Suchergebnisse

Local state-owned enterprises (SOEs) in China continue to face government interference in their operations. They are influenced both by the government's "grabbing hand" and by its "helping hand." Our study examines how SOE chairmen with connections to government influence their firm's employment policies and the economic consequences of overstaffing. Using a sample of China's listed local state-owned enterprises, we find that the scale of overstaffing in these SOEs is negatively related to the firms' political connections to government. However, this relationship turns positive when the firm's chairman has a government background. Appointing chairmen who have government backgrounds is a mechanism through which the government can intervene in local SOEs and influence firms' staffing decisions. We also find that in compensation for the expenses of overstaffing, local SOEs receive more government subsidies and bank loans. However, the chairmen themselves do not get increased pay or promotion opportunities for supporting overstaffing. Further analysis indicates that whereas the "grabbing hand" of government does harm to a firm's economic performance, the "helping hand" provides only weak positive effects, and such government intervention actually reduces the efficiency of social resource allocation.

ABSTRACTWe collect basic federal tax laws over a 64-year period in order to simulate the historical effective tax rates of median income wage-earning couples. We find that effective income tax rates have decreased over the sample period; however, when payroll taxes are included in our calculations, total tax burdens have increased significantly. Interestingly, this increase in middle-class wage taxation has occurred over an historical period in which total federal tax revenue relative to GDP has remained somewhat constant. This implies that the middle class has borne an increasing relative tax burden in recent years. We hope that our analyses inform both the taxpaying public and policy makers of the historical status of middle-class wage earners.

AbstractThis paper considers the problem of computing E(X−n; X > t) when X is a normal variate having the property that the mean is substantially larger than the standard deviation. An approximation is developed which is determined from the mean, standard deviation, and the cumulative standard normal distribution. Computations comparing the approximate moments with the actual are reported for various values of the relevant parameters. These results are applied to the problem of computing the expected number of shortages in a lead‐time for a single product which exhibits continuous exponential decay.

AbstractAugmented reality (AR) offers a transforming user experience and has been increasingly integrated into entertainment and service contexts. Drawing on experience economy theory and employing a mixed‐methods approach, this research evaluates the antecedents and consequences of four realms of viewer experiences: entertainment, educational, aesthetic, and escapist experiences, in AR‐infused sports videos. A qualitative study of semi‐structured interviews highlights three critical AR features in sports videos (i.e., novelty, vividness, and informativeness) in shaping viewer experiences. Subsequently, a research model is formulated to elucidate the relationships among AR features, four realms of viewer experiences, and behavioral intentions. A quantitative analysis based on survey data reveals that AR features exert varying effects on viewers' entertainment, educational, and aesthetic experiences, yet none significantly affects escapist experience, which is relatively trivial in viewers' overall experience. Entertainment, educational, aesthetic, and escapist experiences have various influences on viewers' intentions to watch again, to recommend, and to pay, except that entertainment and educational experiences do not significantly affect intention to pay. This research stresses the importance of understanding multiple aspects of user experiences in AR research and provides useful guidelines for AR features and viewer experience design in sports videos.

PurposeThis research investigates the impact of learners' non-substantive responses in online course forums, referred to as online listening responses, on e-learning performance. A common type of response in online course forums, online listening responses consist of brief, non-substantive replies/comments (e.g. "agree," "I see," "thank you," "me too") and non-textual inputs (e.g. post-voting, emoticons) in online discussions. Extant literature on online forum participation focuses on learners' active participation with substantive inputs and overlooks online listening responses. This research, by contrast, stresses the value of online listening responses in e-learning and their heterogeneous effects across learner characteristics. It calls for recognition and encouragement from online instructors and online forum designers to support this activity.Design/methodology/approachThe large-scale proprietary dataset comes from a leading MOOC (massive open online courses) platform in China. The dataset includes 68,126 records of learners in five MOOCs during 2014–2018. An ordinary least squares model is used to analyze the data and test the hypotheses.FindingsOnline listening responses in course forums, along with learners' substantive inputs, positively influence learner performance in online courses. The effects are heterogeneous across learner characteristics, being more prominent for early course registrants, learners with full-time jobs and learners with more e-learning experience, but weaker for female learners.Originality/valueThis research distinguishes learners' brief, non-substantive responses (online listening responses) and substantive inputs (online speaking) as two types of active participation in online forums and provides empirical evidence for the importance of online listening responses in e-learning. It contributes to online forum research by advancing the active-passive dichotomy of online forum participation to a nuanced classification of learner behaviors. It also adds to e-learning research by generating insights into the positive and heterogeneous value of learners' online listening responses to e-learning outcomes. Finally, it enriches online listening research by introducing and examining online listening responses, thereby providing a new avenue to probe online discussions and e-learning performance.