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Challenges Faced by Good Harvest

Discuss about the Consumer Consumption and Perception.

Good harvest is a company located in Sunshine Coast that offers delivery services for their organic products that they plant and grow on their farms. This is the company’s second year in business, meaning it is still in the startup phase. Like any startup business, it faces a wide range of challenges ranging from raising finance, establishing customer loyalty and retention, competition from established businesses, hiring employees, etc. Like most startups, Good Harvest faces the challenges of low average sales, low revenue, and high cost of goods, which makes it difficult to get new customers and retain the ones they have. The business also suffers from a low workforce, with which they have to manage and operate the business.

The purpose of this analysis therefore, is to determine the financial performance of the business and the products by looking into its sales, profits, and cost of goods. By doing so, the research will be able to come up with solutions and recommendations that could ensure the business doesn’t go under like most startups, but instead, ensure its success by continuous growth in sales, increase in profits and increased retention of profits.

The biggest challenges faced by any company usually involves finances. There are other challenges such as poor management that a business can come back from, but the issue of money usually leads to the utter and complete downfall of a business. Being a new business, this could not only lead to extreme financial challenges, but to the closure of the business as a whole (Davidsson et al. 2006). For new businesses, they face the challenge of setting their prices to a certain balance where they can gain profits and at the same time, not scare away their customers. This is among the toughest decisions to be made by startups (Nwobu 2016). Companies which deal in organic products also at times suffer from bad products which may be a result of wrong season plantation or disease and nutrient-management. It is therefore important that a farmer is fully knowledgeable of the ‘on’ and ‘off’ seasons for their products, be aware of what effects rainfall and lack off, will have in the productivity, sales and profitability of the said products (Finckh 2006). Finally, bringing to order, the sales department of a business may result in a huge financial breakthrough (Ganesan 2016)s. The sales department is the money-generating branch of any given business, the steady revenues attained from selling products can be used for managing the operations of the other departments in the business. By solving these three main challenges of the business, we could be able to confirm the success of the new company in this startup-hostile environment.

Descriptive Statistics

Statistical analysis for this report is performed using two datasets for the food shop, where one dataset is made up of data for product mix whereas the second dataset is made up of data on the sales summary. The product mix dataset has ten variables each with 1034 observations, while the sales summary dataset consists if eighteen variables each with 366 observations.  The data in these datasets contains both quantitative and qualitative data.

For this analysis, I changed the variable type of Product Class category from Ordinal to Nominal and variable type of Product Category from Ordinal to Nominal. This is because both variables are categorical variables based on neither merit nor order; hence they are nominal variables.

N

Minimum

Maximum

Mean

Std. Deviation

Net Profit ($)

1034

0

8703

164.74

482.106

Valid N (listwise)

1034

Table 1: Summary: Net Profit

N

Minimum

Maximum

Mean

Std. Deviation

Total Sales ($)

1034

0

17276

369.96

1014.719

Valid N (listwise)

1034

Table 2: Summary: Total Sales

N

Minimum

Maximum

Mean

Std. Deviation

Cost of Goods ($)

1034

0

8573

205.22

561.072

Valid N (listwise)

1034

Table 3: Summary: Cost of Goods

The top selling product with a maximum sales of 17276 is Bananas Cavendish while the worst selling product with the lowest score of 0 is Scarves Small. The Statistical research questions that this report seeks to answer are:

  1. Is there a difference in payments methods?
  2. Is there differences in sales performance based on where the product is located in the shop?
  • Is there a difference in sales and gross profits between different months of the year?
  1. Are their differences in sales performance between different seasons?
  2. How does rainfall and profit relate?

Analysis of Variance (ANOVA) will be used in these analyses when determining the difference in mans for the various categories such as payment methods, seasons, months of the year, etc., as stated in the research questions. ANOVA will be used to answer all the questions except the last research question, where we’ll use correlation analysis.

The p-value from the ANOVA output on table 4, page 14 is 0.00, which is less than alpha (0.05). This provides sufficient evidence to reject the null hypothesis in favor of the alternate hypothesis; we reject the null hypothesis. This means that there is a difference in payment methods among the four payment methods.

A post hoc analysis (Tukey HSD) is then performed to determine where the difference exists among the four payment methods (cash, credit card, visa card and MasterCard). The results of the post hoc analysis as per table 5 on page 14, reveals that there exists a difference between cash, and credit cards, visa card and MasterCard. There also exists a difference between MasterCard, and cash, credit card and visa card. There is no significant difference between credit card and visa card modes of payments. Cash appears to be the most popular payment mode while MasterCard is the least popular.

The p-value from the ANOVA output on table 6, page 15 is 0.00, which is less than alpha (0.05). This provides sufficient evidence to reject the null hypothesis in favor of the alternate hypothesis; we therefore reject the null hypothesis. This means that there is a difference in sales performance for products based on their location in the shop.  

Statistical Analysis and Results

A post hoc analysis (Tukey HSD) is then performed to determine where the difference exists among the five locations in the shop (left, right, front, outside front and rear). The results of the post hoc analysis as per table 7 on page 15, reveals that there exists a difference in sales between front and rear locations of the shop. A difference also exists in sales between right and left locations in the shop. The products in the left location had the best sales performances followed by products on the right location. Products on outside front had the poorest sales performance.

The p-value from the ANOVA output on table 10, page 16 is 0.814, which exceeds the alpha value of 0.05. This provides insufficient evidence to reject the null hypothesis in favor of the alternate hypothesis; we therefore fail to reject the null hypothesis. This means that there is no difference in sales performance between different seasons.

We performed a correlation analysis to establish presence of or lack of a relationship between rainfall and profits. The results if this analysis as per table 11, on page 16 reveals a correlation coefficient of 0.885. This suggests presence of a linear relationship between rainfall and profits. To mean that as one variable increases, so does the other, i.e., as rainfall increases, so does profits.

Conclusions

The results attained from examining the financial status of the organic firm business, brings to our attention that the performance of the business varies according to the different seasons and months of the year. There are certain months when sales performance is high and and profit recorded are high. There are certain weather conditions such as rainfall that guarantees much higher profits compared to profits garnered during non-rainy seasons. Finally, there are some locations in the shop that guarantees better sales performance compared to other locations. With this deeper insight into the operation of the organic firm business, the C.E.O is able to make better more informed decisions. For instance, the products that don’t sell most and therefore garner the lowest profits can be put in the location of the shop with the best sales performance. This will ensure improved performance of these products. Of course the other products that have gained favor among the customers will continue to sell despite their locations. Customers will go the extra mile to get their regular products. Another recommendation is that the company takes advantage of the rainy seasons and gain the highest profits possible, so that even during the non-rainy seasons when the profits are low, the business maintains a balance. Implementation of these fully informed suggestions will without a doubt result in an excellent performance of the business financially, which will eventually ensure its success.

References

Finckh, M. R., Schulte-Geldermann, E., & Bruns, C. (2006). Challenges to organic potato farming: disease and nutrient management. Potato Research, 49(1), 27-42.

Davidsson, P., Delmar, F., & Wiklund, J. (2006). Entrepreneurship as growth; growth as entrepreneurship. Entrepreneurship and the Growth of Firms, 21-38.

Nwobu, U. (2016, August 25). Most Common Challenges Faced By Start-Ups. Retrieved from https://www.huffingtonpost.com/ursula-nwobu/most-common-challenges-faced-by-start-ups_b_11701900.html

Wei, Y. S., Samiee, S., & Lee, R. P. (2014). The influence of organic organizational cultures, market responsiveness, and product strategy on firm performance in an emerging market. Journal of the Academy of Marketing Science, 42(1), 49-70.

Ganesan, S. (2016, August 22). 6 challenges faced by early-stage startups that some effective tools can help you combat. Retrieved from https://yourstory.com/2016/08/challenges-early-stage-startups/

Miller, D. (1983). The correlates of entrepreneurship in three types of firms. Management science , 29(7), 770-791.

Thompson, G. D. (1998). Consumer demand for organic foods: what we know and what we need to know. American Journal of Agricultural Economics, 80(5), 1113-1118.

Bezawada, R., & Pauwels, K. (2013). What is special about marketing organic products? How organic assortment, price, and promotions drive retailer performance. Journal of Marketing, 77(1), 31-51.

Chinnici, G., D’Amico, M., & Pecorino, B. (2002). A multivariate statistical analysis on the consumers of organic products. British Food Journal, 104(3/4/5), 187-199.

Radman, M. (2005). Consumer consumption and perception of organic products in Croatia. British food journal, 107(4), 263-273.

Tsakiridou, E., Boutsouki, C., Zotos, Y., & Mattas, K. (2008). Attitudes and behaviour towards organic products: an exploratory study. International Journal of Retail & Distribution Management, 36(2), 158-175.

Jánský, J. (2005). Analysis of current situation in sales of selected organic products in the Czech Republic. Zem?d?lská ekonomika, 51(7), 309-313.

Aryal, K. P., Chaudhary, P., Pandit, S., & Sharma, G. (2009). Consumers’ willingness to pay for organic products: a case from Kathmandu valley. Journal of Agriculture and Environment, 10, 15-26.

Casadesus?Masanell, R., Crooke, M., Reinhardt, F., & Vasishth, V. (2009). Households' willingness to pay for “green” goods: evidence from Patagonia's introduction of organic cotton sportswear. Journal of Economics & Management Strategy, 18(1), 203-233.

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