EXPRESS: How Does the Adoption of Ad Blockers Affect News Consumption?

Ad blockers allow users to browse websites without viewing ads. Online news publishers that rely on advertising income tend to perceive users' adoption of ad blockers purely as a threat to revenue. Yet, this perception ignores the possibility that avoiding ads—which users presumably dislike—may affect users' online news consumption behavior in positive ways. Using 3.1 million visits from 79,856 registered users on a news website, this research finds that ad blocker adoption has robust positive effects on the quantity and variety of articles users consume. Specifically, ad blocker adoption increases the number of articles that users read by 21.5%-43.3%, and it increases the number of content categories that users consume by 13.4%-29.1%. These effects are stronger for less-experienced users. The increase in news consumption stems from increases in repeat visits to the news website, rather than in the number of page impressions per visit. These post-adoption visits tend to start from direct navigation to the news website, rather than from referral sources. The authors discuss how news publishers could benefit from these findings, including exploring revenue models that consider users' desire to avoid ads.

Precise Marketing Strategy Optimization of E-Commerce Platform Based on KNN Clustering

With the development of computer technology and the arrival of the era of artificial intelligence, the analysis of user demand bias is of great significance to the operation optimization of e-commerce platforms. Combined with CS domain signaling data, IP packet data of PS domain, and customer CRM data provided by operators, this research studies each dimension index of operator user portrait, after that the operator user portrait platform is divided into some individual subunits, and then the corresponding data mining technology is carried out to study the implementation scheme of each subunit. The system can process and mine multidimensional data of operators’ users and form user portraits on the basis of user data aggregation. Finally, based on the operator user portrait platform studied in this paper, the operator user data are analyzed from both the user’s mobile phone use behavior and user consumption behavior. Furthermore, the application value of this research in the precision marketing and personalized service of operators is illustrated.

Accuracy of Risk Perception of Zoonoses Due to Intensive Animal Farming and People’s Willingness to Change Their Animal Product Consumption

Zoonoses have become more frequent and intense. As intensive animal farming plays a role in the emergence of zoonoses, the increase in intensive animal farming increases the risk of future zoonotic outbreaks. This raises the question of to what extent people are aware that intensive animal farming poses a risk to zoonoses. Furthermore, if people would be made aware, would they be willing to take protective measures, such as reducing their animal food consumption? This was investigated in a representative descriptive study of 1009 Dutch citizens. We measured participants’ perception of the risk of intensive animal farming and their perception of the way animals are treated. We measured their willingness to consume fewer animal products and their opinions on governments banning intensive animal farms. Additionally, participants estimated the percentage of meat from intensive farms that they consume. The main results showed that most participants were aware that zoonoses can occur through intensive animal farming, but not where their meat comes from. The majority of participants were willing to change their animal consumption behavior if this could reduce future zoonotic outbreaks.

The Consumer Role for Sustainable Development

The term sustainable consumption is not only a behavior type in marketing and a just consumption behavior, it is more than this. Sustainable or responsible consumption behavior can change the world. Sustainable consumption concept has been investigated widely in the literature and factors that effecting sustainable consumption or being a green consumer has been investigated recently, too. But the relationship between sustainable development and consumer behavior isn't investigated sufficiently. After 2030 Sustainable Development Goals set up, responsibilities and roles have been an important issue to achieve sustainable development in the long term. In this point, this study aims to investigate the consumer role for sustainable development goals through sustainable consumption patterns and trends.

Climate Change and the Circular Economy

The world is presently facing a climate catastrophe of its own making through the unabated increase in greenhouse gas emissions. Global consumption patterns are to blame, as presently, the global annual demand for resources outpaces the annual rate of the earth's ability to regenerate those resources. Thus, there is an urgent need to reduce the global demand for resources to a sustainable level, through the adoption of a circular economy. Individual consumption behavior habits form the basis of global consumption patterns, and therefore, adoption of sustainable consumption habits and lifestyles are necessary for addressing the climate crisis. In this chapter, the authors assess the potential for addressing the climate crisis through the adoption of a circular economy and sustainable consumption behavior. The authors also evaluate the extent of adoption of sustainable consumption behavior in India and make recommendations for adopting a circular economy in India.

Residential Short-Term Load Forecasting during Atypical Consumption Behavior

Short-term load forecasting (STLF) is a fundamental tool for power networks’ proper functionality. As large consumers need to provide their own STLF, the residential consumers are the ones that need to be monitored and forecasted by the power network. There is a huge bibliography on all types of residential load forecast in which researchers have struggled to reach smaller forecasting errors. Regarding atypical consumption, we could see few titles before the coronavirus pandemic (COVID-19) restrictions, and afterwards all titles referred to the case of COVID-19. The purpose of this study was to identify, among the most used STLF methods—linear regression (LR), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN)—the one that had the best response in atypical consumption behavior and to state the best action to be taken during atypical consumption behavior on the residential side. The original contribution of this paper regards the forecasting of loads that do not have reference historic data. As the most recent available scenario, we evaluated our forecast with respect to the database of consumption behavior altered by different COVID-19 pandemic restrictions and the cause and effect of the factors influencing residential consumption, both in urban and rural areas. To estimate and validate the results of the forecasts, multiyear hourly residential consumption databases were used. The main findings were related to the huge forecasting errors that were generated, three times higher, if the forecasting algorithm was not set up for atypical consumption. Among the forecasting algorithms deployed, the best results were generated by ANN, followed by ARIMA and LR. We concluded that the forecasting methods deployed retained their hierarchy and accuracy in forecasting error during atypical consumer behavior, similar to forecasting in normal conditions, if a trigger/alarm mechanism was in place and there was sufficient time to adapt/deploy the forecasting algorithm. All results are meant to be used as best practices during power load uncertainty and atypical consumption behavior.