THE USE OF NEURO-MARKETING IN MANAGEMENT
Kata Kunci:
marketing, neural networks, genetic algorithm, artificial intelligenceAbstrak
In the present era, in many cases, machines have been replaced by humans, and much of the physical work done by humans in the past is carried out by machines today. Although the power of computers in information storage and retrieval, office automation, and so on is undeniable, there are still some issues that the human has to do work itself. However, in general, machine-related items include systems in which, due to the complicated relationship between components, the human brain is incapable of understanding the mathematics of these relationships. The human brain can, over time, detect system habits, to some extent, by observing the sequence of system behavior and sometimes testing a result obtained by manipulating one of the system components. This learning process leads to the acquisition of experience by observing a variety of examples from the system. In such systems, the brain is not capable of analyzing the internal system and only estimates and predicts the internal system performance due to external behavior. One of the critical research issues in the field of computer science is the implementation of a model similar to the human brain’s internal system for analyzing various systems based on experience. In this regard, neural networks are one of the most active areas of research in contemporary times, attracting many individuals from various scientific fields. The use of neural networks in solving complex application problems has become more and more common these days. In this paper, after a brief introduction of neural networks and genetic algorithms, their relationship and contribution to marketing and business are taken into account.
Referensi
Akimova, Irina (2000). “Development of Market Orientation and Competitiveness of Ukrainian Firms” European Journal of Marketing, Vol. 34, No. 9/10, pp. 1128-1148.
Ambashta Ajitabh, Momaya K. (2002) “Competitive of Firms: Review of Theory, Frameworks, and Models” Singapore Management Review, 26(1).
Barney, J., Wright, M., & Ketchen, D., J. (2001). “The Resource-based View of the Firm: Ten Years after 1991” Journal of Management, 27, Pergamon, pp 625-641.
Bartlett, A., & Ghoshal, S., (1989). “Managing across Borders” Harvard Business School Press, Boston, MA. 1-402.
Bhatnagar, R. and Sohal, A. S. (2005). “Supply chain competitiveness: measuring the impact of location factors, uncertainty and manufacturing practices” Technovation 25, 443–456.
Cardinal, L. B., Alessandri, T.M. and Turner, S.F. (2001) “Knowledge codifiability, resources and science based innovation” Journal of Knowledge Management, 5:2, 195-204.
Metaxiotis, Kostas & John Psarras (2004) "The Contribution of Neural networks and genetic algorithms to business decision support management decision", vol 42, no. 2, Emerald group publishing limited, pp. 229-242.
Curry, B. & L. Moutinho (1993) "Neural Network in marketing: Modelling consumer Responses to Advertising Stimuli" European Journal of Marketing, vol. 27, no. 7, MCB university press, pp 5-20.
Wray, B., A. Palmer & D. Bejou (1994) "Using Neural Network Analysis to evaluate Buyer – Seller Relationships" European Journal of Marketing, vol. 28, no. 10, MCB university press, pp 32-48.
Venugopal V. & W. Beats (1994) "Neural networks and Statistical Techniques in marketing research" Marketing intelligence & planning, vol. 12, no. 7, MCB university press, pp 30-38.
Davies, F, L. Moutinho & B. Curry (1996) "ATM user attitudes: a neural network analysis”, marketing intelligence & planning, vol. 14, no. 2, MCB university press, pp 26-32.
