This paper investigates the determinants of wood pricing in Ghana. The purpose of this study is to assess the factors that influence the prices of wood product at the various wood markets and retailers in Kumasi which have large variety of wood and different categories of retailers in the industry. The study employs a survey design where respondents are conveniently selected. A semistructured questionnaire was designed to gather data from the respondents. STATA version 12.0 software and several sets of statistical analyses such as frequency tables, percentages, and mean point values were performed. Linear regression was employed to estimate the determinants of wood product prices in the wood markets and retail outlets in Kumasi metropolis. The findings of this study suggest that, most of the price determining factors have a positive relationship with the price of wood sold whilst others such as quantity of wood sold and low quality wood products have a negative relationship with the price of wood product sold. Based on the findings of the study, the researchers recommend to future researchers to concentrate further studies on how government policies affect wood prices and also types of wood and their influences on prices.
The importance of capable pricing practice cannot be underrated. The difficulty in pricing in the wood industry can also not be underestimated. The nature of demand, competitiveness, cost, company objective and marketing conditions are the factors that need critical analysis in the Ghanaian wood industry. Such variables may have enormous implication for an industry that faces regulation and pricing difficulties. According to [
Prices of wood products like many other goods can generally be assumed to be determined by demand and supply, where prices adjust to bring demand and supply into balance [
Pricing decision is a crucial decision every organization has to make, because this will eventually affect their corporate objectives, either directly or indirectly [
Pricing strategy is the reasoned choice from a set of alternative prices (or price schedules) that aim at profit maximization within a planning period in response to a given scenario [
The pricing strategy is one of the elements widely occurring in the context of pricing by many authors [
In the pricing strategy [
The second element is the Environment which according to the authors should have an effect on pricing strategy. There can be government regulations such as price control, duty requirements, import taxes or quotes which can have an influence in the pricing possibilities. If the country has hyperinflation and there are price increase limitations, the company should ask for higher possible price from new products. There is also a possibility of currency risks where firms need to analyze the stability of the currency and decide if it is important to revise prices continually or seldom. Also the relative growth or descent of different economies can have an effect on pricing decisions [
The third element in pricing strategy consideration is Distribution Channels. One reason for the distribution channel consideration is the distribution costs which can have a noteworthy influence on the offerings eventual price. The gray market re-export risk can also have an effect on pricing. This appears when there are price differences in different areas which enable the possibility for a distributor to overbuy and sell the surplus in different areas profitably [
The Competitors are a fourth element which is one critical viewpoint to be taken into consideration when exploiting the pricing strategy. In a highly competitive market such as the wood market in Ghana, competition based pricing cannot be underestimated. One essential matter is to compare the companies’ offerings benefits and deficiencies closely between competitors. This is not always possible if the bidding is done rarely, which makes it difficult to sustain consciousness about competitors pricing. The second question related to the costs of competitors. This, as well as pricing can be as well as the pricing to prove to be a difficult task [
The fifth element in the pricing is the Customer. In this segment the marketing unit should create understanding on how the customers are practicing their business. This is used when the price is constructed with the true value drivers which the customer is valuing to determine the true value-in-use prices. The determination of the value-drivers is not always just the customer’s perception from the product but also from the manufacturer itself. Other side of the customer’s consideration is the paying ability. If the customer does not have the amount of funds needed to acquire the products or services, alternative payment methods could be used such as: countertrades, or long-term payment programs [
A slightly older framework created by [
Pricing strategy is a sophisticated and well-thought-out pricing structure that can help to prevent the product from being commoditized [
As a conclusion by linking the different pricing strategy point of views presented earlier, the pricing strategy is a widely general area which contains many factors to consider. The factors include matters such as: the market environment which can contain political factors and competition, the firm with its products is needed to compare with to its competitors; also the distribution channel can also be important matter to consider. From the literature covering the area of pricing strategies, there is a lack of literature covering the area of pricing strategies in the context of service pricing. This may be due to generalizability of the two strategic pricing frameworks, where the outlining of products is not considered to be relevant. The application of any of the pricing strategies in the wood products industry in Ghana can add to the continuous development of the pricing literature.
The subjects of pricing objectives and policies are widely used in the pricing literature to form the guidelines for the pricing. These two matters are taken into consideration by many authors who have made their own pricing models [
Because in the literature pricing objectives and pricing methods are strongly related to each-other a clarification of pricing objectives is in order. [
In the literature there is a wide area of different views how on the pricing objectives are set. Normally the pricing objectives can have some specific goals which can be related to: sales profits, return on investment, sales revenues, market size objectives, channel relationship, or product line consideration [
[
Pricing objectives can be divided into six different categories relating to their content. These categories are: quantitative objectives qualitative objectives, objectives with maximum level of attainment, objectives with satisfactory, level of attainment, short-term objectives and long-term objectives [
The study adopted case study design since it is useful in investigating a contemporary phenomenon [
The researchers assessed the factors that influence the pricing of wood products in the wood markets and outlets across the city. The targeted population for the study consisted of retailers and suppliers of wood products in the Kumasi metropolis.
The primary data for the study was generated by means of a well-structured questionnaire instrument. The first section of the questionnaire was based on the personal data of the responses while the second section of the questions sought to relate to the subject matter on the basis of the research questions. The questions in the questionnaire were focused on the relationship between factors in the wood product markets and their influence on pricing variables in the wood product market. The questionnaire was carefully administered and a total of hundred and twenty three (123) respondents were selected for the purpose of this study.
Simple random technique was adopted for this study to ensure that each member in the population will have equal chance of being selected. The sampling was done randomly such that the respondents represented the entire players in the value chain process of the wood product market. This could to some extent give a basis for generalization. The survey was carried at the various wood retail outlets in the Kumasi metropolis.
Completed questionnaire from the field was edited and coded appropriately to make meaning out of them. Data cleaning was performed to correct errors, check for non-responses and accuracy and remove outliers. Coding was done to facilitate a comprehensive analysis of the data. To arrive at the intended analysis, the responses were keyed into STATA version 12.0 software and several sets of statistical analyses such as percentages, and mean point values were performed. Linear regression was employed to estimate the determinants of wood product prices in the wood markets and retail outlets in Kumasi.
With respect to socio-demographic characteristics, key information that the study intended including were age, gender, level of education etc. Findings obtained from this analysis are shown in the
Variable | Frequency (N) | Percentage (%) |
---|---|---|
Age | ||
20 - 29 | 37 | 30.08 |
30 - 39 | 40 | 32.52 |
40 - 49 | 38 | 30.89 |
≥50 years | 8 | 6.50 |
Gender | ||
Male | 75 | 60.98 |
Female | 48 | 39.02 |
Education | ||
Tertiary | 15 | 12.20 |
Secondary education | 20 | 16.26 |
Technical/Vocational | 56 | 45.53 |
MSLC/JHS | 20 | 16.26 |
No formal Education | 12 | 9.76 |
Source: Author’s computation, 2015.
of male wood retailers than their female counterpart in the industry. With regards to the educational qualification of the respondents, the study revealed that majority (45.53%) of the respondents have attained Secondary Education; 20 (16.26%) of the respondents have attained Technical/Vocational education whilst another 20 (16.26%) were within the category of MLCE/JHS qualification, 15 (12.20%) attained a tertiary qualification and 12 (9.76%) of the respondents have no formal education. In the wood industry it is to be expected that majority have formal education, even though it is within the bracket of MSLC and secondary and technical education. The educational background provides the study with the extent to which respondents appreciate their pricing practices and their implication on the wood industry.
The study sought to find the summary statistics of the variables used in the price determinants model. The findings of this analysis are shown in the
After the researchers had determined the knowledge base of respondents on wood prices and wood price determining factors, the researchers further went on to determine the correlation between wood price determining factors (independent variable) and prices of wood (dependent variable) of the wood dealers and retailers in Kumasi by the use of Pearson rank correlation coefficient (
The Pearson rank correlation was calculated for each price determining factor in the wood product industry. The results in table 4.3 show that the correlation between quantity of wood sold per month by the wood dealers and the prices of wood is −0.962 and highly significant at 1% (0.01) level of testing. This implies that there is a negative significant relationship between the quantity of wood sold per month and the price of wood. Thus it can be concluded that as the prices of wood increase, the quantity of wood sold decreases since consumers of these wood product tend to reduce the quantity of wood they buy as a result of higher prices. Again, it was also observed that the correlation for transportation cost is 0.756 and highly significant at 1% level of testing implying that there is a strong positive correlation between the cost of transporting wood to sales point and the prices at which the dealers sell the wood. Thus, it can be concluded that as the cost of transporting the wood increases, the prices of wood product increase since the wood dealers tend to pass on the extra cost of transportation to the consumers (buyers) of the wood product. Considering carriage cost, a correlation coefficient of 0.791 was obtained between carriage cost and the price of wood product and this is highly significant at 1% level of testing. This implies that there is a strong correlation between carriage cost and the price of wood sold. Hence, like in
Variable | Unit | Mean | Min | Max |
---|---|---|---|---|
Quantity | Pieces/Month | 1613.9 | 150 | 2579 |
Transportation | GH¢ | 1500.1 | 269 | 3919 |
Carriage cost | GH¢ | 157.3 | 101 | 905 |
Seasoning cost | GH¢ | 2100 | 2791 | 5000 |
Price | GH¢ | 45.3 | 33.5 | 90.1 |
Source: Author’s computation, 2015.
Variable | Price | Qty. | TC | CC | SC |
---|---|---|---|---|---|
Price | 1 | −0.962*** | 0.756*** | 0.791*** | 0.818*** |
Qty. | 1 | 0.690*** | 0.299 | 0.216 | |
TC | 1 | 0.813*** | 0.742*** | ||
CC | 1 | 0.801*** | |||
SC | 1 |
Source: Author’s computation, 2015 (***indicates correlation is significant at the 1% level). Note: Qty. = Quantity Sold per month; TC = Transportation Cost; CC = Carriage Cost and SC = Seasoning Cost.
the case of transportation cost, it can be inferred that as the cost of carriage increases, the price of wood sold also increases because the wood dealers tend to pass on the extra cost of carriage to the final consumers (buyers) of the wood. Lastly the study revealed that the correlation between seasoning cost and the price of wood sold was 0.818 and this is highly significant at 1%. This also implies that there is a strong positive correlation between seasoning cost and the price of wood sold.
In assessing the effect of price determining factors and the price of wood sold by the dealers, an Ordinary Least Square (OLS) regression model was adopted for the analysis. The results of the OLS are shown in
The results in
The results in
The results in
Variables | Coef. | Std. Error | t-Value | P-Value |
---|---|---|---|---|
Primary Factors | ||||
Qty. | −0.4483*** | 0.1326 | −3.38 | 0.004 |
Transportation | 0.6899*** | 0.1310 | 5.27 | 0.000 |
Carriage Cost | 0.2932*** | 0.1221 | 2.40 | 0.009 |
Seasonal Cost | 0.1017* | 0.0576 | 1.77 | 0.096 |
High Quality Type | ||||
Odum | 0.8550*** | 0.0996 | 8.59 | 0.000 |
Dahoma | 0.4891*** | 0.1187 | 4.12 | 0.000 |
Mahogany | 0.2191** | 0.1076 | 2.04 | 0.042 |
Mansonia | 0.1964*** | 0.0503 | 3.91 | 0.000 |
Wawa | 0.1953*** | 0.0217 | 9.02 | 0.000 |
Low Quality Type | ||||
Otie | −0.0339* | 0.0198 | −1.71 | 0.087 |
Yaya | −0.0797*** | 0.0293 | −2.72 | 0.007 |
Chenchen | −0.0789** | 0.0305 | −2.59 | 0.010 |
Waterpou | −0.1057*** | 0.0252 | −4.19 | 0.000 |
Cebia | −0.0403* | 0.0222 | −1.81 | 0.070 |
Constant | 0.9715 | 0.0483 | 20.12 | 0.000 |
N | 90 | |||
F-Statistics | 61.31 | |||
P > F | 0.0000 | |||
R-Squared | 0.9369 | |||
Adj. R-Squared | 0.9279 |
Source: Author’s computation (***, ** and * indicate significance at 1%, 5% and 10% respectively).
disaster, seasonal changes etc.
Research on factors that affect wood pricing and the price of wood products sold has progressively become the yardstick for the development of the local wood industry in the Ghanaian private wood business sector. Employing effective research techniques include the attempts to measure efficient and effective wood pricing practices in Ghanaian Wood industry.
It was realized that the types of wood have greater influence in determining pricing of wood products since they consider some wood to be of good quality than others. Cost of transportation also plays a significant role in the pricing of the wood product. Suppliers and retailer should therefore come together to form association in order to come out with a standardised transportation cost.
In this study the research sought to investigate factors that influence pricing of wood product in Ghana with specific reference to wood products markets and outlets in Kumasi. The study though has established grounds for understanding pricing practices in the wood industry in Ghana, it also important that future researchers take into consideration government policies on wood and how they affect pricing in the Ghanaian wood market. For the purposes of expanding knowledge in the field of wood pricing, future researchers can also revise and study the wood market through comparative analysis of sources of supplier of the wood to the various market.
A future study can also build models on the value of individual brand of the various woods on the market. The current study established that there are many brands of wood on the market. This means that the value frame of specific type of wood can be studied for the purposes of building theories in the wood pricing literature.
CollinsKankam-Kwarteng,JacobDonkor,StephenAcheampong, (2016) Determinants of Wood Prices: Analysis of Wood Retailers in Kumasi. Open Journal of Business and Management,04,36-44. doi: 10.4236/ojbm.2016.41004