Percepciones sobre la responsabilidad social corporativa de las empresas: relación con las actitudes y conductas de sus clientes  <span class="so-article-trans-title" dir="auto"> Translated title: Perceptions of corporate social responsibility: relationship with customers’ attitudes and behaviors </span>

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Abstract

Resumen El presente estudio explora la relación entre las percepciones de una muestra de clientes sobre la Responsabilidad Social Corporativa (RSC) de las empresas del IBEX 35 y sus actitudes y conductas. Las percepciones de los clientes sobre la RSC de las empresas del IBEX 35 se relacionan con el beneficio percibido, con el valor percibido de los servicios o productos de la empresa y con la intención de compra o adquisición futura del producto o servicio. La muestra ha estado formada por 1011 clientes de las empresas cotizantes en el IBEX 35. A partir de los resultados se afirma que las tres dimensiones de la RSC percibida por los clientes son diferentes entre sí, y que se relacionan positiva y significativamente con el beneficio, el valor percibido y la intención de compra de los clientes. Las aportaciones de este estudio permitirán enlazar las actividades sociales de las empresas y el grado de motivación de los consumidores, relaciones éstas que se convierten en esenciales a la hora del diseño de los productos y / o servicios de las empresas.

Translated abstract

Abstract The present study aims to empirically explore the relationship between perceptions of Corporate Social Responsibility (CSR) of IBEX 35 companies' customers, their attitudes and behaviors. The relationship between the customers' perception of CSR of the IBEX 35 companies with their products or services' perceived benefit and perceived value and with customers' buying intention of their products or services is empirically analyzed. The sample includes 1011 clients of the IBEX 35 companies. The results supported that the three dimensions of the CSR perceived by the clients are different from each other, and that they are related positively and significantly with the benefit, the perceived value and the customers' purchase intention. The study contributions will link the Firm's CSR activities with the degree of motivation of consumers, relationships that become essential when designing companies' products and / or services.

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Identifying careless responses in survey data.

Adam Meade, S Bartholomew Craig (2012)

When data are collected via anonymous Internet surveys, particularly under conditions of obligatory participation (such as with student samples), data quality can be a concern. However, little guidance exists in the published literature regarding techniques for detecting careless responses. Previously several potential approaches have been suggested for identifying careless respondents via indices computed from the data, yet almost no prior work has examined the relationships among these indicators or the types of data patterns identified by each. In 2 studies, we examined several methods for identifying careless responses, including (a) special items designed to detect careless response, (b) response consistency indices formed from responses to typical survey items, (c) multivariate outlier analysis, (d) response time, and (e) self-reported diligence. Results indicated that there are two distinct patterns of careless response (random and nonrandom) and that different indices are needed to identify these different response patterns. We also found that approximately 10%-12% of undergraduates completing a lengthy survey for course credit were identified as careless responders. In Study 2, we simulated data with known random response patterns to determine the efficacy of several indicators of careless response. We found that the nature of the data strongly influenced the efficacy of the indices to identify careless responses. Recommendations include using identified rather than anonymous responses, incorporating instructed response items before data collection, as well as computing consistency indices and multivariate outlier analysis to ensure high-quality data.