Improved resource efficiency and cascading utilisation of renewable materials

In light of various environmental problems and challenges concerning resource allocation, the utilisation of renewable resources is increasingly important for the efficient use of raw materials. Therefore, cascading utilisation (i.e., the multiple material utilisations of renewable resources prior to their conversion into energy) and approaches that aim to further increase resource efficiency (e.g., the utilisation of by-products) can be considered guiding principles. This paper therefore introduces the Special Volume “Improved Resource Efficiency and Cascading Utilisation of Renewable Materials”. Because both research aspects, resource efficiency and cascading utilisation, belong to several disciplines, the Special Volume adopts an interdisciplinary perspective and presents 16 articles, which can be divided into four subjects: Innovative Materials based on Renewable Resources and their Impact on Sustainability and Resource Efficiency, Quantitative Models for the Integrated Optimisation of Production and Distribution in Networks for Renewable Resources, Information Technology-based Collaboration in Value Generating Networks for Renewable Resources, and Consumer Behaviour towards Eco-friendly Products. The interdisciplinary perspective allows a comprehensive overview of current research on resource efficiency, which is supplemented with 15 book reviews showing the extent to which textbooks of selected disciplines already refer to resource efficiency. This introductory article highlights the relevance of the four subjects, presents summaries of all papers, and discusses future research directions. The overall contribution of the Special Volume is that it bridges the resource efficiency research of selected disciplines and that it presents several approaches for more environmentally sound production and consumption

The effects of product placement, in films, on the consumer's purchase intentions

Traditional forms of communication have been losing their once golden effectiveness due to, mainly, consumer saturation. Since the modern world is characterized by an advertising clutter, in wich each citizen living in urban areas is exposed daily to an average of 3,500 stimuli, companies are now looking for ways to differentiate themselves from competitors, and are now immersing their ways onto product placement. As investment in product placements increases over the years, making it now a billion dollar industry, there is a necessity to deeply explore this theme to better understand what its main effects on consumers are. Purchase intention is a step that mediates consumer attitude and effective behavior. The link between this stage of consumer behavior and product placement represents an area in which previous scholars’ results have not been overly consistent, making it not as crystal clear as desired. The present research conveys an experimental design resorting to eye tracking tools and softwares, providing data concerning the viewers’ levels of attention. As this experiment also aims to denote differences between reactions to product placements in different contexts (comedy vs. drama), two experimental and two control groups were defined, totaling a sample of 85 subjects. This paper covers the effects that the variables identified on the literature review have on brand attitudes, and subsequentely, on purchase intentions. The formulated hypotheses provide an in-depth look to how product placement affects consumers.As formas de comunicação tradicionais têm vindo a perder a sua eficácia devido à, essencialmente, saturação do consumidor. Dado que o mundo moderno é caracterizado por um ambiente com excesso de publicidade, em que um cidadão residente em áreas urbanas é exposto, em média, a 3.500 estímulos diários, as empresas estão à procura de novas formas de se diferenciarem da concorrência apostando cada vez mais em product placement. À medida que o investimento em product placement aumenta, tornando-o numa indústria que movimenta milhares de milhões de dólares, surge a necessidade de explorar profundamente este tema almejando uma melhor compreensão dos efeitos que tem no consumidor. A intenção de compra representa o primeiro passo no processo de decisão de compra do consumidor, e a ligação entre este patamar e o product placement representa uma área que não tem suscitado coerência em estudos passados, tornando-a pouco clara. O presente estudo experimental recorre a ferramentas e software de eye tracking, proporcionando informação acerca dos níveis de atenção dos espectadores em relação aos estímulos definidos. Dado que esta experiência também pretende identificar diferenças nas reacções ao product placement em contextos distintos (comédia vs. drama), dois grupos experimentais e dois grupos de controlo foram definidos, totalizando uma amostra de 85 elementos. Esta dissertação cobre os efeitos que diferentes variáveis identificadas na revisão de literatura têm nas atitudes do consumidor perante a marca, e consequentemente, nas suas intenções de compra. As hipóteses formuladas oferecem um olhar aprofundado sobre a forma de como é que o product placement afecta os consumidores

A Novel Contextual Information Recommendation Model and Its Application in e-Commerce Customer Satisfaction Management

In the current supply chain environment, distributed cognition theory tells us that various types of context information in which a recommendation is provided are important for e-commerce customer satisfaction management. However, traditional recommendation model does not consider the distributed and differentiated impact of different contexts on user needs, and it also lacks adaptive capacity of contextual recommendation service. Thus, a contextual information recommendation model based on distributed cognition theory is proposed. Firstly, the model analyzes the differential impact of various sensitive contexts and specific examples on user interest and designs a user interest extraction algorithm based on distributed cognition theory. Then, the sensitive contexts extracted from user are introduced into the process of collaborative filtering recommendation. The model calculates similarity among user interests. Finally, a novel collaborative filtering algorithm integrating with context and user similarity is designed. The experimental results in e-commerce and benchmark dataset show that this model has a good ability to extract user interest and has higher recommendation accuracy compared with other methods

4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 4th International Conference on Advanced Research Methods and Analytics (CARMA) is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges. Due to the covid pandemic, CARMA 2022 is planned as a virtual and face-to-face conference, simultaneouslyDoménech I De Soria, J.; Vicente Cuervo, MR. (2022). 4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2022.2022.1595

A novel data analytic model for mining user insurance demands from microblogs

This paper proposes a method based on LDA model and Word2Vec for analyzing Microblog users' insurance demands. First of all, we use LDA model to analyze the text data of Microblog user to get their candidate topic. Secondly, we use CBOW model to implement topic word vectorization and use word similarity calculation to expand it. Then we use K-means model to cluster the expanded words and redefine the topic category. Then we use the LDA model to extract the keywords of various insurance information on the “Pingan Insurance” website and analyze the possibility of users with different demands to purchase various types of insurance with the help of word vector similarity. Finally, the validity of the method in this paper is verified against Microblog user information. The experimental results show that the accuracy, recall rate and F1 value of the LDA-CBOW extending method have been proposed compared with that of the traditional LDA model, respectively, which proves the feasibility of this method. The results of this paper will help insurance companies to accurately grasp the preferences of Microblog users, understand the potential insurance needs of users timely, and lay a foundation for personalized recommendation of insurance products

Management Responses to Online Reviews: Big Data From Social Media Platforms

User-generated content from virtual communities helps businesses develop and sustain competitive advantages, which leads to asking how firms can strategically manage that content. This research, which consists of two studies, discusses management response strategies for hotel firms to gain a competitive advantage and improve customer relationship management by leveraging big data, social media analytics, and deep learning techniques. Since negative reviews' harmful effects are greater than positive comments' contribution, firms must strategise their responses to intervene in and minimise those damages. Although current literature includes a sheer amount of research that presents effective response strategies to negative reviews, they mostly overlook an extensive classification of response strategies. The first study consists of two phases and focuses on comprehensive response strategies to only negative reviews. The first phase is explorative and presents a correlation analysis between response strategies and overall ratings of hotels. It also reveals the differences in those strategies based on hotel class, average customer rating, and region. The second phase investigates effective response strategies for increasing the subsequent ratings of returning customers using logistic regression analysis. It presents that responses involving statements of admittance of mistake(s), specific action, and direct contact requests help increase following ratings of previously dissatisfied returning customers. In addition, personalising the response for better customer relationship management is particularly difficult due to the significant variability of textual reviews with various topics. The second study examines the impact of personalised management responses to positive and negative reviews on rating growth, integrating a novel method of multi-topic matching approach with a panel data analysis. It demonstrates that (a) personalised responses improve future ratings of hotels; (b) the effect of personalised responses is stronger for luxury hotels in increasing future ratings. Lastly, practical insights are provided

Investigating the Effects of Dimension-Specific Sentiments on Product Sales: The Perspective of Sentiment Preferences

While the literature has reached a consensus on the awareness effect of online word-of-mouth (eWOM), this paper studies its persuasive effect—specifically, dimension-specific sentiment effects on product sales.We examine the sentiment information in eWOM along different product dimensions and reveal different persuasive effects on consumers’ purchase decisions based on consumers’ sentiment preference, which is defined as the relative importance that consumers place on various dimension-specific sentiments. We use an aspect-level sentiment analysis to derive dimension-specific sentiment and PVAR (panel vector auto-regression) models, and estimate their effects on product sales using a movie panel dataset. The findings show that three dimension-specific sentiments (star, genre, and plot) are positively related to movie sales.Regarding consumers’ sentiment preferences, we find a positive relationship to movie sales that is stronger for plot sentiment, relative to star sentiment for low-budget movies. For high-budget movies, we find a positive relationship to movie sales that is stronger for star sentiment, relative to plot or genre sentiment

Pollution-Reducing and Resource-Saving Technological Progress

In this paper we survey the theoretical literature on both pollution-reducing and resource-saving technological progress. The literature can be divided into two strands. One strand deals with microeconomic models which investigate incentives to adopt and to develop environmentally more friendly technologies for different policy tools and in different economic environments, such as market structure or timing and commitment structures. It turns out that, firstly, price based instruments such as emission taxes and tradable permits perform better than command and control policies, and secondly, that under competitive conditions ex ante end ex post optimal policies are equivalent. Under imperfect market conditions the policy conclusions are more subtile. The second strand of literature deals with both pollution-reducing and resource-saving technological progress within endogenous growth models. Most of these models are characterized by three market imperfections : market power for new (intermediate) products, positive R&D spillovers, and pollution. These imperfections can be mitigated by subsidies on intermediate products, subsidies on R&D effort, and a tax on emissions. Moreover, in most models there occurs a trade-off between the speed of growth and environmental quality. --pollution-reducing technological progress,resource-saving technological progress,environmental innovation,endogenous growth models