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This paper proposes a methodology based on an uncertain mining technology that identifies the linguistic relationships of ESG and its components with a financial performance metric to help the sustainability diagnosis of a region, specifically Latin America.

First, based on a relevant dataset of companies in a region, a procedure is formulated whereby an uncertain mining technology extracts the mathematically significant linguistic relationships of ESG and its components with a financial performance metric. Second, a knowledge management process is designed based on the linguistic summaries obtained from the mining process. As a final step and drawing upon the two preceding processes, a diagrammatic system of signals is proposed for diagnosing the sustainability of the region as contributed by its companies.

After this methodology is instantiated on a group of Multilatinas, it is observed that their sustainability contributions to the region are limited and that none of the identified linguistic relationships between ESG and the financial performance metric are favorable for the region.

This is the first proposal of its kind and it can be applied to any region of the world to assess the financial performance of its companies regarding their ESG commitments. In addition, it enables the region to comprehensively monitor compliance with the 2030 SDG agenda.

Purpose: Innovation is a multi-dimensional phenomenon influenced at the organisational level by internal and external factors that can determine how innovative an organisation can be, determining a firm’s business performance. This chapter measures and predicts how innovative a company can be, considering key internal factors using modern data analytics/science.

Need for Study: The increasing challenge of modern business operations is affected by how quickly, sustainably, effectively, and efficiently companies can innovate to mitigate the dynamic challenges of current business environments and evolving customer needs. The ability to predict, measure, and manage innovation becomes necessary to ensure that businesses are fit for purpose.

Methodology: A model was designed following the study hypotheses and statistically tested. A historical data sample from the OECD global industry dataset for eight years was used for the analysis. The ordinary least square method was used to test for model fit. Also, in machine learning engineering, predictive analysis using the multivariate linear regression analysis method was carried out.

Findings: The results support the hypotheses that an organisation’s capacity to be innovative can be measured and predicted, and it is influenced by a good number of internal factors or independent variables at various degrees.

Practical Implications: Managers must understand how to measure and predict innovation metrics to manage innovation better, ultimately leading to better business outcomes and performance. Also proposed are new measurement matrices for innovation management: innovation capacity (I_C), business innovation value (BIV), innovation creation factor (ICF), and a practical data-driven innovation management and prediction system.

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.

We explore the effect of selective schooling, where students are assigned to different schools by ability, on adult health, well-being and labour market outcomes. We exploit the 1960s transition from a selective to a non-selective secondary schooling system in England and Wales. The introductio3n of mixed-ability schools decreased average school quality and peer ability for high-ability pupils, while it increased them for low-ability pupils. We therefore distinguish between two treatment effects: that of high-quality school attendance for high-ability pupils and that of lower-quality school attendance for low-ability pupils, with mixed-ability schools as the alternative. We address selection bias by balancing individual pre-treatment characteristics via entropy balancing, followed by ordinary least squares (OLS) regression. Selective schooling does not affect long-term health and well-being, while it marginally raises hourly wages, compared to a mixed-ability system, and school aspirations for high-ability pupils. Cognitive and non-cognitive abilities measured prior to secondary school are significantly and positively associated with all adult outcomes.

This study evaluates how well Victorian local councils’ procurement policies align with the sustainable and circular economy (CE) approach that prioritises sustainable and regenerative practices. It proposes a set of criteria designed to effectively integrate environmental sustainability issues into purchasing policies.

Employing the Specific, Measurable, Assignable, Realistic and Time-bound (SMART) framework, a multi-dimensional content analysis guided by the goal-setting theory was applied to evaluate all 79 Victorian local councils’ procurement policies. This approach provided an assessment of policy specificity, measurability, assignability, realism and time sensitivity in promoting environmental sustainability through purchasing policies.

The findings underscored a significant deficiency in policy adherence to all SMART criteria concerning environmental sustainability, hindering the effective green purchasing decisions within government entities. This lack of integration of greening in purchasing policy poses challenges for manufacturers of waste-derived goods, obscuring the procurement objectives of these critical public sector customers.

The paper contributes to the sustainable procurement (SP) discourse by proposing guidelines aimed at improving the efficacy of governmental purchasing of sustainable products. These guidelines address the broader imperative to mitigate the environmental impacts of governmental spending on less sustainable goods, thereby fostering ecological sustainability and promoting responsible consumption.

While past studies have often relied on subjective content analysis methods, the SMART assessment used to develop the environmental sustainability criteria for purchasing policies, which distinguishes this study from previous governmental policy evaluation studies. This approach marks a departure from traditional governmental policy evaluation studies, offering a more structured analysis of policy effectiveness in promoting SP practices.