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Weekly UpdatesWhile much has been written regarding the export activities of sawmills in the United States, little attention has been paid to determining the important transportation and competitiveness factors that differ among exporting and other hardwood lumber manufacturers. This study surveyed exporting and other lumber manufacturers in the United States to determine the factors associated with exporting. Analyzing the results from this study will help nonexporters understand what important transportation and competitiveness factors are needed for exporting.
Exportation can offer several advantages for small hardwood producing companies. These advantages include the potential for increased profits, market expansion, and economic stability resulting from diversification. Small companies may recognize the potential advantages of exporting but not enter the market due to perceived barriers such as unfamiliar procedures and market conditions, inadequate information, complicated trade regulations, and alternative transportation modes (Parhizkar et al. 2007). Once a company decides to begin exporting, its need for information becomes more specific. Information concerning marketing, transportation, and production are appropriate at this point (Ifju and Bush 1994).
Literature review
Hardwood sawmills in the United States, while large in total production, are comprised of many small facilities, most employing less than 50 people. Approximately 25,000 workers are employed by an estimated 3,500 hardwood sawmills in the United States, mostly located in the eastern half of the country (USDC 2005). Hardwood lumber is one of the most important commodity groups exported and has had a significant impact on the balance of trade in the United States (Peck 2002). In 2007, hardwood lumber accounted for 24 percent of total U.S. wood exports by value, or $1.6 billion. The production of hardwood lumber is, therefore, an integral part of the economy of the country. The estimated North American hardwood lumber production was 10 billion board feet (BBF) in 2007 (USCB 2008). The Appalachian region alone accounts for over 55 percent of the hardwood lumber produced in the eastern United States. Roughly 50 percent of the hardwood sawn in the United States is red and white oak. Poplar comprises another 11 percent of the total production. Of the total amount of hardwood lumber production, an estimated 1.4 BBF (3.3 million [m.sup.3]) was exported to overseas markets in 2006 (AHMI 2007).
Hardwood lumber situation
Unfortunately, the production of U.S. hardwood lumber has fallen 25 percent (more than 4 billion ft) since 2000 (AHEC 2006) (Fig. 1). Historically, the sawmill industry has been a low profit margin business; log prices have driven profit margins to less than 4 percent of total sales in the past few years, forcing many sawmills out of business, thereby reducing overall hardwood production (Luppold 2006). Consequently, many firms are now closing, and the resulting loss of employment opportunities throughout the United States from this sector has obviously caused major difficulties for many individuals and their communities. The demands of the marketplace have forced sawmills to change, and the ability of sawmills to export efficiently has become crucial (HPC 2006).
Globalization and forest products export
The need to take advantage of different market opportunities and to serve customers in the global market environment has affected the international market behavior of firms. Therefore, introducing products more quickly in different international markets is necessary in the highly competitive global environment (Rundh 2003). Globalization issues ranked among the most important concerns of hardwood sawmill managers (Buehlmann et al. 2007). Issues such as transportation costs and changes in domestic markets are challenges that mill managers must address. Cheap transportation from competing suppliers is strongly impacting the competitiveness of sawmills in the Appalachian region because reliance on trucking, coupled with increasing fuel costs, is reducing sawmill competitiveness in domestic and global markets (Miller et al. 2005). The normal rationale behind globalization is value creation through lower supply chain (e.g., transportation) costs and access to local markets (Rossi and Robert 2005). But, it is important to understand what role globalization plays in a sawmill's exporting activities in today's business environment.
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Reliance on trucking, coupled with increasing fuel costs, is reducing its competitiveness in domestic and global markets (Parhizkar et al. 2007). Intennodal transportation was defined as the successive transportation of a loaded container or trailer from its place of origin to its place of destination by more than one mode of transportation in interstate or foreign commerce as an alternative to trucking (Boske et al. 2005). Intermodal and transloading facilities can assist the Appalachian region to meet the demands of a global marketplace. Smith et al. (2006) found that exporting firms in this region were more in favor of using multi-modal transportation and railroads than restricting themselves to trucking. In a follow-up study, Parhizkar et al. (2007) reported that most responding sawmills in the Appalachian region were in favor of utilizing or establishing more intermodal facilities throughout the region and recommended the introduction of new and expanded intermodal facilities throughout Appalachia, as well as the dissemination of information to the wood products industry on the feasibility of using intermodal facilities.
Buehlmann et al. (2007) assessed the impacts of global competition on the Appalachian hardwood industry and revealed that the majority of sawmills were working harder to develop relationships with their customers as a result of globalization and were more aggressive in searching for new markets. During the last two decades, several studies of forest product industries have been conducted to determine the factors that indicate export success. Exporters of wood building materials to Japan from the Pacific Northwest indicated that export success was closely related to finn size, shortened distribution channels, product mix, having a company presence in Japan, and maintaining a close relationship with Japanese customers even during an economic downturn (Eastin et al. 2004).
This present study surveyed exporting and other hardwood lumber manufacturers in the United States to obtain an overview of the business practices of exporters and other hardwood lumber manufacturers and to determine the factors associated with exportation in today's global environment.
Objectives
The objective of this study was to compare hardwood lumber exporting finns to other hardwood lumber firms with respect to possible transportation and competitiveness factors for international trade.
Methods
Questionnaire development
The questionnaire included general questions (categorized as transportation and competitiveness factors) directed to both exporters and other hardwood lumber manufacturers. It was reviewed by (Wood Science and Forest Products) faculty members from Virginia Tech and the University of Southern Mississippi. The questionnaire was pretested among 12 hardwood lumber firms. After the development of the sample frame and pretesting, the initial mailing, which consisted of a personalized cover letter and the questionnaire, was sent in August 2006. The first mailing was followed by a reminder postcard about two weeks later. A second questionnaire was mailed two weeks after the reminder postcard, and a second reminder postcard was mailed after an additional two weeks.
Sampling
A mail survey was conducted to collect data from U.S. hardwood lumber firms. A sample of 428 nationwide hardwood lumber exporters and 438 randomly chosen other hardwood lumber manufacturers in the United States were selected in 2006 from the membership directories of National Hardwood Lumber Association (NHLA), the Harris data provided by the Appalachian Regional Commission (ARC), and the membership directories from the Center for Forest Products Marketing and Management at the Virginia Tech.
Data analysis
The analysis was based on the firms that produced more than 90 percent of their lumber from hardwood species and were active in exporting. This was determined by asking the firms about the percent by species of their total production and the number of years that they have been active in exporting. All of the firms that had sold lumber to overseas markets within the past 3 years were considered "exporters." All other responding firms were considered "other hardwood lumber manufacturers" in the analysis.
Data measurement
The data collected were considered numerical (from numerical and categorical questions) or quasi-numerical (from rating questions with the Likert scale, from 1 to 7). Therefore, the t-test was used for the comparison of the means for the collected data.
Data were incorporated into the Statistical Analysis System (SAS[R]). First, the number of responses (no.) and the average response (mean) were calculated for both responding exporters and other hardwood firms. The input in the mean column (Tables 1 through 5) was calculated as average response for each variable (sum of responses divided by the number of respondents). Then, a t-test was run to test for differences between the means for these data. The computed absolute t-value, and the significance level (p-value) were calculated for both exporters and other hardwood lumber manufacturers.
Why t-test?
The t-test procedure was conducted to test the hypothesis that the means of the observations of the variables from the exporters and from the other hardwood lumber manufacturers were equal (Blankenship et al. 1998). Since the t-test is robust with respect to the non-normality of the data (the ordered categorical type of data is generally not normally distributed), it can be used to test the significance of the difference between the means for ranking questions (Dilorio and Hardy 1995). The collected data from the categorical questions were intrinsically numerical and were approximated by the middle of the interval. Therefore, using the t-test was a proper way of determining the differences between the means for data (numerical and categorical questions) from this study. The calculated p-value helped determine the significant variables that were different between exporters and other hardwood firms, as is explained below.
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