درخت حوزه‌های تخصصی

Uncertainty and critical conditions have become one of the most important challenges in the world today. In order to achieve superiority in economic competition, the supply chain must be designed in such a way that it has the necessary resilience in critical situations. One of the things that can be considered in this regard is the role of social media in the resilience of the supply chain. Considering the impact that social media has on business processes and considering the rapid and significant advances in information and communication technology, the use of social media in risk situations can be very effective. This research examines the role of social media in the resilience of the digital supply chain with a hybrid approach. The results of this research include 118 open codes, 43 core codes, and 13 main categories. The 13 selected codes of this research are placed in the form of five general categories of causal conditions, contextual conditions, intervening conditions, strategies, and consequences. Causal conditions include 2 selective codes, 8 concepts, and 21 open codes. The contextual conditions include 3 selected codes, 10 concepts, and 25 open codes. Intervening conditions include 2 optional codes, 7 concepts, and 21 open codes. The strategies include 2 selected codes, 7 concepts, and 19 open codes. The results include 4 selected codes, 11 concepts, and 32 open codes.

Considering that today's competitive environment is growing and the movement of organizations towards complexity and dynamism has started, the ability to learn earlier and faster than competitors will be the only relative advantage in the next decade and should The prospects and long-term and short-term contributions of organizations should be examined in a different way, and creativity, innovation along with the use of current information should be an inseparable part of managers' decision-making, and only an organization can claim superiority and success that To use the capabilities, commitment and learning capacity of employees at all levels of the organization in the best way. Based on this, the current research has studied the Board influence on the role of environmental innovation in the success and stock price risk. The statistical population of the research is all the Companies listed in Tehran Stock Exchange to the stock exchange, the period from 2014 to 2019 and the sample is 153 companies. Based on the investigations, the findings showed that environmental innovation has a positive and significant effect on the company's long-term success, and with the Board influence in the company, the role of environmental innovation on the company's long-term success shows a better effect. Also, environmental innovation has a positive and significant effect on the company's success in the short term, and with the Board influence in the company, the role of environmental innovation on the company's short-term success becomes more colorful (intense). Finally, environmental innovation has a negative and significant effect on the Stock price risk in the long run.

This paper describes a series of experiments in using logistic regression machine learning as a method for entity resolution. From these experiments the authors concluded that when a supervised ML algorithm is trained to classify a pair of entity references as linked or not linked pair, the evaluation of the model’s performance should take into account the transitive closure of its pairwise linking decisions, not just the pairwise classifications alone. Part of the problem is that the measures of precision and recall as calculated in data mining classification algorithms such as logistic regression is different from applying these measures to entity resolution (ER) results.. As a classifier, logistic regression precision and recall measure the algorithm’s pairwise decision performance. When applied to ER, precision and recall measure how accurately the set of input references were partitioned into subsets (clusters) referencing the same entity. When applied to datasets containing more than two references, ER is a two-step process. Step One is to classify pairs of records as linked or not linked. Step Two applies transitive closure to these linked pairs to find the maximally connected subsets (clusters) of equivalent references. The precision and recall of the final ER result will generally be different from the precision and recall measures of the pairwise classifier used to power the ER process. The experiments described in the paper were performed using a well-tested set of synthetic customer data for which the correct linking is known. The best F-measure of precision and recall for the final ER result was obtained by substantially increasing the threshold of the logistic regression pairwise classifier.