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The literature on green consumer behaviour recently focuses upon the Asian markets. Though environmental consciousness in Indian consumers is observed in the literature, their purchase behaviour towards green products is not yet clearly understood. So, the purpose of this paper is to study the factors affecting consumers’ green product purchase decisions in India.

The research employs a survey-based method to test a theoretically grounded set of hypotheses. Using a 38-item questionnaire and snowball sampling method, the data were collected from 403 working Indian respondents in Mumbai. The data were analysed using exploratory and confirmatory factor analyses. Structural equation modelling was used to test the proposed hypotheses.

The results witnessed that the respondents possess willingness to support environmental protection, realization of environmental responsibilities, and inclination towards searching green product-related information and learning about green products. Supporting environmental protection, drive for environmental responsibility, green product experience, environmental friendliness of companies and social appeal are identified as important factors affecting green product purchase decisions.

Results of the research are useful for marketing professionals for green products to develop effective green marketing strategies emphasizing personal relevance, social importance and environmental significance of purchasing, using and disposing green products that produce increased levels of satisfaction for customers and influence their decisions to buy green products.

This research provides valuable insights into green consumer behaviour in Indian context by examining the factors that influence their purchase decisions towards green products.

The purpose of this paper is to determine the extent of greening retail taking place in Indian context and identifies its core groups of green retail practices.

A questionnaire was developed and used to collect data from retailers of green products, and caselets were developed.

Nine core groups of green retail practices are identified – distinctness of green products, promoting sustainable business practices, use of environmental keywords, promotion for awareness, promotional offers for sale, ensuring availability and visibility of green products, approval for environmental claims, environmentally friendly appeal of store and consumer involvement approaches.

Findings of the study should be generalised with its limitations; though are useful in understanding greening retail concept.

This paper is original in terms of its value addition to the green marketing literature and extends the concept of sustainability to retailing.

Driving cycle is an essential requirement to evaluate the exhaust emissions of various types of vehicles on the chassis dynamometer test. This study presents a real world comparison of the driving cycles of Edinburgh motorcycles in two world cities; Edinburgh in Scotland and Delhi in India. The two driving cycles (EMDC & DMDC) driving cycle (EMDC) that were was developed through the analysis of experimental data. This data was collected from trips on a number of routes in each city. In Edinburgh, five different routes between the home addresses in the surrounding areas and place of work at Edinburgh Napier University in Edinburgh were selected. In Delhi data were collected in East Delhi (Geeta Calony) to Central Delhi (Raisena Road). The data collected data was divided into two categories of urban and rural roads in the case of Edinburgh while it was only the urban route in Delhi.. Forty four trips were made on the five designated routes in both urban and rural areas and 12 trips were made in Delhi. The aims of the study were to assess the various parameters (i.e. motorcycle speed, cruise, accelerations and decelerations and percentage time spent in idling) and their statistical validity over total trip lengths for producing a real world EMDC in each of the two cities. The results show that EMDC in Edinburgh, the EMDC has a cycle length of 770 and 656 seconds for urban and rural trips, respectively, which was found more than ECE cycle length. Time spent in acceleration and deceleration modes were found to be significantly higher than any other driving cycle reported to date for motorcycles, reflecting a typical characteristic of the driving cycle in Edinburgh; this was presumably due to diverse driving conditions of motorcycles in the city. In Delhi on the other hand, the DMDC has a cycle length of 847.5 seconds for the urban trips, which higher than that of the EMDC length. The overall percentage time spent in acceleration in Delhi was higher than that of Edinburgh while the time spent in deceleration was lower in Delhi. The overall average speed in the case of Delhi was slightly higher than that of Edinburgh.

The compressive strength of concrete depends on many interdependent parameters; its exact prediction is not that simple because of complex processes involved in strength development. This study aims to predict the compressive strength of normal concrete and high-performance concrete using four datasets.

In this paper, five established individual Machine Learning (ML) regression models have been compared: Decision Regression Tree, Random Forest Regression, Lasso Regression, Ridge Regression and Multiple-Linear regression. Four datasets were studied, two of which are previous research datasets, and two datasets are from the sophisticated lab using five established individual ML regression models.

The five statistical indicators like coefficient of determination (R²), mean absolute error, root mean squared error, Nash–Sutcliffe efficiency and mean absolute percentage error have been used to compare the performance of the models. The models are further compared using statistical indicators with previous studies. Lastly, to understand the variable effect of the predictor, the sensitivity and parametric analysis were carried out to find the performance of the variable.

The findings of this paper will allow readers to understand the factors involved in identifying the machine learning models and concrete datasets. In so doing, we hope that this research advances the toolset needed to predict compressive strength.

Reputation risk onsets in focal firm whenever any entity of its supply chain (SC) faces risk-crisis event. A framework for modeling and predicting holistic SC reputation risk is proposed by integrating operational risk (OR) drivers originating from upstream and downstream partners and focal firm. A fuzzy cognitive map (FCM) is then developed to predict and quantify Pharmaceutical SC reputation risk.

Using event study methodology, SC reputation risk framework with 13 input OR drivers was developed. Based on pharmaceutical supply chain experts’ opinion, the correlation between reputation risk and its input drivers was estimated. The developed FCM tool was validated using nine real-life instances. A series of “what-if” scenario analyses were performed to demonstrate effectiveness of proactive and reactive mitigation strategies against reputation risk.

Quality and unethical governance risks significantly impacted reputation in Pharmaceutical SC and a firm should prefer “risk avoidance” against these risks. The upstream risks significantly affect reputation in a Pharmaceutical SC as compared to the downstream risks. Proactive mitigation strategies and assertive crisis communication are suggested for upstream risks while diminishment/ bolstering/rebuilding reactive crisis communication is recommended for downstream risks.

Reputation risk is often overlooked in SC literature. This work develops a model to quantify the reputation risk considering the indirect consequences of the ORs that originates at any point in a SC. The proposed FCM tool aids SC manager to focus on higher attribution risk events and devise an optimal combination of proactive and reactive mitigation strategies to avoid/minimize the economic loss due to reputation crisis.

Option pricing theory enables computation of the price of an option using different variables associated with the underlying security and options contract. The purpose of this study is to assess research trends that emerged in the field of option pricing. This study reviews existing literature of the option pricing domain, both qualitatively and quantitatively, and identifies potential themes for future research.

This study adopts bibliometric analysis method to explore literature published in the option pricing domain. As part of bibliometric analysis, this study considers both descriptive and network analysis to assess publication trends. For descriptive analysis, the “bibliometrix” package proposed by Aria and Cuccurullo (2017) is used and for network analysis, VOS viewer (Van Eck and Waltman, 2017) and Gephi (Bastian et al., 2009) are used.

This study identifies research trends, top researchers, articles, journals and contributions from institutions and countries in the option pricing domain. It identifies four clusters that show different directions and also focuses on past studies on the same subject. It explores research gaps by performing an in-depth analysis of existing literature on option pricing and suggests the way forward for research in this area.

To the best of the authors’ knowledge, no previous studies have attempted to analyze the literature published in the option pricing domain. This study fulfils this research gap by conducting a comprehensive analysis of studies in the option pricing area. This study identifies quality research work published in the domain, research trends, contribution by most relevant researchers, contributions across geographies and institutions and the connections among these aspects. This study also identifies important themes and provides directions for future research.

This study aims to provide insights into different aspects of the extant literature on the effects of dividend announcements. Along with other outputs of a bibliometric study, this study provides deeper insights into the concentration of the extant literature and suggest future research agendas.

This study uses the bibliometric, network and content analysis of the dividend announcement literature indexed in Scopus. This study presents the temporal analysis, the network of authors, countries, author citations and the co-occurrence of author keywords. This study provides the concentration of the extant literature in three clusters and unearth some key future research areas. This study uses the latent Dirichlet allocation method for robustness.

A total of 54 documents examining the US sample have received 1,804 citations. Interestingly, the first article on emerging markets was published in 2002, when at least 34 articles on developed markets had already been published from 1982 to 2001. The content analysis of top-cited literature unveils diverse insights into dividend announcements’ effects on financial markets. Contagion effects negatively impact non-announcing banks, particularly larger ones. Dividend maintenance affects stock market momentum, influencing loser returns. While current dividend/earnings news may not predict future company performance, information content dominates bond market reactions to post-dividend announcements. Concomitantly, while financially constrained firms exhibit short-term gains but worse long-term performance following dividend increases, larger stock dividends send stronger market signals in China.

This study significantly contributes to the bibliometric and content analysis literature by analyzing the sample documents based on the sample examined. To the best of the authors’ knowledge, no previous bibliometric study in this domain has been conducted to explore the markets (developed and emerging) to which the samples examined belong and the quality of publications from developed and emerging markets.

Block chain technology (BCT) has apparent capability of handling information in digital format, which has dragged attention of the practitioners for its utility in industrial and manufacturing practices. Conversely, the managerial adoption of BCT is relatively limited, which motivated the authors to identify crucial dimensions that can persuade the acceptance of BCT from an executive perspective. Thus, the present study is aimed to conduct to understand crucial barriers under BCT for managerial implementation in supply chain management (SCM) of small and medium enterprises (SMEs).

The present study investigated evident barriers to understand implementation of BCT. A questionnaire based survey is performed to collect primary data from service and manufacturing based companies in India. Survey responses are received online and the data is analyzed in a scorecard. The scorecard embedded the scribed entries of Likert scale to determine the relative score.

In present study, sixteen barriers from three categories named as technological, organizational and environmental are evaluated, where, five sub-barriers from technological domain, seven sub-barriers from organizational domain and four sub-barriers from environmental domain are evaluated. The findings of the study determined that the three factors, i.e. “complexity in setup/use”, “Security and privacy concern” and “Technological awareness” mostly affect the adaptation of BCT in SCM. Conversely, “Market dynamics”, “Scalability” and “Cost” do not influence the intention to adopt the technology.

Only few studies have endeavored to ascertain the BCT adoption in SCM of SMEs in developing country like India. Thus, the study is filling a momentous gap of mapping BCT dimensions in the scholastic literature. The findings are expected to enable SMEs to understand important factors to be considered for adopting BCT in their curriculum. Furthermore, the study may benefit the BCT developers and suppliers to endure customized solutions based on the findings.

Development of (1T-type) ferroelectric random access memory (FeRAM) has most actively progressed since 1995 and motivated by the physical limits and technological drawbacks of the flash memory. 1T-type FeRAM implements ferroelectric layer at the field effect transistor (FET) gate. During the course of the investigation, it was very difficult to form a thermodynamically stable ferroelectric-semiconductor interface at the FET gate, leading to the introduction of one insulating buffer layer between the ferroelectric and the silicon substrate to overcome this problem. In this study, Al₂O₃ a high-k buffer layer deposited by plasma enhanced atomic layer deposition (PEALD) is sandwiched between the ferroelectric layer and silicon substrate.

Ferroelectric/high-k gate stack were fabricated on the silicon substrate and pt electrode. Structural characteristics of the ferroelectric (PZT) and high-k (Al₂O₃) thin film deposited by RF sputtering and PEALD, respectively, were optimized and investigated for different process parameters. Metal/PZT/Metal, Metal/PZT/Silicon, Metal/PZT/Al₂O₃/Silicon structures were fabricated and electrically characterized to obtain the memory window, leakage current, hysteresis, PUND, endurance and breakdown characteristics.

XRD pattern shows the ferroelectric perovskite thin Pb[Zr_0.35Ti_0.65]O₃ film with (101) tetragonal orientation deposited by sputtering and PEALD Al₂O₃ with (312) orientation showing amorphous nature. Multiple angle analysis shows that the refractive index of PZT varies from 2.248 to 2.569, and PEALD Al₂O₃ varies from 1.6560 to 1.6957 with post-deposition annealing temperature. Increase in memory window from 2.3 to 8.4 V for the Metal-Ferroelectric-Silicon (MFS) and Metal-Ferroelectric-Insulator-Semiconductor (MFIS) structure has been observed at the annealing temperature of 500°C. MFIS structure with 10 nm buffer layer shows excellent endurance of 3 × 109 read-write cycles and the breakdown voltage of 33 V.

This paper shows the feature, principle and improvement in the electrical properties of the fabricated gate stack for 1T-type nonvolatile FeFET. The insulating buffer layer sandwiched between ferroelectric and silicon substrate acts as a barrier to ferroelectric–silicon interdiffusion improves the leakage current, memory window, endurance and breakdown voltage. This is perhaps the first time that the combination of sputtered PZT on the PEALD Al₂O₃ layer is being reported.