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

Thrilled or Angry: Consumer Emotions on Black Friday

The purposes of this research are to identify personal (prior experience and expectations) and situational (goal blockage) factors evoking consumer emotions on Black Friday (BF) and examine the role consumer emotions play in influencing consumer evaluation of BF. This study employs an online survey with the manipulation of goal blockage (Doorbuster, Stockout, and Wait in line conditions) on BF. A total of 339 people with prior BF experience participated in the online survey. The findings of this study provide insights into the BF shopping experience, especially the process by which personal and situational factors drive consumer BF experience. This study further shows how goal blockage, that can be managed by retailers, plays an important role in eliciting positive and negative emotion

Speech Enhancement for Virtual Meetings on Cellular Networks

We study speech enhancement using deep learning (DL) for virtual meetings on cellular devices, where transmitted speech has background noise and transmission loss that affects speech quality. Since the Deep Noise Suppression (DNS) Challenge dataset does not contain practical disturbance, we collect a transmitted DNS (t-DNS) dataset using Zoom Meetings over T-Mobile network. We select two baseline models: Demucs and FullSubNet. The Demucs is an end-to-end model that takes time-domain inputs and outputs time-domain denoised speech, and the FullSubNet takes time-frequency-domain inputs and outputs the energy ratio of the target speech in the inputs. The goal of this project is to enhance the speech transmitted over the cellular networks using deep learning models

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

Who Are Social Entrepreneurs? Connecting the Stories of Women in the Global Textile and Apparel Industry

Current definitions of social entrepreneurs appear limited in view, delineating social-entrepreneurs as outside elites with special qualities and their work to be complex and lavish. Definitions of social entrepreneurs fail to capture and illustrate the multitudes and diversity of social entrepreneurship. Thus, social entrepreneurship needs refashioning to address the multiple types of intentions (feasibility and desirability) to act, opportunities, and capacities. The present interpretation lacks a holistic standpoint. Using a scenario of analysis of the textile and apparel industry, it becomes evident that micro-entrepreneurs engage daily in solving the complex problem of poverty, unemployment, exploitation, and other social issues through self-employment. They are by their very nature practicing social entrepreneurship. The purpose of this concept paper is not to dispute current definitions of social entrepreneurs but to help make definitions more holistic, by recognizing the contributions of the multiple types of people and organization who attempt to solve societal concerns

Enhancing Breast Cancer Risk Prediction by Incorporating Prior Images

Recently, deep learning models have shown the potential to predict breast cancer risk and enable targeted screening strategies, but current models do not consider the change in the breast over time. In this paper, we present a new method, PRIME+, for breast cancer risk prediction that leverages prior mammograms using a transformer decoder, outperforming a state-of-the-art risk prediction method that only uses mammograms from a single time point. We validate our approach on a dataset with 16,113 exams and further demonstrate that it effectively captures patterns of changes from prior mammograms, such as changes in breast density, resulting in improved short-term and long-term breast cancer risk prediction. Experimental results show that our model achieves a statistically significant improvement in performance over the state-of-the-art based model, with a C-index increase from 0.68 to 0.73 (p < 0.05) on held-out test sets