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With the rise of social media platforms, an increasing number of cases of cyberbullying has reemerged. Every day, large number of people, especially teenagers, become the victim of cyber abuse. A cyberbullied person can have a long-lasting impact on his mind. Due to it, the victim may develop social anxiety, engage in self-harm, go into depression or in the extreme cases, it may lead to suicide. This paper aims to evaluate various techniques to automatically detect cyberbullying from tweets by using machine learning and deep learning approaches.

The authors applied machine learning algorithms approach and after analyzing the experimental results, the authors postulated that deep learning algorithms perform better for the task. Word-embedding techniques were used for word representation for our model training. Pre-trained embedding GloVe was used to generate word embedding. Different versions of GloVe were used and their performance was compared. Bi-directional long short-term memory (BLSTM) was used for classification.

The dataset contains 35,787 labeled tweets. The GloVe840 word embedding technique along with BLSTM provided the best results on the dataset with an accuracy, precision and F1 measure of 92.60%, 96.60% and 94.20%, respectively.

If a word is not present in pre-trained embedding (GloVe), it may be given a random vector representation that may not correspond to the actual meaning of the word. It means that if a word is out of vocabulary (OOV) then it may not be represented suitably which can affect the detection of cyberbullying tweets. The problem may be rectified through the use of character level embedding of words.

The findings of the work may inspire entrepreneurs to leverage the proposed approach to build deployable systems to detect cyberbullying in different contexts such as workplace, school, etc and may also draw the attention of lawmakers and policymakers to create systemic tools to tackle the ills of cyberbullying.

Cyberbullying, if effectively detected may save the victims from various psychological problems which, in turn, may lead society to a healthier and more productive life.

The proposed method produced results that outperform the state-of-the-art approaches in detecting cyberbullying from tweets. It uses a large dataset, created by intelligently merging two publicly available datasets. Further, a comprehensive evaluation of the proposed methodology has been presented.

The study aims to present demand chain management (DCM) modeling of Indian apparel retailers. This will result in a structured model presenting contextual interrelationship among DCM variables so that retailers can proactively manage their demand chain.

The research follows an exploratory research design. It initially involves identification and analysis of influential factors of the implementation of DCM practices through the review of literature. Then, these variables were analyzed using total interpretive structural modeling or TISM followed by a statistical verification and case-based validation of the model.

The major findings of the paper are: top-management commitment and support, information management and supply chain agility in supply chain are the most significant enablers with the highest driving power. The other apparel retail specific significant variables are assortment planning, category management and marketing orientation. The model also indicates that the firms that implement customer-centric DCM practices do well in terms of organizational performance and thereby achieve differential advantage over their competitors.

Because the literature on DCM is still in nascent stage, the study bases itself on interpretive method; that is, TISM of analysis with a limited number of experts. Future studies may consider larger sample with more advanced statistical tools such as structural equation modeling for further validation of the findings.

The novelty of the paper lies in the study of an emerging supply chain philosophy; that is, DCM and its key practices per se. It has rarely been studied from the theory building perspective hitherto. Moreover, TISM-based approach is applied for the first time to study the DCM practices and its drivers vis-à-vis dependents.

This study aims to examine factors influencing purchase intentions of households toward solar rooftops, using diffusion of innovation theory.

Six dimensions from the diffusion of innovation theory have been contemplated to assess households’ purchase intentions toward solar rooftops. Primary data was collected from 863 respondents by using a purposive sampling technique from different parts of India. The study used structural equation modeling analysis to examine the data.

Results confirmed that the dimensions, namely, relative advantage, compatibility, complexity, innovations and observability, positively influence households’ purchase intentions of solar rooftops, except for the trialability dimension.

Data for the study is based on a purposive sampling technique that restricts the representation of the whole population. There may be variance in public perception from place to place due to demographic and psychographic differences.

The present study would facilitate devising a roadmap to improve the quality of the environment. Policy planners, researchers and professionals would find the implications of this study helpful in developing critical strategies.

The research practically explores households’ intention to purchase solar rooftops by filling the gap in previous literature. Findings of the study can equip firms with several advantages. Through the present study, one gets to know that developed and developing economies have similar energy usage issues. Further, it empirically examines the applicability of diffusion of innovation theory in the solar rooftops context.

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 paper aims to explore the application of drumstick (Moringa oleifera) flower (DF) as a functional antioxidative ingredient in goat meat product.

Dried DF was included in the product formulation at 1% (Treatment I) and 2% (Treatment II) levels. The physicochemical, colour, textural and sensory quality as well as storage stability of nuggets with DF were determined against control.

The dried DF was found to be rich source of protein and dietary fibre, possessing good antioxidant potential. Chromatographic analysis of DF extract showed presence of 14 active principles known to have antioxidative properties. Inclusion of dried DF decreased pH values of emulsion (p = 0.005) as well as nuggets (p < 0.001) and increased (p < 0.001) the ash, dietary fibre and phenolic contents. The added DF affected the product’s lightness (p = 0.017), yellowness (p < 0.001, hardness (p < 0.001), adhesiveness (p = 0.032), cohesiveness (p = 0.006), gumminess and chewiness (p < 0.001). Sensory characteristics of control and product with DF were statistically similar except low (p = 0.002) flavour score for Treatment II. DF inclusion lowered (p < 0.001) thiobarbituric acid reactive substances number and total plate count.

DF can be used as a source of antioxidants and dietary fibre in goat meat nuggets to enhance their health value, functionality and storage stability.

Foods including goat meat nuggets enriched with goodness of functional ingredients like dietary fibre and natural antioxidants are gaining consumer’s preference globally. Inclusion of drumstick flower in goat meat nuggets significantly increases the dietary fibre and antioxidants making such products healthier and more stable. Consumption of goat meat nuggets added with drumstick flower is expected to improve consumer’s well-being as well.

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.

This case describes the events following an incident of a rape in a taxi associated with Uber, by its driver. Uber was an application based taxi operator. The events raised several issues for government systems and processes, such as need for regulation of new formats of business like application based taxi services, integrated databases, checks against forgery and holistic approach towards women safety. The case also brings out how an e-commerce business raises regulatory concerns.

The present study aimed to examine the relationship between information and communication technologies (ICT) diffusion, financial development and economic growth in the panel of developing countries for 2005–2019.

The study employed the principal component analysis (PCA) to extract the index of ICT diffusion. First-generation panel unit root tests such as Levine Lin Chu (LLC), Im Pesaran Shin (IPS), Augmented Dickey-Fuller (ADF) and Phillips and Perron (PP) were employed to check the stationarity of the variables. Pedroni and Kao co-integration techniques were used to examine the existence of the long-run relationship, and co-integration coefficients were estimated using FMOLS and dynamic ordinary least squares (DOLS). The panel Granger causality approach examined the short-run and long-run causality.

The results confirmed that ICT diffusion, financial development and trade openness accelerate growth, whereas inflation dampens economic growth. Further, the causality test showed bidirectional causality between ICT growth and financial development growth but a unidirectional causality from financial development to ICT diffusion in developing countries.

The study recommends synchronizing public and private sector investment for a synergistic effect on ICT infrastructure and adequate investment in the financial sector to increase the growth rate in developing countries. Economic policies should be adopted toward incentives and subsidies to ensure affordable ICT services for disadvantaged communities. Also, training programs focussing on enhancing digital literacy to enable all segments of the population to use digital platforms for financial services are recommended.

The public distribution system (PDS) is an Indian food security system established to manage the distribution of food grains at affordable prices. As a result of the population explosion, the long-established PDS system finds it challenging to maintain operational efficiency, quality, trust and transparency. This paper explores the possibility of leveraging blockchain technology to overcome these operational hurdles.

Through a literature review and expert interactions, the present research identifies critical success factors in terms of enablers and barriers that influence the adoption of blockchain technology in PDS. Furthermore, we propose two independent interpretive structural models (ISM) and MICMAC to characterize these attributes.

The research identifies 15 distinct enablers and ten barriers that influence the diffusion of the latest technology in the sector at focus. The analyses disclose the interrelationships/dependencies among these enablers and between barriers, along with their individual driving power and dependence power.

The research showcases the importance of automating the system and illustrates how the features of blockchain technology can assist in augmenting stakeholder satisfaction levels. However, poor or nonexistent government regulations and patronage are found to be the major impediments to adoption. The research also delineates the cost implications of this barrier through its interrelationships with other barriers.

Interesting inferences are drawn from the models that offer actionable insights for the industry, government and technologists for improving PDS performance. Such interventions will ensure national food security through enhanced trust and transparency, which can further improve efficiency and effectiveness.