Factors Affecting on Online Shopping Behaviour of Consumers - Coursework Example

Risk as a major factor affecting the behaviour of online shoppers All businesses face major risks in the course of delivering their business objective and in doing the business. In is explained to be the future uncertainty of the business in making profits or also avoidance of making losses due to some unforeseen and therefore unaccountable future events (Smart shopping math 2011). Business risks are divided into two major categories the internal risks and the external risks. Internal risks are the ones that arise from within the business organization and they include human factors such as the talent, strikes by the workers or even lack of dedication by the employees. An example is the cost of production, and more so the transportation cost on the other hand. This is a risk that the online shoppers have to undertake in the course of decision making. It affects the prices of the products in that the higher the cost of production the higher the prices. Online shoppers only shop where either the seller offers the after sales services to ferry the products to the shoppers’ premises or where it is cheap to sell for the shopper to transport the products. Consequently, the lower the cost of production the lower the prices of commodities are. This affects both the basic and the luxurious goods (Coughlin & D'Ambrosio 2012). There is also the external risks are the risks that are caused by events that take place outside of the organization which may include factors that cannot be controlled by the business such as economic factors like the marker risks and the pricing pressures. It may also include the natural factors and other political factors in the economy (Coverdale & Wilbon 2013). In this study, online shoppers and businesses will be focused on and how their sales respond to the change in the prices and the risks that the consumers consider in making their shopping decisions. The dependent variable is the online behaviour while the independent variable are the risks to be taken into consideration are financial risks, product risk, convenience risk, non-delivery risk, service and infrastructural variables as well as a good return policy. Increasing the prices of the products is not necessarily the answer to increasing profits for a business. Online shopping has helped in ordering of goods without necessarily having to travel outside the country which would be expensive for the business. However, if a business is able to increase the sales, it would register a profit, especially in a country with poor economy. A research conducted shows that many businesses fail to prosper or even maintain themselves in the recession times due to ordering expenses and travelling overheard, overpricing, and they are easily driven out of the market by their competitors (Huddleston & Minahan 2011). Adopting a good budgetary strategy for the marketing businesses and customers is essential, since customers conduct a research on the markets before they buy. It is also essential for the businesses to create a good relation with the online shoppers to help them reduce the risks that they face. This is because, businesses may either over-price or even under-price the products, which makes them lose money or lose customers. An increase in price causes the customers and more specifically the online shoppers because they have a variety of places to compare the pricing and buy, to move away from the products. In addition, a decrease in price causes more sales though at a lower price, which may result in low profits or even losses. However, setting of prices is not just a thing that depends on one factor and it affects other issues in the economy (O'Rourke 2012). However, online shopping and business is affected by other risks that have been found to be the most influential in the business by researchers. Some of the risks that affect the online shopping include the compliance risk, financial risks, products risk, convenience risks, non-delivery risks, service and infrastructural risks as well as easy and convenient return policy (Yang, Liu & Cai 2013). From the results in Table 7, there is an indication that 13.1% of the variance in Online Behaviour is predictable from the independent variables chosen which include financial risks, convenience risk, product risk, service and infrastructural risk, return policy as well as non-delivery risk. 13.1% is very low in this research and therefore many reasons can be attributed to this factor. This probably can be because of the sample chosen when giving out the questionnaires. This percentage does not even reflect half of the predictable behaviours. On the other hand it can be too low due to the sample probably being too low as a representative of the whole population of the online shoppers. From the ANOVA analysis, the significant level is 0.004 which is way far smaller than 0.05. ANOVA is used to reflect the reliability of the variables used to predict of the research done. If it is below 0.05 it is, therefore, reliable to use in the research. Therefore, this value indicates that the independent variables can be used in any research to reliably give results that predict online behaviour. This means that very few of the factors that had been pointed out really affect the business of online shopping. It has been established that a variance that is below 50% is not appropriate since it shows that most of the independent variables used to determine the dependent factors do not relate or affect the results of the research in any way (Close 2012). A major reason can be the design of the questionnaire. If the questionnaire is a closed ended type and it has only few choices to choose from, the respondent may have been limited in his choices. On the other hand, if the questionnaire is open ended, the respondent may have a wide range of choices which may be different from what the researcher is looking for. Testing the hypotheses This study has six hypotheses that are to be tested and the results from the analysis are as follows. The table number 9 has figures that explain the results for hypotheses. On the significant level for financial risk is showed at 0.249 greater than 0.05 levels. Therefore, hypothesis one is rejected. Hypothesis 1 is about financial risk in which it concludes that financial risk is not one of the essential factors that affects online behaviour according to the research done. Financial risks are the risks associated with the structure of the finances of the business as well as the number of transactions of the given industry. An online shopper is specifically determined to know how his potential seller conducts his business, the financial muscles of the business so as to determine if the business is able to conduct the business according to his wishes. From the results above for this research it shows that financial risks always have a negative effect on the consumer behaviour in the business of online shopping and therefore shoppers are not affected by this risk. On the contrary, several other researches done before this type of risk was identified as a factor and especially where there is a distance between the seller and the buyer. This is because payment is only made after delivery has happened and therefore it no longer becomes a risk (Nelson 2011). On the second hypothesis, hypothesis 2, the significant level for Product risk was 0.468 which is far way greater than 0.05 levels. Therefore, hypothesis 2 was rejected. From this, a conclusion is made that Product risk is not one of the essential factors that affects consumers’ behaviour of online shopping. Product risk is a risk that the consumer takes. This is the risk where the consumers are not certain that the product ordered is delivered as it was during the time of ordering. An online shopper works with the images of a product and hence takes a risk since the products may not be in their proper state. From the results provided product risk causes a negative effect to the consumer behaviour towards online shopping. Product information disbursement is a crucial item in the business of the online shopping (Bamfield 2012). Therefore, a good and friendly website increases the convenience in advertisement and therefore this creates a positive influence to the online shoppers towards their shopping. However, the shoppers are not sure if the goods that they order resemble what was on the website. This is because the seller cannot verify the products before they are delivered. However, due to the good history of delivery with the different businesses, online shoppers do not consider this to be a major risk. Furthermore, the better the website, the more information that the shopper receives and this is what the customers want. Businesses have developed their websites and therefore, the shoppers have no risk to think of (Cebi & Social Commerce 2013). Hypothesis 3 is different from the other hypotheses as it is accepted as a factor that affect the behaviours of the consumers. The significant level for Convenience risk has been analysed to be at 0.038 which is less than 0.05 levels. Therefore, this concludes that hypothesis 3 is accepted. This concludes that Convenience risk is one of the essential factors that affect consumer behaviour of online shopping. This shows that convenience in the sale of the product is the major issues that affect the online shoppers before they make any decision. How they goods will be transferred, at what time are the major logistics that the buyer has to deal with. In addition, convenience in terms of time wastage and involvement in the shipment are also considered. Different researchers concluded that many shoppers don’t want to get involved in the transport but likes the goods delivered to their door step (Kim et al 2013). Hypothesis 4 also has a significant level for non-delivery risk that is presented at 0.034 which is less than 0.05 levels. This therefore, concludes that hypothesis 4 is accepted. This concludes that Non-delivery risk is one of the major and essential factors that affect the consumer behaviour of online shopping. Another major risk that faces the online shoppers, and affects them while making the decision when buying the products, is the non-delivery risk. This is the type of risk where the consumer makes payment for a certain purchase and all the transactions are successful the seller fails to deliver the product as agreed with the shopper. From the results provided non delivery risk have a negative effect on the consumer behaviour in the business of online shopping. A slight history of non-delivery impacts a major effect to the potential online shoppers (iCETS 2012 & Khachidze 2012). Hypothesis 5 has a significant level for service and infrastructural variables are given at 0.172 which is greater than the set level of 0.05. Therefore, hypothesis 5 is rejected. The results conclude that Service and Infrastructural variables risk is not an essential factor that affects consumer online behaviour. Not many shoppers think of infrastructure as a major issue because they prefer the goods delivered to them and hence the issue of transport infrastructure is not a major issue. Online shoppers do a lot of research before they do any transaction mostly on the service that the good is to offer once it is bought and how much of the risk might be involved. They only order for the goods that they are satisfied they will offer the service required and expected by the customer. This makes this risk not a major consideration by the shoppers. Infrastructure on the other hand, does not affect the shopping habit of the online shoppers due to the fact that the shoppers depend on the sellers to deliver the goods to the customers. Transportation cost and process has been in the recent times been shifted to the seller as a way to entice the customers to buy from them and also as an after sales service (Zhou & Ali 2012). Hypothesis 6 has a significant level for Return policy given from the results at 0.022 which is less than the level of 0.05. Hence, hypothesis is accepted as a factor. This concludes that Return risk is one of the essential factors that affect consumer online behaviour when shopping. After sales services provided by the selling company is another major impact in the decision and behaviour of the consumer. Low shipping fee, free delivery and cyber laws create a positive influence on the consumer behaviour. Another factor that helps is the easy and a convenient way to return good in case of any defaults. Therefore, a good return policy, impacts positively on consumer behaviour in terms of online shopping. This is mainly because not all shoppers trust their suppliers and hence they must think of the return policy. According to the researchers, different goods may require a special attention since they may be ferried with a defect and hence a good return policy is necessary (Evelyn 2012). Therefore it is concluded from the results that from the six hypotheses only three of them are acceptable. This means that financial risks, product risk and service and infrastructural variables do not by any manner affect the decisions made by the online shoppers and hence their hypotheses rejected. Convenience risk, non-delivery risk and return policy are the factors that affect the consumers’ behaviour for online shoppers (Liao & Keng 2013). Sales strategy is very crucial for a business growth and avoids such risks, since a customer is the most important element of the business. Online shoppers are very crucial to the growth of the business and therefore, the businesses always take time to consider them in their plans. The world, since becoming a global village, business has been made simple and therefore this ensures that shoppers can even buy and sell without incurring much of the travelling and ferrying expenses (United States 2011). Different strategies are used by different business though they aim at one thing, which is increasing the profits of the business. Most of the business owners and the operators, including the marketing managers, work towards a common goal. They try to determine what the competitors charge as their prices and modify their prices from that. They always aim at beating or meeting the prices set by others in the industry and advertising themselves online. In the short run, this strategy is successful; though this is not the only way that helps them make a decision due to the fact that customers make decisions of purchases based on other considerations and not price alone. Online shoppers as seen by different researchers always make a decision depending on the best prices and other services including the after sales services (Evelyn 2012). References List Bamfield, J. 2012. Shopping and crime. Houndmills, Basingstoke, Hampshire: Palgrave Macmillan. Cebi, S., & Social Commerce. April 01, 2013. A quality evaluation model for the design quality of online shopping websites. Electronic Commerce Research and Applications, 12, 2, 124-135. Close, A. 2012. Online consumer behaviour: Theory and research in social media, advertising, and e-tail. New York: Routledge. Coughlin, J., & D'Ambrosio, L. 2012. Aging America and Transportation: Personal Choices and Public Policy. New York: Springer Pub. Co. Coverdale, T. S., & Wilbon, A. D. January 01, 2013. The Impact of In-Group Membership on e-Loyalty of Women Online Shoppers: An Application of the Social Identity Approach to Website Design. International Journal of E Adoption, 5, 1, 17-36. Evelyn, J. 2012. Online shopping - Unabridged Guide. Dayboro: Emereo Publishing. Huddleston, P., & Minahan, S. 2011. Consumer behaviour: Women and shopping. New York, N.Y.] (222 East 46th Street, New York, NY 10017: Business Expert Press. iCETS 2012, & Khachidze, V. 2012. Contemporary research on E-business technology and strategy: International Conference, iCETS 2012, Tianjin, China, August 29-31, 2012, Revised selected papers. Berlin: Springer. Khosrowpour, M., & IGI Global. 2013. E-commerce for organizational development and competitive advantage. Hershey, Pa: IGI Global (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA. Kim, H., Suh, K. S., & Lee, U. K. June 01, 2013. Effects of collaborative online shopping on shopping experience through social and relational perspectives. Information & Management, 50, 4, 169-180. Liao, T. H., & Keng, C. J. July 01, 2013. Online shopping delivery delay: Finding a psychological recovery strategy by online consumer experiences. Computers in Human Behaviour, 29, 4, 1849-1861. Nelson, S. L. 2011. Quicken 2011 for dummies. Hoboken, NJ: Wiley. O'Rourke, D. 2012. Shopping for good. Cambridge, Mass: MIT Press. Smart shopping math. 2011. Costa Mesa, Calif.: Saddleback Educational Publishing. United States. 2011. 9 online shopping tips. Washington, D.C.: Federal Trade Commission. Yang, Y., Liu, H., & Cai, Y. December 01, 2013. Discovery of online shopping patterns across websites. Informs Journal on Computing, 25, 1, 161-176. Yılmaz, M. 2010. OpenCart 1.4 beginner's guide: Build and manage professional online shopping stores easily using OpenCart. Birmingham, UK: Packt Pub. Zhou, Y., & Ali, F. 2012. Factors affecting consumer behaviour in online shopping: A study of students purchasing clothing in UK online market. Saarbrücken: LAP Lambert Academic Publishing. Read More