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Milk is often referred to as the ultimate food because it meets the nutritional needs of infants, children and adults alike. It is a rich source of protein, fat, sweetness, vitamins and minerals. Because of its widespread usage as a healthy dairy product, the issue of milk adulteration is of global significance. The increasing frequency of fraudulent methods in the dairy business raises concerns about its purity and quality.

A study was conducted and reviewed that looked at several approaches for detecting milk adulteration during the past 15 years. This study examines the current state of research and analyzes recent advances in development.

There are ways and technology available that can effectively put an end to the abhorrent practice of milk adulteration.

This research takes a unique approach, focusing on the application of milk adulteration. It provides an overview of milk adulteration detection and investigates the effectiveness of biosensors in identifying common milk adulterants.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.

The study directs attention to the psychological conditions experienced and knowledge management practices leveraged by faculty in higher education institutes (HEIs) to cope with the shift to emergency remote teaching caused by the COVID-19 pandemic. By focusing attention on faculty experiences during this transition, this study aims to examine an under-investigated effect of the pandemic in the Indian context.

Interpretative phenomenological analysis is used to analyze the data gathered in two waves through 40 in-depth interviews with 20 faculty members based in India over a year. The data were analyzed deductively using Kahn’s framework of engagement and robust coding protocols.

Eight subthemes across three psychological conditions (meaningfulness, availability and safety) were developed to discourse faculty experiences and challenges with emergency remote teaching related to their learning, identity, leveraged resources and support received from their employing educational institutes. The findings also present the coping strategies and knowledge management-related practices that the faculty used to adjust to each discussed challenge.

The study uses a longitudinal design and phenomenology as the analytical method, which offers a significant methodological contribution to the extant literature. Further, the study’s use of Kahn’s model to examine the faculty members’ transitions to emergency remote teaching in India offers novel insights into the COVID-19 pandemic’s effect on educational institutes in an under-investigated context.

This paper has a dual purpose: to produce a clear panorama of microfactors behind the implementation of environmental, social and governance (ESG) in emergent economies, and to identify long-term versus short-term implications of ESG and its impacts on sustainable transformation. In particular, the paper investigates the moderating role of ownership concentration on ESG performance and firm value relationship in Southeast Asia during 2010–2022 and COVID-19 period 2020–2022.

By adopting stakeholder and agency theory lenses, this study analyzes 591 nonfinancial listed companies in Southeast Asia from 2010 to 2022 with 2,673 firm-year observations. Data has been collected from Refinitiv and companies' annual reports. Ordinary least squares (OLS) and two-stage least squares (2SLS) estimators are main strategies.

During 2010–2022, the links between ESG performances and firm value are negative. Ownership concentration negatively moderates the nexus between governance pillar and firm value in both short and long run. In COVID-19, ownership concentration also plays an antagonistic moderating role in ESG combined score-firm value association. The results show a crucial role of blockholders in Southeast Asian firms and their strong support to ESG in conquering crisis period, suggesting that managers develop balancing mechanisms in making ESG-related decisions; policymakers and regulators improve effective control instruments with strong legal systems and enhanced law enforcement to protect minority shareholders.

This is the first study to test the connection between ESG performance, ownership concentration and firm value in Southeast Asia that has: (1) utilized different proxies of firm value and ownership concentration in robustness tests, (2) controlled heteroskedasticity defects, (3) eliminated companies in the Banking and Finance sector from the sample to avoid distorting the conclusions and (4) empirically verified the driven role of governance pillar in ESG performance and ownership concentration reversely moderated the impact of governance pillar on firm value.

The purpose of this study is to explore the relationship between career competencies and leadership aspiration among women IT/ITeS professionals in South India, examining the mediating effects of work role salience and occupational self-efficacy, along with the moderating effect of achievement aspiration.

The sample consists of 348 women professionals working in the IT/ITeS industry in South India. The study adopts a descriptive methodology and employs a cross-sectional research design.

The result shows that work role salience mediates the relationship between career competencies and leadership aspiration and that this mediation is moderated by achievement aspiration. Additionally, occupational self-efficacy is found to have a supplementary effect on leadership aspiration, further contributing to the model.

Despite the limitations of online data collection, the study showcases adaptability, providing valuable insights into women's career aspirations. It acknowledges opportunities for future research improvements, such as implementing longitudinal frameworks and incorporating a more diverse sample, to enhance the robustness and applicability of findings.

The study offers valuable insights for managers, researchers and academia, aiding in the identification of crucial competencies for women aspiring to leadership roles, and fostering the retention of top talent in a diverse and inclusive work environment. Individuals can leverage these insights for enhanced career development by recognizing and emphasizing strengths while addressing weaknesses through accurate self-assessments.

This study offers a novel perspective by identifying the essential competencies that are crucial for women to achieve leadership positions, thus making a valuable contribution to the existing literature in the field.

Identifying and prioritizing supply chain risk is significant from any product’s quality and reliability perspective. Under an input-process-output workflow, conventional risk prioritization uses a risk priority number (RPN) aligned to the risk analysis. Imprecise information coupled with a lack of dealing with hesitancy margins enlarges the scope, leading to improper assessment of risks. This significantly affects monitoring quality and performance. Against the backdrop, a methodology that identifies and prioritizes the operational supply chain risk factors signifies better risk assessment.

The study proposes a multi-criteria model for risk prioritization involving multiple decision-makers (DMs). The methodology offers a robust, hybrid system based on the Intuitionistic Fuzzy (IF) Set merged with the “Technique for Order Performance by Similarity to Ideal Solution.” The nature of the model is robust. The same is shown by applying fuzzy concepts under multi-criteria decision-making (MCDM) to prioritize the identified business risks for better assessment.

The proposed IF Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) for risk prioritization model can improve the decisions within organizations that make up the chains, thus guaranteeing a “better quality in risk management.” Establishing an efficient representation of uncertain information related to traditional failure mode and effects analysis (FMEA) treatment involving multiple DMs means identifying potential risks in advance and providing better supply chain control.

In a company’s supply chain, blockchain allows data storage and transparent transmission of flows with traceability, privacy, security and transparency (Roy et al., 2022). They asserted that blockchain technology has great potential for traceability. Since risk assessment in supply chain operations can be treated as a traceability problem, further research is needed to use blockchain technologies. Lastly, issues like risk will be better assessed if predicted well; further research demands the suitability of applying predictive analysis on risk.

The study proposes a hybrid framework based on the generic risk assessment and MCDM methodologies under a fuzzy environment system. By this, the authors try to address the supply chain risk assessment and mitigation framework better than the conventional one. To the best of their knowledge, no study is found in existing literature attempting to explore the efficacy of the proposed hybrid approach over the traditional RPN system in prime sectors like steel (with production planning data). The validation experiment indicates the effectiveness of the results obtained from the proposed IF TOPSIS Approach to Risk Prioritization methodology is more practical and resembles the actual scenario compared to those obtained using the traditional RPN system (Kim et al., 2018; Kumar et al., 2018).

This study provides mathematical models to simulate the supply chain risk assessment, thus helping the manufacturer rank the risk level. In the end, the authors apply this model in a big-sized organization to validate its accuracy. The authors validate the proposed approach to an integrated steel plant impacting the production planning process. The model’s outcome substantially adds value to the current risk assessment and prioritization, significantly affecting better risk management quality.

This study is the maiden attempt to understand the effect of specific human resource practices (HRPs) on employee retention (ER) with the mediation of job satisfaction (JS) and moderation of work experience (WE) and job hopping (JH) in the context of the textile industry.

This study adopted a quantitative methodology and applied quota sampling to gather data from employees (n = 365) of leading textile companies in India. The conceptual model and hypotheses were tested with the help of Partial Least Squares-Structural Equation Modelling (PLS-SEM).

The findings of a path analysis revealed that compensation and performance appraisal (CPA) have the highest impact on JS followed by employee work participation (EWP). On the other hand, EWP had the highest impact on ER followed by grievance handling (GRH). The study revealed that JS significantly mediates between HRPs like CPA and ER. During Multi-group analysis (MGA) it was found that the importance of EWP and health and safety (HAS) was more in employee groups with higher WE, but it was the opposite in the case of CPA. In the case of JH behavior, the study observed that EWP leads to JS in loyal employees. Similarly, JS led to ER, and the effect was more pronounced for loyal employees.

In the context of the Indian textile industry, this work is the first attempt to comprehend how HRPs affect ER. Secondly, it confirmed that JS is not a guaranteed mediator between HRPs and ER, it could act as an insignificant, partial or full mediator. Additionally, this study establishes the moderating effects of WE and JH in the model through multigroup analysis.

In the era of big data, various industries are generating large amounts of text data every day. Simplifying and summarizing these data can effectively serve users and improve efficiency. Recently, zero-shot prompting in large language models (LLMs) has demonstrated remarkable performance on various language tasks. However, generating a very “concise” multi-document summary is a difficult task for it. When conciseness is specified in the zero-shot prompting, the generated multi-document summary still contains some unimportant information, even with the few-shot prompting. This paper aims to propose a LLMs prompting for multi-document summarization task.

To overcome this challenge, the authors propose chain-of-event (CoE) prompting for multi-document summarization (MDS) task. In this prompting, the authors take events as the center and propose a four-step summary reasoning process: specific event extraction; event abstraction and generalization; common event statistics; and summary generation. To further improve the performance of LLMs, the authors extend CoE prompting with the example of summary reasoning.

Summaries generated by CoE prompting are more abstractive, concise and accurate. The authors evaluate the authors’ proposed prompting on two data sets. The experimental results over ChatGLM2-6b show that the authors’ proposed CoE prompting consistently outperforms other typical promptings across all data sets.

This paper proposes CoE prompting to solve MDS tasks by the LLMs. CoE prompting can not only identify the key events but also ensure the conciseness of the summary. By this method, users can access the most relevant and important information quickly, improving their decision-making processes.

Much of the existing research conducted on dynamic capabilities and the microfoundations of innovation has focused either on individual or organizational factors without considering mechanisms. This paper aims to address this “process” gap by developing an integrated conceptual framework based on individual, processual and structural microfoundations as well as the interaction between and among them with respect to innovation.

To understand the theoretical and empirical landscape in building our conceptual model, we conducted a content analysis of existing research microfoundations, dynamic capabilities and innovation. Using NVivo 12, we identified and examined the individual and organizational behavior microfoundations and their interplay to propose possible processual mechanisms. We framed these process mechanisms using the sensing, seizing and reconfiguring dynamic capabilities framework.

The study emphasizes certain microfoundations that facilitate innovation-dynamic capabilities at various organizational levels. It is posited that both formal and informal strategic intelligence processes, along with directed and undirected information research methods, constitute crucial microfoundations for identifying opportunities for innovation. For the internal capture and seizing of these opportunities, we assert that the diversity of individual internal networks and the mechanisms for social integration will prove to be critical. Furthermore, the paper suggests that reconfiguring microfoundations, specifically an organization’s flexible structure and the involvement of external directors with diverse experiences, are pivotal in spurring innovation.

We combine the microfoundations approach (individual, structural and processual) with the dynamic capabilities theory (sensing, seizing and reconfiguring) to offer an integrated conceptual framework underlying innovation’s dynamic capabilities. This sets us apart from existing research by both introducing processual aspects and their multilevel interactions.

Employing the “Job demands-resources (JD-R)” framework, this study examines the impact of co-occurring social supports (supervisor, coworker, and family support) on the telework environment and employee engagement.

The study uses a multimethod approach. Data from 294 employees belonging to Indian technology organizations were collected and analyzed using the partial least squares (PLS)-based structure equation modeling software SmartPLS4. Following this, necessary condition analysis (NCA) was carried out using the NCA package for R.

Telework environment was found to mediate the relationship between social support and work engagement. Supervisor support and instrumental family support were identified as predictors as well as necessary conditions for telework environment. Coworker support was identified both as a predictor and necessary condition for telework environment. Although emotional family support was found to be a predictor of telework environment, it was not identified as a necessary condition.

The findings indicate that coworker support and family instrumental support are as important for telework success as supervisor support. Moreover, our findings suggest that varying levels of telework environments (low, moderate, and high) may necessitate distinct social support configurations. Consequently, organizations should match their social support configuration to match their overall teleworking strategy.

A basic premise of the JD-R framework is that resources exist in caravans (bundles). However, previous research (in telework) has concentrated on only one or two kinds of social support, that too in varying situational contexts, limiting generalizability of the findings. This has also produced inconsistent conclusions concerning the role of support providers such as coworkers and family. Recent developments in JD-R also suggest that the role of resources may vary in terms of their importance (necessity) for work engagement. By augmenting standard regression-based techniques with NCA, the authors explore these issues to provide a more thorough understanding of the influence of social supports on work engagement in telework situations.