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This paper aims to assess the impact of digital financial innovation on financial system development in Common Market for eastern and Southern Africa (COMESA). This paper evaluates the dynamic relationship between digital financial innovation measures and financial system development using time series data from COMESA countries for the period 1997–2019.

A dynamic autoregressive distributed lag model (ARDL) was adopted and the mean group (MG), pooled mean group (PMG) and dynamic fixed effect (DFE) of the model were estimated to evaluate the short- and long-run impact. In addition, the dynamic generalized method of moments (DGMM) was adopted for a robustness check. The Hausman test results show PMG to be the most consistent and efficient estimator, while the coefficient of lagged dependent variable of different GMM is less than the fixed effect coefficient, and, as such, suggests system GMM is the most suitable estimator. Data for the study were sourced from World Bank Development Indicator (WDI, 2020), World Governance Indicator (WGI, 2020) and World Bank Global Financial Development Database (GFD, 2020).

The result shows that digital financial innovation significantly impacts financial system development in the long run. As such, the evidence revealed that automated teller machines (ATMs), point of sale (POS), mobile payments (MP) and mobile banking are significant and contribute positively to financial system development in the long run, while mobile money (MM) and Internet banking (INB) are insignificant but exhibit positive and inverse relationship with financial development respectively. Further investigation revealed that institutional quality and a stable macroeconomic environment including their interactive term are significantly imperative in predicting financial system development in the COMESA region.

Researchers recommend a cohesive and conscious policy that would checkmate the divergence in the short run and suggest a common regional innovative financial strategy that could be pursued to incentivize technology transfer needed to promote financial system development in the long run. More so, plausible product and process innovations may be adapted to complement innovative institutions in the different components of the COMESA financial system.

Digital financial innovation services if well managed increase the inherent benefits in financial system development.

To the best of the authors’ knowledge, this paper presents new background information on digital financial innovation that may stimulate the development of the financial system, particularly in the COMESA region. It also exposes the relevance of digital financial innovation, institutional quality and stable macroeconomic environment as well as their interactive effect on COMESA financial system development.

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.

Personal remittance and ready-made garments (RMG) export incomes have emerged as the largest source of foreign income for Bangladesh's economy. The study investigates their impact on income inequality and gross domestic product (GDP) as a control variable, using time-series yearly data from 1983 to 2018.

It employs the Autoregressive Distributed Lag (ARDL) estimation and the Toda-Yamamoto (T-Y) causality approach. The ARDL estimation outcomes confirm a long-run association among the above variables and validate the autoregressive characteristic of the model.

Personal remittances positively contribute to reducing the income gap among the people of the society and declining income inequality. In contrast, RMG export income and economic growth contribute to further income inequality. The T-Y causality analysis follows the ARDL estimation outcomes and authenticates their robustness. It reveals a feedback relationship between remittance inflow and the Gini coefficient, unidirectional causalities from RMG export income to income inequality and economic growth to income inequality.

The finding has important policy implications to limit the income gaps between low and high-income groups by channeling incremental income to the lower-income group people. The policymakers may facilitate further international migration to attract further remittances and may upgrade the minimum wage of the RMG workers.

The study is original. As far as the authors' knowledge goes, this is a maiden attempt to investigate the impact of personal remittances and RMG export income on income disparity in the case of Bangladesh.

Economic literature highlights both positive and negative impact of FDI on economic growth. The purpose of this study is to confirm the relationship between various economic factors and FDI equity inflows and find out deviations, if any. This is investigated using standard time-series econometric models. The long and short run relationship is inquired with respect to market size, inflation rate, level of infrastructure, domestic investment and openness to trade. The choice of variables for Indian economy is purely based on empirical observations obtained from scientific literature review.

The study involves application of autoregressive distributive lag (ARDL) model to investigate the relationship. The long run co-integration between FDI and economic growth is tested by Pesaran ARDL model. The stationarity of data is tested by augmented Dickey Fuller test and Phillip–Perron unit root test. Error correction model is applied to study the short run relationship using Johansen’s vector error correction model method besides other tests.

The results show that the domestic investment, inflation rate, level of infrastructure and trade openness influence inward FDI flows. These factors have both long and short-term relationship with FDI inflows. However, market size is insignificant in influencing the foreign investments inflows. There lies an inverse relation between FDI and inflation rate.

To the best of the authors’ knowledge, the study is original. The methodology and interpretation of results are distinct and different from other similar studies.

A promising solution to meet the challenge of sustainability and ensure the protection of the environment consists in acting considerably on the adoption and use of new information and communication technologies. The latter can act on the protection of the environment; completely change manufacturing processes into energy-efficient, eco-friendly techniques or influence institutions and governance. The article attempts to cover shortcomings in the literature by providing a couple of theoretical frameworks and grounded empirical proofs for the dissemination of green technologies and the interaction of the latter with institutional quality.

The sample is made up of 43 African countries covering the period 2000–2020 and a panel VAR modeling approach is employed.

Our results show that an attenuation of CO2 emissions amplifies the diffusion of digital technologies (mobile telephones and Internet). Efficiency in the institutional quality of African countries is mandatory for environmental preservation. Moreover, the provision of a favorable institutional framework in favor of renewable energy helps to stimulate environmental performance in African states.

This study complements the extant literature by assessing nexuses between green technology and CO2 emissions in environmental sustainability.

Our study aims to evaluate the impact of infrastructure and public investment on private investment in machinery and equipment in Brazil from 1947 to 2017. The contribution of our article to the existing literature lies in providing a more comprehensive understanding of the presence or absence of the crowding effect in the Brazilian economy by leveraging an extensive historical database. Our central argument posits that the recent decline in private capital accumulation over the last few decades can be attributed to shifts in economic policies – moving from a developmentalist orientation to nondevelopmental guidance since the early 1990s, which is reflected in the diminished levels of public investment and infrastructure since the 1980s.

We conducted a series of econometric regressions utilizing the autoregressive distributed lag (ARDL) model as our chosen econometric methodology.

Employing two different variables to measure public investment and infrastructure, our results – robust across various specifications – have substantiated the existence of a crowding-in effect in Brazil over the examined period. Thus, we have empirical evidence indicating that the state has influenced private capital accumulation in the Brazilian economy over the past decades.

Our article contributes to the existing literature by offering a more comprehensive understanding of the crowding effect in the Brazilian economy, utilizing an extensive historical database.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

This study aims to explore how sustainable business models (SBM) can be developed within agri-innovation systems (AIS) and emphasize an integration of the two with a systemic understanding for reducing food loss and value loss in postharvest agri-food supply chain.

This study conducted longitudinal qualitative research in a developing country with food loss challenges in the postharvest supply chain. This study collected data through multiple rounds of fieldwork, interviews and focus groups over four years. Thematic analysis and “sensemaking” were used for inductive data analysis to generate rich contextual knowledge by drawing upon the lived realities of the agri-food supply chain actors.

First, this study finds that the value losses are varied in the supply chain, encompassing production value, intrinsic value, extrinsic value, market value, institutional value and future food value. This happens through two cumulative effects including multiplier losses, where losses in one model cascade into others, amplifying their impact and stacking losses, where the absence of data stacks or infrastructure pools hampers the realisation of food value. Thereafter, this study proposes four strategies for moving from the loss-incurring current business model to a networked SBM for mitigating losses. This emphasises the need to redefine ownership as stewardship, enable formal and informal beneficiary identification, strengthen value addition and build capacities for empowering communities to benefit from networked SBM with AIS initiatives. Finally, this study puts forth ten propositions for future research in aligning AIS with networked SBM.

This study contributes to understanding the interplay between AIS and SBM; emphasising the integration of the two to effectively address food loss challenges in the early stages of agri-food supply chains. The identified strategies and research propositions provide implications for researchers and practitioners seeking to accelerate sustainable practices for reducing food loss and waste in agri-food supply chains.

This study aims to empirically examine the influence of financial constraints on firm carbon emissions. In addition to the role of financial constraints in firm-level carbon emissions, this study also examines this influence in the presence of governance, environmental orientation and firm-level attributes.

Using pooled ordinary least square, this study examines the impact of financial constraints on firm-level carbon emissions using a panel of 1,536 US firm-year observations from 2008 to 2019. This study also used two-step generalized method of moment–based dynamic panel data and two-stage least square approaches to address potential endogeneity. The results are robust to endogeneity and collinearity issues.

The results suggest that financial constraints enhance the carbon emissions of the firms. The economic significance of financial constraints on carbon emissions is more pronounced for the firms that do not report environment-related expenditure investment and those that are highly leveraged. The authors further document that firms with a nondiverse gender board signify a statistically significant impact of financial constraints on carbon emissions. These results are also economically significant, as one standard deviation increase in financial constraints is associated with a 3.340% increase in carbon emissions at the firm level.

Some implicit and explicit factors like corporate emissions policy and culture may condition the relationship of financial constraints with carbon emissions. Therefore, it would be worthwhile to consider these factors for future research. In addition, it is beneficial to identify the thresholds and/or quantiles at which financial constraints may significantly make a difference in enhancing carbon emissions.

The findings offer policy implications for investment in stakeholder engagement for capital acquisitions, thereby effectively enforcing environmental innovation and leading to a reduction in carbon emissions.

This study integrated governance and environment-oriented variables in the model to empirically examine the role of financial constraints on the carbon emissions of the firms in the USA over and above what has already been documented in the earlier literature.

The purpose of this study was to develop a scale that can be used to assess inequality at work based on gender, age and ethnicity that is grounded in Acker’s (2006) inequality regimes.

The authors used three representative samples (total N = 1,806) of Swedish teachers, nurses and social workers to develop and validate the scale. The validation process included the assessment of content validity, confirmatory factor analysis for factorial validity, internal consistency and associations with theoretically warranted outcomes and related constructs to assess criterion-related validity and convergent validity.

The authors found evidence supporting the content, factorial, criterion-related and convergent validity of the InEquality in organisations Scale (InE-S). Furthermore, the scale demonstrated high internal consistency.

The newly developed scale InE-S may be used to further the understanding of how inequality at work influences employees. This study makes a contribution to the current literature by providing a scale that, for the first time, can test Acker’s hypotheses using quantitative methods to demonstrate the consequences of inequality at work.