The trade credit in the Spanish agrofood industry

Selling on credit is rather frequent in Mediterranean countries. Its generalized use can lead to excessive enlargements of the payment periods and consequently can deteriorate the profitability of firms. In spite of the relevance of this problem there are few empirical researches. This work intends to fill this gap and to shed light on the factors related to the extension of trade credit. In the theoretical and empirical literature, different motives have been proposed to explain this issue: a mechanism to reduce transaction costs, a financial alternative to the bank system and an additional tool to improve commercial activities. To contrast these ideas a panel of 388 firms of the Spanish agrofood industry has been taken, and static and dynamic regression models have been estimated by using robust methods to heteroskedasticity, autocorrelation and endogeneity of the explanatory variables. The results confirm that trade credit receivable is associated with more active firms and with cheaper bank financing. Other factors with positive relationships are short-term bank debts and accounts payable. These findings are consistent with commercial motives, rather than a pure financial view, in the sense that financial distressed producers extend trade credit as a way of promoting their products and in turn increasing their sales.

Generating context-aware recommendations using banking data in a mobile recommender system

The increasing adoption of smartphones by the society has created a new area of research in recommender systems. This new domain is based on using location and context-awareness to provide personalization. This paper describes a model to generate context-aware recommendations for mobile recommender systems using banking data in order to recommend places where the bank customers have previously spent their money. In this work we have used real data provided by a well know Spanish bank. The mobile prototype deployed in the bank Labs environment was evaluated in a survey among 100 users with good results regarding usefulness and effectiveness. The results also showed that test users had a high confidence in a recommender system based on real banking data.