The impact of brand communication on brand equity through Facebook

Purpose: The purpose of this study is to fill the gap in the discussion of the ways in which firm-created and user-generated social media brand communication impacts consumer-based brand equity metrics through Facebook. Design/methodology/approach: We evaluated 302 data sets that were generated through a standardized online-survey to investigate the impact of firm-created and user-generated social media brand communication on brand awareness/associations, perceived quality, and brand loyalty across 60 brands within three different industries: non-alcoholic beverages, clothing, and mobile network providers. We applied structural equation modeling techniques (SEM) to investigate the effects of social media brand communication on consumers’ perception of brand equity metrics, as well as in an examination of industry-specific differences. Findings: The results of our empirical studies showed that both firm-created and user-generated social media brand communication influence brand awareness/associations; whereas, user-generated social media brand communication had a positive impact on brand loyalty and perceived brand quality. Additionally, there are significant differences between the industries being investigated. Originality/value: This article is pioneering in that it exposes the effects of two different types of social media brand communication (i.e., firm-created and user-generated social media communication) on consumer-based brand equity metrics, a topic of relevance for both marketers and scholars in the era of social media. Additionally, it differentiates the effects of social media brand communication across industries, which indicate that practitioners should implement social media strategies according to industry specifics to lever consumer-based brand equity metrics

A scale to measure consumer’s engagement with social media brand-related content

The purpose of this study is to fill the gap in the literature concerning to the measurement of consumer’s engagement with social media brand-related content (hereafter, CESBC). We introduce empirical evidence for the development and measurement of CESBC scale. The scale is based on the consumer's online brand-related framework and comprises three dimensions: consumption, contribution, and creation. We used qualitative techniques to prepare an initial list of items and tested and validated the CESBC scale with confirmatory factor analysis (CFA). Results (n = 2252) confirmed the three-factor structure of the CESBC and indicated its good psychometric properties

Free analysis and planar algebras

We study 2-cabled analogs of Voiculescu's trace and free Gibbs states on Jones planar algebras. These states are traces on a tower of graded algebras associated to a Jones planar algebra. Among our results is that, with a suitable definition, finiteness of free Fisher information for planar algebra traces implies that the associated tower of von Neumann algebras consists of factors, and that the standard invariant of the associated inclusion is exactly the original planar algebra. We also give conditions that imply that the associated von Neumann algebras are non-$\Gamma$ non-$L^2$ rigid factors

Determination of the X-ray reflection emissivity profile of 1H 0707-495

When considering the X-ray spectrum resulting from the reflection off the surface of accretion discs of AGN, it is necessary to account for the variation in reflected flux over the disc, i.e. the emissivity profile. This will depend on factors including the location and geometry of the X-ray source and the disc characteristics. We directly obtain the emissivity profile of the disc from the observed spectrum by considering the reflection component as the sum of contributions from successive radii in the disc and fitting to find the relative weightings of these components in a relativistically-broadened emission line. This method has successfully recovered known emissivity profiles from synthetic spectra and is applied to XMM-Newton spectra of the Narrow Line Seyfert 1 galaxy 1H 0707-495. The data imply a twice-broken power law form of the emissivity law with a steep profile in the inner regions of the disc (index 7.8) and then a flat region between 5.6rg and 34.8rg before tending to a constant index of 3.3 over the outer regions of the disc. The form of the observed emissivity profile is consistent with theoretical predictions, thus reinforcing the reflection interpretation.Comment: 9 pages, 10 figures. Accepted for publication in MNRA

User-generated images and its impact on consumer-based brand equity and on purchase intention

Researchers and brand managers have limited knowledge of the effects that different types of user-generated content (UGC) have on consumers’ perception of brands and behavior. In this study we investigated 301 authors of a specific category of UGC, i.e., user-generated images (UGI) on a social networking site to confirm the relationships of four drivers reported in literature (co-creation, empowerment, community, and self-concept) to the consumers involvement with UGI, and consequently how it impacts consumer-based brand equity and purchase intention. When analyzing the data, we applied the structural equation modeling technique with Mplus software. The results of the empirical study showed that from the four drivers, only the perception of community influenced the consumers’ involvement with the creation of brand-related images. Subsequently, the consumers’ involvement with UGI directly affected both brand equity and brand purchase intention

Deep Learning and Statistical Models for Time-Critical Pedestrian Behaviour Prediction

The time it takes for a classifier to make an accurate prediction can be crucial in many behaviour recognition problems. For example, an autonomous vehicle should detect hazardous pedestrian behaviour early enough for it to take appropriate measures. In this context, we compare the switching linear dynamical system (SLDS) and a three-layered bi-directional long short-term memory (LSTM) neural network, which are applied to infer pedestrian behaviour from motion tracks. We show that, though the neural network model achieves an accuracy of 80%, it requires long sequences to achieve this (100 samples or more). The SLDS, has a lower accuracy of 74%, but it achieves this result with short sequences (10 samples). To our knowledge, such a comparison on sequence length has not been considered in the literature before. The results provide a key intuition of the suitability of the models in time-critical problems