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In a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.

The present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.

Empirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.

As a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.

Based on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.

Purpose. This chapter discusses the challenges and different strategies to increase skill development for the future workforce.

Methodology. Multiple sources on the topic were studied and reviewed in this chapter. The idea of skill and its development is discussed in the literature review.

Findings. Different nations’ governments have promoted human capital development by providing up-skilling and retraining programs to balance supply and demand. Skills gaps need to be brought to the attention of stakeholders, such as governments, businesses, and the educational system. Teachers, employers, and other stakeholders need to develop strategies and action plans to ensure that the skills gaps are appropriately identified and adequately addressed. These initiatives must be developed with input from various stakeholders.

Practical Implications. The research results would inform the curriculum, incorporating skill development processes tailored to various scenarios. These findings would aid business organisations in crafting skill development programs that address identified skill gaps. Challenges in skill development would be taken into account during course development, and relevant teaching–learning materials would be created. Key stakeholders, such as accrediting organisations, employers, and students, should exert more influence on academic institutions to prioritise societal demands for economic development.

Originality/Value. The uniqueness and significance of this chapter lie in its concise summary of the strategies to tackle the hurdles in skill development.

This study aims to analyse the systemic risk emitted by all publicly listed commercial banks in a key emerging economy, India.

The study makes use of the Tobias and Brunnermeier (2016) estimator to quantify the systemic risk (ΔCoVaR) that banks contribute to the system. The methodology addresses a classification problem based on the probability that a particular bank will emit high systemic risk or moderate systemic risk. The study applies machine learning models such as logistic regression, random forest (RF), neural networks and gradient boosting machine (GBM) and addresses the issue of imbalanced data sets to investigate bank’s balance sheet features and bank’s stock features which may potentially determine the factors of systemic risk emission.

The study reports that across various performance matrices, the authors find that two specifications are preferred: RF and GBM. The study identifies lag of the estimator of systemic risk, stock beta, stock volatility and return on equity as important features to explain emission of systemic risk.

The findings will help banks and regulators with the key features that can be used to formulate the policy decisions.

This study contributes to the existing literature by suggesting classification algorithms that can be used to model the probability of systemic risk emission in a classification problem setting. Further, the study identifies the features responsible for the likelihood of systemic risk.

This study aims to understand the impact of negative supervisor gossip on job performance among South Indian hotel employees. The focus is not just on the direct influence, but also on the mediating role of feedback-seeking behaviour (FSB) and the moderating effects of the agreeableness trait.

Through purposive sampling, data was garnered from South Indian hotel employees. Comprehensive analyses were performed using partial least squares structural equation modelling.

The analysis shows that FSB plays a mediating role in the positive relationship between negative supervisor gossip and job performance. In addition, the influence of gossip on FSB and subsequent job performance was more pronounced for employees with high agreeableness.

This research underscores the complex interplay between negative supervisor gossip and job performance, revealing that such gossip can catalyze FSB process in employees. It suggests that under certain conditions, negative gossip can be transformed into a constructive force that enhances job performance, challenging traditional perceptions of gossip in the workplace.

The findings underscore the importance of understanding the effects of workplace dynamics, like supervisor gossip, on employee behaviour and performance. Recognizing the influence of individual personality traits, such as agreeableness, can guide management strategies for fostering a productive work environment.

This research sheds light on the intricate interplay between negative supervisor gossip, FSB and agreeableness, offering a novel perspective on their combined impact on job performance. It not only enriches the existing literature on workplace communication but also broadens the understanding of the role of personality traits in shaping employee responses and outcomes.

Green HRM practices in the hospitality sector are now receiving growing interest. However, the extent to which these practices contribute towards employee non-green workplace outcomes remains largely unknown. This study explores the relationships among green HRM practices, happiness at work, employee resilience, and feedback-seeking behaviour.

The study employs two-wave data from a sample of 306 five-star hotel employees in India. Using partial least square-structural equation modelling, the relationships are tested.

The study’s results demonstrate that green HRM practices positively impact happiness at work, employee resilience, and feedback-seeking behaviour. Additionally, the relationship between green HRM practices and feedback-seeking behaviour and employee resilience is mediated by happiness at work.

Drawing on the Job Demands-Resources Theory, Social Exchange Theory, and Broaden and Build theory, this paper proposes that green HRM practices can contribute to happiness at work, employee resilience, and feedback-seeking behaviour.

To establish a positive connection between green HRM practices and employee outcomes, organizations must recognize the vital role played by happiness at work as a mediator. This means that organizations must implement green HRM practices and ensure their positive impact on employee happiness at work.

The originality of this research lies in its holistic approach to green HRM outcomes, suggesting that the benefits of these practices extend beyond environmental impacts to influence the psychological and behavioural dimensions of employees.

Technology advancement has changed how banks operate. Modernizing technology has, on the one hand, made it simpler for banks to do their daily business, but it has also increased cyberattacks. The purpose of the study is to to determine the factors that have the most effects on online fraud detection and to evaluate the advantages of AI and human psychology research in preventing online transaction fraud. Artificial intelligence has been used to create new techniques for both detecting and preventing cybercrimes. Fraud has also been facilitated in some organizations via employee participation.

The main objective of the research approach is to guide the researcher at every stage to realize the main objectives of the study. This quantitative study used a survey-based methodology. Because it allows for both unbiased analysis of the relationship between components and prediction, a quantitative approach was adopted. The study of the body of literature, the design of research questions and the development of instruments and procedures for data collection, analysis and modeling are all part of the research process. The study evaluated the data using Matlab and a structured model analysis method. For reliability analysis and descriptive statistics, IBM SPSS Statistics was used. Reliability and validity were assessed using the measurement model, and the postulated relationship was investigated using the structural model.

There is a risk in scaling at a fast pace, 3D secure is used payer authentication has a maximum mean of 3.830 with SD of 0.7587 and 0.7638, and (CE2).

This study focused on investigating the benefits of artificial intelligence and human personality study in online transaction fraud and to determine the factors that affect something most strongly on online fraud detection. Artificial intelligence and human personality in the Indian banking industry have been emphasized by the current research. The study revealed the benefits of artificial intelligence and human personality like awareness, subjective norms, faster and more efficient detection and cost-effectiveness significantly impact (accept) online fraud detection in the Indian banking industry. Also, security measures and better prediction do not significantly impact (reject) online fraud detection in the Indian banking industry.

Automation of detecting cracked surfaces on buildings or in any industrially manufactured products is emerging nowadays. Detection of the cracked surface is a challenging task for inspectors. Image-based automatic inspection of cracks can be very effective when compared to human eye inspection. With the advancement in deep learning techniques, by utilizing these methods the authors can create automation of work in a particular sector of various industries.

In this study, an upgraded convolutional neural network-based crack detection method has been proposed. The dataset consists of 3,886 images which include cracked and non-cracked images. Further, these data have been split into training and validation data. To inspect the cracks more accurately, data augmentation was performed on the dataset, and regularization techniques have been utilized to reduce the overfitting problems. In this work, VGG19, Xception and Inception V3, along with Resnet50 V2 CNN architectures to train the data.

A comparison between the trained models has been performed and from the obtained results, Xception performs better than other algorithms with 99.54% test accuracy. The results show detecting cracked regions and firm non-cracked regions is very efficient by the Xception algorithm.

The proposed method can be way better back to an automatic inspection of cracks in buildings with different design patterns such as decorated historical monuments.

While sustainable growth extends the use of resources, it is crucial to explore green growth (GG) that ensures growth sustainability through the adoption of renewable energy. Thus, this study is motivated to investigate the influence of renewable energy on GG in 19 emerging countries spanning a decade and a half (2000–2020). This study aims to provide a quantitative examination of how renewable energy contributes to sustainable economic growth.

This study uses advanced dynamic common correlated effect techniques to assess the long-term effectiveness of renewable energy on GG. Additionally, it uses Dumitrescu and Hurlin causality tests to identify synchronicity between the respective variables.

The findings of this study reveal that the adoption and utilisation of renewable energy effectively promote GG in emerging economies. However, in contrast, the significantly greater negative influence of trade openness on GG compared to renewable energy highlights the inadequacy and limited impact of cleaner energy alone.

To the best of the authors’ knowledge, existing literature predominantly focuses on investigating the relationship between renewable energy and economic growth, with only a limited number of studies exploring the impact on GG. To the best of the authors’ knowledge, this study would be the first to analyse this relationship in these emerging countries. Furthermore, previous estimation frameworks used in prior studies often overlook the crucial factor of cross-sectional dependence (CSD) among countries. Therefore, this study addresses this issue using a contemporary econometric approach that deals not only with CSD but other biases, like endogeneity, autocorrelation, small sample bias, etc.

This study investigates how performance pressure affects feedback-seeking and innovative work behaviors. The study also examines the effect of extraversion on the performance pressure–FSB relationship.

The hypotheses in this study were tested by analyzing two-wave data collected from a sample of employees in the information technology sector in India using the PLS-SEM approach.

Our findings revealed that individuals possessing extraverted personality traits exhibited a positive response to performance pressure, thereby enhancing their FSB. Moreover, our results demonstrated that FSB mediates the relationship between performance pressure and IWB.

The results underscore the importance of individual variations in personality traits, particularly extraversion, in influencing how employees respond to performance pressure. By providing insights into the mediating mechanism of feedback-seeking behavior, our study contributes to a deeper understanding of the interplay between performance pressure, feedback-seeking behavior and innovative work behavior.

Managers should consider extraversion as a factor in the relationship between performance pressure and FSB, adapting strategies and support systems accordingly. Creating a feedback-oriented culture and providing resources for extroverts during high-pressure periods can enhance their coping mechanisms.

Previous research has provided a limited exploration of the mechanisms that establish the connection between job demands and innovative work behaviors. This study contributes by uncovering the previously unexplored relationship between performance pressure, extraversion, feedback-seeking behavior and, subsequently, innovative work behavior.

The majority of previous studies made on Recycled Concrete Aggregates (RCA) are limited to the utilisation of non-structural grade concrete due to unfavourable physical characteristics of RCA including the higher absorption of water, tending to increased water requirement of concrete. This seriously limits its applicability and as a result it reduces the usage of RCA in structural members. In the present study, the impact of hybrid fibres on cracking behaviour of RCA concrete beams along with the inclusion of reinforcing steel bars under two-point loading system exposed to different sustained elevated temperatures are being investigated.

RCA is substituted for Natural Coarse Aggregates (NCA) at 0, 50 and 100 percentages. The study involves testing of 150 mm cubes and beams of size (700 × 150 × 150) mm, i.e. with steel reinforcing bars along with the addition of 0.35% Steel fibres+ 0.15% polypropylene fibres. The specimens are being exposed to temperatures from 100° to 500°C with 100° interval for 2 h. Studies were made on the post crack analysis, which includes the measurement of crack width, crack length and load at first crack. The crack patterns were analysed in order to understand the effect of fibres and RCA at sustained elevated temperatures.

The result shows that ultimate load carrying capacity of reinforced concrete beams and load at first crack decreases with the raise in temperatures and increased percentage of RCA content in the mix. Further that 100% RCA replacement specimens showed lesser cracks when compared to the other mixes and the inclusion of fibres enhances the flexural capacity of members highlighting the importance of fibres.

RCA can be used for structural purposes and the study can be projected for assessing the performance of real structures with the extent of fire damage when recycled aggregates are used.

Most of recycled materials can be used in the regular concrete which solves two problems namely avoiding the dumping of C&D waste and preventing the usage of natural aggregates. Hence the study provides sustainable option for the production of concrete.

The reduction in capacity of flexural members due to the utilisation of recycled aggregates can be negated by the usage of fibres. Hence improved flexural performance is observed for specimens with fibres at sustained elevated temperatures.