scholarly journals Generalized Trust, External Sourcing, and Firm Performance in Economic Downturns

2021 ◽  
Author(s):  
Bart S. Vanneste ◽  
Ranjay Gulati
Keyword(s):  
Machine Learning ◽  
Firm Performance ◽  
Financial Crisis ◽  
Selection Procedure ◽  
Regional Level ◽  
Economic Downturn ◽  
Trust Regions ◽  
Generalized Trust ◽  
Economic Downturns ◽  
Performance Effects

Going beyond prior research that has focused on dyadic, party-specific trust, this study investigates the importance of generalized trust, which is not specific to a counterparty and originates from a broader context. We analyze how generalized trust at the regional level affects the extent to which a firm relies on external suppliers and the performance effects of doing so. Furthermore, we assess how these relationships are impacted by an economic downturn. We exploit differences in generalized trust across 145 regions in 12 European countries and use data on more than a million small- and medium-sized enterprises (SMEs) before and during the Great Financial Crisis (from 2008 to 2010). Control variables are selected via a double-selection procedure based on machine learning. We find that firms in high generalized trust regions, compared with those in low generalized trust regions, source more externally (but do not reduce external sourcing less in an economic downturn) and benefit more from external sourcing during an economic downturn.

scholarly journals Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1993
Author(s):  
Fernando Pérez-Sanz ◽  
Miriam Riquelme-Pérez ◽  
Enrique Martínez-Barba ◽  
Jesús de la Peña-Moral ◽  
Alejandro Salazar Nicolás ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Transplantation ◽  
Learning Algorithms ◽  
Selection Procedure ◽  
Machine Learning Algorithms ◽  
Liver Biopsies ◽  
Convenient Technique ◽  
End Stage ◽  
Macrovesicular Steatosis

Liver transplantation is the only curative treatment option in patients diagnosed with end-stage liver disease. The low availability of organs demands an accurate selection procedure based on histological analysis, in order to evaluate the allograft. This assessment, traditionally carried out by a pathologist, is not exempt from subjectivity. In this sense, new tools based on machine learning and artificial vision are continuously being developed for the analysis of medical images of different typologies. Accordingly, in this work, we develop a computer vision-based application for the fast and automatic objective quantification of macrovesicular steatosis in histopathological liver section slides stained with Sudan stain. For this purpose, digital microscopy images were used to obtain thousands of feature vectors based on the RGB and CIE L*a*b* pixel values. These vectors, under a supervised process, were labelled as fat vacuole or non-fat vacuole, and a set of classifiers based on different algorithms were trained, accordingly. The results obtained showed an overall high accuracy for all classifiers (>0.99) with a sensitivity between 0.844 and 1, together with a specificity >0.99. In relation to their speed when classifying images, KNN and Naïve Bayes were substantially faster than other classification algorithms. Sudan stain is a convenient technique for evaluating ME in pre-transplant liver biopsies, providing reliable contrast and facilitating fast and accurate quantification through the machine learning algorithms tested.

scholarly journals Big data analytics and firm performance: Effects of dynamic capabilities

2017 ◽  
Vol 70 ◽  
pp. 356-365 ◽  
Author(s):  
Samuel Fosso Wamba ◽  
Angappa Gunasekaran ◽  
Shahriar Akter ◽  
Steven Ji-fan Ren ◽  
Rameshwar Dubey ◽  
...  
Keyword(s):  
Big Data ◽  
Firm Performance ◽  
Data Analytics ◽  
Dynamic Capabilities ◽  
Big Data Analytics ◽  
Performance Effects

scholarly journals Credit Growth, the Yield Curve and Financial Crisis Prediction: Evidence from a Machine Learning Approach

2020 ◽  
Author(s):  
Kristina Bluwstein ◽  
Marcus Buckmann ◽  
Andreas Joseph ◽  
Miao Kang ◽  
Sujit Kapadia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Financial Crisis ◽  
Yield Curve ◽  
Learning Approach ◽  
Credit Growth ◽  
Machine Learning Approach

scholarly journals FINANCING OF WORKING CAPITAL REQUIREMENT, FINANCIAL FLEXIBILITY AND SME PERFORMANCE

2016 ◽  
Vol 17 (6) ◽  
pp. 1189-1204 ◽  
Author(s):  
Sonia BAÑOS-CABALLERO ◽  
Pedro J. GARCÍA-TERUEL ◽  
Pedro MARTÍNEZ-SOLANO
Keyword(s):  
Firm Performance ◽  
Financial Crisis ◽  
Method Of Moments ◽  
Generalized Method Of Moments ◽  
Capital Requirement ◽  
Working Capital ◽  
Financial Flexibility ◽  
Method Of Moments Estimator ◽  
Financing Performance ◽  
Financing Strategy

This paper investigates the relation between the financing strategies of working capital requirement and firm performance for the period 1997 to 2012. Using the two-step generalized method of moments estimator, we find that a suitable financing strategy can help firms improve their performance. Moreover, the results indicate that the working capital requirement financing-performance relation changes during a financial crisis. Finally, we also find that this relation depends on a firm’s financial flexibility. The findings are of interest for managers and researchers and show that managers should not only be concerned about investing in working capital requirement but also consider how this investment is to be financed. To the best of our knowledge, this is the first paper to analyse how the financing strategy selected by firms to finance their working capital requirement affects their performance.

Making Moving Happen

2017 ◽  
Author(s):  
Naomi Haynes
Keyword(s):  
Financial Crisis ◽  
Social Life ◽  
Social Ties ◽  
Social World ◽  
Social Resources ◽  
Economic Downturn ◽  
Economic Factors ◽  
A Value ◽  
Credit Associations ◽  
The Social

This chapter explores moving as a value, an animating idea that gives social life on the Copperbelt its shape. It shows how people in Nsofu structure their relationships around the possibility of moving through two types of social ties. Most important here are relationships of patronage, or “dependence,” which connect poorer people to those with greater economic and social resources. People also move through relationships that produce alternating indebtedness, including rotating credit associations and the “committees” that finance expensive events like weddings. In both cases moving requires asymmetry, which makes these ties particularly vulnerable to the leveling forces of economic downturn, and the chapter concludes by describing how events like the 2008–2009 financial crisis have impacted the social world of Nsofu. It is these economic factors, coupled with a cultural emphasis on novelty, that make Pentecostalism especially compelling.

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