The Circular Economy Business Model: Examining Consumers’ Acceptance of Recycled Goods

[Abstract] The circular economy strategy supports the transformation of the linear consumption model into a closed-production model to achieve economic sustainability, with the consumers’ acceptance of circular products being one of the major challenges. Further, one important aspect of product circularity remains unexplored, such as the consumers’ purchase intention of recycled circular goods. In this context, the present study proposes and tests a conceptual model on consumers acceptance of recycled goods through PLS Structural Equation Modeling (PLS-SEM), based on the data obtained from 312 respondents. Results indicate that the positive image of circular products is the most important driver of consumers’ acceptance, followed by the product perceived safety. This study provides an empirical foundation for the important role of consumers in circular economy business models through the examination of consumers’ acceptance of recycled goods

Examining the Influence of Store Environment in Hedonic and Utilitarian Shopping

[Abstract] Much of the literature on the attractiveness and pleasantness of retail stores has focused on the critical influence of store atmosphere or ambient attributes, which influence customer satisfaction and store choice. However, little is known about the environmental cues that influence customers’ satisfaction in different shopping contexts. In this context, the present research aims to answer the following questions: “Are the store atmospheric variables equally relevant in hedonic and utilitarian shopping?”; and further: “Does the influence of store environment on customer satisfaction vary depending on the type of shopping?”. For this purpose an empirical research is developed through PLS Structural Equation Modeling (PLS-SEM) based on data obtained from hedonic (n = 210) and utilitarian (n = 267) shopping contexts. Results indicate that customers perceive differently store atmospherics in utilitarian and in hedonic shopping. More precisely, findings report that customer satisfaction is driven by internal ambient and merchandise layout in hedonic shopping contexts; while the external ambient and the merchandise layout are major atmospheric cues in utilitarian shopping. Interestingly, store crowding does not influence customers’ satisfaction. This study provides a deeper understanding into the specific store attributes that influence customer satisfaction, which could be used by retailers to differentiate themselves from competitors

Examining the role of product involvement in consumption elicited emotions

La implicación tiene una influencia muy importante en el comportamiento del consumidor. Este trabajo aborda las siguientes preguntas: “¿Influye la implicación con el producto en cómo las emociones crean la satisfacción con los productos?”, y “¿la implicación con el producto desempeña un papel moderador en las relaciones emociones-satisfacción?”. Basándonos en la Teoría de la Asimetría Hedónica, mediante un Modelo de Ecuaciones Estructurales (SEM) analizamos la influencia de las emociones en la satisfacción. Se recogió una muestra de 570 consumidores de un producto de alta implicación –vino-, y una muestra de 431 consumidores para el producto de baja implicación –una taza de café-. Los resultados muestran que las emociones positivas ejercen una influencia mayor en la satisfacción de los consumidores para el producto de baja implicación que para el producto de alta implicación, sugiriendo los factores situacionales –como la ocasión de consumo- podrían estar actuando como potenciadores de las emociones positivas. Adicionalmente, se ofrece evidencia empírica del rol moderador de la implicación en la relación que existe entre las emociones derivadas del consumo y la satisfacción del consumidor.Involvement has a major impact on consumer behavior. This study addresses the following questions: “Does product involvement influence how emotions drive satisfaction with products?”, and “does product involvement play a moderating role in the relationship emotions-satisfaction?”. Based on the Theory of the Hedonic Asymmetry we test through Structural Equation Modeling (SEM) how emotions drive consumer satisfaction. A sample of 570 respondents was gathered for a high involvement product –wine-, while a sample of 431 consumers was collected for a low involvement product –a cup of coffee-. Findings show that positive emotions exert a higher influence on satisfaction in low involvement products, compared to high involvement products, suggesting that situational factors, such as the occasion of consumption, could be acting as qualifiers of positive emotions. Additionally, we provide empirical support for the moderating role of product involvement as influencing the relationship between consumption elicited emotions and consumer satisfaction

Un modelo multifactorial con variables macroeconómicas en el mercado de capitales español: un análisis de estructuras de covarianzas

[Resumen] Se analiza la relación entre las rentabilidades de las acciones y un conjunto de variables macroeconómicas en el mercado de capitales español a partir del análisis de estructuras de covarianzas. De la prueba realizada con una muestra de 70 títulos se obtiene que la rentabilidad del mercado –representado por un índice– es la única variable explicativa de la variación conjunta de las rentabilidades de las acciones. El resto de variables macroeconómicas probadas (la producción industrial, las relacionadas con el sector exterior, el riesgo de crédito y las que definen la estructura temporal de los tipos de interés) no parecen tener poder explicativo alguno.[Abstract] In this research the relationship between stockholder return and macroeconomic variables in the Spanish stockmarket will be analysed using structural equation modeling methodology. A test with many independent variables (Industrial Production, Credit Risk, and other variables defining interest rates, etc.) will be carried out, enlightening those which have a significant relationship on Market Retur

The Emotional Edge of Financial Predators: a Four Group Longitudinal Study

En los últimos años, los inversionistas han sido engañados por sus propios expertos financieros. A pesar de las advertencias de las organizaciones reguladoras, como la Comisión de Seguridad de Valores Mobiliarios o los informes publicados por periódicos y revistas especializados, muchas personas se sienten atrapadas en los esquemas de Ponzi. La pregunta es ¿por qué? En este trabajo se plantea la hipótesis de que gran parte de los inversionistas basó sus decisiones en torno a los asesores o agentes financieros poco escrupulosos que capitalizaron la emoción primitiva. Se realiza una investigación longitudinal con cuatro grupos para un periodo de seis meses en donde se muestra que la gente se involucra en la negociación financiera con el corazón, no sólo con sus pensamientos y calculadoras.In the last few years, a number of investors from all walks of life have been duped by their once-trusted financial advisors. Despite warnings by regulatory bodies such as the Security Exchange Commission or educated reports published by newspapers and magazines, people still get caught in the likes of Ponzi schemes. The question is why? This paper hypothesizes that a large part of the blind eye turned onto financial advisors and brokers finds its source in primitive emotion. A four-group longitudinal study spread over six months shows that people engage in financial negotiation with their hearts and guts, not only with their thoughts and calculators

Learning the Combinatorial Structure of Demonstrated Behaviors with Inverse Feedback Control

International audienceIn many applications, such as virtual agents or humanoid robots, it is difficult to represent complex human behaviors and the full range of skills necessary to achieve them. Real life human behaviors are often the combination of several parts and never reproduced in the exact same way. In this work we introduce a new algorithm that is able to learn behaviors by assuming that the observed complex motions can be represented in a smaller dictionary of concurrent tasks. We present an optimization formalism and show how we can learn simultaneously the dictionary and the mixture coefficients that represent each demonstration. We present results on a idealized model where a set of potential functions represents human objectives or preferences for achieving a task

Learning to recognize parallel combinations of human motion primitives with linguistic descriptions using non-negative matrix factorization

International audienceWe present an approach, based on non-negative matrix factorization, for learning to recognize parallel combinations of initially unknown human motion primitives, associated with ambiguous sets of linguistic labels during training. In the training phase, the learner observes a human producing complex motions which are parallel combinations of initially unknown motion primitives. Each time the human shows a complex motion, he also provides high-level linguistic descriptions, consisting of a set of labels giving the name of the primitives inside the complex motion. From the observation of multi-modal combinations of high-level labels with high-dimensional continuous unsegmented values representing complex motions, the learner must later on be able to recognize, through the production of the adequate set of labels, which are the motion primitives in a novel complex motion produced by a human, even if those combinations were never observed during training. We explain how this problem, as well as natural extensions, can be addressed using non-negative matrix factorization. Then, we show in an experiment in which a learner has to recognize the primitive motions of complex human dance choreographies, that this technique allows the system to infer with good performance the combinatorial structure of parallel combinations of unknown primitives

Feature learning for multi-task inverse reinforcement learning

In this paper we study the question of life long learning of behaviors from human demonstrations by an intelligent system. One approach is to model the observed demonstrations by a stationary policy. Inverse rein-forcement learning, on the other hand, searches a reward function that makes the observed policy closed to optimal in the corresponding Markov decision process. This approach provides a model of the task solved by the demonstrator and has been shown to lead to better generalization in un-known contexts. However both approaches focus on learning a single task from the expert demonstration. In this paper we propose a feature learn-ing approach for inverse reinforcement learning in which several different tasks are demonstrated, but in which each task is modeled as a mixture of several, simpler, primitive tasks. We present an algorithm based on an al-ternate gradient descent to learn simultaneously a dictionary of primitive tasks (in the form of reward functions) and their combination into an ap-proximation of the task underlying observed behavior. We illustrate how this approach enables efficient re-use of knowledge from previous demon-strations. Namely knowledge on tasks that were previously observed by the learner is used to improve the learning of a new composite behavior, thus achieving transfer of knowledge between tasks

Learning Semantic Components from Subsymbolic Multimodal Perception

International audiencePerceptual systems often include sensors from several modalities. However, existing robots do not yet sufficiently discover patterns that are spread over the flow of multimodal data they receive. In this paper we present a framework that learns a dictionary of words from full spoken utterances, together with a set of gestures from human demonstrations and the semantic connection between words and gestures. We explain how to use a nonnegative matrix factorization algorithm to learn a dictionary of components that represent meaningful elements present in the multimodal perception, without providing the system with a symbolic representation of the semantics. We illustrate this framework by showing how a learner discovers word-like components from observation of gestures made by a human together with spoken descriptions of the gestures, and how it captures the semantic association between the two

Unsupervised learning of simultaneous motor primitives through imitation

We propose to build a system able to learn motor primitives from simultaneous demonstrations of several such primitives. Our approach is based on compact local descriptors of the motor trajectory similar to those used to learn acoustic words amongst sentences or objects inside visual scenes