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This study aims to determine whether there is an effect of internal and public debt on income inequality in Kenya for the period 1970–2018.

The relationship is examined by using the Autoregressive Distributed Lag (ARDL) model by Pesaran et al. (2001) and Toda Yamamoto causality by Toda and Yamamoto (1995).

Our findings suggest that both internal and public debt harm inequality in Kenya in the long term. Furthermore, a one-way causality from internal debt to income inequality is also obtained while no causality relationship is found to exist between public debt and income inequality. Based on these findings, the study recommends that to reduce income inequality levels in Kenya, other methods of financing other than debt financing should be preferred because debt financing is not pro-poor.

This study is unique based on the fact that no previous paper has analysed the debt and inequality relationship in Kenya. To the best of our knowledge, this will be the first study to analyse the applicability of redistribution effect of debt in Kenya. The study is also different in that it provides separate analysis for public debt and internal debt on their effects on income inequality.

The study aims to use individuals using the internet and fixed broadband subscriptions as a proxy for digitalization to empirically assess the effects of Foreign Direct Investment (FDI), digitalization and their interaction on income inequality in developed and developing countries from 2002 to 2019.

The paper used the system general method of moments estimators for 30 developed and 35 developing countries.

FDI increases income inequality in developed countries but decreases it in developing countries, digitalization reduces income inequality in both groups and interaction term narrows income inequality in developed countries but widens it in developing countries.

The paper is the first to introduce digitalization into the FDI – income inequality relationship. Furthermore, it provides empirical evidence to show the difference in the role of digitalization in this relationship between developed and developing countries.

The purpose of this paper is to investigate whether real estate investment trusts (REITs) have any significant cost-efficiency advantages over real estate operating companies (REOCs).

The data for listed companies were extracted from the Bloomberg terminal. The authors analyzed financial ratios and conducted a non-parametric data envelope analysis (DEA) for 534 firms in the USA, Canada and some EU member states.

The results suggest that REITs were much more cost-efficient than REOCs by all the parameters in the DEA model during the entire three-year period under consideration. Although the debt-to-equity levels were similar, REOCs were more relying on short-term than long-term maturities, which made them more vulnerable against market corrections or shocks. Being larger in asset size did not necessarily guarantee greater economies of scale. Both – the cases of increasing economies of scale and diseconomies – were detected. The time period 2015–2017 showed the general trend of decreasing efficiency.

Very few papers on the topic of REITs have attempted to find out whether a different firm structure displays any differences in efficiency. Because the question of REITs and sustainable growth of the real estate market has become a prominent issue, this research can help EU countries to consider the option of adopting a REIT system. If this system were successfully implemented, the EU member states could benefit from a more sustainable and more rapid growth of their real estate markets.

Environmental concern is getting increasing importance in consumer shopping decisions. Nevertheless, to date, sustainable packaged foods are not always the first option when consumers go shopping. This paper analyses how environmental concern moderates the role played by external factors – preference towards sustainable retailers and trust in sustainable producers – in determining consumer purchase intentions for sustainable packaged foods. Consumer involvement in eco-friendly labels, increasingly present in food packages, is investigated as indirectly impacting pro-environmental purchase intentions.

An online survey administered to a sample of Italian food shoppers is used for the empirical analysis. A total of 278 structured questionnaires were modelled using a structural equation modelling approach.

Findings show that producers and retailers' policies in favour of sustainability are key in determining consumers' sustainable purchase intentions. Further, coherent uses of labels and logos in light of sustainability can support consumer purchase decisions. Relevant is the influence played by the environmental concern in both supporting pro-environmental purchase intentions and in amplifying the trust in sustainable producers-purchase intentions path.

This study contributes to the literature on sustainability showing how producers and retailers may together influence consumers' pro-environmental purchase intentions. Findings extend the retail literature on the impact of producers and retailers' policies on consumers' sustainable purchases. Further, environmental concern is investigated in its moderating role on the impact of external factors on consumers' pro-environmental purchase intentions.

Improving business processes provides companies with advantages in terms of efficiency and profitability, as well as competitiveness against other companies in the market. Companies that integrate business processes with enterprise resource planning (ERP) systems into digital platforms also have the opportunity to strengthen their weaknesses by recognizing disruptions and bottlenecks in inefficient business processes thanks to this digital transformation. Descriptive and bibliometric analyses were performed in this study for a systematic evaluation of studies on artificial intelligence (AI) in the ERP literature. The studies in which the keywords determined from the AI literature were firstly used together with ERP were investigated from the Scopus database. 837 publications meeting the search criteria were reached and a descriptive analysis of these publications was presented. Then, bibliometric analysis was performed using common author, common citation, and common keyword analysis methods for 296 publications in the article type. Tsinghua University and Obuda University have the most publications according to the results. The most commonly used AI keywords in the ERP studies were “genetic algorithm”, “fuzzy logic”, and “machine learning”. This study aims to guide future studies by providing a systematic and new perspective to researchers and experts working on ERP-AI.

The purpose of this study is to investigate the impact of terrorist attacks on the volatility and returns of the stock market in Tunisia.

The employed sample comprises 1250 trading day from the Tunisian stock index (Tunindex) and stock closing prices of 64 firms listed on the Tunisian stock market (TSM) from January 2011 to October 2015. The research opts for the general autoregressive conditional heteroscedasticity (GARCH) and exponential generalized conditional heteroscedasticity (EGARCH) models framework in addition to the event study method to further assess the effect of terrorism on the Tunisian equity market.

The baseline results document a substantive impact of terrorism on the returns and volatility of the TSM index. In more details, the findings of the event study method show negative significant effects on mean abnormal returns with different magnitudes over the events dates. The outcomes propose that terrorism profoundly altered the behavior of the stock market and must receive sufficient attention in order to protect the financial market in Tunisia.

Very few evidence is found on the financial effects of terrorism over transition to democracy cases. This paper determines the salient reaction of the stock market to terrorism during democratic transition. The findings of this study shall have relevant implications for stock market participants and policymakers.

Quality environment is argued to be essential for ensuring food security. The effect of environmental degradation on agriculture has thus gained the attention of researchers. However, the analyses of aggregate and sectoral effect of carbon dioxide emissions on agricultural development are limited in the literature. Consequently, this study examines the effect of aggregate and sectoral carbon emissions on Ghana's agricultural development.

Time-series data from 1971 to 2017 are employed for the study. Regression analysis and a variance decomposition analysis are employed in the study.

The results show that the country's agricultural development is negatively affected by aggregate carbon emission while financial development, labour and capital increases agricultural development. Further, industrial development and emissions from transport sector, industrial sector and other sectors adversely affect Ghana's agriculture development. The contribution of carbon emission together with other explanatory variables to the changes in agricultural development generally increases over the period.

This study analyses the aggregate and sectoral carbon dioxide emission effect on Ghana's agricultural development.