Experts vs. Public, Who Knows Better? Factors Affecting High Growth Entrepreneurship in Developed and Developing Countries

This paper uses the Global Entrepreneurship Monitor data of approximately 200,000 surveys conducted on industry experts and general population to examine factors that have a significant impact on high growth Total Early-Stage Entrepreneurial Activity (TEA), with a focus on developed countries with GDP per capita of USD 20,000 or above. The results suggest that expert opinion has a significant positive correlation with high growth TEA in developed countries, while only the public sentiment has a meaningful relationship with high growth TEA in developing countries. Among the specific categories of the survey, access to funding and government regulations and support had the largest impact

Response of U.S.A. owned hotels to the drug-free workplace act of 1988: A Case study

Alcohol and drug abuse has been causing tremendous problems in American workforces everyday. The misuse of alcohol and drug finally lead the government to react to the problems. In March 1989, Drug-Free Workplace Act of 1988 went into effect, establishing the goal of a drug-free workplace. The purpose of this study is to find out how 14 major U.S. owned hotels are responding to Drug-Free Workplace Act of 1988. In order to find out, a 10 item questionnaire was developed and used to interview human resources executives. From the sample of 14 hotels, 11 hotels (78.5%) responded. The survey results showed that 63.6% of hotels have responded directly to the Act of 1988. However, 90% ofhotels have had written policies on alcohol and substance abuse which can be interpreted as maybe some hotels were not aware of the Act of 1988. Also, 90% of hotels provide education programs on alcohol and drug problems, as well as treatment programs (72.7%). Most hotels use outside treatment programs, while only a few hotels (18%) have in-house programs or the combination of in-house and outside programs. For some cases, even if they do not have a treatment program, they at least have alternative programs to assist troubled employees. One interesting factor was that only 27.2% of hotels responding said they required drug- screening tests, however, 64% of hotels required drug tests according to their written policies on alcohol and drug abuse. Therefore, quite a few respondents may not be aware of their own policies, or the policies are not applied as what it is stated. Most hotels (72.7%) see the problem quite seriously. On a scale of 1 (less serious) to 5 (very serious), the mean was 3.5. The remaining hotels did not respond to this question

Unsupervised Text Embedding Space Generation Using Generative Adversarial Networks for Text Synthesis

Generative Adversarial Networks (GAN) is a model for data synthesis, which creates plausible data through the competition of generator and discriminator. Although GAN application to image synthesis is extensively studied, it has inherent limitations to natural language generation. Because natural language is composed of discrete tokens, a generator has difficulty updating its gradient through backpropagation; therefore, most text-GAN studies generate sentences starting with a random token based on a reward system. Thus, the generators of previous studies are pre-trained in an autoregressive way before adversarial training, causing data memorization that synthesized sentences reproduce the training data. In this paper, we synthesize sentences using a framework similar to the original GAN. More specifically, we propose Text Embedding Space Generative Adversarial Networks (TESGAN) which generate continuous text embedding spaces instead of discrete tokens to solve the gradient backpropagation problem. Furthermore, TESGAN conducts unsupervised learning which does not directly refer to the text of the training data to overcome the data memorization issue. By adopting this novel method, TESGAN can synthesize new sentences, showing the potential of unsupervised learning for text synthesis. We expect to see extended research combining Large Language Models with a new perspective of viewing text as an continuous space