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This work aims to investigate the feasibility of recycling waste plastic (polyethylene terephthalate) as a coarse aggregate for producing blended cement concrete modified with fly ash and pond ash.

The low, medium and high controlled strength blended cement concrete modified with varied proportions of fly and pond ashes were produced. Manufactured sand and recycled plastic coarse aggregate (RPCA) replaced normal fine and coarse aggregates. Concrete samples were tested for workability, mechanical and durability characteristics. Microstructural analysis was performed on cement concrete blended with fly and pond ashes and compared to conventional concrete samples.

All concrete mixes showed better flowability with values greater than 200 mm. Besides, the maximum flow time was approximately 8 s. The wet density of blended cement concrete-RPCA-based concretes was approximately 30% lower than that of conventional concrete. The compressive strengths of the controlled strength mix at 7 and 28 days were within the specified ranges. While the conventional concrete had slightly higher permeability, the blended cement concrete-RPCA-based concretes had better thermal resistivity and lower thermal conductivity. The scanning electron microscopy analysis revealed the densification of the microstructure due to the filler effects of fly and pond ashes.

This study establishes the prospects of substituting RPCA with normal coarse aggregate in the production of controlled low-strength blended cement concrete, offering benefits of structural fill concrete, lower permeability and thermal conductivity, higher thermal resistivity and reduced density and shrinkage.

This study aims to develop two sentiment indices sourced from news stories and corporate disclosures of the firms in the National Stock Exchange NIFTY 50 Index by extracting sentiment polarity. Subsequently, the two indices would be compared for the predictive accuracy of the stock market and stock returns during the post-digitization period 2011–2018. Based on the findings this paper suggests various options for financial strategy.

The news- and disclosure-based sentiment indices are developed using sentiment polarity extracted from qualitative content from news and corporate disclosures, respectively, using qualitative analysis tool “N-Vivo.” The indices developed are compared for stock market predictability using quantitative regression techniques. Thus, the study is conducted using both qualitative data and tools and quantitative techniques.

This study shows that the investor is more magnetized to news than towards corporate disclosures though disclosures contain both qualitative as well as quantitative information on the fundamentals of a firm. This study is extended to sectoral indices, and the results show that specific sectoral news impacts sectoral indices intensely over market news. It is found that the market discounts information in disclosures prior to its release. As disclosures in quarterly statements are delayed information input, firms can use voluntary disclosures to reduce the communication gap with investors by using the internet. Managers would do so only when the stock price is undervalued and tend to ignore the market and the shareholder in other cases. Otherwise, disclosure sentiment attracts only long horizon traders.

Finance managers need to improve disclosure dependence on investors by innovative disclosure methodologies irrespective of the ruling market price. In this context, future studies on investor sentiment would be interesting as they need to capture man–machine interactions reflected in market sentiment showing the interplay of human biases with machine-driven decisions. The findings would be useful in developing the financial strategy for protecting firm value.

This study is unique in providing a comparative analysis of sentiment extracted from news and corporate disclosures for explaining the stock market direction and stock returns and contributes to the behavioral finance literature.

The purpose of this paper is to study the integration between fused deposition modeling (FDM) technology and abrasive flow machining process to improve the surface quality of FDM printed parts. FDM process has some limitations in terms of accuracy and surface finish. Hence, post-processing operations are essential to increase the quality of the part.

Initially, a sustainable polymer abrasive gel-based media (SPAGM) using natural polymer and natural additives (waste vegetable oil) was prepared using different combinations of (abrasive mesh size, percentage of abrasives and percentage of liquid synthesizer); then the characterization of media was done to check various properties. As media is an essential part in the process which helps in increase the surface finish, it needs to have some desired characteristics such as the following: the developed SPAG needs to hold the abrasives; its viscosity has to be medium so that it can easily flow through the machine; and its thermal stability caused by the increase in the temperature during various cycles of operation. For that, it is characterized rheologically as well as thermally to find its various properties.

Experiments were performed on FDM-printed parts using an L9 orthogonal array with different parameters to find their effect on the workpiece. Scanning electron microscope images of SGAPM showed sharp edges of abrasive particles and bonding pattern between polymer chain molecules. Good surface finish and material removal rate (MRR) was observed at high pressure and long finishing time with 50 per cent abrasive concentration.

The authors confirm that this work is original and has neither been published elsewhere nor is it currently under consideration for publication elsewhere.

A composite sentiment index (CSI) from quantitative proxy sentiment indicators is likely to be a lag sentiment measure as it reflects only the information absorbed in the market. Information theories and behavioral finance research suggest that market prices may not adjust to all the available information at a point in time. This study hypothesizes that the sentiment from the unincorporated information may provide possible market leads. Thus, this paper aims to discuss a method to identify the un-incorporated qualitative Sentiment from information unadjusted in the market price to test whether sentiment polarity from the information can impact stock returns. Factoring market sentiment extracted from unincorporated information (residual sentiment or sentiment backlog) in CSI is an essential step for developing an integrated sentiment index to explain deviation in asset prices from their intrinsic value. Identifying the unincorporated Sentiment also helps in text analytics to distinguish between current and future market sentiment.

Initially, this study collects the news from various textual sources and runs the NVivo tool to compute the corpus data’s sentiment polarity. Subsequently, using the predictability horizon technique, this paper mines the unincorporated component of the news’s sentiment polarity. This study regresses three months’ sentiment polarity (the current period and its lags for two months) on the NIFTY50 index of the National Stock Exchange of India. If the three-month lags are significant, it indicates that news sentiment from the three months is unabsorbed and is likely to impact the future NIFTY50 index. The sentiment is also conditionally tested for firm size, volatility and specific industry sector-dependence. This paper discusses the implications of the results.

Based on information theories and empirical findings, the paper demonstrates that it is possible to identify unincorporated information and extract the sentiment polarity to predict future market direction. The sentiment polarity variables are significant for the current period and two-month lags. The magnitude of the sentiment polarity coefficient has decreased from the current period to lag one and lag two. This study finds that the unabsorbed component or backlog of news consisted of mainly negative market news or unconfirmed news of the previous period, as illustrated in Tables 1 and 2 and Figure 2. The findings on unadjusted news effects vary with firm size, volatility and sectoral indices as depicted in Figures 3, 4, 5 and 6.

The related literature on sentiment index describes top-down/ bottom-up models using quantitative proxy sentiment indicators and natural language processing (NLP)/machine learning approaches to compute the sentiment from qualitative information to explain variance in market returns. NLP approaches use current period sentiment to understand market trends ignoring the unadjusted sentiment carried from the previous period. The underlying assumption here is that the market adjusts to all available information instantly, which is proved false in various empirical studies backed by information theories. The paper discusses a novel approach to identify and extract sentiment from unincorporated information, which is a critical sentiment measure for developing a holistic sentiment index, both in text analytics and in top-down quantitative models. Practitioners may use the methodology in the algorithmic trading models and conduct stock market research.

The paper aims to build a customized hybrid multi-criteria model to identify the top three utilities of drones at both personal and community levels for two use cases: firefighting in high-rise buildings and logistic support.

A hybrid multi-criterion model that integrates fuzzy analytical hierarchy process (AHP), Best Worst, fuzzy analytical network process (ANP), fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) is used to compute the criteria weights. The weights are validated by a novel ensemble ranking technique further whetted by experts at the community and personal levels to two use cases.

Drones' fire handling and disaster recovery utilities are the most important to fight fire in high-rise buildings at both personal and community levels. Similarly, drones' urban planning, municipal works and infrastructure inspection utilities are the most important for providing logistics support at personal and community levels.

The paper presents a novel multi-criteria approach, i.e. ensemble ranking, by combining the criteria ranking of individual methods – fuzzy AHP, Best-Worst, fuzzy ANP and fuzzy DEMATEL – in the ratio of optimal weights to each technique to generate the consolidated ranking. Domain experts also validate this ranking for robustness. This paper demonstrates a viable methodology to quantify the utilities of drones and their capabilities. The proposed model can be recalibrated for different use case scenarios of drones.

The paper aims to identify the most supply chain resilient company suitable for the customized preferences of partner firms in the context of the Chinese supply chain framework during the COVID-19 pandemic.

A hybrid multicriteria model, i.e. Fuzzy Analytical Hierarchy Process (AHP), was used to assign weights to each criterion, which was subsequently analyzed by three approaches, namely Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory), and Evaluation Based on Distance from Average Solution (EDA), to rank the top ten companies in descending order of supply chain resilience. Further, sensitivity analysis is performed to identify the consistency in ranking with variation in weights. The rankings are validated by a novel Ensemble Ranking algorithm and by supply chain domain experts.

The rankings suggest the company “China Energy Construction Group Tianjin Electric Power Construction Co., Ltd” is the most feasible and resilient company, presenting interesting findings to partner firms, and Bosch is the least reliable supply chain company of the ten firms considered, thus presenting interesting findings to partner companies.

“Crisis Management Beforehand” is most critical in the current pandemic scenario. This implies that companies need to first prioritize taking proactive steps in crisis management followed by the need to minimize the “Expected impact of pandemic.” Performance factors also need to be regulated (sales, supply chain rank and financial performance) to maintain the company's overall reputation. Considering the consistent performance of the China Energy Construction Group Tianjin Electric Power Construction Co., Ltd., it is recommended as the most reliable supply chain firm to forge strategic partnerships with other supply chain stakeholders like suppliers and customers. On the other hand, Bosch is not recommended as a supply chain reliable company and needs to improve its crisis management capabilities to minimize the pandemic impact.

The paper aims to identify the most supply chain resilient company suitable for the customized preferences of partner firms in the context of the Chinese supply chain framework during the COVID-19 pandemic. The rankings suggest the company “China Energy Construction Group Tianjin Electric Power Construction Co., Ltd” is the most feasible and resilient company, presenting interesting findings to partner firms, and Bosch is the least reliable supply chain company of the ten firms considered, thus presenting interesting findings to partner companies.

India has a distinct family hierarchy and a vertical collectivistic culture. Indian traditional cultural norms discourage young adults from having romantic or sexual relationships before marriage. Romantic liaisons and marriages are fiercely opposed outside the caste and social network. Despite this cultural practice, research in the last decade demonstrated that more young adults in India engage in premarital romantic relationships and prefer a marriage of choice. However, they strongly wish their parents to approve of their partner and arrange their marriage. This increasing trend of love-cum-arranged marriages could be considered how Indian culture adapts to the demands of modernization strongly impelled by globalization. This chapter discusses the dynamics of change in the romantic and marital agency among young adults in India. First, the authors provide a brief historical introduction to the Indian marital system. Then, the authors discuss the changing cultural dimensions that promote marital choice and independence in partner selection (e.g., filial piety). Further, the authors provide an overview of the trends in premarital romantic relationships in India. In addition, the authors discuss the distress and conflict in Indian families due to the increasing premarital romantic and sexual relationships among Indian youth, as evidenced by eloped marriages, forced marriages, and honor killings.

This study aims to examine the influence of international business competence (IBC) on innovation performance of organizations activating in global markets. The study also explores whether ambidextrous organizational culture (AOC) acts as an antecedent of IBC and whether the environmental dynamism affects the IBC–innovation performance relationship.

The authors assessed the hypothesized relationships using data collected from a sample of companies operating in the Norwegian seafood industry. The direct, mediating and moderating effects were tested using partial least squares (PLS) with SmartPLS software application.

The empirical analysis revealed that AOC is positively associated with IBC, while IBC is a significant predictor of innovation performance. The findings also corroborated the proposed mediation effect of IBC, but refuted the moderating role of environmental dynamism.

This study contributes to the international business literature by suggesting that companies equipped with IBC can excel in innovative undertakings and that organizational culture can be effectively leveraged to develop such competences.

The paper aims to study the relationship between corporate social responsibility, green supply chain management, and operational performance and the moderating effects of relational capital on these relationships.

The authors conduct an empirical study with a structural equation modeling approach to investigate the relationship between corporate social responsibility—constructed by the quality and environmental responsibility, green supply chain management—including green supplier and customer management and operational performance—manifested by quality, cost, flexibility, and delivery performance using data from 308 manufacturers in China. Besides, the authors explore the moderating effect of supplier and customer relational capital on these relationships.

The findings indicate that a company's quality and environmental responsibility significantly impacts its green supply chain management practices, which further improve its operational performance in quality, cost, flexibility, and delivery. In addition, supplier and customer relational capital strengthens the influence of environmental responsibility on green supply chain management. While supplier relational capital reinforces the impact of green supplier management on flexibility and delivery performance, customer relational capital only strengthens the influence of green customer management on flexibility performance.

The study enriches the extant literature by developing a holistic framework integrating corporate social responsibility, green supply chain management, relational capital, and operational performance and unraveling their intricate relationships. The authors’ findings help practitioners prioritize proactive steps in environmental conservation more than achieving operational performance.

This study aims to analyze the association of information technology and non-information technology reviewer information and advertisement format, and also demographics and theory of planned behavior variables for watching a new movie release.

The authors surveyed 809 college students about various advertisement topics of traditional media/print, Internet, social media, and both print and online reviews.

For advertising, outdoor billboards and YouTube video were each positively associated with watching a new movie release while reviewer information of critic reviews in print, critic reviews online, and user reviewers were each not associated with watching a new movie release. For race/ethnicity, Hispanics were positively associated with watching a new movie release while South Asians were negatively associated with watching a new movie release. For theory of planned behavior variables, behavioral control and intentions were each positively associated with watching a new movie release.

In conclusion, movie production companies should dedicate large portions of their advertising budget to YouTube and outdoor billboard advertising. Also, movie production companies should continue to advertise and possibly even tailor advertising to Hispanics.

This is the first study for watching a new movie release to simultaneously include predictors of advertisement format including from many types of social media platforms, reviewer information whether from professional critics or user reviews, and the theory of planned behavior.