Consumer behaviour is affected by both internal and external factors. In particular, consumer behaviour is mainly dictated by external factors that are attributable to the companies that are selling products and services via online platforms. External factors are beyond the control of consumers who rely on available information pertaining to shopping through online media. Internal factors also play a role in determining consumer tendencies based on the buyer’s attitudes, perception of risk and demographic aspects. Unlike the external factors, internal factors can be controlled by the consumer who might decide to take them into consideration or to overlook their implications. By using primary and secondary sources of data, it is possible for Zara to deduce factors affecting online shopping, the challenges impeding them and also possible recommendation for encouraging online purchases.
The most effective primary source of consumer data is questionnaires. The use of questionnaires is an efficient research method that can be utilized to acquire relevant information on hopping behaviour ( Chen, Yan, Fan, & Gordon, 2015) . Closed-ended questionnaires can be distributed to capture information on the consumer’s age group, gender and other essential demographic data. The questionnaires can also enquire about the facets controlling consumer patterns, especially the trends and driving factors exhibited in a given market. In this source, data is collected from a fixed number of recipients who depict the variables and characteristics that are under investigation.
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Sampling is another essential tool that will enable the identification of the best statistical models for selecting questionnaire recipients as in the investigation of consumer behaviour. In sequential analysis, the sample size is not fixed in advance since subjects are selected during the actual experiment (Chen et al., 2015). The next step of research is the analysis phase and it entails contextualizing the knowledge gap that is hypothesized. Intelligent data analysis can be employed to enable the modelling and discovery of quantitative data as well as the description of qualitative information. Therefore, the analysis phase involves uncovering new data and also validating the hypotheses by either supporting it or contradicting it through conclusive data.
The internet and social media platforms provide a valuable source of secondary data about consumer behaviour. Nowadays, the growing popularity of the internet affects how customers do their shopping mainly through the emergence of online shopping platforms. In this case, drivers are the tangible and intangible factors that can affect the decisions made by customers with regards to their online shopping tendencies ( Fang, Wen, George, & Prybutok, 2016) . Furthermore, the internet has severe implications to organizations such that it offers stability through market benefits and pricing incentives. By evaluating the drivers of online shopping behaviour of consumers in, it is possible to predict future trends in buying behaviour as well as enable the formulation of optimal business decisions.
Social media is now recognized as an emerging alternative to traditional marketing tools that are utilized by organizations. Companies that utilize social platforms are able to establish a desired brand image to consumers thereby affecting online shopping behaviour ( Mikalef, Giannakos, & Pateli, 2013) . In established consumer markets, social media will allow Zara to generate brand loyalty in customers.
Overall, there are various sources of primary and secondary data that Zara can use to examine consumer behaviour including questionnaires, sampling, the internet, and social media. These sources of data illustrate how consumer behaviour is influenced by both internal and external factors. Research into consumer buying behaviour reveals several aspects about the relationship between online shopping and the core factors affecting it. Through this research, Zara can determine the main considerations that motivate specific shopping patterns depicted by buyers. Secondly, it can establish the obstacles that impede online shopping from two perspectives: consumer’s viewpoint and seller’s outlook. The ultimate goal is for Zara to formulate recommendations for optimizing the online shopping experience for consumers.
References
Chen, Y., Yan, X., Fan, W., & Gordon, M. (2015). The joint moderating role of trust propensity and gender on consumers’ online shopping behaviour. Computers in Human Behavior , 43 , 272-283.
Fang, J., Wen, C., George, B., & Prybutok, V. R. (2016). Consumer heterogeneity, perceived value, and repurchase decision-making in online shopping: The role of gender, age, and shopping motives. Journal of Electronic Commerce Research , 17 (2), 116.
Mikalef, P., Giannakos, M., & Pateli, A. (2013). Shopping and word-of-mouth intentions on social media. Journal of theoretical and applied electronic commerce research, 8(1), 17-34
