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The purpose of this paper is to provide an overview of the current state of knowledge on the economic performance of genetically modified (GM) cotton worldwide based on a wide range of data and source from available literature, and second to assess yield gain and economic performance.

A systematic review was captured to provide the evidence of potential benefits of GM cotton. A country-specific analysis was conducted in order to compare economic indicators and employed meta-analysis to find out the significance of the different of GM cotton over its counterpart.

This paper depicts positive impact of commercialized GM cotton in terms of net revenue, and the benefits, especially in terms of increased yields, are greatest for the mostly farmers in developing countries who have benefitted from the spill over of technology targeted at farmers in industrialized countries.

Due to the variability of the data which came from different methodologies, it is difficult to determine the differences of the performances each individual study.

This, it is believed that results from this study can be useful for operations of all sizes as the authors think about what needs to be focussed on for long-term producers survival.

The paper clearly indicates that China is the highest cotton yield of GM cotton, the lowest cost of GM seed and the lowest cost of chemical spray compare to any other countries. Therefore, this is the fact that the adoption of GM cotton has been widely spread among the farmers across the regions in China.

A wetland is a place of tourist attraction, and tourism values play a key role in economic development. Among various services provided by a wetland, recreational services are increasingly valuable in the tourism sector. This paper aims to unfold the potential recreational values of the Dal Lake in Jammu and Kashmir, India.

The study uses individual travel cost methods (TCMs) and assesses its impact on regional development in terms of income and employment generation. A sample of 200 tourists is selected through an on-site survey on Dal Lake, and the demand for recreational visits and its value is estimated by employing the truncated Poisson regression model (TPRM) and un-truncated Poisson regression model (UTPRM). The consumers' surplus is estimated and tourists' benefit to visiting the wetland is explored.

On average, estimated consumers' surplus per visitor is Rs 6,250 (US$96.15) and Rs 25,000 (US$384.61) from respective models. The annual total recreational value of the lake is accounted for Rs 1713m (US$ 26m). This high consumer surplus (CS) and recreational values of the lake indicate large demand for its recreational facilities.

The study is based on primary data and thus, is original. The paper has implications for the policymakers to formulate sustainable management plans for the proper use of Dal Lake and tourism development.

This study aims to advance knowledge of channel integration, a key feature of omnichannel retailing, by investigating the role of specific touchpoints in delivering a consistent integration perception.

Quantitative methods were adopted, by testing a model built on the stimulus-organism-response framework. Data collection used a panel survey across the grocery and fashion sectors (1,031 and 739 consumers, respectively). An ordinary least squares regression with clustered standard errors was conducted, combined with a multiple correspondence analysis, followed by a mediation analysis.

This study identifies touchpoints relevant for channel integration perception and shows that they differ across product category and customer types (first time vs repeat customers). Furthermore, it pinpoints touchpoints that are directly and indirectly related to patronage intention, thereby exposing the mediating role of channel integration. By drawing on categorization theory, it discusses individual touchpoints’ contribution to channel integration perception, at general level and on different customer targets.

This study offers a new vision of channel integration perception that highlights touchpoints’ role. It contributes to the established channel integration quality framework by showing that integrated information is concerned not only with consistency of information across channels but also with the specific touchpoints through which such information is disseminated.

This study provides directly actionable managerial implications, by through strategic insights for customer journey and customer experience design/redesign and by offering a practical methodology for retailers to identify the touchpoints they can leverage to improve their customers’ channel integration perceptions – with consequences for patronage intention.

The purpose of this study is to explore the customer shopping experience in the innovative technology setting. Specifically, the purpose is to understand how do innovative technologies influence the customer shopping experience?

This qualitative, explorative study has characteristics of a phenomenological research strategy. The data were collected from four focus groups and ten in-depth interviews with consumers. Abductive approach with an implementation of content analysis was used as a method of analysis.

The results show that there are three critical factors in customer's shopping experience in the context of innovative technologies; (1) channel choice, (2) value dimensions related to convenience and enjoyment, and (3) social interaction. All factors are highly intertwined and influence each other.

This study contributes to customer experience literature by offering a framework for understanding customer shopping experiences in the innovative technology setting. These findings have important implications for retail managers seeking to enhance customer experience and achieve a competitive advantage by utilizing innovative technology.

Transparent and fair price discovery is essential to commodity market participants in the trade value chain for competitive benefit. The purpose of this paper is to investigate the price discovery of Indian cardamom at e-auction, spot and futures markets in addition to the existence of the day of the week effect at e-auction apart from exploring a novel price risk management framework.

This study used Johansen co-integration, vector error correction model, Granger causality and regression with dummy variables to understand a day of the week effect in high-value agri-commodity of cardamom e-auction prices. These price data were based on authenticated sources of Spices Board India and Multi Commodity Exchange of India Ltd.

The statistical results indicate price discovery exists in the e-auction market and it leads to spot and futures prices. cardamom e-auction prices are negatively related to cardamom futures and positively related to spot prices. It also finds the non-existence of the day of the week effect in the high-value cardamom e-auction system in India. The study revealed that a cardamom e-auction is more active in price discovery than a cardamom futures contract.

These results shall facilitate policymakers to explore intervention of online forward market mechanism at the national level to ensure price discovery and market efficiency. However, the study did not explore reasons for the non-equilibrium of a cardamom futures contract with spot and e-auction market.

The results of this study are useful in understanding the price discovery of cardamom e-auction and its role in the spot and futures market. Cardamom price discovery depends upon the e-auction system; any change of auction policy shall be binding on Indian cardamom prices. The introduction of an online forward market mechanism as described in the paper shall facilitate price risk management apart from improving the efficiency of price discovery.

This is the first study considering cardamom e-auction, spot and futures prices in the price discovery process in India. Statistical results of a day of the week effect clearly show no significant volatility of cardamom prices during the week. Besides, this study did not find the role of cardamom futures contracts intended to serve the economic function of price discovery and price risk management. Hence, suggests policy intervention for implementing an online Forward Market mechanism for Indian cardamom to ensure market efficiency and manage price risk.

Petroleum hydrocarbons are naturally occurring flammable fossil fuels used as conventional energy sources. It has carcinogenic, mutagenic properties and is considered a hazardous pollutant. Soil contaminated with petroleum hydrocarbons adversely affects the properties of soil. This paper aim to remove pollutants from the environment is an urgent need of the hour to maintain the proper functioning of soil ecosystems.

The ability of micro-organisms to degrade petroleum hydrocarbons makes it possible to use these microorganisms to clean the environment from petroleum pollution. For preparing this review, research papers and review articles related to petroleum hydrocarbons degradation by micro-organisms were collected from journals and various search engines.

Various physical and chemical methods are used for remediation of petroleum hydrocarbons contaminants. However, these methods have several disadvantages. This paper will discuss a novel understanding of petroleum hydrocarbons degradation and how micro-organisms help in petroleum-contaminated soil restoration. Bioremediation is recognized as the most environment-friendly technique for remediation. The research studies demonstrated that bacterial consortium have high biodegradation rate of petroleum hydrocarbons ranging from 83% to 89%.

Proper management of petroleum hydrocarbons pollutants from the environment is necessary because of their toxicity effects on human and environmental health.

This paper discussed novel mechanisms adopted by bacteria for biodegradation of petroleum hydrocarbons, aerobic and anaerobic biodegradation pathways, genes and enzymes involved in petroleum hydrocarbons biodegradation.

This paper examines the process optimization method of the online sales model of information product demand concerning the spillover effect. It illustrates the spillover effect (SE) of online product demand compared with traditional market demand. Also, optimized the SE for the ethical and ordinary consumer.

This article primarily focused on two types of models for online marketing: one is wholesales, and another is the agency. Firstly, the wholesale and agency models without SE and the wholesale and agency models with SE are constructed, respectively, to realize the SE in different sales models. Secondly, online channel participants' optimal price, demand and profit under variant conditions are compared and analyzed. Finally, efficient supply chain theory is optimized for the decision-making of online marketing consumers using an equation-based comparative analysis method.

The study found that when SEs are not considered, stronger piracy regulation makes online channel participants more beneficial. When the positive SE is strong, it is detrimental to manufacturers. When SEs are not considered, online channel participants only reach Pareto in agency mode. Pareto optimality can be achieved in wholesale and agency modes when SEs are considered.

The research has practical implications for an effective supply chain model for online marketing. This is the first algorithm-based comparative study concerning theoretical spillover effect analysis in supply chain management.

This study aims to solve the problem of weld quality inspection, for the aluminum alloy profile welding structure of high-speed train body has complex internal shape and thin plate thickness (2–4 mm), the conventional nondestructive testing method of weld quality is difficult to implement.

In order to solve this problem, the ultrasonic creeping wave detection technology was proposed. The impact of the profile structure on the creeping wave detection was studied by designing profile structural test blocks and artificial simulation defect test blocks. The detection technology was used to test the actual welded test blocks, and compared with the results of X-ray test and destructive test (tensile test) to verify the accuracy of the ultrasonic creeping wave test results.

It is indicated that that X-ray has better effect on the inspection of porosities and incomplete penetration defects. However, due to special detection method and protection, the detection speed is slow, which cannot meet the requirements of field inspection of the welding structure of aluminum alloy thin-walled profile for high-speed train body. It can be used as an auxiliary detection method for a small number of sampling inspection. The ultrasonic creeping wave can be used to detect the incomplete penetration welds with the equivalent of 0.25 mm or more, the results of creeping wave detection correspond well with the actual incomplete penetration defects.

The results show that creeping wave detection results correspond well with the actual non-penetration defects and can be used for welding quality inspection of aluminum alloy thin-wall profile composite welding joints. It is recommended to use the echo amplitude of the 10 mm × 0.2 mm × 0.5 mm notch as the criterion for weld qualification.

The purpose of this study is to determine what the history of research in marketing implies for the reaction of the field to recent developments in technology due to the internet and associated developments.

This paper examines the introduction of new research topics over 10-year intervals from 1960 to the present. These provide the basic body of knowledge that drives the field at the present time.

While researchers have always borrowed techniques, they have refined them to make them applicable to marketing problems. Moreover, the field has always responded to new developments in technology, such as more powerful computers, scanners and scanner data, and the internet with a flurry of research that applies the technologies.

Marketing will adapt to changes brought on by the internet, increased computer power and big data. While the field faces competition for other disciplines, its established body of knowledge about solving marketing problems gives it a unique advantage.

This paper traces the history of academic marketing from 1960 to the present to show how major changes in the field responded to changes in computer power and technology. It also derives implications for the future from this analysis.

El objetivo de este estudio es examinar qué implica la historia de la investigación académica en marketing en la reacción del campo de conocimiento a los recientes desarrollos tecnológicos como consecuencia de la irrupción de Internet.

Esta investigación analiza la introducción de nuevos temas de investigación en intervalos de diez años desde 1960 hasta la actualidad. Estos periodos proporcionan el cuerpo de conocimiento básico que conduce al ámbito del marketing hasta el presente.

Aunque los investigadores tradicionalmente han tomado prestadas ciertas técnicas, las han ido refinando para aplicarlas a los problemas de marketing. Además, el ámbito del marketing siempre ha respondido a los nuevos desarrollos tecnológicos, más poder de computación, datos de escáner o el desarrollo de Internet, con un amplio número de investigaciones aplicando tales tecnologías.

El marketing se adaptará a los cambios provocados por Internet, aumentando el poder de computación y el big data. Aunque el marketing se enfrenta a la competencia de otras disciplinas, su sólido cuerpo de conocimiento orientado a la resolución de problemas le otorga una ventaja diferencial única.

Describe la historia académica del marketing desde 1960 hasta la actualidad, para mostrar cómo los principales cambios en este campo respondieron a los cambios tecnológicos. Se derivan interesantes implicaciones para el futuro.

Historia, Revisión, Cambio, Tecnología, Conocimiento, Internet, Datos, Métodos

Revisión general

Cardiac arrest is a severe heart anomaly that results in billions of annual casualties. Smoking is a specific hazard factor for cardiovascular pathology, including coronary heart disease, but data on smoking and heart death not earlier reviewed. The Heart Rate Variability (HRV) parameters used to predict cardiac arrest in smokers using machine learning technique in this paper. Machine learning is a method of computing experience based on automatic learning and enhances performances to increase prognosis. This study intends to compare the performance of logistical regression, decision tree, and random forest model to predict cardiac arrest in smokers. In this paper, a machine learning technique implemented on the dataset received from the data science research group MITU Skillogies Pune, India. To know the patient has a chance of cardiac arrest or not, developed three predictive models as 19 input feature of HRV indices and two output classes. These model evaluated based on their accuracy, precision, sensitivity, specificity, F1 score, and Area under the curve (AUC). The model of logistic regression has achieved an accuracy of 88.50%, precision of 83.11%, the sensitivity of 91.79%, the specificity of 86.03%, F1 score of 0.87, and AUC of 0.88. The decision tree model has arrived with an accuracy of 92.59%, precision of 97.29%, the sensitivity of 90.11%, the specificity of 97.38%, F1 score of 0.93, and AUC of 0.94. The model of the random forest has achieved an accuracy of 93.61%, precision of 94.59%, the sensitivity of 92.11%, the specificity of 95.03%, F1 score of 0.93 and AUC of 0.95. The random forest model achieved the best accuracy classification, followed by the decision tree, and logistic regression shows the lowest classification accuracy.