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This study aims to treat the development and application of sequence characterised amplified region (SCAR) markers for the detection of plant based adulterants (dried red beet pulp and powdered Ziziphus nummularia fruits) in traded ground chilli.

Adulterant‐specific DNA fragments (red beet pulp specific – “Beet 01” and Z. nummularia specific – “Ziz 01”) identified by random amplified polymorphic DNA polymerase chain reaction (RAPD‐PCR) analysis were cloned and sequenced for SCAR marker development. Red beet pulp specific SCAR primer pair, B1, and Z. nummularia specific SCAR primer pair, Z1, were designed from the corresponding RAPD marker sequences to amplify SCAR markers of 320 bp and 389 bp, respectively. The utility of the SCAR markers for adulterant detection was verified in model blends of chilli powder with the adulterants. Six commercial samples of ground chilli powder were analysed using the SCAR markers.

SCAR markers could detect the adulterants at a concentration as low as 10 g adulterant kg⁻¹ of blended sample. The Z. nummularia SCAR marker could detect the presence of Z. nummularia fruit adulteration in one of the commercial samples. All the market samples tested were free from red beet pulp adulteration.

The PCR‐based method developed in the study is simple, rapid, and sensitive and has the potential to be developed into a quantitative analytical method and for commercial PCR kits for the large‐scale screening of ground chilli to detect and prevent plant‐based adulterants. The work has public health significance too, as ground chilli is one of the major spices consumed worldwide.

The study is the first report on the development of SCAR markers for adulterant detection in ground chilli. This work has relevance, as adulteration is a major concern of the sanitary and phytosanitary issues of the World Trade Organization (WTO) agreement.

This research study proposes a feature-centric spam email detection model (FSEDM) based on content, sentiment, semantic, user and spam-lexicon features set. The purpose of this study is to exploit the role of sentiment features along with other proposed features to evaluate the classification accuracy of machine learning algorithms for spam email detection.

Existing studies primarily exploits content-based feature engineering approach; however, a limited number of features is considered. In this regard, this research study proposed a feature-centric framework (FSEDM) based on existing and novel features of email data set, which are extracted after pre-processing. Afterwards, diverse supervised learning techniques are applied on the proposed features in conjunction with feature selection techniques such as information gain, gain ratio and Relief-F to rank most prominent features and classify the emails into spam or ham (not spam).

Analysis and experimental results indicated that the proposed model with sentiment analysis is competitive approach for spam email detection. Using the proposed model, deep neural network applied with sentiment features outperformed other classifiers in terms of classification accuracy up to 97.2%.

This research is novel in this regard that no previous research focuses on sentiment analysis in conjunction with other email features for detection of spam emails.

This paper aims to identify, examine, and present an empirical research design of behavioral finance of potential investors during Covid-19.

A well-structured questionnaire was designed; a survey was conducted among potential investors using convenience sampling, and 200 valid responses were collected. The research work uses multiple regression and discriminant function analysis to evaluate the influence of cognitive factors on the financial decision-making of investors.

Recency and familiarity bias are proven to have the highest significant impact on the financial decisions of investors followed by confirmation bias. Overconfidence bias had a negligible effect on the decision-making process of the respondents and found insignificant.

Covid-19 is a temporary phase that may lead to changes in financial behavior and investors’ decisions in the near future.

The paper will help academicians, scholars, analysts, practitioners, policymakers and firms dealing with capital markets to execute their job responsibilities with respect to the cognitive bias in terms of taking financial decisions.

The present investigation attempts to fill the gap in the literature on the intended topic because it is evident from literature on the chosen subject that no study has been undertaken to evaluate the impact of cognitive biases on financial behavior of investors during Covid-19.

This study aims to identify the major factors influencing the consumers to prefer milk products and also to analyze the awareness level of the Indian consumers.

In this study, the data is obtained through a structured questionnaire from Indian consumers considering convenience sampling under the nonprobability sampling technique. The consumer preference is explained using a multiple-regression model followed by analysis of variance (ANOVA), which shed insight on the significant differences between the variables that influence consumer preference for dairy products.

Investigation is done to analyze the factors influencing the consumers' buying behavior toward milk and its products. The results showed that quality, health consciousness, price and availability are the most influencing factors to buy milk products. Quantity of milk showed a significant relationship between age, monthly income and family size.

This study helps marketing managers to frame the marketing strategies based on consumer preference, quality, health consciousness, price and availability. The research outcome will not only be advantageous for the entrepreneurial perspective but also takes care of consumer likeliness. Though the research reveals the opinion of Indian consumers, it limits the likeliness of the western world. Because of the scarcity of resources, several dairy products are unexplored, which could pave the future scope of research.

The novelty of this study is to identify the quality, health consciousness, price and availability are the most influencing factors to buy milk products considering ANOVA and the multiple regression model.

Climate change and its imminent threat to human survival adversely impact the agriculture sector. In an impoverished country like The Gambia, economic costs of climate change are colossal. This study aims to establish a computable general equilibrium (CGE) model for The Gambia’s agriculture sector to examine the effects of climate change on crops, livestock and sea-level rise.

This study used a CGE model with other climate change impact models to compute the impacts of climate change on The Gambia’s agriculture sector. The social accounting matrix calibrates the results from the various models, thereby generating the baseline results which exemplify a “steady-state” and policy shock results illustrating the medium- and long-term effects of climate change on the country’s agriculture sector.

The baseline results indicate the status quo showing the neglect of the agriculture sector due to limited investment in the sector. Hence, the sector is the “hardest hit” sector as a result of climate change. When the model factored in climate change in the medium term (2055) and long term (2085), the macroeconomic indicators of gross domestic product, national savings, wages, disposable income and consumer price index deteriorated, elucidating the vulnerability of the economy to climate change. The consumption of groundnuts, cattle and fish will decline by 5%, 5% and 4%, respectively, in the long term. However, the production of all agricultural commodities will decline by an average of 35% for the same period. The results for international trade show that exportation would decline while importation will increase over time. The general price level for agricultural commodities would increase by 3% in 2055 and 5% in 2085. Generally, the results manifest the severity of climate change in the agriculture sector which will have a multiplier effect on the economy. The impact of climate change would result in agriculture and economic decline causing hunger, poverty and human misery.

The caveat of this study revealed the nuances not captured by previous Gambian climate change studies, thus the novelty of the study.

Social comments are rich in information and useful in evaluating, ranking or retrieving different kinds of materials. However, their merits in representing or providing added values to scientific articles have not yet been studied. Therefore, the present study investigates the informativeness of open review reports as a kind of social comments in a scholarly setting.

A test collection was built consisting of 100 randomly selected queries, 1,962 reviewed documents and their reviewers' open reports from F1000Research. They were analyzed using natural language techniques. The comments' salient words were compared to the documents' and also to the Medical Subject Headings (MeSH) salient words. The receiver operating characteristic (ROC) curve was used to test the accuracy of the comments in representing their related articles.

The papers' contents and comments have a considerable number of salient words in common. The comments' salient words are also largely found in the MeSH, signifying their consistency with the knowledge tree and their potential to add some complementary features to their related items. The ROC curves confirm the accuracy of the comments in retrieving their related papers.

This research is the first to reveal the merits of open review reports on scientific papers, in terms of their relatedness to their mother articles, in specific, and to the knowledge tree, in general. They are found informative in not only representing the reviewed papers but also in adding values to the contents of the papers.

The future of management studies is invariably steering towards online and hybrid modes of course delivery. Therefore, assessing the effectiveness of online course delivery is exceptionally crucial. This study attempts to evaluate the effectiveness of online course delivery in management education involving the instructor, participant and technological component. This study contributes to the body of knowledge in three ways. First, the study proposes an approach to assess the effectiveness of online courses in management education. The study demonstrates this by taking a case study of a business school (B-school) in southern India. Second, the study identifies the shortcomings and areas that need improvement to enhance the overall effectiveness further. Third, the study outlines suggestive measures to improve the effectiveness of online course delivery by addressing technical, infrastructural, instructor and student behavioral components.

To accomplish the objectives, a case study approach has been adopted and fuzzy logic has been used as a methodology to assess the effectiveness of online course delivery in management education.

The findings suggest that instructors' use of cases and animation during online sessions, use of whiteboards, digital pens and other tools, attempts to draw participant's interest and the users' sense of belongingness in the online cohort, self-discipline and motivation from students' side, easy to use Learning Management System (LMS), audio-visual platforms, active electronic communication and training on the technical aspect of the online platform need to be improved to enhance the effectiveness of online course delivery further. The current effectiveness of online course delivery in the case of B-school was found to be “Fair,” which is average in relation to the effectiveness labels.

This study doesn't investigate the factors that moderate the effectiveness of online course delivery and how the factors influence each other. Future research endeavors can be extended in this direction to enrich the body of knowledge with new insights. Apart from this, the results outlined in this study are about the status quo of the case B-school and can't be generalized. However, the methodology and approach can be adopted by other B-schools or higher educational institutes to measure the schools' and institutes' current level of effectiveness in online teaching.

So far, only a few studies have paid attention to the empirical assessment of the effectiveness of online course delivery consisting of engagement from the technical, instructor and participants' dimensions. This study proposes a novel approach to measure the level of effectiveness and identifies the shortfalls that impede good effectiveness in online course delivery.

The objective to achieve economic growth and sustainable development (SD) within the maritime industry has ever since been the ultimate goal of the International Maritime Organization and its stakeholders. Coupled with this effect, the United Nations organization has also mandated all its bodies to adopt sustainable working policies and practices towards the achievement of SD in its 2030 Agenda. From the standpoint of an emerging economy, this study aims to examine green maritime practices adopted by maritime authorities towards the achievement of SD in the maritime industry of Ghana. The proposed conceptual model of this study supports the natural resource-based view theory advocated by Hart (1995).

The dataset of this study was gathered using semi-structured questionnaires. A total of 635 valid responses were received as feedback which were tested and analyzed using partial least square structural equation modelling. The rationale for the adoption of this analytical tool is its resilient ability to handle a relatively small quantity of datasets. It is also suitable for empirical studies involving model development and at the early stage of theory development.

The findings of the study are as follows; firstly, quality maritime education and training directly and significantly influence green maritime transport (GMT), clean ocean and maritime resource conservation (COMRC), green port operations and services (GPOS), SD and waste management and treatment systems (WMTS). Secondly, GMT, COMRC, GPOS and WMTS have a direct significant influence on SD. Lastly, GMT, COMRC, GPOS and WMTS partially mediate the relationship between quality maritime education and training and SD.

This study proposes a conceptual model that attempts to explain to maritime authorities and stakeholders that although the adoption of green maritime practices significantly influences SD, yet, it may be insufficient without quality maritime education and training provided to maritime professionals. Hence, emphasizing that all maritime personnel receive quality maritime education and training to enhance the long-term achievement of SD in the maritime industry. It also attempts to prove and suggest to maritime authorities how they can collectively integrate both onshore and offshore green maritime practices to achieve SD.

The originality of this study shows in testing a conceptual model that affirms that, achieving SD in the maritime industry is dependent on quality maritime education and training received by maritime personnel, hence, demonstrating the significant role of maritime training institutions towards the maritime industry and the achievement of SD.

Portfolio optimization is the process of making an investment decision on a set of assets to realize high returns with low risk. It has three major stages: asset selection, asset weighting and asset management. Asset selection is an important phase because it influences asset allocation and ultimately affects the returns of a portfolio. Today, there is an increase in the number of listings on a stock exchange. Therefore, it is important for an investor to screen and select stocks for investment. This study focuses on the first stage of the portfolio optimization problem, namely, asset selection. The purpose of this study is to evaluate and select profitable stocks quoted on National Stock Exchange (NSE) for portfolio optimization.

Financial ratios are considered as the input and output parameters for evaluating the financial performance of the firms. This study adopts a hybrid principal component analysis (PCA) and data envelopment analysis (DEA) approach to evaluate the efficiency of the firms. Based on the efficiency scores, the firms are selected for the investment process.

The model helps to determine the relative efficiencies of the firms. The efficient firms are considered to be the potential stocks for investment. It helps the investors to screen the stocks from a large number of stocks quoted on NSE.

One of the limitations of the standard DEA model is that it fails to discriminate the firms when the number of input and output parameters are larger than the number of firms. To overcome this problem, either a parameter can be ignored or weight-restricted DEA can be applied. When an input/output parameter is dropped, the information in that variable is lost. Weight-restricted DEA model uses expert opinion for measuring the relative importance of input and output parameters. Expert opinion is subjective and might be biased. The PCA-DEA model helps to identify the efficient firms by improving the discriminatory power of standard DEA without any loss of information and without the need for expert opinion, which might be biased.

Asset selection is an important stage in the investment process. Selection of stocks based on the efficiency score is an easier option available to the investors. But the misclassification of firms either due to biased expert opinion or discrimination inability of DEA can be costly to an investor. The PCA-DEA model overcomes both these limitations. Investors can select the potential candidates for asset allocation based on the efficiency scores obtained using the PCA-DEA model. Further, the relative efficiencies obtained can help the firms to benchmark their performance against the best performing firms within their industry.

This paper is one of few papers to adopt the PCA-DEA framework to select stocks in the Indian stock market.

In India, one of the prime focuses of a post-graduate management program is to prepare students and make them job-ready. Masters in Business Management (MBA) program helps students to imbibe theoretical and practical skills which are required by the industry, which can make them hit the ground running from the day they start their career. Many students (almost 40–50%) get pre-placement offers based on their performance in summer internship. The selection for summer interns by the corporate happens within a few months of the student joining the MBA program. Signaling theory in education indicates that the level of productivity of an individual is independent of education, but the educational qualification acts as a testimony for higher ability. However, this theory does not explain the reason for the mismatch between “education and work” or “education and the disparity in salary” between individuals who earn differently but have the same qualification. The paper aims to explore three attributes namely – “employability”– the chance of being employable; “pre-placement offers” – the chance of securing a job offer based on the performance in internship and “salary” – the chance of bagging a good job offer with a high salary.

The authors have used longitudinal data consisting of 1,202 students who graduated from reputable business schools (B-Schools) in India. In the study, the authors have used predictive analytics on six years data set that have been gathered. The authors have considered 24 attributes including educational background at the graduate level (BE, B Tech, B Com, BSc, BBA and others), score secured in class ten (high, medium and low), score secured in class twelve (high, medium and low), score secured in graduation (high, medium and low), competency in soft skills (high, medium and low), participation in co-curricular activities (high, medium and low) and social engagement status (high, medium and low).

The findings of the study contradict the signaling theory in education. The findings suggest that the educational qualification alone cannot be the predictor of the employability and the salary offered to the student. The authors note that the better performance at a lower level of qualification (class 12) is the strong predictor in comparison to the student performance at their graduation and post-graduation level. The authors further observed at the post-graduate management education level that soft skills and participation in co-curricular activities are the major deciding factors to predict employability and pre-placement job opportunity and marks secured in class 12 is one more factor that gets added to this list to predict salary. The paper can immensely help management graduates to focus on key aspects that can help to hone appropriate skills and also can help management institutions to select the right students for management programs.

The analysis and the predictive model may apply to Indian B-Schools wherein the quality of students are almost the same or better. Predictive analytics has been used to explain the employability of management graduates alone and not any other.

The authors' study might be useful for those students who often fail to understand “what” skills are the most important predictors of their performance in the pre-placement and final-placement interviews. Moreover, the study may serve as a useful guide to those organizations that often face dilemmas to understand “how” to select an ideal candidate for the particular job profile from a campus.

The authors believe that the current study is one of the few studies that have attempted to examine the employability of management graduates using predictive analytics. The study further contradicts that the signaling theory in education does not help better explain the employability of the students in extremely high-paced business environments.