Human resource is the most important investment level economic entity and it is the only able to increase its value over time. The burden of managing human factors is quite high. Both employers and employees are seeking optimal solutions on the type of employment that will bring the greatest benefits.
Promoting entrepreneurship is an essential component to ensure economic development at the national and the regional level. Entrepreneurship in young people may directly stimulate them and positively influence the generations and communities in which it operates.
Successful young entrepreneurs in identifying those aspects hold ideas that will contribute to the success of the business and have availability to conquer niches of business that other entrepreneurs have ignored them or have them watched in disbelief.
Over the past few years, employment of young remains one of the main problems that persist, problem on the development of market economy has boosted it and deepened it, emphasizing the correlation between the supply and demand of labour force as a whole.
In any field of economic, political or social activities, the issue of risk emerges, which implies consequences that cannot be always anticipated or predicted. Lack of knowledge of the risks that may affect the achievement of public entity objectives, of causality factors, and of action measures to materialize them may lead to negative consequences for the entire economic activity.
In this context, identifying and assessing risks (probability of occurrence and impact on activity) are mandatory and determinant actions in the process of knowing the mechanism of action of each risk and implementing appropriate protection strategies. Based on the documentary research, fieldwork and applicable legislation, and empirical research conducted within a public entity, we have concluded that risk management within the public entity is a process that leads to improved performance and governance, even if it is an additional process to existing ones.
Amassoma Ditimi, Keji Sunday and Onyedikachi O. Emma-Ebere
This study empirically investigates the upshot of money supply on inflation in Nigeria using annual time series data spanning from 1970 to 2016. Co-integration and Autoregressive Dynamic Error Correction Model (ADLECM) approach was utilized. The results showed that money supply does not considerably influence inflation both in the long and short run possibly because the country is in recession. The ECM has the correct sign of negative and it is significant meaning that about 21% of the errors are corrected yearly. The Granger causality outcome demonstrates that, there is no causality between money supply and inflation in Nigeria within the study period and vice-versa. The implication of this is often that there are different economic conditions which are key determinant of inflation in Nigeria. The study recommends that the government should diversify the economy, minimize importation by encouraging local production of products and services. The CBN should guarantee an exchange rate policy that is essentially determined by the state of the economy and not by speculators being a net importation economy. Also, the CBN should look inwards into the current interest rate and see how it can be regulated in such a way that will encourage private and foreign investors to be able to invest in the country. This in turn, successively increases income, infrastructure development and economic growth at large.
Drawing upon the resource-based and relational view, this study examines how the three types of IT competencies (i.e., IT objects, IT operations, and IT knowledge) differentially affect firm performance and how such effects are moderated by interorganizational communication (IOC). We test the hypotheses of interest with data collected from 258 firms in China. The results of hierarchical regression analysis reveal that IT operations and IT knowledge significantly improve firm performance, while IT objects are found to be insignificant. In addition, the moderating effect of IOC on the relationship between the three types of IT competencies and firm performance varies across diffenent types of IT competencies. Specifically, IOC positively moderates the relationship between both IT operations and IT knowledge and firm performance. However, the moderating effect of IOC on the relationship between IT objects and firm performance is not significant.
Our motivation for conducting this research is driven by the lack of studies focusing on the acknowledgments sections of published papers. Another motivation is the lack of a study examining the countries and organizations mentioned in the acknowledgments section and their influence—something that cannot be analyzed using a citation or co-authorship relationship. Concentrating on the qualitative aspects of acknowledgments has been limited because of the atypical pattern of the acknowledgment section. Our research aims to identify useful information hidden within the acknowledgment sections of the articles stored in the PubMed Central database and to analyze a map of influence via a country-acknowledgment network. To solve the problems, we use the topic modeling to analyze topics of acknowledgments and conduct a basic network analysis to find the difference in the co-the country network and acknowledgment network. A word-embedding model is used to compare the semantic similarity that exists between the authors and countries extracted from our original dataset. The result of topic modeling suggests that funding has become a critical topic in acknowledgments. The results of network analysis indicate that some large countries work as hubs in terms of both implicitly and explicitly while revealing that some countries such as China do not frequently work with other countries. The word-embedding model built by acknowledgments suggests that the authors frequently referenced in acknowledgments are also likely to be referred to in a similar context. It also implies that the publishing country of a paper has little effect on whether it receives an acknowledgment from any other specific country. Through these results, we conclude that the content in acknowledgments extracted from the papers can be divided into two categories—funding and appreciation. We also find that there is no clear relationship between the publication country and the countries mentioned in the acknowledgment section.
With vast amount of biomedical literature available online, doctors have the benefits of consulting the literature before making clinical decisions, but they are facing the daunting task of finding needles in haystacks. In this situation, it would be of great use to the doctors if an effective clinical decision support system is available to generate accurate queries and return a manageable size of highly useful articles. Existing studies showed the usefulness of patients’ diagnosis information in supporting effective retrieval of relevant literature, but such diagnosis information is often missing in most cases. Furthermore, existing diagnosis prediction systems mainly focus on predicting a small range of diseases with well-formatted features, and it is still a great challenge to perform large-scale automatic diagnosis predictions based on noisy medical records of the patient. In this paper, we propose automatic diagnosis prediction methods for enhancing the retrieval in a clinical decision support system, where the prediction is based on evidences automatically collected from publicly accessible online knowledge bases such as Wikipedia and Semantic MEDLINE Database (SemMedDB). The assumption is that relevant diseases and their corresponding symptoms co-occur more frequently in these knowledge bases. Our methods use Markov Random Field (MRF) model to identify diagnosis candidates in the knowledge bases, and their performance was evaluated using test collections from the Clinical Decision Support (CDS) track in TREC 2014, 2015, and 2016. The results show that our methods can automatically predict diagnosis with about 75% accuracy, and such predictions can significantly improve the related biomedical literatures retrieval. Our methods can generate comparable retrieval results to the state-of-the-art methods, which utilize much more complicated methods and some manually crafted medical knowledge. One possible future work is to apply these methods in collaboration with real doctors.
Notes: a portion of this work was published in iConference 2017 as a poster, which won the best poster award. This paper greatly expands the research scope over that poster.
Book search is far from a solved problem. Complex information needs often go beyond bibliographic facts and cover a combination of different aspects, such as specific genres or plot elements, engagement or novelty. Conventional book metadata may not be sufficient to address these kinds of information needs. In this paper, we present a large-scale empirical comparison of the effectiveness of book metadata elements for searching complex information needs. Using a test collection of over 2 million book records and over 330 real-world book search requests, we perform a highly controlled and in-depth analysis of topical metadata, comparing controlled vocabularies with social tags. Tags perform better overall in this setting, but controlled vocabulary terms provide complementary information, which will improve a search. We analyze potential underlying factors that contribute to search performance, such as the relevance aspect(s) mentioned in a request or the type of book. In addition, we investigate the possible causes of search failure. We conclude that neither tags nor controlled vocabularies are wholly suited to handling the complex information needs in book search, which means that different approaches to describe topical information in books are needed.
Although user information disclosure behavior in the context of social network service(SNS) has been well studied in previous literature, there is a lack of understanding about user information withholding behavior. To fill this research gap, the present study assumes that there might be a three-way interaction among information sensitivity, prevention focus, and interdependent self-construal regarding information withholding. The proposed model is empirically tested through an online survey of 479 users in the context of WeChat, one of the most popular SNSs in China. The results of hierarchical regression analysis verify the three-way interaction that prevention focus positively moderates the relationship between information sensitivity and information withholding, and interdependent self-construal strengthens the moderating effect of prevention focus. Findings in light of theoretical and practical implications as well as limitations of the study are discussed.