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The paper aims to discuss error detection and correction in Kashmiri carpet weaving (KCW), mediated by cryptographic code, Talim which is held to guarantee accurate information transference from designing to weaving, even after hundred years. Yet, carpets often show errors on completion.

Human factors analysis revealed error emergence, detection and correction in this practice whose task domains are distributed over large geographies (from in-premises to several kilometers) and timescales (from days to decades). Using prospective observation method, production process of two research carpets from their design, coding and weaving was observed while noting the errors made, identified and corrected by actors in each phase.

The errors were found to emerge, identified and corrected during different phases of designing, coding and weaving while giving rise to fresh errors in each phase, due to actors’ normal work routines.

In view of this, usual branding of “weaver-error” behind flawed carpet turns out to be misplaced value judgment passed in hindsight.

Of the global carrot production, 20–30% is outgraded as carrot rejects and waste (CRW) at the primary processing level, which is partially used toward animal feed and the remaining ends in the landfills. This study was undertaken to identify the hurdles and seek potential solutions for using CRW in food processing.

CRW were procured from the processing unit in Ontario, Canada, as (1) outgraded carrots (OGCs) and (2) processed discards (PDs). The physical parameters of CRW, imperfections responsible for their separation from the graded carrots and shelf-life studies were recorded.

A significant difference with p ≤ 0.05 was recorded for both the physical parameters and the nature of imperfections in CRW. Discolored carrots (42.37 ± 3.59%) and the presence of vertical splits (52.71 ± 3.18%) were among the top defects in the OGCs. In contrast, the presence of broken tips (54.83 ± 2.52%) and vertical splits (40.56 ± 2.65%) were among the primary cause for the generation of PDs. In total, five percent of CRW were initially infected, which later increased to 30% during the seven days storage period.

The limitation of the study was that only two varieties of carrots were considered and these were procured from one processor (the authors’ industry partner) at different time intervals of the year. Microbiological analysis could not be completed and reported due to prevailing coronavirus disease 2019 (COVID-19) situation but is included for future studies.

Development of specialized post-harvest packaging and handling protocols and separation of infected fragments are essential before suggesting the use of CRW in food processing.

Numerous studies report on the post-harvest management and processing of graded carrots, but limited to no studies are published on the usage of CRW in food processing.

Financial inclusion is a prerequisite for inclusive development. In 2014, the Indian Government introduced the Pradhan Mantri Jan-Dhan Yojana (PMJDY) with a similar objective. The study aims to analyse the effectiveness of banks in the implementation of financial inclusion policy, i.e. PMJDY.

To evaluate the effectiveness of Indian banks, the study used the data over a seven-year period, from 2014–2015 to 2020–2021. Data are analysed by using the data envelopment analysis technique.

The study discovered that public sector banks performed better than private sector banks (PVBs) in terms of boosting financial inclusion under the PMJDY scheme. In terms of implementing the PMJDY programme, the State Bank of India rated first.

Results recommended that policymakers set goals for banks. In order to encourage consumers to utilise their accounts, banks ought to introduce supplementary financial products and implement incentive programs.

The study is the first of its kind to measure the performance of Indian banks in the implementation of the PMJDY scheme.

This paper aims to consolidate and review the literature in the field of market-calibrated option pricing analysis. By doing so, the paper brings out the gaps in the extant literature and makes suggestions for future researchers in the field.

The methodology used in this research is inspired by the works of Ferreira et al. (2016), Jabbour (2013), Lage Junior and Godinho Filho (2010), Seuring (2013) and Sharma et al. (2018). A total of 1,500 papers written on the pricing of options globally are collated from the Web of Science ranging across 2010-2018.

Most of the research papers present mathematical proposals to value options; without calibrating it with real market data points. The authors bring out five important gaps in the extant literature.

This is arguably the first study that consolidates the literature in the field of market calibrated option pricing analysis with a view to suggest directions for future researchers.

The purpose of this study is to examine the impact of corporate governance (CG) on chief executive officer compensation (CEO COMP) and pay–performance relationship (PPR) in Indian listed firms.

A sample of 196 companies listed on the S&P BSE 500 (Standard and Poor's Bombay Stock Exchange 500) Index has been analyzed using the panel (random effects) regression technique over the period 2010–2019. In addition, the system GMM technique was used to deal with the endogeneity issue.

The study found that block ownership and ownership concentration negatively impact COMP measures and PPR. Board size also had a negative direct and moderating impact on CEO COMP; however, the linkages were generally insignificant, especially for total pay. Similarly, outsider blockholders were found to be playing an insignificant role. Further, board independence positively influences COMP levels and PPR, though the results were mixed with respect to significance. Finally, CEO duality positively and significantly influences CEO COMP and PPR. A comparison before and after the new Indian Companies Act 2013 also revealed similar results, particularly in the after period. It suggests that the new legislative initiative was not effective enough in improving the CG and, hence, the alignment of pay with performance.

This study investigates the direct and moderating impact of CG on CEO COMP in the context of emerging economy India. Further, it makes a comparison before and after the introduction of the new governance reform, that is, the Indian Companies Act, 2013. Moreover, providing support to the entrenchment effect, the study reveals that large shareholders expropriate minority shareholders’ wealth by not aligning CEO pay with performance, making agency problems graver in emerging economies like India.

The purpose of this study is to explore whether different board diversity attributes (corporate governance aspect) can be used to predict financial distress. This study also aims to identify what type of prediction models are more applicable to capture board diversity along with conventional predictors.

This study used Chinese A-listed companies during 2007–2016. Board diversity dimensions of gender, age, education, expertise and independence are categorized into three broad categories; relation-oriented diversity (age and gender), task-oriented diversity (expertise and education) and structural diversity (independence). The data is divided into test and validation sets. Six statistical and machine learning models that included logistic regression, dynamic hazard, K-nearest neighbor, random forest (RF), bagging and boosting were compared on Type I errors, Type II errors, accuracy and area under the curve.

The results indicate that board diversity attributes can significantly predict the financial distress of firms. Overall, the machine learning models perform better and the best model in terms of Type I error and accuracy is RF.

This study not only highlights symptoms but also causes of financial distress, which are deeply rooted in weak corporate governance. The result of the study can be used in future credit risk assessment by incorporating board diversity attributes. The study has implications for academicians, practitioners and nomination committees.

To the best of the authors’ knowledge, this study is the first to comprehensively investigate how different attributes of diversity can predict financial distress in Chinese firms. Further, this study also explores, which financial distress prediction models can show better predictive power.