An Empirical Analysis of Risks, Involved in Marketing of Civil Engineering Products

Abstract

This research paper is an effort to highlight prominent key risks which are involved in marketing of civil engineering products. Construction moguls hire the services of third party to market and sale the engineering products but they are not fully experts in construction affairs. A framework of marketing risks has been devised to help the third party personnel during sales of completed or under process civil projects. These risks have been evaluated by the sales managers and site engineers through survey process. Last three decades have witnessed the Chinese economy growing at very rapid rate especially for massive construction developments. Rapid developments in building designs, customers ever changing demands, state policies and geographical locations have made the marketing strategies risk prone in China. Marketing concept was introduced during 60s to get the cmpettive advanteges by business moguls. His concepts caused a paradigm shift in business concept to suggests that business should satisfy their customers needs which are the heart of business. Customers loyalty and satisfaction were put at first in priority. The results indicate that the key risks as: economic slowdown, price inflation, liquidity risks, capital risks, competition, interest rates, land acquisitions, payment risks, investors changes, inaccurate cost estimation, design variations, administrative issues, poor competency of third party, non-familiarity with construction affairs, poor safety measures, political stability, law and order situation and problems for approval and permission process cause to push construction entrepreneurs towards complex scenarios. These results have been extracted on the basis of research survey. 313 paper base and online questionnaires were sent to solicit responses from sales professionals and site engineers during survey. Overall, 113 responses were collected during research survey. Responses were scaled on 5-likert scale. Data has been analyzed using SPSS software.