Relationship lending and the use of trade credit: the role of relational capital and private information

Using a unique sample of Italian manufacturing firms, we investigate the impact of relationship lending on firms’ use of trade credit. We find that firms maintaining close and long-lasting relationships with their main banks are associated with higher amounts of trade credit extended by suppliers. This result is robust to alternative measures of trade credit and relationship lending, and to different estimation techniques. We also analyze the mechanisms driving the association between relationship lending and the use of trade credit. Regression results suggest that the positive link between accounts payable and relationship lending is especially significant for firms that use to provide soft information to their lenders and for companies with greater relational abilities.Plain English Summary The existence of close and long lasting lending relationships positively affects the amount of trade credit manufacturing firms receive from their suppliers. By relying on the Survey on Italian Manufacturing Firms, we show that the positive link between relationship lending and the use of trade credit is driven by two channels: private information and relational capital. In a policy perspective, our findings reveal a need for banking regulation and supervision to encompass banking business models in evaluating banks. The current approach might not be suitable for local banks investing in soft information acquisition and could weaken SMEs’ chances to receive both bank financing and trade credit from suppliers. Moreover, from a managerial point of view, our results uncover the relevance of firms’ ability to create strong relationships with banks, suppliers, and other companies that may help alleviating financial constraints.

Soft and hard information in equity crowdfunding: network effects in the digitalization of entrepreneurial finance

As a digital financial innovation, equity crowdfunding (ECF) allows investors to exploit the complementarity of information provision and network effects in a reduced transaction cost environment. We build on the underlying distinction between soft and hard information and show that ECF platforms create an environment of greater information pooling that benefits from network externalities. We test our hypotheses using a unique proprietary dataset and find that soft information has a greater impact than hard on the likelihood that a financing pitch will be successful. Moreover, the effects of soft information are amplified by the size of the investor network on the platform and network size also positively moderates the effect of information on the amount invested during each pitch. We conclude that ECF platforms can successfully exploit low transaction costs of the digital environment and bring network externalities to bear on investor decisions. Taken together that these increase the supply of funds to entrepreneurs.

Ranking Effectiveness of COVID-19 Tests Using Fuzzy Bipolar Soft Expert Sets

The theory of fuzzy bipolar soft sets is an efficient extension of soft sets for depicting the bipolarity of uncertain fuzzy soft information; however, it is limited to a single expert. The present research article introduces the theory of an innovative hybrid model called the fuzzy bipolar soft expert sets, as a natural extension of two existing models (including fuzzy soft expert sets and fuzzy bipolar soft sets). The proposed model is highly suitable for describing the bipolarity of fuzzy soft information having multiple expert opinions. Some fundamental properties of the developed hybrid model are discussed, including subset, complement, union, intersection, AND operation, and OR operation. The proposed concepts are explained with detailed examples. Moreover, to demonstrate the applicability of our initiated model, an application of the proposed hybrid model is presented along with the developed algorithm to tackle the real-world group decision-making situation, that is, ranking effectiveness of tests in spread analysis of COVID-19. Finally, a comparative analysis of the developed model with some existing mathematical tools such as fuzzy soft expert sets and fuzzy bipolar soft sets is provided to show the cogency and reliability of the initiated model.

Application of Induced Preorderings in Score Function-Based Method for Solving Decision-Making with Interval-Valued Fuzzy Soft Information

Ranking interval-valued fuzzy soft sets is an increasingly important research issue in decision making, and provides support for decision makers in order to select the optimal alternative under an uncertain environment. Currently, there are three interval-valued fuzzy soft set-based decision-making algorithms in the literature. However, these algorithms are not able to overcome the issue of comparable alternatives and, in fact, might be ignored due to the lack of a comprehensive priority approach. In order to provide a partial solution to this problem, we present a group decision-making solution which is based on a preference relationship of interval-valued fuzzy soft information. Further, corresponding to each parameter, two crisp topological spaces, namely, lower topology and upper topology, are introduced based on the interval-valued fuzzy soft topology. Then, using the preorder relation on a topological space, a score function-based ranking system is also defined to design an adjustable multi-steps algorithm. Finally, some illustrative examples are given to compare the effectiveness of the present approach with some existing methods.