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The purpose of this paper is to explore the interplay of institutional quality and market potential factors on the agglomeration of foreign fast-food franchises in major cities in Central America.

The authors approached the research question through a regression analysis of the main fast-food chains operating in the 41 largest cities in Central America. The exploratory analysis in this paper attempted to discover the statistical relationship between institutional quality and market potential factors on the agglomeration of fast-food chains in specific cities. The paper also examined the spatial distribution of fast-food units in selected cities to try to discover specific patterns on the selection of specific locations within each city.

The findings of this paper suggest that population size and institutional quality in terms of regulatory efficiency were the two most significant predictors of fast-food chains agglomerations in selected Central American cities. The authors also found a negative interaction between market potential and institutional quality on agglomeration of fast-food restaurants, whereby a relatively weak institutional environment might deter investors, even if initially a market offers moderate potential. Finally, they found specific geographic patterns for the chosen locations of fast-food places that signal to a preference for urban locations with easy access to main thoroughfares, high commercial traffic and more affluence.

The small sample size was a major constraint. Moreover, population size as a measure of market potential was available for all cities, but other city-level indicators were only available for a small number of cities. The preliminary results aligned with the predictions in this paper, yet the generalizability of the findings of this paper is limited by the sampling and measurement issues noted above. Finally, the paper did not include all fast-food chains in the cities examined, and inclusion of more foreign and domestic chains should be considered in future studies.

Local governments should consider the factors that impact franchise chains’ decisions to enter a market and the specific locations in which they choose to locate their units. Improving the quality of local institutions could be instrumental in attracting investment.

Very few studies have focused on Central America as a recipient of investment by fast-food chains. The region is less than attractive in terms of both market potential and risk. Yet fast-food franchises have continued to grow over the past two decades, making the examination of their investment decisions worth studying. The inclusion of institutional quality at the city level is an additional contribution of this paper. This paper furthers our understanding of the factors that drive investment decisions of global franchisors in regions with low to medium market potential and medium to high levels of institutional risk.

The best algorithm that was implemented on this Brazilian dataset was artificial immune system (AIS) algorithm. But the time and cost of this algorithm are high. Using asexual reproduction optimization (ARO) algorithm, the authors achieved better results in less time. So the authors achieved less cost in a shorter time. Their framework addressed the problems such as high costs and training time in credit card fraud detection. This simple and effective approach has achieved better results than the best techniques implemented on our dataset so far. The purpose of this paper is to detect credit card fraud using ARO.

In this paper, the authors used ARO algorithm to classify the bank transactions into fraud and legitimate. ARO is taken from asexual reproduction. Asexual reproduction refers to a kind of production in which one parent produces offspring identical to herself. In ARO algorithm, an individual is shown by a vector of variables. Each variable is considered as a chromosome. A binary string represents a chromosome consisted of genes. It is supposed that every generated answer exists in the environment, and because of limited resources, only the best solution can remain alive. The algorithm starts with a random individual in the answer scope. This parent reproduces the offspring named bud. Either the parent or the offspring can survive. In this competition, the one which outperforms in fitness function remains alive. If the offspring has suitable performance, it will be the next parent, and the current parent becomes obsolete. Otherwise, the offspring perishes, and the present parent survives. The algorithm recurs until the stop condition occurs.

Results showed that ARO had increased the AUC (i.e. area under a receiver operating characteristic (ROC) curve), sensitivity, precision, specificity and accuracy by 13%, 25%, 56%, 3% and 3%, in comparison with AIS, respectively. The authors achieved a high precision value indicating that if ARO detects a record as a fraud, with a high probability, it is a fraud one. Supporting a real-time fraud detection system is another vital issue. ARO outperforms AIS not only in the mentioned criteria, but also decreases the training time by 75% in comparison with the AIS, which is a significant figure.

In this paper, the authors implemented the ARO in credit card fraud detection. The authors compared the results with those of the AIS, which was one of the best methods ever implemented on the benchmark dataset. The chief focus of the fraud detection studies is finding the algorithms that can detect legal transactions from the fraudulent ones with high detection accuracy in the shortest time and at a low cost. That ARO meets all these demands.