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This study examines the semi-strong market efficiency of the Indian agricultural commodity market in light of market reforms and policies. This study investigates whether the market reforms have boosted the speed of price adjustment and influenced the market quality.

The study used the daily data of nine agricultural commodities. To precisely capture the effects of market microstructure changes, this study split the whole data into pre- and post-ban and pre- and post-reform eras. To ascertain the velocity of price adjustment, the authors used the ARMA (1,1) model, and the ADD VRatio was employed to identify the price movement on a specific day.

This study found that full incorporation of information happens sometimes. The authors noticed no gradual progress in the quickness of price adjustment. Since both methods suggested the same result for the period, the authors confirm that market microstructure changes do not enhance market quality.

This research has implications for academicians, policymakers and market players.

The paper has twofold novelty. First, this is a contemporary topic, and very few studies have been done in the Indian agriculture context. Second, the study has implications for policymakers and government because it highlights the effects of structural changes on market quality and market efficiency.

The purpose of this study is to determine the enablers of the quality of work life (QWL) of employees working in the Garment industries.

The study was carried out in a fivefold step. In the first step, the enablers of QWL were identified through an exhaustive literature survey, in the second step identified vital few components through Pareto analysis. Then the third step was followed by exploratory factor analysis (EFA) to further, to identify the precise components and validate the same using confirmatory factor analysis in fourth step. The final step included interpretive structural modeling and Cross-Impact Matrix Multiplication Applied to Classification analysis to model the validated components and determine the interrelationships and linkages.

Predominant QWL enablers of employees working in the garment industries are training and development, satisfaction in job, compensation and rewards, relation and co-operation, grievance handling, work environment, job nature, job security and facilities.

In this study, the interpretive structural model is designed based on the opinion of the experts who are working in the garment industry considering the responses from employees in garment sectors. The framework can be extended further to the other sectors.

In future, the researchers in QWL may develop a model to quantify the level of employees’ QWL who are working in different sectors. Enablers of QWL are essential, and based on this further statistical analysis can be carried out. This study will provide limelight to the researchers in choosing the valid and reliable set of enablers for the empirical studies. Organizations can get benefit by implementing the outcome of this research for the enhancement of the QWL of employees.

The study was carried out in 133 garment industries where 851 workers constituted the final valid responses that were considered for analysis. The outcomes from the study help administrators, policy and decision-takers in taking decisions to enhance QWL.

In this study, the authors examine artificial knowledge as a fundamental stream of knowledge management for sustainable and resilient business models in supply chain management (SCM). The study aims to provide a comprehensive overview of artificial knowledge and digitalization as key enablers of the improvement of SCM accountability and sustainable performance towards the UN 2030 Agenda.

Using the SCOPUS database and Google Scholar, the authors analyzed 135 English-language publications from 1990 to 2022 to chart the pattern of knowledge production and dissemination in the literature. The data were collected, reviewed and peer-reviewed before conducting bibliometric analysis and a systematic literature review to support future research agenda.

The results highlight that artificial knowledge and digitalization are linked to the UN 2030 Agenda. The analysis further identifies the main issues in achieving sustainable and resilient SCM business models. Based on the results, the authors develop a conceptual framework for artificial knowledge and digitalization in SCM to increase accountability and sustainable performance, especially in times of sudden crises when business resilience is imperative.

The study results add to the extant literature by examining artificial knowledge and digitalization from the resilience theory perspective. The authors suggest that different strategic perspectives significantly promote resilience for SCM digitization and sustainable development. Notably, fostering diverse peer exchange relationships can help stimulate peer knowledge and act as a palliative mechanism that builds digital knowledge to strengthen and drive future possibilities.

This research offers valuable guidance to supply chain practitioners, managers and policymakers in re-thinking, re-formulating and re-shaping organizational processes to meet the UN 2030 Agenda, mainly by introducing artificial knowledge in digital transformation training and education programs. In doing so, firms should focus not simply on digital transformation but also on cultural transformation to enhance SCM accountability and sustainable performance in resilient business models.

This study is, to the authors' best knowledge, among the first to conceptualize artificial knowledge and digitalization issues in SCM. It further integrates resilience theory with institutional theory, legitimacy theory and stakeholder theory as the theoretical foundations of artificial knowledge in SCM, based on firms' responsibility to fulfill the sustainable development goals under the UN's 2030 Agenda.

The purpose of this paper is to investigate risk propagation and resilience in the agribusiness supply chain (ASC).

The paper undertakes a systematic literature review (SLR). Overall, 94 articles from six databases published between 2000 and 2022 underwent descriptive and thematic analysis.

There is a lack of research on risk propagation and resilience in the ASC for more than two decades. Accordingly, this research fills the gap in the extant literature by advancing the construct of risk propagation and resilience in the ASC and developing a framework proposing directions in risk propagation and resilience in ASC research.

Firstly, only the Web of Science and Scopus databases were mostly used as primary sources while other databases were used as secondary sources to validate search results. Secondly, SLR is based on the peer-reviewed articles, books and conference papers; other non-academic sources relevant to the topic were not included in this paper.

The paper offers a set of constructs for understanding risk propagation and resilience in the ASC, develops a framework proposing directions in risk propagation and resilience in the ASC research and recommends three key themes for future research directions, namely, keep updated with recent constructs of risk propagation and resilience in the ASC, conduct case studies based on empirical studies to determine the current risk dependency and propagation in the ASC and conduct case studies based on empirical studies to determine resilience and sustainability in the ASC.