The impact of utilitarian product reviews on brand perception

The impact of online reviews on consumer behavior has been increasingly studied as online retail platforms have grown exponentially, and internet research used prior to purchasing products has become more common. However, limited research has examined the impact of those product reviews on the overall perception of the brands selling these products. This study exclusively looked at product reviews for high and low-involvement utilitarian products and analyzed how those reviews affect consumers\u27 perception of a brand. Taking a sample of 301 participants, findings showed that star ratings had a drastic effect on consumers\u27 perception of a brand, associating a low star-rated review with poor brand perception and vice versa. The research also found that low-involvement utilitarian products were highly affected by star ratings, especially concerning purchases of future products from that brand. Those findings suggest that for products associated with a low involvement thought process, consumers are willing to purchase different products from that brand purely from seeing a high-rated star review. However, for products associated with a higher involvement thought process, consumers will conduct more future research before deciding to purchase different products from that brand. Additionally, the findings strengthen the importance of a brand building its image and following, as they showcase how one visual review can deter consumers from wanting to buy not only a specific product but any other products from that brand

Early Detection of Alzheimer’s Disease-Related Pathology Using a Multi-Disease Diagnostic Platform Employing Autoantibodies as Blood-Based Biomarkers

Background: Evidence for the universal presence of IgG autoantibodies in blood and their potential utility for the diagnosis of Alzheimer’s disease (AD) and other neurodegenerative diseases has been extensively demonstrated by our laboratory. The fact that AD-related neuropathological changes in the brain can begin more than a decade before tell-tale symptoms emerge has made it difficult to develop diagnostic tests useful for detecting the earliest stages of AD pathogenesis. Objective: To determine the utility of a panel of autoantibodies for detecting the presence of AD-related pathology along the early AD continuum, including at pre-symptomatic [an average of 4 years before the transition to mild cognitive impairment (MCI)/AD)], prodromal AD (MCI), and mild-moderate AD stages. Methods: A total of 328 serum samples from multiple cohorts, including ADNI subjects with confirmed pre-symptomatic, prodromal, and mild-moderate AD, were screened using Luminex xMAP ® technology to predict the probability of the presence of AD-related pathology. A panel of eight autoantibodies with age as a covariate was evaluated using randomForest and receiver operating characteristic (ROC) curves. Results: Autoantibody biomarkers alone predicted the probability of the presence of AD-related pathology with 81.0% accuracy and an area under the curve (AUC) of 0.84 (95% CI = 0.78–0.91). Inclusion of age as a parameter to the model improved the AUC (0.96; 95% CI = 0.93–0.99) and overall accuracy (93.0%). Conclusion: Blood-based autoantibodies can be used as an accurate, non-invasive, inexpensive, and widely accessible diagnostic screener for detecting AD-related pathology at pre-symptomatic and prodromal AD stages that could aid clinicians in diagnosing AD.</jats:p

Digital Technology Differentiates Graphomotor and Information Processing Speed Patterns of Behavior

Background: Coupling digital technology with traditional neuropsychological test performance allows collection of high-precision metrics that can clarify and/or define underlying constructs related to brain and cognition. Objective: To identify graphomotor and information processing trajectories using a digitally administered version of the Digit Symbol Substitution Test (DSST). Methods: A subset of Long Life Family Study participants (n = 1,594) completed the DSST. Total time to draw each symbol was divided into 'writing' and non-writing or 'thinking' time. Bayesian clustering grouped participants by change in median time over intervals of eight consecutively drawn symbols across the 90 s test. Clusters were characterized based on sociodemographic characteristics, health and physical function data, APOE genotype, and neuropsychological test scores. Results: Clustering revealed four 'thinking' time trajectories, with two clusters showing significant changes within the test. Participants in these clusters obtained lower episodic memory scores but were similar in other health and functional characteristics. Clustering of 'writing' time also revealed four performance trajectories where one cluster of participants showed progressively slower writing time. These participants had weaker grip strength, slower gait speed, and greater perceived physical fatigability, but no differences in cognitive test scores. Conclusion: Digital data identified previously unrecognized patterns of 'writing' and 'thinking' time that cannot be detected without digital technology. These patterns of performance were differentially associated with measures of cognitive and physical function and may constitute specific neurocognitive biomarkers signaling the presence of subtle to mild dysfunction. Such information could inform the selection and timing of in-depth neuropsychological assessments and help target interventions. </p

Digital Technology Differentiates Graphomotor and Information Processing Speed Patterns of Behavior

Background: Coupling digital technology with traditional neuropsychological test performance allows collection of high-precision metrics that can clarify and/or define underlying constructs related to brain and cognition. Objective: To identify graphomotor and information processing trajectories using a digitally administered version of the Digit Symbol Substitution Test (DSST). Methods: A subset of Long Life Family Study participants (n = 1,594) completed the DSST. Total time to draw each symbol was divided into ‘writing’ and non-writing or ‘thinking’ time. Bayesian clustering grouped participants by change in median time over intervals of eight consecutively drawn symbols across the 90 s test. Clusters were characterized based on sociodemographic characteristics, health and physical function data, APOE genotype, and neuropsychological test scores. Results: Clustering revealed four ‘thinking’ time trajectories, with two clusters showing significant changes within the test. Participants in these clusters obtained lower episodic memory scores but were similar in other health and functional characteristics. Clustering of ‘writing’ time also revealed four performance trajectories where one cluster of participants showed progressively slower writing time. These participants had weaker grip strength, slower gait speed, and greater perceived physical fatigability, but no differences in cognitive test scores. Conclusion: Digital data identified previously unrecognized patterns of ‘writing’ and ‘thinking’ time that cannot be detected without digital technology. These patterns of performance were differentially associated with measures of cognitive and physical function and may constitute specific neurocognitive biomarkers signaling the presence of subtle to mild dysfunction. Such information could inform the selection and timing of in-depth neuropsychological assessments and help target interventions.</jats:p