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The Relationship Between the Use of Electronics and Consumer Behaviors


Choice is the primary issue in consumer behavior. The customer is obliged to deal with uncertainty, or risk, because the outcome of a choice can only be understood in the future. Risk perception is an important part of consumer behavior since risk is frequently viewed as painful in the sense that it might induce anxiety, in which case it must be approached by the customer according to the situation.

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With the growth and spread of the Internet, a new type of word of mouth (WOM) has emerged: electronic word of mouth (eWOM). Given the recent technological developments it is now considered one of the most important informal media among customers, businesses, and the general public. Most of the papers utilized build on these ideas by reviewing the relevant literature, analysing the impact of traditional WOM and eWOM in the field of consumer behavior, and highlighting the key differences between the two types of recommendations, in order to contribute to a better understanding of their potential.

The credibility of the information source, however, is often viewed as a downside of online communications in relation to marketing and consumer behavior. The anonymity of internet messages has been claimed to have a negative impact on their reliability. Other research, on the other hand, claim that consumers use eWOM to lower risk while making decisions. Similarly, when a consumer uses eWOM for the first time, it is more trustworthy.

Traditional WOM data is shared through private, real-time, face-to-face dialogs and chats, therefore message privacy is another element that distinguishes the two mediums. eWOM has the capacity to reach a wider audience since personal connection is no longer required for the information exchange. In addition, evaluations can be viewed at different times. Consumers and businesses can verify eWOM reviews at any time because they are written, as opposed to traditional WOM, which tends to fade away once the message has reached the receiver.

The outlined specifications complicate the ongoing attempts to analyze the specific dynamic between eWOM and risk in consumer behavior. One of the key ways to better comprehend the dynamic between the factors relies on structural analysis that systemizes the estimations of the social media use. There has been ongoing discussion on rival models for predicting social networking service usage. The authors of this study develop and test a model that incorporates elements from the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), as well as (1) a mediator between the two models, (2) perceived risk, and (3) trust. The empirical findings confirm the prediction that perceived ease of use (as defined by TAM theory) greatly amplifies (positively moderates) the effect of perceived behavioral control (as defined by TPB theory) on the intention to utilize social networks for transactions. In brief, combining concepts from both models rather than choosing one over the other in study and practice has advantages (Hansen et al., 2018). The findings also show that perceived risk and trust are important antecedents in consumer decision-making, and that risk-taking propensity has a direct relationship with behavioral intention.


The hypothesis for this research assignment can be formulated as follows: it is expected for the respondents with higher use of technology to be more easily stimulated and susceptible to risk in their purchases. To test this idea, a quantitative survey was used, and its findings were then analyzed through the set of standard descriptive statistics tests. The outlined literature review was then utilized to interpret the findings of the questionnaire.


Sample Description

The sample for the research consistent of young people in college and was thus affected by the demographic trends typical for this category. Colleges have recently become more racially diverse, echoing national trends, with Hispanic and Asian students making some of the most significant gains. Almost a third of all students are the first in their family to go to college. Within three years, about a third of these first-generation students have dropped out. With diversity in all of its forms being so important to the educational environment and students’ preparation for a diverse world, the report also highlights how many institutions fall short in terms of supporting underrepresented students and mirroring the growing student diversity with more diversity among faculty and leadership.

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Data Collection

Snowball sampling involves ever-expanding chain-referral and is frequently applied in cases where simple random sampling is not applicable due to the specific characteristics being requried. This is a technique for recruiting samples for a research project in which existing individuals refer new subjects. A primary data source nominates other possible data sources who will be able to participate in the research investigations using this sampling strategy. The snowball sampling approach is solely referral-based, and it is through recommendations that a researcher is able to obtain a sample. As a result, this method is also known as chain-referral sampling.

Snowball sampling is a common business research technique due to the frequently sufficient emotional distance between the subjects and the topic. This form of sampling is only ethically acceptable if the research topic is not of a sensitive nature. The snowball sampling approach is often employed when a population is unknown or rare, and it is difficult to select participants for study samples. The main advantage of the snowball sampling lies in its predisposition to geometric progression, where each sample member can contribute to the sample growth until the sufficient number of participants is reached.

Measures Used

The responses of the participants were then analyzed through SPSS software, with regression and R2 elements respectively measuring the existing relationships between the variables. The analysis accounted for the standard sociologically relevant differences, such as class, race, gender, etc. Class in particular was put in the spotlight due to an expected association between one’s purchasing behavior and financial status.

Statistical Analysis

SPSS (Statistical Package for the Social Sciences) is a collection of software tools packaged as a single package. It is predominantly used in the social research field to systemize and visually present collected data. This information can be utilized for market research, surveys, and data mining, among other things. Researchers can quickly evaluate the demand for a product on the market using the statistical data they’ve gathered, and adjust their plan accordingly. SPSS stores and organizes the data first, then compiles the data set to generate appropriate output. SPSS has been intended to work with a wide range of variable data formats.


The results of the questionnaire demonstrate that there exists a positive correlational relationship between the two variables. The greater is the use of technology and electronics, the more college students are likely, as consumers, to initiate risky materialist purchases. Potential reasons behind this dynamic are to be explored further, with some of the existing information discussing the click-bait nature of online advertising and speed of spread for the eWOM.

Recommendations and Conclusion

Further research on the matter is recommended, since the topic is actively relevant to the currently central topic of social media marketing. The more active users of social media can be cathegorized as a demographic of their own with special purchasing patterns. It is recommended for marketeers to focus more on the intended audience when designing their online advertisement campaigns, since the frequency of the electronics use has became an important variable of its own.


Hansen, J., Saridakis, G., & Benson, V. (2018). Risk, trust, and the interaction of perceived ease of use and behavioral control in predicting consumers’ use of social media for transactions. Computers In Human Behavior, 80, 197-206. Web.

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