The Effects of Leadership in Corporate Social Advocacy on Positive Employee Outcomes

Despite the growing attention to corporate social advocacy in the extant literature, little empirical research has examined the effects of corporate social advocacy in the context of employees. The purpose of this study was to delve into the impact of leadership in corporate social advocacy (CSA) on positive employee outcomes, using data from an online survey of full-time employees working in various corporations in the United States. Controlling for the participants’ tenure, demographic information, and company size, this study found that leaders’ facilitation of corporate social advocacy strongly influenced employee advocacy for their organizations, which was also significantly mediated by employees’ personal identification with the leader and by employee–organization relationship (EOR) quality

Design Framework for Multimodal Reading Experience in Cross-Platform Computing Devices - Focus on a Digital Bible

With the emergence of cloud computing, a diverse range of content can be read across platforms. Here, we extend our previous research (Kang and Eune 2011), where we proposed three aspects to reinforce seamless reading across different platforms: coherence, immersion, and multimodality. The reading process is classified as pre-reading, during the reading, and post-reading, where each step requires different goals, functions, and types of reading. An appropriate platform among smart phones, Tablet PCs, and PCsis chosen to play a main role at each step. In this research, we verify the design framework of our previous study by analyzing a digital Bible, Youversion, across platforms. In a comparison of the digital Bible and a digital newspaper,which wasthe case from the previous research, we find common characteristics of multimodalreading in each platform. This research contributes to finding a brand new content market for the cloud eco-system while offering a way to enjoy the contentwith an enhanced sense of immersion when analog and digital content types are brought together in a cross-platform environment.OAIID:oai:osos.snu.ac.kr:snu2012-01/102/0000025799/3SEQ:3PERF_CD:SNU2012-01EVAL_ITEM_CD:102USER_ID:0000025799ADJUST_YN:NEMP_ID:A075458DEPT_CD:611CITE_RATE:0FILENAME:____2013_DRS[1].pdfDEPT_NM:디자인학부EMAIL:[email protected]:

Exploring the Interrelationship and Roles of Employee–Organization Relationship Outcomes between Symmetrical Internal Communication and Employee Job Engagement

Purpose This paper aims to investigate how employee–organization relationship (EOR) outcomes – types and qualities – are interrelated and how employees\u27 perceptions of types (exchange and communal EORs) and qualities (trust, satisfaction, commitment, and control mutuality) play a role in their evaluations of symmetrical internal communication (SIC) and employee job engagement (EJE). Design/methodology/approach This study conducted an online survey of full-time employees (N = 804) from major US industries. This study performed a confirmatory factor analysis to check the validity and reliability of the measurement model using latent variables and then conducted structural equation modeling. Findings The findings demonstrate that employees\u27 perceptions of both exchange and communal EORs are associated with each of the four EOR qualities. The results also show that only communal EORs have a significant relationship with perceived SIC and that employees\u27 perceptions about one of the EOR quality indicator, satisfaction with an organization, has a significant association with their perceived EJE. Originality/value This study contributes to relationship management theory within the internal context by examining the interrelationship between each of the EOR types and qualities that are perceived by employees. This paper also suggests the practical importance of developing not only communal but also exchange EORs to enhance EOR quality. Additionally, the results imply that SIC programs could help to enhance employees\u27 perceptions of communal EORs and employees could be engaged in their workplace when they are satisfied with their organizations

Influence of Job Stress, Fatigue, Happiness on Sleep Quality of Shift Work Nurses

Purpose: This descriptive study aimed to identify relationships among shift work nurses’ job stress, fatigue, happiness, and sleep quality and the factors causing decrease in sleep quality to establish fundamental data for effective intervention strategies. Methods: The data were collected using a structured questionnaire from June 11 to 19, 2018, and study participants were 198 nurses working rotating shifts in a university hospital. Results: The quality of sleep showed a mean of 15.77±5.30 and was varied depending on the number of night shifts (p=.001), health status (p=.008), and the number of discomforts at work (p\u3c.001). Sleep quality also showed a significant relationship with job stress (r=.15, p=.040), fatigue (r=.35, p\u3c.001), and happiness (r=-.24, p=.001). Multiple regression analysis (Adj R2=.275) indicated that factors affecting the quality of sleep included the number of discomforts (β=.31, p\u3c.001), fatigue (β=.22, p=.002), and the number of night shifts (β=.19, p=.002). Conclusion: This study suggests that individuals, hospitals, and social systems need to coordinate the number of night shifts and improve discomfort and fatigue by considering factors influencing rotating shift work nurses’ sleep quality

An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery

Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neural network model for AC and cyanobacteria estimation, without considering the physical formulation. The stacked autoencoder (SAE) network was adopted for the feature extraction and dimensionality reduction of hyperspectral imagery. The artificial neural network (ANN) and support vector regression (SVR) were sequentially applied to achieve AC and estimate cyanobacteria concentrations (i.e., SAE-ANN and SAE-SVR). Further, the ANN and SVR models without SAE were compared with SAE-ANN and SAE-SVR models for the performance evaluations. In terms of AC performance, both SAE-ANN and SAE-SVR displayed reasonable accuracy with the Nash???Sutcliffe efficiency (NSE) > 0.7. For PC and Chl-a estimation, the SAE-ANN model showed the best performance, by yielding NSE values > 0.79 and > 0.77, respectively. SAE, with fine tuning operators, improved the accuracy of the original ANN and SVR estimations, in terms of both AC and cyanobacteria estimation. This is primarily attributed to the high-level feature extraction of SAE, which can represent the spatial features of cyanobacteria. Therefore, this study demonstrated that the deep neural network has a strong potential to realize an integrative remote sensing application

Immunological Conversion of Solid Tumours Using a Bispecific Nanobioconjugate for Cancer Immunotherapy

Solid tumours display a limited response to immunotherapies. By contrast, haematological malignancies exhibit significantly higher response rates to immunotherapies as compared with solid tumours. Among several microenvironmental and biological disparities, the differential expression of unique immune regulatory molecules contributes significantly to the interaction of blood cancer cells with immune cells. The self-ligand receptor of the signalling lymphocytic activation molecule family member 7 (SLAMF7), a molecule that is critical in promoting the body\u27s innate immune cells to detect and engulf cancer cells, is expressed nearly exclusively on the cell surface of haematologic tumours, but not on solid ones. Here we show that a bispecific nanobioconjugate that enables the decoration of SLAMF7 on the surface of solid tumours induces robust phagocytosis and activates the phagocyte cyclic guanosine monophosphate-adenosine monophosphate synthase-stimulator of interferon genes (cGAS-STING) pathway, sensitizing the tumours to immune checkpoint blockade. Our findings support an immunological conversion strategy that uses nano-adjuvants to improve the effectiveness of immunotherapies for solid tumours

ChimerDB 2.0—a knowledgebase for fusion genes updated

Chromosome translocations and gene fusions are frequent events in the human genome and have been found to cause diverse types of tumor. ChimerDB is a knowledgebase of fusion genes identified from bioinformatics analysis of transcript sequences in the GenBank and various other public resources such as the Sanger cancer genome project (CGP), OMIM, PubMed and the Mitelman’s database. In this updated version, we significantly modified the algorithm of identifying fusion transcripts. Specifically, the new algorithm is more sensitive and has detected 2699 fusion transcripts with high confidence. Furthermore, it can identify interchromosomal translocations as well as the intrachromosomal deletions or inversions of large DNA segments. Importantly, results from the analysis of next-generation sequencing data in the short read archives are incorporated as well. We updated and integrated all contents (GenBank, Sanger CGP, OMIM, PubMed publications and the Mitelman’s database), and the user-interface has been improved to support diverse types of searches and to enhance the user convenience especially in browsing PubMed articles. We also developed a new alignment viewer that should facilitate examining reliability of fusion transcripts and inferring functional significance. We expect ChimerDB 2.0, available at http://ercsb.ewha.ac.kr/fusiongene, to be a valuable tool in identifying biomarkers and drug targets