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Strategic planning (SP) enables enterprises to plan management and operations activities efficiently in the medium and large term. During its implementation, many processes and methods are manually applied and may be time consuming. The purpose of this paper is to introduce an automatic method to define strategic plans by using text mining (TM) algorithms within a generic SP model especially suited for small- and medium-sized enterprises (SMEs).

Textual feedbacks were collected through a SWOT matrix during the implementation of a SP model in a company dedicated to the local distribution of food. A four-step TM process (performing acquisition, pre-processing, processing, and validation tasks) is applied via a framework developed under the cloud computer paradigm in order to determine the strategic plans.

The use of categorization and clustering algorithms show that unstructured textual information produced during the SP can be efficiently processed and capitalized. Collected evidence reveals the potential to enhance the strategic plans creation with less effort and time, improving the relevance, and producing new technological resources accessible to SMEs.

An innovative framework especially suited for the SMEs based on the synergy assumption of the coupling between TM and a generic SP model.

From the perspective of business history, this chapter presents an overview of the development of the tourism sector in South America, placing special emphasis on the Peruvian case. The chapter explores various topics related to the tourism chain, such as hotel networks, the role of the state, tour operators, micro- and small enterprises, linkages between tourism and sustainability, the formation of clusters in the sector, and interactions between different entrepreneurs in the chain. Special emphasis is placed on the Peruvian case, especially when it comes to discussing the role of micro- and small enterprises in the sector.

This paper tests the impact of the financial structure of banks on the bank lending channel of monetary policy transmission in Colombia.

We use a monthly panel of 51 commercial banks for the period 1996:4–2014:8.

An increase in the monetary policy interest rate significantly reduces bank loan growth. The magnitude of this effect depends on banks’ financial structure. Additionally, we identify an asymmetric effect in which the bank lending channel is stronger in monetary contractions than during expansions. We show that this behavior is due to the heterogeneous response of banks with different levels of solvency. This finding has important implications for the design and implementation of monetary policy and coordination of central bank’s policy with key economic agents.

The fact that the BLC is stronger in times of monetary contraction is quite interesting for central banking, as it shows that monetary policy transmission is harder during macroeconomic downturns. When investment plans are depressed, monetary stimulus may prove insufficient to reactivate credit demand. This has proven to be true in advanced economies after a strong recession and our results suggest that is also true in emerging market economies for economic downturns in general. Central banks may have to provide stronger shocks to reactivate private credit when the economy is facing a slow economic recovery.

Our findings point out that an increase in the monetary policy interest rate significantly reduces bank loan growth. However, the magnitude of this effect critically depends on two aspects. First, bank heterogeneity matters. Particularly, the loan supply of better capitalized banks is less sensitive to monetary policy shocks. Second, the response of credit supply to shifts in short-term interest rates critically depends on the monetary policy stance. The BLC is stronger in times of monetary contraction than during expansions. Moreover, we show that this asymmetric behavior is due to the heterogeneous response of banks with different levels of solvency to the monetary policy stance.