Search results

The purpose of this chapter is to develop the indicators that are necessary for monitoring and controlling the efficiency of the process of implementation of information economy’s optimization model. For this, the authors used general scientific research methods – systemic, problem, structural, and functional analysis to determine the key components of balancing, along with a specific method of economics – the balance method – for determining the method of balancing of indicators.

On the basis of the complex analysis performed with the application of the above methods, the authors determined the main indicators of efficiency of the process of implementation of information economy’s optimization model essential for its successful monitoring and control. These indicators are systematized in connection to the noosphere levels and are presented in this work. The offered indicators of efficiency of the process of implementation of the information economy’s optimization model are recommended for systemic national and international monitoring and control during model implementation. The indicators for the current year, in comparison with the dynamics of previous years, could be used. The balance coefficients pose the largest value and provide the highest level of information as they reflect the results of analysis of intermediary indicators and offer information in the form that is ready for regulators. The offered indicators have to supplement the indicators used for evaluating the efficiency of economy’s formation according to the its old model.

The indicators offered in this chapter focus on the balance model of information economy, however to compile a complete picture on the progress of its formation requires consideration of the values of the indicators used within the old model.

India’s manufacturing sector employs about 12% of the labour force and contributes to about 17% of the nation’s GDP. The Indian government intends to implement several initiatives under the “Make in India” and Atma Nirbhar Bharat banners to increase the manufacturing sector’s share of the nation’s GDP to 25% by 2025. Applying lean manufacturing, green manufacturing and Six Sigma is crucial to ensure that India’s manufacturing sectors grow sustainably in international markets. This study aims to identify sustainability indicators and ascertain their respective weights to evaluate the sustainability performance of the Indian manufacturing industry.

This research identifies 25 sustainability indicators and classifies them into the triple bottom line of sustainability based on an evaluative literature review and expert opinion. The Best Worst Method was utilised to determine the weights of the sustainability indicators. The sustainability index was developed to evaluate economic, social and environmental sustainability.

The sustainability performance of a foundry in a significant Western Indian State city was assessed by applying the developed sustainability index. After the adoption of integrated lean, green and Six Sigma (LG&SS) strategies and related practices in the foundry, there has been a notable improvement of 68.03% in the economic index, 61.62% in the social index and 13.24% in the environmental index.

The proposed sustainability index is applied and evaluated specifically for assessing the sustainability performance of Indian manufacturing SMEs. It can be used to substantiate firm’s sustainability performance and also to assess the improvement in firm’s performance in economic, environmental and social dimensions after implementing various operational excellence practices. However, it cannot serve as a benchmark tool across similar companies or organisations.

The developed sustainable index can be used to analyse the company or organisation’s sustainability performance and see how various strategies have improved things. Practitioners can use this index to assess social, economic and environmental performance and focus on areas that need improvement.

The proposed sustainability index serves as a vital tool for monitoring a firm’s progress in triple bottom line (TBL) dimensions of sustainability, tracking a diverse range of indicators and encouraging sustainable organisational practices.

This study attempts to assess the economic, social and environmental performance of Indian Manufacturing SMEs by proposing a sustainability index.

The purpose of this paper is to construct a grey incidence model for panel data that can reflect the incidence direction and degree between indicators.

Firstly, this paper introduces the concept of a negative matrix and preprocesses the data of each indicator matrix to eliminate differences in dimensions and magnitudes between indicators. Then a model is constructed to measure the incidence direction and degree between indicators, and the properties of the model are studied. Finally, the model is applied to a practical problem.

The grey-directed incidence degree is 1 if and only if corresponding elements between the feature indicator matrix and the factor indicator matrix have a positive linear relationship. This degree is −1 if and only if corresponding elements between the feature indicator matrix and the factor indicator matrix have a negative linear relationship.

The example shows the number of days with good air quality is negatively correlated with the annual average concentration of each pollutant index. PM_2.5, PM₁₀ and O₃ are the main pollutants affecting air quality in northern Henan.

This paper introduces the negative matrix and constructs a model from the holistic perspective to measure the incidence direction and level between indicators. This model can effectively measure the incidence between the feature indicator and factor indicator by integrating information from the point, row, column and matrix.

This study aims to explore the interrelationships among quality and safety metrics within the Global Food Security Index (GFSI). Its primary objective is to identify key indicators and their respective influences on food security outcomes, thereby enriching comprehension of the intricate dynamics within global food security.

The analysis encompasses data from 113 countries for the year 2022, utilizing Bayesian Belief Network (BBN) models to identify significant drivers of both the GFSI and quality and safety dimensions. This methodological approach enables the examination of probabilistic connections among different indicators, providing a structured framework for investigating the complex dynamics of food security.

The study highlights the critical role of regulatory frameworks, access to clean drinking water, and food safety mechanisms in fostering food security. Key findings reveal that “nutrition monitoring and surveillance” has the highest probability (75%) of achieving a high-performance state, whereas “national dietary guidelines” have the highest probability (41%) of achieving a low-performance state. High GFSI performance is associated with excelling in indicators such as “access to drinking water” and “food safety mechanisms”, while low performance is linked to underperformance in “national dietary guidelines” and “nutrition labeling”. “Protein quality” and “dietary diversity” are identified as the most critical indicators affecting both the GFSI and quality and safety dimensions.

This research operationalizes a probabilistic technique to analyze the interdependencies among quality and safety indicators within the GFSI. By uncovering the probabilistic connections between these indicators, the study enhances understanding of the underlying dynamics that influence food security outcomes. The findings highlight the critical roles of regulatory frameworks, access to clean drinking water, and food safety mechanisms, offering actionable insights that empower policymakers to make evidence-based decisions and allocate resources effectively. Ultimately, this research significantly contributes to the advancement of food security interventions and the achievement of sustainable development goals related to food quality and safety.

The purpose of this study is to identify sustainability assessment indicators of road infrastructure projects by analyzing and synthesizing existing literature, considering that, despite being essential to achieving the sustainable development goals, sustainability performance in road infrastructure projects is a persistent challenge. Therefore, determining how sustainability can be assessed in these projects is crucial, necessitating a systematic review of the sustainability assessment indicators of road infrastructure projects.

Using PRISMA principles, 54 peer-reviewed papers from 2010 to 2022 were retrieved from three search databases (Scopus, Research for Life and Google Scholar) as part of a systematic literature review. Data analysis techniques included both descriptive and content analysis.

The study identified 22 indicators. The results emphasized that the sustainability of road infrastructure projects should consider all three sustainability dimensions to provide development while meeting human needs, preserving the planet Earth and enhancing economic growth. Similarly, indicators should be incorporated from the design phase to implement and attain sustainability successfully.

The outcome of this study will serve as a guide to road infrastructure stakeholders to understand the relevant sustainability indicators to assess the sustainability performance of their projects.

The study’s findings provide the sustainability assessment indicators for roads, which serve as a foundation for developing a sustainability assessment indicator framework for road infrastructure projects. Future research can look at establishing the indicators for the end-of-life phase of the project lifecycle.

This study aims to review the computational framework of SDGs in India, so that a mid-course correction can be contemplated.

This study deploys, inter alia, econometric analysis to probe the robustness of indicators of SDG India Index 3.0. Methodologically, the study intensively probes the robustness of SDG India index and extensively refers to the global SDG indexes for cross-checking.

Though the three editions of SDGI index mark significant efforts taken towards measuring the progress of SDGs in India, the paradigm suffers from the problem of too many indicators chasing only few targets, quantitative and qualitative issues with indicators, vintage pollution, partial coverage of targets and robustness issues.

This study has the limitation that it could not check the robustness of SDG scores with different weights assigned to indicators and future researchers can take up that interesting assignment.

Since measuring the SDG progress through SDG index is a global endeavour, the findings of this study are important for almost all countries of the world, as it is still not too late to do mid-course correction because it is not the measurement that matters at the end of the day, rather it is the outcome of sustainable development that every country cares about.

The obfuscation of layers of SDG index in crafty, glossy and power-point-presentation-oriented SDG reports should get the reality check through such review of the computational framework of SDGs.

This is the first study that unpacks the layers of SDG index computation in general and comprehensively reviews the Indian SDG indexing method in particular.

Real estate projects are capital-intensive and deeply intertwined with economic factors, making them subject to various influences besides local housing needs. This paper aims to comprehensively understand the dynamics of the Egyptian real estate market, examining real estate cycles, driving factors and their correlation and scale of impact.

The study conducts a literature review to explore real estate cycles and their driving factors, along with the relationship between real estate and macroeconomic cycles. It then delves into the dynamics of the Egyptian real estate market, followed by a time series analysis that incorporates five key indicators: economic indicator, demand indicator, supply indicator, capital flow indicator and cost indicator over a 12-year interval (2012–2023), to examine short-term cycle factors, followed by correlation and multi-linear regression analysis to elucidate interrelations among these factors.

Through measuring and comparing the prementioned indicators with different economic and social events, the study paints a comprehensive picture of the macroeconomic environment and the real estate cycle in Egypt. Where demand has been found to be more sensitive and directly affected by macroeconomic factors than the supply. With the economic factor as the factor with the highest impact, especially in times of economic fluctuations, the impact has been immediate and short-term. These findings support the idea that the demand in Egypt is speculative, laying a threat of longer recession periods in the long term and having greater and more direct impact.

This paper contributes to the understanding of the Egyptian real estate market by integrating insights from real estate cycles, macroeconomics and specific market dynamics. The application of time series, correlation and multi-linear regression analysis provides a nuanced understanding of the interplay between several factors shaping the real estate cycle. Ultimately, the findings offer valuable insights for decision-makers involved in urban development planning, facilitating more informed and precise decision-making processes.

Design of architecture, engineering and construction (AEC) project organizations expose project management practitioners (PMPs) to poor mental health due to the influence of project organization designs on project management activities assigned to the PMPs. The AEC project organization design comprises the integration of permanent organization, project organization and external environment layers. In spite of the link between project organization design and mental health, limited studies have examined the impact of permanent organization factors, project organization factors and external environmental factors on mental health management practices. Therefore, this study aims to examine the interactive relationships between permanent organization factors, project organization factors, external environment factors and mental health management indicators.

Four organizational theories: institutional theory, agency theory and resource-based theory were integrated to develop a theoretical model guiding the aim of the study. Eighty-two survey data were collected from PMPs in AEC firms in Australia. Structural equation modelling was used to test the relationships between the constructs.

The study found that mental health management indicators are predicted by the interactive and direct effects of permanent organizational factors, project organizational factors and external environmental factors. The results of the interactive effects of the factors and mental health management indicators revealed that 20 of 26 proposed hypotheses were supported. Based on the established hypotheses, economic factors, technological factors, environmental factors, legal factors and organizational culture positively correlated with mental health management indicators. Likewise, human resources management (HRM), corporate governance, project governance and integrated project delivery (IPD) positively impact mental health management indicators. However, political factors, social factors, knowledge management and project management skills negatively impact mental health management indicators. Moreover, political factors, economic factors, technological factors, environmental factors, legal factors and organizational culture are positively related to corporate governance. Additionally, organizational culture positively impacts corporate governance, project governance and HRM, whereas project governance positively correlated with IPD and knowledge management.

The findings provide guidelines to AEC firms on achieving positive mental health management indicators through concentration on project organization design.

This study thoroughly examines the relationship between environmental, social and governance (ESG) scores and their subcategories with the investment decisions made by foreign institutional investors (FII). These subcategories include resource use, emission reduction and innovation under the environmental pillar, workforce, human rights, community and product responsibility under the social pillar and management, shareholders and CSR strategy under the governance pillar.

A machine learning technique known as “topic modeling” is used to analyse the current literature on ESG. To investigate the correlation between ESG scores and their subcategories with the investment decisions made by FII and to address concerns regarding multicollinearity and overfitting, a penalty-based regression model is employed.

The findings indicate that FIIs invest in firms with higher emission reduction and innovation scores under the environmental indicator. Additionally, firms with high human rights, community and product responsibility scores under the social indicator category have a positive relationship with FII investors. All subcategories of governance indicators, such as corporate social responsibility (CSR), strategy, shareholders and management scores, also positively impact FII investment. Of the three indicators, i.e. ESG, non-promoter FIIs give maximum weightage to governance indicators.

Since ESG is a contemporary topic, the findings on the relationship between different categories of ESG on FII investment will support managers in their FII investment. Also, the study will help the government frame policy decisions on ESG.

Previous studies have explored the impact of the overall ESG indicators on FII investments, but they have not specifically studied the influence of sub-indicators within these categories on investment decisions. By addressing this gap, the study enhances stakeholder theory by identifying and prioritizing the various subcategories of ESG indicators that impact FII investment decisions.

The purpose of this paper is to examine the technical, social and moral aspects of accounting through the implementation of a novel balanced scorecard (BSC) that addresses the United Nations Sustainable Development Goal (UN SDG) 6 – Clean Water and Sanitation – within the Portuguese water utilities sector.

A novel research design is adopted, using actor network theory (ANT) as a broad approach to frame the study. ANT emphasizes the importance of ever-evolving networks of relationships and how concepts such as the BSC are just as important in structuring social practice. A set of expert interviews was conducted with stakeholders in the water utilities sector in Portugal, which led to the iterative development of a context-relevant BSC proposal and associated indicators.

A novel BSC architecture to achieve UN SDG 6 is proposed through a unique engagement between professionals and academics. The BSC, and the specific definition of indicators for an entire sector (water), contribute to bridging business processes with the common good to improve life and planetary conditions. Ultimately, the study discusses how the technical aspects of accounting can be enhanced to achieve social and moral imperatives. The paper also reflects on the limitations of broadening existing technical practices.

There is a burgeoning literature on how organizations are engaging with the UN SDG agenda. However, there is a dearth of studies on how management control systems are currently addressing, or can potentially contribute to measuring and managing specific UN SDGs such as Clean Water and Sanitation. This study makes a unique contribution to the literature by developing a novel BSC solution to SDG 6 measurement and management using a novel practitioner-led approach. Ultimately, our study highlights how accounting can be broadened to enhance technical practices while also serving a moral and social purpose.