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Transformational leaders have long been known to use emotions to motivate their followers and guide their energy toward the vision set forth by the leader. Much of the past research and theory on this topic has exhibited a bias toward positively valenced emotions. Negative emotions have received limited attention relative to positive emotions, and this imbalance has led to a skewed understanding of the relationship between emotions and transformational leadership (TL).

The study reviews the organizational literature regarding negative emotion expression in TL.

The study integrates research regarding negative emotions and TL with the existing body of research regarding positive emotions and TL. The authors argue that the range of emotions considered needs to be broadened and rebalanced. Practical and theoretical implications are also discussed.

The study integrates the benefits of negative emotions and TL the more well-known and explored the benefits of positive emotions and TL. The study uses the four components of TL theory, i.e. inspirational motivation (IM), idealized influence (II), individualized consideration (IC) and intellectual stimulation (IC), to explore how transformational leaders can effectively display negative emotions. The study ultimately presents a more balanced overview of emotions and TL.

Procrastination is regularly presented as a behavior to avoid, but this paper argues that individuals who strategically engage in procrastination may experience unique performance benefits that non-procrastinators do not. The purpose of this paper is to present a balanced framework from which procrastination, beginning with a review of the procrastination performance literature and historical stance on the behavior, can be understood.

This paper presents and reviews the use of procrastination in organizations.

Our findings indicate that while procrastination can be dysfunctional, it can prove to be strategically valuable. To summarize, this paper recommends a holistic conceptualization of procrastination that refrains from value judgment and calls for rethinking the stigma associated with the behavior.

This paper highlights both the theoretical and practical importance of exploring the benefits of procrastination in an organizational context.

In this global digital era, health-care companies are increasing their presence on the internet through branded content that serves as a connecting link between customers and brands. However, there is a limited understanding of branded content’s impact on customers. Thus, this paper aims to empirically analyse customer engagement for branded content in the health-care sector.

The factors impacting customer engagement for branded content were identified and analysed using the Decision-Making Trial and Evaluation Laboratory approach to get their significance and the cause and effect relationship.

It emerged that co-creation is the most significant factor, having a substantial relationship with all other factors. It is substantiated that health-care companies can increase the intensity of customer engagement by delivering more authentic and relevant content and having an appealing look in a time-bound manner. This will increase the usefulness and entertaining value of the content.

The research findings contribute to the customer engagement dimension in the health-care sector and help the companies construct effective branded content leading towards higher customer engagement.

In this chapter, drone and vision camera technology have been combined for monitoring the crop product quality. Three vegetable crops such as tomato, cauliflower, and eggplant are considered for quality monitoring; hence, image datasets are collected for those vegetables only. The proposed method classified the vegetables into two classes as rotten and nonrotten products so the images were collected for rotten and nonrotten products. Three different features information such as chromatic features, contour features, and texture features have been extracted from the dataset and further used to train a Gaussian kernel support vector machine algorithm for identifying the product quality. The system utilized multiple features such as chromatic, contour, and texture features in classifier training which enhances the accuracy and robustness of the system. Chromatic features were utilized for detecting the crop while other features such as contour and texture features were utilized for further classifier building to identify the crop product quality. The performance of the system is evaluated based on the true positive rate, false discovery rate, positive predictive value, and accuracy. The proposed system identified good and bad products with a 97.9% of true positive rate, 2.43 % of false discovery rate, 97.73% positive predictive value, and 95.4% of accuracy. The achieved results concluded that the results are lucrative and the proposed system is efficient in agriculture product quality monitoring.

The growing environmental awareness all through the world has motivated a standard change toward planning and designing better materials having good performance, which are very much suited to the environmental factors. The purpose of this study is to investigate the impact on mechanical, thermal and water absorption properties of sawdust-based composites reinforced by epoxy, and the amount of sawdust in each form.

Manufacturing of the sawdust reinforced epoxy composites is the main area of the research for promoting the green composite by having good mechanical properties, biodegradability or many applications. Throughout this research work, the authors emphasize the importance of explaining the methodology for the evaluation of the mechanical and water absorption properties of the sawdust reinforced epoxy composites used by researchers.

In this paper, a comprehensive review of the mechanical properties of sawdust reinforced epoxy composite is presented. This study is reported about the use of different Wt.% of sawdust composites prepared by different processes and their mechanical, thermal and water absorption properties. It is studied that after optimum filler percentage, mechanical, thermal properties gradually decrease, but water absorption property increases with Wt.% of sawdust. The changes in the microstructure are studied by using scanning electron microscopy.

The novelty of this study lies in its use of a systematic approach that offers a perspective on choosing suitable processing parameters for the fabrication of composite materials for persons from both industry and academia. A study of sawdust reinforced epoxy composites guides new researchers in the fabrication and characterization of the materials.

The increased demand for high-quality, nutritionally rich processed food has led to non-thermal food processing technologies like high pressure processing (HPP), a novel process for microbial inactivation with minimal loss of nutritional and sensory properties. The purpose of this paper is to highlight the impact of HPP on the microbiological, nutritional and sensory properties of food.

Recent research on the role of HPP in maintaining food quality and safety and the impact of process conditions with respect to various food properties have been explored in this paper. Also, the hurdle approach and the effectiveness of HPP on food quality have been documented.

HPP has been verified for industrial application, fulfilling the consumer demand for processed food with minimum nutrition loss at low temperatures. The positive impact of HPP with other treatments is known as the hurdle approach that enhances its impact against microorganism activity and minimizes the effects on nutrition and sensory attributes.

This paper highlights the impact of HPP on various food properties and a good alternative as non-thermal technology for maintaining shelf life, sensory properties and retention of nutrients.

This paper aims to explore the last-mile (LM) challenges faced by on-demand food delivery (ODFD) riders during the coronavirus pandemic. This study contributes to the literature on the less-explored domain of ODFD services.

A grounded theory methodology is used. Riders working for multiple ODFD firms in various urban and semi-urban areas of India were interviewed. Open, axial and selective coding of interview transcripts was done.

A grounded model is developed consisting of riders' challenges represented broadly under four core categories: Operational, Customer-related, Organisational and Technological issues. The study indicates that while some of the challenges are inherent to the ODFD supply chain, these have been visibly exposed and intensified by COVID-19, while other challenges are specific to the pandemic.

The model is a qualitative proposition representing LM delivery issues in ODFD services faced by the riders in India's urban and semi-urban areas during the COVID-19 pandemic. Other countries may face similar problems, but further studies are necessary to confirm or refute the findings.

ODFD companies must address the riders' issues to better adapt to the current and future disruptions and improve riders' quality of work–life to achieve operational excellence.

This study builds on the extant ODFD literature by focusing on one of its less addressed aspects: the working conditions of the riders. This work is conducted amid the COVID-19 pandemic in the context of a developing country and aims to study the challenges in ODFD operations.

This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies.

In the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks.

The authors got very satisfactory classification results.

DDPML system is specially designed to smoothly handle big data mining classification.

This study aims to examine the impact of customer brand value dimensions on relationship marketing dimensions through consumer hope in the context of digital payment applications (apps) services. The study considers the role of consumer engagement using a moderated mediation, and applies customer perceived value, affect theory of social exchange and relationship marketing theories.

The study is based on data collected from 301 digital payment app users. Structural equation modeling results were analyzed using Smart PLS. The authors performed moderated mediation, with different levels of customer engagement as a moderating variable, using Model 8 of PROCESS. The authors considered customer perceived value dimensions, digital quality value, perceived value, hedonic value and social value as antecedents to consumer hope and explored the role of trust, commitment and continued usage as a consequence of consumer hope.

Three levels of perceived consumer value, digital quality, price value and social value, positively influenced consumer hope, which has positively influenced trust, commitment and continuance usage. Using moderated mediation analysis, consumer hope influenced continuance usage through trust at different levels of engagement, but consumer hope did not influence continuance usage through commitment at different levels of engagement.

The study highlights the role of consumer hope in linking customer value dimensions with relationship marketing dimensions. The study can guide managers to ensure continued usage of digital payment apps, which is a strategic objective. The results are relevant for the digital setting.