Social Media and the Mediating Role of Perceived Authenticity in Covert Celebrity Endorsement: Influencing Factors

This thesis analyses the factors that influence the celebrity endorser’s perceived authenticity and its impact on the promoted brand in covert social media marketing. To examine consumer behaviour, the Persuasion Knowledge Model and Attribution Theory were integrated, and a theoretical framework was then developed. In total, 653 social media users were recruited to participate in the research, and structural equation modelling was conducted to test the proposed model. The results confirm that (1) activated persuasion knowledge negatively influences celebrity endorser’s perceived authenticity in covert social media marketing; (2) celebrity-brand congruity does not have a significant impact on the endorser’s perceived authenticity; (3) celebrity’s expertise positively influences the celebrity endorser’s perceived authenticity when endorsing products related to his or her area of expertise; (4) the celebrity’s perceived attractiveness has a positive impact on the celebrity’s perceived authenticity when endorsing attractiveness enhancing products covertly in social media; and (5) perceived authenticity of a celebrity endorser positively influences brand attitudes and, consequently, behavioural intentions. Both theoretical and managerial implications are drawn, suggesting directions for future studies

An Adaptive Neuro-Fuzzy Inference System Based Approach to Real Estate Property Assessment

This paper describes a first effort to design and implement an adaptive neuro-fuzzy inference system based approach to estimate prices for residential properties. The data set consists of historic sales of homes in a market in Midwest USA and it contains parameters describing typical residential property features and the actual sale price. The study explores the use of fuzzy inference systems to assess real estate property values and the use of neural networks in creating and fine tuning the fuzzy rules used in the fuzzy inference system. The results are compared with those obtained using a traditional multiple regression model. The paper also describes possible future research in this area.

Auxiliary medical practices: a guide to students who fail to gain admission to medical school.

Thesis (M.A.)--Boston UniversityIt is the purpose of this thesis to analyze the various auxiliary medical occupations which are open to students who have failed to gain admission to medical school. An attempt has been made to evaluate each occupation in the light of the following criteria: a) the job, b) qualifications, c) employment outlook, d) the worker, e) earnings, hours, working conditions. [TRUNCATED

Analysis of the Early-time Optical Spectra of SN 2011fe in M101

The nearby Type Ia supernova (SN Ia) SN 2011fe in M101 (cz = 241 km s^(–1)) provides a unique opportunity to study the early evolution of a "normal" SN Ia, its compositional structure, and its elusive progenitor system. We present 18 high signal-to-noise spectra of SN 2011fe during its first month beginning 1.2 days post-explosion and with an average cadence of 1.8 days. This gives a clear picture of how various line-forming species are distributed within the outer layers of the ejecta, including that of unburned material (C+O). We follow the evolution of C II absorption features until they diminish near maximum light, showing overlapping regions of burned and unburned material between ejection velocities of 10,000 and 16,000 km s^(–1). This supports the notion that incomplete burning, in addition to progenitor scenarios, is a relevant source of spectroscopic diversity among SNe Ia. The observed evolution of the highly Doppler-shifted O I λ7774 absorption features detected within 5 days post-explosion indicates the presence of O I with expansion velocities from 11,500 to 21,000 km s^(–1). The fact that some O I is present above C II suggests that SN 2011fe may have had an appreciable amount of unburned oxygen within the outer layers of the ejecta

A Model for Investigating Internal Control Weaknesses

Scandals in corporate finance in the early 2000s and subsequent policy changes led corporate executives to adopt a more risk-based approach in corporate governance. Therefore, identification and assessment of risks became extremely important. Risk assessment poses a particular challenge for auditors due to the highly complex structure and processes of internal control systems. Extant research in this area mostly focused on probabilistic models and expert systems that capture and model heuristic knowledge. However, evidence suggests that knowledge of the structure of the internal control system is also essential. There is relatively little research that focuses on the modeling of the structural aspects of financial processes and their internal control systems as a means of helping corporate executives and auditors perform their respective tasks of risk management and assessment. This article proposes an approach to risk management and assessment in internal control systems that models the structure and financial processes of an internal control system. The model uses a directed graph to represent the various elements in an internal control system, such as financial statement assertions, control activities, financial processes, and the causal relationships that exist among these elements. The article demonstrates the usefulness of the model by presenting and discussing algorithms based on this model to help corporate executives manage risk and to help internal and external auditors assess risk, for designing substantive testing and for tracing sources of errors

Effects of Noise in a Cortical Neural Model

Recently Segev et al. (Phys. Rev. E 64,2001, Phys.Rev.Let. 88, 2002) made long-term observations of spontaneous activity of in-vitro cortical networks, which differ from predictions of current models in many features. In this paper we generalize the EI cortical model introduced in a previous paper (S.Scarpetta et al. Neural Comput. 14, 2002), including intrinsic white noise and analyzing effects of noise on the spontaneous activity of the nonlinear system, in order to account for the experimental results of Segev et al.. Analytically we can distinguish different regimes of activity, depending from the model parameters. Using analytical results as a guide line, we perform simulations of the nonlinear stochastic model in two different regimes, B and C. The Power Spectrum Density (PSD) of the activity and the Inter-Event-Interval (IEI) distributions are computed, and compared with experimental results. In regime B the network shows stochastic resonance phenomena and noise induces aperiodic collective synchronous oscillations that mimic experimental observations at 0.5 mM Ca concentration. In regime C the model shows spontaneous synchronous periodic activity that mimic activity observed at 1 mM Ca concentration and the PSD shows two peaks at the 1st and 2nd harmonics in agreement with experiments at 1 mM Ca. Moreover (due to intrinsic noise and nonlinear activation function effects) the PSD shows a broad band peak at low frequency. This feature, observed experimentally, does not find explanation in the previous models. Besides we identify parametric changes (namely increase of noise or decreasing of excitatory connections) that reproduces the fading of periodicity found experimentally at long times, and we identify a way to discriminate between those two possible effects measuring experimentally the low frequency PSD.Comment: 25 pages, 10 figures, to appear in Phys. Rev.

Topological model of soap froth evolution with deterministic T2-processes

We introduce a topological model for the evolution of 2d soap froth. The topological rearrangements (T2 processes) are deterministic (unlike the standard stochastic model): the final topology depends on the areas of the neighboring cells. The new model gives agreement with experiments in the transient regime, where the previous models failed qualitatively, and also improves agreement in the scaling state.Comment: latex, 12 pages, 2 figure

An Innovative Clustering Approach to Market Segmentation for Improved Price Prediction

A main obstacle to accurate prediction is often the heterogeneous nature of data. Existing studies have pointed to data clustering as a potential solution to reduce heterogeneity, and therefore increase prediction accuracy. This paper describes an innovative clustering approach based on a novel adaptation of the Fuzzy C-Means algorithm and its application to market segmentation in real estate. Over 15,000 actual home sales transactions were used to evaluate our approach. The test results demonstrate that the accuracy in price prediction shows notable improvement for some clustered market segments. In comparison with existing methods our approach is simple to implement. It does not require additional collection of data or costly development of models to incorporate social-economic factors on segmentation. Finally our approach is not market specific and can be easily applied across different housing markets