improving the supply of seafood in developing South Asian countries. The study proposes that the sustainable management of water resources requires an alliance across nation states. The alliance will be useful in understanding the concept of the blue economy and the role it plays in ensuring economic growth in developing nations throughout the world.

This study aims to examine the phenomenon of climate variability and its implications for pastoralists and agro-pastoralists food security in Amibara and Awash Fentale districts of the Afar region, Ethiopia.

The study relied on meteorological records of temperature and rainfall in the study area between 1988 and 2018. Besides, literature on the topic was reviewed to make caveats on the literal picture that comes from quantitative data, and that is the contribution of this study to the existing debate on climate change and variability. The spatiotemporal trend was determined using the Mann–Kendall test and Sen’s slope estimator, while variability was analyzed using the coefficient of variation and standardized anomaly index, and standardized precipitation index/standardized precipitation evapotranspiration index were applied to determine the drought frequency and severity.

The result reveals that the mean seasonal rainfall varies from 111.34 mm to 518.74 mm. Although the maximum and minimum rainfall occurred in the summer and winter seasons, respectively, there has been a decrease in seasonal and annual at the rate of 2.51 mm per season and 4.12 mm per year, respectively. The study sites have been experiencing highly seasonal rainfall variability. The drought analysis result confirms that a total of nine agricultural droughts ranging from moderate to extreme years were observed. Overall, the seasonal and annual rainfall of the Amibara and Awash Fentale districts showed a decreasing trend with highly temporal variations of rainfall and ever-rising temperatures, and frequent drought events means the climate situation of the area could adversely affect pastoral and agro-pastoral households’ food security. However, analysis of data from secondary sources reveals that analyzing precipitation just based on the meteorological records of the study area would be misleading. That explains why flooding, rather than drought, is becoming the main source of catastrophe to pastoral and agro-pastoral livelihoods.

The analysis of temperature and rainfall dynamics in the Afar region, hence the inception of all development interventions, must take the hydrological impact of the neighboring regions which appears to be useful direction to future researchers.

The research is originally conducted using meteorological and existing literature, and hence, it is original. In this research, we utilized a standardized and appropriate methodology, resulting in insights that augment the existing body of knowledge within the field. These insights serve to advance scholarly discourse on the subject matter.

The purpose of this paper is to investigate the relational dynamics for innovation and, in particular, the impact of the openness of innovation process on the innovation capacity of organisations in restricted geographical contexts.

Through a negative binomial regression, the work analyses how the characteristics of the openness of the organisation’s innovation process in the period 2004-2010 influence the firm’s patent productivity in the following period (2011-2016).

The breadth of the open innovation (OI) process, here measured by the number of external network ties that an organisation realises for the realisation of its patents, has a positive effect on patent productivity. The depth of the openness, that is, the intensity of external network ties, has an equally positive influence on the innovative performance. However, after a tipping point, the patent productivity tends to decrease, underlining the costs and problems of OI practices.

This study considers only patent collaborations in the city of Florence. Therefore, it focusses on codified innovations and on a single territorial case study.

The results underline the importance of the adoption of OI practices in restricted geographical contexts (such as cities, clusters or industrial districts) but with several limitations. Only collaborating more with others does not foster the organisation’s invention productivity, but different types of evidence are found here.

An original database has been created, containing all the information on patents realised in the area of Florence from 2004 until 2016, and a social networks analysis was applied to identify the local innovation networks.

Timber export is an important economic development pillar in Ghana, which has a direct effect on Ghana’s domestic forestry industry development, local communities’ income and sustainable forest management and deforestation. China, as the Ghana’s largest timber export destination, brought significant impacts on Ghana’s timber export. However, there is a lack of quantitative analyses on impact factors of timber trade between the two countries in the past, which this paper sought to do.

The authors first collected Ghana’s timber exports to China from 1997 to 2017, and then based on the literature review and trade theories, the authors set up a least squares estimate (LSE)-based multiple linear regression (MLR) model to analyse the specific impact factors. In addition, multi-collinearity, autocorrelation and heteroscedasticity issues of the impact factors were checked to guarantee the accuracy of the results.

The results showed China timber import volume, Ghana cedi (GHC)/US$ exchange rate, Ghana’s gross domestic product (GDP) and China timber consumption volume in construction sector had positive impacts; Ghana timber production volume and Ghana average timber export price had negative impacts. The results confirmed that China’s timber consumption had a positive linkage with Ghana’s timber export. The article also reviewed possible impacts caused by change in timber trade policies, which proved important but was hardly directly quantified. The authors pointed out the importance of optimizing these impact factors to make sure Ghana’s timber export to China went on the sustainable track.

There is no literature about timber trade and its impact factors between Ghana and China, which used econometric models. This paper provided new perspectives on the impact factors in timber trade between the two countries.

Recent evidence from a linear econometric framework infers that housing search activity, captured from Google Trends data, can predict housing returns for the USA at a national and regional (metropolitan statistical area [MSA]) level. Based on search theory, the authors, however, postulate that search activity can also predict housing returns volatility. This study aims to explore the possibility of using online search activity to predict both housing returns and volatility.

Using a k-th order non-parametric causality-in-quantiles test allows us to test for predictability in a robust manner over the entire conditional distribution of both housing price returns and its volatility (i.e. squared returns) by controlling for nonlinearity and structural breaks that exist in the data.

The analysis over the monthly period of 2004:01 to 2021:01 produces results indicating that while housing search activity continues to predict aggregate US house price returns, barring the extreme ends of the conditional distribution, volatility is relatively strongly predicted over the entire quantile range considered. The results carry over to an alternative (the generalized autoregressive conditional heteroskedasticity-based) metric of volatility, higher (weekly)-frequency data (over January 2018–March 2021) and to over 84% of the 77 MSAs considered.

To the best of the authors’ knowledge, this is the first study regarding predictability of overall and regional US housing price returns and volatility using search activity, based on a non-parametric higher-order causality-in-quantiles framework, which is insightful to investors, policymakers and academics.

Due to the constantly increasing competitiveness along with the complexity of knowledge, firms perceive collaboration as a key strategy that preserves firms' radical innovation performance. In this context, this paper aims to examine how firms’ partners’ diversity in open innovation activities influences the development of radical innovations, critical for social development. In particular, this study analyzes how the functional and geographical breadth of the firm’s collaboration portfolio affects its radical innovation performance. Furthermore, it also explores the role of firm size as a moderator in the relationships proposed.

This research employs panel data analysis, using a sample of 4,677 Spanish firms, with data sourced from the PITEC database.

The results of this study show that there is an inverted U-shaped relationship between the functional and the geographical breadth of collaborations and the firms’ radical innovation performance. Moreover, this study finds partial support for the moderating role of firm size, in the sense that small and medium-sized enterprises (SMEs) and large firms vary in their optimal number of diversity of partners.

This research provides a better understanding on how partners’ functional and geographical diversity, along with organizational characteristics such as firm size, affect how firms benefit from collaboration for innovation. This study shows that both SMEs and large firms experience diminishing returns when their collaboration networks become overly diverse in pursuit of radical innovation, due to increased costs. However, in SMEs, the turning point occurs at a later stage, consistent with the idea that small firms need broader functional networks to access complementary and novel resources they usually lack.

The purpose of this paper is to investigate the role of firms’ features on innovation performance in Industry 4.0, focusing on the concepts of breadth and depth of openness in the innovation process.

Using data gathered from 96 firms active in Industry 4.0 (I4.0) in Italy, a Poisson regression analysis is conducted to investigate the relationship between the openness of firms’ innovation processes at the level of knowledge sources and their innovation performance in I4.0.

The results highlight the relationship between the level of openness and innovative performance in I4.0. In particular, the breadth of the openness of the innovation process of enterprises is curvilinearly related to innovation in I4.0, taking an inverted U-shape.

Managers of firms operating in I4.0 should consider openness as a strategic response to the knowledge requirements and risks associated with the innovation process in complex technologies.

Through the questionnaires administered mainly to highly qualified individuals, an original and unique database has been created with information on the openness of the innovative process and the innovation performances in I4.0.