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Business has a critical part to play in transforming the global economy and society to achieve sustainable development. Many granular sustainability accounting and management tools have been offered. To systematize these piecemeal developments, this paper aims to develop a framework for analysis of the potential role of sustainability management accounting (SMA). The key challenge addressed is how SMA could be extended to support future-oriented, long-term, pro-active management of multiple issues to contribute towards strong sustainable development at the macro-economy level.

This conceptual paper examines SMA within a multi-level, context-action-transformation framework which can move organizations and society towards sustainability. Based on normative stakeholder theory, including concern for mainstreaming marginalized stakeholders, the paper discusses the role of SMA and how it can contribute necessary information to sustainable development of the company and beyond its boundaries.

Guided by a SMA framework linking context, action and transformation and normative stakeholder theory, which considers all stakeholders, the paper shows how the present lack of progress towards macro-level sustainable development can be addressed. This requires a focus on measuring and assessing positive impacts and forward-looking, long-term and proactive management of multiple sustainability issues as typified by the Sustainable Development Goals (SDGs).

The paper distinguishes between two aspects of SMA – a focus on reducing unsustainability and a focus on transformations towards sustainability. It is observed that there is insufficient emphasis on the latter at present if SMA is to provide comprehensive support to achieving the SDGs. A set of supportive tools is presented as a guide to practice and future developments.

The paper considers how SMA can enable and support transformations towards sustainability at the macro- and meso-level. Different transformational challenges and opportunities are discussed. In particular, the need to balance consideration of time, proactivity and multiplicity, as highlighted in the SDGs, is identified as the central way forward for SMA.

The purpose of this study is to introduce a new type of sensor which uses microwave metamaterials and direct-coupled split-ring resonators (DC-SRRs) to measure the dielectric properties of solid materials in real time. The sensor uses a transmission line with a bridge-type structure to measure the differential frequency, which can be used to calculate the dielectric constant of the material being tested. The study aims to establish an empirical relationship between the dielectric properties of the material and the frequency measurements obtained from the sensor.

In the proposed design, the opposite arm of the bridge transmission line is loaded by DC-SRRs, and the distance between DC-SRRs is optimized to minimize the mutual coupling between them. The DC-SRRs are loaded with the material under test (MUT) to perform differential permittivity sensing. When identical MUT is placed on both resonators, a single transmission zero (notch) is obtained, but non-identical MUTs exhibit two split notches. For the design of differential sensors and comparators based on symmetry disruption, frequency splitting is highly useful.

The proposed structure is demonstrated using electromagnetic simulation, and a prototype of the proposed sensor is fabricated and experimentally validated to prove the differential sensing principle. Here, the sensor is analyzed for sensitivity by using different MUTs with relative permittivity ranges from 1.006 to 10 and with a fixed dimension of 9 mm × 10 mm ×1.2 mm. It shows a very good average frequency deviation per unit change in permittivity of the MUTs, which is around 743 MHz, and it also exhibits a very high average relative sensitivity and quality factor of around 11.5% and 323, respectively.

The proposed sensor can be used for differential characterization of permittivity and also as a comparator to test the purity of solid dielectric samples. This sensor most importantly strengthens robustness to environmental conditions that cause cross-sensitivity or miscalibration. The accuracy of the measurement is enhanced as compared to conventional single- and double-notch metamaterial-based sensors.

This paper aims to investigate the determinants of global interest in central bank digital currency (CBDC). It assessed whether global interest in sustainable development and cryptocurrency are determinants of global interest in CBDC.

Google Trends data were analyzed using two-stage least square regression estimation.

There is a significant positive relationship between global interest in sustainable development and global interest in CBDC. There is a significant positive relationship between global interest in cryptocurrency and global interest in the Nigeria eNaira CBDC. There is a significant negative relationship between global interest in CBDC and global interest in the eNaira CBDC. There is a significant positive relationship between global interest in CBDC and global interest in the China eCNY. There is a significant negative relationship between global interest in cryptocurrency and global interest in the Sand Dollar and DCash.

The literature has not empirically examined whether global interest in sustainable development and cryptocurrency are factors motivating global interest in CBDC. This study fills a gap in the literature by investigating whether global interest in sustainable development and cryptocurrency are factors motivating global interest in CBDC.

This study examines potential ways to break the inequality reproduction circle faced by ethnic minority health workers and sustained by key performance indicators (KPIs)-centred management in the National Health Service (NHS) in England. It does so through the lens of signalling theory.

Three years panel data for 2018–2020 covering 207 hospitals was compiled from the annual NHS staff survey and matched with relevant administrative records. Structural equation modelling was used to test the proposed hypotheses at the organisational level.

The moderated mediating model reveals that persistent racial discrimination by managers and coworkers can disadvantage the career progression of ethnic minority health workers, which in turn reinforces and reproduces economic and health inequalities among them. More importantly, we show how the collective agreement that the senior management team acts (SMTA) on staff feedback can break this vicious circle.

While our research focuses on the not-for-profit health care sector, it opens important opportunities to extend the proposed model to understand organisational inequality and how to address it.

Perceived SMTA can send strong signals to reduce deep-rooted discrimination (race, gender, age, etc.) through resource allocations and instrumental functions. This is also a way to address the current staff burnout and shortage issues in the healthcare sector.

This article reveals why the purpose of organisations that provide public service to reduce social inequality was comprised during their business-like operations and more importantly, how to reflect their foundational purpose through management practice.

This study offers a way forward to resolve one of the unintended consequences of KPI-centred management in the not-for-profit sector through unpacking the process of inequality reproduction and, more importantly, how it is possible to break this vicious circle.

The purpose of this paper is to investigate investor herding behaviour and the effect of presidential elections on investor herding behaviour in African stock markets at the sector level.

The study segregates listed firms into financial, consumer goods, consumer services and basic materials sectors and uses the cross-sectional absolute deviation approach as a metric of detecting herding in each of the sectors. The authors extend the model to tease out the effect of presidential elections on investor herding behaviour.

The study reveals that sectoral differences are fundamental to the evolution of herding. Herding is prominent in a financial services sector dominated by banks. The phenomenon also prevails in markets with smaller consumer goods and services sectors. A post-presidential election effect on investor herding is found for the consumer goods and services sectors of Ghana and a pre-presidential election effect is documented in Nigeria's consumer services sector. The authors conclude that post-presidential election effect is as a result of political connections whilst a pre-presidential election effect is attributable to political business cycles.

The study is based on four African countries due to data constraints. Nonetheless, the study is the first in Africa to the best of the authors' knowledge, and the results are very solid and have a lot of practical and policy implications.

The study has implications for investors as it guides investment behaviour in pre- and post-presidential election periods.

Past studies on investor herding behaviour in African stock markets have largely concentrated on the aggregate market. Knowledge on sectoral differences in investor herding is almost non-existent for African stock markets. Furthermore, premised on the fact that stock markets react to presidential elections, there is no known study that have attempted to examine the effect of presidential elections on investor herding behaviour. This paper contributes to the literature by providing evidence on sectoral differences in investor herding behaviour and the effect of presidential elections on sectoral herding behaviour.

Education had proven to be one of the main determinants of economic growth, and it is a reason of the variations in economic growth levels between developed and developing countries. One of the main dimensions in studding the relationship between economic growth and education is the gender dimension or the importance of gender equality or female education in achieving economic growth. This chapter aims to test the hypothesis of the existence of a positive relationship between female education and economic growth in Egypt since 1990.

To address this question, Auto Regression Distributed Lag (ARDL) Bound test approach is conducted to analyze the co-integration between female education and economic growth using Egyptian Data for the period 1990–2022. The Empirical analysis for Egypt suggests the existence of positive significant relationship both in the short run and long run and that the impact of female education on economic growth is larger than the impact of education in general on growth. This could be explained by the existence of gender gap in Egypt, labor market, and thus, more educated girls able to enter the labor market will affect the economic growth more than the education of both sexes, in other words, there is still a room for improvement in the female labor market opportunities than for both sexes. The chapter also confirms the existence of a direct link between education in general and economic growth and thus confirms the hypothesis of the positive impact of education economic growth.

The present study distinguishes itself by pioneering an innovative framework that integrates key elements of prospect theory and the fundamental principles of electronic word of mouth (EWOM) to forecast Bitcoin/USD price fluctuations using Twitter sentiment analysis.

We utilized Twitter data as our primary data source. We meticulously collected a dataset consisting of over 3 million tweets spanning a nine-year period, from 2013 to 2022, covering a total of 3,215 days with an average daily tweet count of 1,000. The tweets were identified by utilizing the “bitcoin” and/or “btc” keywords through the snscrape python library. Diverging from conventional approaches, we introduce four distinct variables, encompassing normalized positive and negative sentiment scores as well as sentiment variance. These refinements markedly enhance sentiment analysis within the sphere of financial risk management.

Our findings highlight the substantial impact of negative sentiments in driving Bitcoin price declines, in contrast to the role of positive sentiments in facilitating price upswings. These results underscore the critical importance of continuous, real-time monitoring of negative sentiment shifts within the cryptocurrency market.

Our study holds substantial significance for both risk managers and investors, providing a crucial tool for well-informed decision-making in the cryptocurrency market. The implications drawn from our study hold notable relevance for financial risk management.

We present an innovative framework combining prospect theory and core principles of EWOM to predict Bitcoin price fluctuations through analysis of Twitter sentiment. Unlike conventional methods, we incorporate distinct positive and negative sentiment scores instead of relying solely on a single compound score. Notably, our pioneering sentiment analysis framework dissects sentiment into separate positive and negative components, advancing our comprehension of market sentiment dynamics. Furthermore, it equips financial institutions and investors with a more detailed and actionable insight into the risks associated not only with Bitcoin but also with other assets influenced by sentiment-driven market dynamics.