Month: January 2013

Competitive Pattern-Based Strategies under Complexity: The Case of Turkish Managers

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This paper aims to augment current Enterprise Architecture (EA) frameworks to become pattern-based. The main motivation behind pattern-based EA is the support for strategic decisions based on the patterns prioritized in a country or industry. Thus, to validate the need for pattern-based EA, it is essential to show how different patterns gain priority under different contexts, such as industries. To this end, this chapter also reveals the value of alternative managerial strategies across different industries and business functions in a specific market, namely Turkey. Value perceptions for alternative managerial strategies were collected via survey, and the values for strategies were analyzed through the rigorous application of statistical techniques. Then, evidence was searched and obtained from business literature that support or refute the statistically-supported hypothesis. The results obtained through statistical analysis are typically confirmed with reports of real world cases in the business literature. Results suggest that Turkish firms differ significantly in the way they value different managerial strategies. There also exist differences based on industries and business functions. Our study provides guidelines to managers in Turkey, an emerging country, on which strategies are valued most in their industries. This way, managers can have a better understanding of their competitors and business environment, and can develop the appropriate pattern-based EA to cope with complexity and succeed in the market.

Ertek, G., Kasap, N., Tokman, S., Bilgin, Ö., İnanoğlu, M. (2013) “Competitive Pattern-Based Strategies under Complexity: The Case of Turkish Managers”, In P. Saha (ed.) A Systemic Perspective to Managing Complexity with Enterprise Architecture. ISBN-10: 1466645180.

Note: This is the final draft version of this paper. Please cite this paper (or this final draft) as above.

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Competitive Pattern-Based Strategies under Complexity: The Case of Turkish Managers

 

 

Text Mining with RapidMiner

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The goal of this chapter is to introduce the text mining capabilities of RAPIDMINER through a use case. The use case involves mining reviews for hotels at TripAdvisor.com, a popular web portal. We will be demonstrating basic text mining in RAPIDMINER using the text mining extension. We will present two different RAPIDMINER processes, namely Process01 andProcess02, which respectively describe how text mining can be combined with association mining and cluster modeling. While it is possible to construct each of these processes from scratch by inserting the appropriate operators into the process view, we will instead import these two processes readily from existing model files. Throughout the chapter, we will at times deliberately instruct the reader to take erroneous steps that result in undesired outcomes. We believe that this is a very realistic way of learning to use RAPIDMINER, since in practice, the modeling process frequently involves such steps that are later corrected.

Ertek, G., Tapucu, D., and Arın, I., 2013. Text Mining with RapidMiner. In: Markus Hofmann, Ralf Klinkenberg (Eds.) RapidMiner: Data Mining Use Cases and Business Analytics Applications. Chapman & Hall/CRC Data Mining and Knowledge Discovery Series. Chapman and Hall/CRC.

Note: This is the final draft version of this paper. Please cite this paper (or this final draft) as above.

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Text Mining With Rapidminer

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TripAdvisor Dataset

Dr. Gürdal Ertek recommends the following related books: