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The moderating roles of consumer-company identification and perceived fit between corporate associations and crisis type in the effectiveness of crisis communication strategies
Many of todayâs corporations face crisis events. A few well-known examples include Tylenolâs poisoned capsules, Odwallaâs contaminated juice, Mattelâs defective toys, Toyotaâs product-recalls, and more recently, BPâs Gulf oil spill. Whether or not it is at fault, a corporation must sometimes overcome negative public sentiment resulting from a crisis and taking a proper action, especially an appropriate response strategy, is necessary for the company to overcome such a predicament. This study, therefore, seeks to systematically investigate how two critical constructs â consumer-company identification and corporate association-crisis type congruence â in a corporate crisis context conjointly determine the effectiveness of crisis communication strategies by the corporation. More specifically, this study examines how varying degrees of consumer identification with a company affect the effectiveness of one of the common crisis response strategies, excuse. It also seeks to discover how the consumer-company identification moderates the effectiveness of different types of crisis communication strategies (compensation vs. apology). In addition, the study attempts to determine how perceived fit between corporate associations and crisis type moderates the effects of crisis communication strategies (excuse vs. apology). The studyâs results show that the excuse strategy is more effective for consumers strongly identified with a company than for those weakly identified with it. However, the level of consumer-company identification is not found to moderate the effectiveness of the compensation and apology strategies. The study also found a significant moderating role of perceived fit between corporate associations and crisis type in determining the effectiveness of the excuse and apology strategies. The findings suggest that when a company faces a crisis that violates its core corporate associations, the apology strategy is more effective; a company involved in a crisis irrelevant to its corporate associations is better served by the excuse strategy.Advertisin
Cug2 is essential for normal mitotic control and CNS development in zebrafish.
Background:
We recently identified a novel oncogene, Cancer-upregulated gene 2 (CUG2), which is essential for kinetochore formation and promotes tumorigenesis in mammalian cells. However, the in vivo function of CUG2 has not been studied in animal models.
Results:
To study the function of CUG2 in vivo, we isolated a zebrafish homologue that is expressed specifically in the proliferating cells of the central nervous system (CNS). Morpholino-mediated knockdown of cug2 resulted in apoptosis throughout the CNS and the development of neurodegenerative phenotypes. In addition, cug2-deficient embryos contained mitotically arrested cells displaying abnormal spindle formation and chromosome misalignment in the neural plate.
Conclusions:
Therefore, our findings suggest that Cug2 is required for normal mitosis during early neurogenesis and has functions in neuronal cell maintenance, thus demonstrating that the cug2 deficient embryos may provide a model system for human neurodegenerative disorders
Synchronous double primary malignant tumor of the gallbladder and liver: a case report
We report a case of synchronous double primary tumor of gallbladder and liver. A 63-year-old male was admitted to the hospital complaining of abdominal discomfort. Enhanced computed tomography of the abdomen showed acute cholecystitis with tiny gallbladder stones and a 2.2 cm size enhanced nodule in the left lobe of the liver. Under the impression of acute cholecystitis with gall bladder stones and hepatocellular carcinoma of the left Liver, the patient underwent a laparotomy. At laparotomy, a mass was palpated on the surface of the neck portion of the gall bladder. Intraoperative frozen diagnosis revealed adenocarcinoma of the gall bladder. The patient was diagnosed as having gall bladder cancer and hepatocellular carcinoma, so extended cholecystectomy with dissection of regional lymph nodes and left hemihepatectomy were performed. Histological examination revealed moderated differentiated adenocarcinoma of gallbladder and hepatocellular carcinoma of liver. To our knowledge, the simultaneous occurrence of primary malignant tumor of the gallbladder and liver has never been published before. The patient is doing well with no evidence of recurrence 17 months after surgery
A homozygous Keap1-knockout human embryonic stem cell line generated using CRISPR/Cas9 mediates gene targeting
Kelch-like ECH-associated protein 1 (keap1) is a cysteine-rich protein that interacts with transcription factor Nrf2 in a redox-sensitive manner, leading to the degradation of Nrf2 (Kim et al., 2014a). Disruption of Keap1 results in the induction of Nrf2-related signaling pathways involving the expression of a set of anti-oxidant and anti-inflammatory genes. We generated biallelic mutants of the Keap1 gene using a CRISPR-Cas9 genome editing method in the H9 human embryonic stem cell (hESC). The Keap1 homozygous-knockout H9 cell line retained normal morphology, gene expression, and in vivo differentiation potential. (C) 2016 The Author(s). Published by Elsevier B.V.
BUTERFLI D3.19 Report
D3.19 Report on the experimental results on transition delay in the âJujuâ TRIN1 wind tunnel by the VR DBD actuators based on wall-normal jet
Hide-and-Tell: Learning to Bridge Photo Streams for Visual Storytelling
Visual storytelling is a task of creating a short story based on photo
streams. Unlike existing visual captioning, storytelling aims to contain not
only factual descriptions, but also human-like narration and semantics.
However, the VIST dataset consists only of a small, fixed number of photos per
story. Therefore, the main challenge of visual storytelling is to fill in the
visual gap between photos with narrative and imaginative story. In this paper,
we propose to explicitly learn to imagine a storyline that bridges the visual
gap. During training, one or more photos is randomly omitted from the input
stack, and we train the network to produce a full plausible story even with
missing photo(s). Furthermore, we propose for visual storytelling a
hide-and-tell model, which is designed to learn non-local relations across the
photo streams and to refine and improve conventional RNN-based models. In
experiments, we show that our scheme of hide-and-tell, and the network design
are indeed effective at storytelling, and that our model outperforms previous
state-of-the-art methods in automatic metrics. Finally, we qualitatively show
the learned ability to interpolate storyline over visual gaps.Comment: AAAI 2020 pape
Discriminative Feature Learning for Unsupervised Video Summarization
In this paper, we address the problem of unsupervised video summarization
that automatically extracts key-shots from an input video. Specifically, we
tackle two critical issues based on our empirical observations: (i) Ineffective
feature learning due to flat distributions of output importance scores for each
frame, and (ii) training difficulty when dealing with long-length video inputs.
To alleviate the first problem, we propose a simple yet effective
regularization loss term called variance loss. The proposed variance loss
allows a network to predict output scores for each frame with high discrepancy
which enables effective feature learning and significantly improves model
performance. For the second problem, we design a novel two-stream network named
Chunk and Stride Network (CSNet) that utilizes local (chunk) and global
(stride) temporal view on the video features. Our CSNet gives better
summarization results for long-length videos compared to the existing methods.
In addition, we introduce an attention mechanism to handle the dynamic
information in videos. We demonstrate the effectiveness of the proposed methods
by conducting extensive ablation studies and show that our final model achieves
new state-of-the-art results on two benchmark datasets.Comment: Accepted to AAAI 2019 !!
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