1.1 Background and Research Motivation
The commitment to new distribution channel in emerging markets is an adamant investment since it is very difficult to repudiate a distributor even though it is not very productive. From the prolific sides, distribution channel is a strategic asset for a firm that would like to attain sustainable competitive advantages over competitors in strategic matching, such as replicating product designs, underselling on price, and counterfeiting advertising and promotional strategies. Distribution intensity, commonly defined as the number of intermediaries at various distribution channel levels, is the critical element in channel structure to implement these activities (Frazier, Sawhney, and Shervani, 1990; Hardy and Magrath, 1988; Onvisit and Shaw, 1990; Rosenbloom, 1995). In addition, it represents the market outgrowth stake invested by the manufacturer to defend its trading territories (Bonoma and Kosnik, 1990; Corey, Cespedes, and Rangan, 1989; Coughlan, Anderson, Stern, and El-Ansary, 2001; Lassar and Kerr, 1996).
Past literature had studied the underlying distribution intensity mechanism and had attempted to explain why firms in similar product category differed in distribution intensity. In their classic works, Frazier and Lassar (1996) proposed several theoretical constructs, such as manufacturer's brand strategy and channel practices, and the moderating effect of retailer requirements that had impacts on distribution intensity. Jain (1993) and Mallen (1996) argued that distribution intensity and the underlying channel structures co-evolved with a host country’s economic developmental stages, while Watson (1997) argued that distribution intensity was the trade-off among customer expectations, company strategy and many other
uncontrollable factors such as a country's political and legal environment. All these studies investigated the factors that influenced distribution intensity in industrialized markets; not many studies examined these variables in an emerging market context.
For emerging markets, such as China and India, the development of economy is unstable. For these potentially largest markets in the world, market structural ambivalence, such as huge trading territories, income inequality, and diversified cultures, abounds in its complicated channel market structure. Distribution of channel intensity is influenced not only by industrial development (Frazier and Lassar, 1996), but also by economic performance (Ingene, 1984; Tang and Li, 1998). Other possible explanation is that some predominant factors might have influence on distribution intensity in this emerging economy. For instance, Li (2003) attempted to identify the determinants of export distribution intensity in emerging markets. His inductive study concerned 18 British manufacturers and their Chinese intermediaries. Five determinants were collected to show a great impact on distribution intensity in emerging markets: behavioral uncertainties, market growth, gray marketing and fake products, distribution capabilities, and transaction-specific investments. Johnson and Tellis (2008) indicated drivers for market entry into China and India are that success is greater with earlier entry, greater control of entry mode, and shorter cultural and economic distances between the home and the host countries, and firms entering more open emerging markets have less success.
All the above studies took a deterministic view to investigate distribution intensity. With modeling consideration, even if many researchers attempted to describe/predict distribution intensity using observed determinants, they rely on random components to recognize not all factors were included. Furthermore, since the underlying distribution intensity mechanism might be different in contingent channel settings, it is more difficult to gauge what elements should be involved.
Krugman (1991) presented an economic geographic framework, showing that the prime impact of most economic activity might be concentrated in one or a few regions.
Similar to the existence of heterogeneous economic performance among cities, huge population resides in a few clusters of metropolitan areas in China. Meanwhile, in channel management practice, one critical question marketer may ask is: Should distribution intensity be always concentrated only on a few regions? More interesting research questions are scrutinized toward whether concentration of the distribution intensity patterns on specific sites needs to dispose the same way among different products, and whether few vital parameters could account for distribution intensities.
1.2 Research Objectives
Most high-technology products are introduced in a turbulent, uncertain, and chaotic environmental setting where the odds of success are often low. As a result, the marketing strategies for 3C (computers, communications, and consumer electronics) products must be carefully implemented to enhance the odds of success; yet marketing is often not a well-developed competency for most product-driven high-tech manufacturers. Since the local channel is an indispensable stake for these firms to sell products in a special market, especially the manufacturers in 3C industry rely heavily on channels to transport, stock, distribute, and promote their products.
This empirical study subsumes four products (i.e., PC, printer, TV, and mobile phone) to represent 3C category that are widespread in 200 distributed cities in China. The database consists of the number of their intermediaries among 200 distributed cities of four benchmark brands in these categories in 2002, including PC for Lenovo, printer for HP, mobile phone for Nokia, and TV for Haier.
The important aspect of this study is to take a new perspective to re-examine the distribution intensity issue in emerging market. In particular, we take into consideration unobserved nature of heterogeneous distribution intensity rates among 200 cities in China by imposing various probability distributions for counting events (Winkelmann, 2008), such as Poisson distribution, Gamma-Poisson mixture distribution (also known as negative binomial distribution, NBD), and Lorenz Curve from NBD (Schmittlein, Cooper, and Morrison, 1993). Since characterizing the right distribution to various channel intensities among cities is more crucial than covariating observed determinants, rashly jumping to import any observed determinant is often exaggerated while the distribution intensity patterns is subtle.
This study enumerates three concentration statistics which are closely related to fitting NBD model, such as coefficient of variation, Pareto Shares, and Gini index, to describe prolific sides of the inherent characteristics of distribution channel structures in these cities. To further detect the determinants of distribution intensity in emerging markets on market growth and distribution capabilities from the propositions (Li, 2003). We explicitly add two proxies for these two important constructs to denote different distribution intensity rates among cities.
Although applying the dataset in the first year after WTO accession, this study was really completed in the early 2009 during the global recession. The Chinese Central Government suggested the implementation of further measures to propel domestic demand and elicit 3C-proudct consumption with a slogan: “Home appliances going to the countryside”. A government-subsidized project aims to expand sales of household electric appliances in the country's vast rural areas at prices about 13 percent lower than those in cities. This kind of project is not only expected to benefit farmers' living standards but is also expected to help the country's suffering manufacturing sector pull out of the global economic winter. Any further distribution
channel allocation decisions taken by manufacturers to address the current chances and challenges should also consider not only the specific circumstances of provinces and regions in China, but also the findings mentioned in this study. Transforming market entry strategies for allocated distribution channels from market leader experience as benchmark learning is of paramount importance.
In brief, not only is this paper an initially empirical study of 3C benchmark brands experience in China, but also confers the distribution intensity patterns in a generalized manner. This study provides a new perspective of 3C channel structures in China market, the factors that account for the way they are structured, as well as evidence of how investment on channel contributes to devise an applicable mechanism in generating channel intensities among different cities in China.