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1. Introduction
The first restrictive measures initiated upon the appearance of the first Covid-19 case in China were enforced by governments worldwide, trying to prevent the progress of this lethal illness. These measures that were oriented against the mobilization of people brought about a change in how products and services were acquired.
In Peru, during the Covid-19 pandemic, multiple forms of conduct of human beings were modified as a consequence of the quarantines imposed by the government. The quarantine represented a strict change in the modality in which consumers would acquire products or services. A majority sector of society, the millennials, had to adapt their diverse consumer behaviors to the new trends of e-commerce. As of March 2021, electronic commerce has become more relevant because of restrictive measures for the mobilization of people.
1.1. Consumption Habits
In this sense, we can say that we learn habits, which are not intrinsic, and they develop according to people’s evolution of people, and besides, they can be positive or negative.
The research sought to analyze consumer habits by referring to millennial shopping behaviors about goods and services for their use; this conduct, however, was influenced by cultural, social, personal, and psychological factors, as well as by social classes (Armstrong and Kotler, 2016).
Consumer habits are related to the consumer market behavior from the time the client starts looking for the product and service up to the post-sales process. The consumer, who knows what he needs, decides what to buy, thus performing a consumer act (Schiffman and Kanuk, 2013).
For Wilkie (1994), consumer habits show how consumers behave within the market, from searching for a product and service up to the completion of what to buy, to fulfill diverse needs and wishes, having in mind that this process involves mental and emotional processes, besides physical acts. Henceforth, it is significant to analyze the conduct of final buyers (individuals and homes) who purchase goods and services to meet their own needs (Armstrong and Kotler, 2016). It can also be said that buying is a unique process since market circumstances and the needs of people are significantly influenced by the situation, options to select, and motivations (Blackwell et al., 2002).
During the decision-to-buy process, the consumer goes through several experiences: learning, selecting, using, and even rejecting a product (Kotler and Keller, 2016). These experiences are part of the process that derives from the use of products or services. After the purchase has been made, the consumer fulfills the need as a result of the decision made. Subsequently, such a person experiences a feeling of satisfaction or dissatisfaction resulting from the purchase; besides, it confronts the expectations with reality. Specifically, the use is based on the decision of having realized the purchase, the acquisition, spending money, and the use and possession of goods or services (Descouvières, 1998).
1.2. Electronic Commerce
The pandemic has forced changes in the daily habits of individuals and displayed a new array of economic and consumer behavior possibilities (Mehta et al., 2020). E-commerce has been used by multiple companies in diverse sectors to develop new sale channels and enable potential consumers to have easy access to their product catalogs, price lists, Etc. Moreover, to be able to have the direct sale of their products or services. For Arellano (2017), electronic commerce transaction facilitates the purchase of goods or services through the Internet. This is how e-commerce has become a globally significant channel of local features that each country has incorporated into its economy and progress.
There are innumerable success cases that prove the benefits technological updating brings about for the country and the companies offering their products or services in the market; therefore, new commercialization channels have had to be adapted with the security measures e-commerce must have while establishing rules and regulations to safeguard customers and companies from unforeseen conducts in electronic commerce (Laudon and Traver, 2021). Web security is an incentive to implement e-commerce; people in recent times are no longer afraid of online transactions, and the significant impact and convenience and time-saving when deciding to go shopping online, transactions and multiple payment options (Jukariya and Singhvi, 2018).
In a purchase and sale process, the distribution of products from the stage of the provider to the supply chain needs to be considered; that is, to make the product available to the final consumer in the requested amount, at the time and place it is needed and at the location, the consumer wants to acquire it. Silva (2009) states that product or service distribution can be improved since there is no need for intermediaries, which represents high-cost savings since communication between the company and the customers is direct.
According to consumer behavior, segmentation is required for the target audience, to have varied groups of clients, about customs, needs, and preferences, that will possibly get different publicity goods. (Monferrer, 2013). Organizations segment heterogeneous markets by dividing them into small and homogeneous segments. Consumers of any market have different wishes, resources, locations, purchase reactions, and purchase practices (Kotler and Armstrong, 2016).
2. Literature review
Likewise, as support for the research carried out, background information was sought to become aware of the previous studies that were used as the core base. In this respect, indicating that:
Palomino et al., (2020) analyzed e-commerce and its importance in COVID-19 times in the Northern section of Peru. They screened the domain of Covid-19 as an attraction for online shopping of people located in Northern Peru. They observed that at times before and during coronavirus, e-commerce had become relevant and placed digital online purchases at the head of the purchase and use preferences of people.
Linero and Botero (2020) sought to determine consumer habits online, concluding that confidence in online use is associated with diverse aspects such as the information provided by the website, the comments of satisfied clients, and the security perceived in such a platform.
Casco (2020) investigated the effects of the pandemic on purchase decisions and consumer conduct. For such purpose, the respective bibliographic review was used, with secondary bibliographic sources and free access databases. Upon completing the research, the conclusion was that it was relevant to question if the consumer impact would be permanent due to COVID-19.
Esteves and Fernández (2019) investigated the placement of credit card offers to promote e-commerce for clients of a bank chain in Peru, deciding it was worthwhile to know what facilities are available to close credit card transactions to stimulate e-commerce.
Garrido (2019), after investigating those elements that influence the client before buying online, determined that there is a high relation in the finality of electronic commerce with the hedonic motivation considered as the pleasure that drives the purchase.
Mercado et al., (2019) carried out Research in Sonora - Mexico trying to determine and classify variables regarding online purchases by establishing relevant factors such as motivation and purchase patterns, preferences, and consumer habits. They concluded that there are three main factors, as follows: purchase motivation, consumer experiences, and behavior. In addition, the results acquired provide information regarding what strategies can be implemented and applied to the design of online platforms to offer a personalized service to consumers.
Salazar et al., (2018) analyzed 12 variables that affect purchases through communication networks. It was confirmed that the indicators that influence Chileans positively to make digital purchases are the use of credit cards, the professional level, skill in the use of the Internet, and the income and age of the individuals.
As is shown in previous studies, there are multiple elements related to the study variables of consumer habits and e-commerce. In this context, it is essential to know how consumer habits are related to consumer habits with e-commerce in Lima millennials - Covid Pandemic - 19/2021. Framing the hypothesis: Consumer habits are significant about e-commerce in Lima millennials - Covid Pandemic - 19/2021.
The research is justified based on updated information regarding the relationship between consumer habits and e-commerce. We know that being faced with sudden changes because of global quarantine measures, the consumer was forced to adopt changes in the way of acquiring goods or services. For that reason, it must be known how these new circumstances affected how Peruvian millennials went shopping. On the other hand, it is a contribution to the different studies carried out regarding consumer conduct faced with new and extreme situations such as those caused by forceful quarantines and immobilization nationwide. Likewise, the results of this research shall serve for the decision-making of any entrepreneur who wishes to start a business using e-commerce as the sales means, having in mind the context of this pandemic.
3. Methodology
The study corresponds to the non-experimental, cross-section design, with a quantitative focus, of a descriptive - correlational type. The probabilistic sample was calculated using the formula for finite population proportions with a result of 384 participants of ages 23 to 37 (millennials). The technique used was surveying, and as the instrument, the questionnaires were validated by an expert judge’s criteria. Regarding reliability, which was calculated with Cronbach Alfa, the results were the following according to Table 1, 2.
Table 1 Instrument reliability
| Variables | Cronbach Alpha | Reliability |
|---|---|---|
| Consumption habits | 0,773 | Reliable |
| Electronic commerce | 0,888 | Reliable |
| Note: The reliability values found through Cronbach Alfa are described herein. |
Source: Authors’ own elaboration.
Table 2 Baremos distribution
| Variable/dimension | N° Questions | Top score | Low | Medium | High |
|---|---|---|---|---|---|
| Consumer behavior | 4 | 20 | [13-30] | [31-48] | [49-65] |
| Obtaining | 5 | 25 | [13-30] | [31-48] | [49-65] |
| Consumption | 4 | 20 | [13-30] | [31-48] | [49-65] |
| Security | 4 | 20 | [15-35] | [36-55] | [56-75] |
| Distribution | 5 | 25 | [15-35] | [36-55] | [56-75] |
| Segmentation | 6 | 30 | [15-35] | [36-55] | [56-75] |
| Consumption habits | 13 | 65 | [13-30] | [31-48] | [49-65] |
| Electronic commerce | 15 | 75 | [15-35] | [36-55] | [56-75] |
Note: Instrument valuation is detailed according to the response level.
Source: Authors’ own elaboration
4. Results
4.1. Descriptive statistics
According to Table 3, it is evident that variable percentages, consumer habits, and electronic commerce are concentrated in 64.2% and 31.9% on the intermediate scale and 35.1% and 67.5% on a large scale.
Table 3 Frequency of traditional consumer habits and e-commerce
| Scale | Consumption habits | Electronic commerce | ||
|---|---|---|---|---|
| Scale | Frequency | Percent | Frequency | Percent |
| Low | 3 | 0,8 | 2 | 0,5 |
| Medium | 246 | 64,2 | 122 | 31,9 |
| High | 135 | 35,1 | 260 | 67,5 |
Note: Showcase of percent value in scales for both variables.
Source: Authors’ own elaboration.
4.2. Inferential statistics
As seen in Table 4, to know the normality of data used in this research, it was subject to the “Kolmogórov-Smirnov” test because it complied with the condition of having more than fifty elements in the sample. All significant amounts are less than P-value = (0.05). Therefore, their distribution is not normal. It must be noted, however, due to findings in the previous paragraph, that a non-parametric” test is to be used for this case, the “Spearman rank correlation coefficient (rho).” Likewise, the confidence level was 95%.
Table 4 Normality test
| Variables and dimensions | Kolmogórov-Smirnov a | ||
|---|---|---|---|
| Statistical | gl | Sig. | |
| Consumption habits | 0,060 | 384 | 0,000 |
| Electronic commerce | 0,117 | 384 | 0,000 |
| Consumer behavior | 0,136 | 384 | 0,000 |
| Obtaining | 0,106 | 384 | 0,000 |
| Consumption | 0,125 | 384 | 0,000 |
Note: Normality values found are described through the Kolmogórov-Smirnov test.
Source: Authors’ own elaboration.
4.2.1. The contrast of the general hypothesis.
Null and alternate hypotheses are used for this process.
H0 “Consumer habits are not related with e-commerce in Lima millennials - Covid Pandemic - 19/2021.”
Ha “Consumer habits are significatively related with e-commerce in Lima millennials - Covid Pandemics -19/2021.”
According to the results obtained in Table 5, it has been determined that the Spearman rank correlation coefficient (rho) is (r=0,371) a low correlation, with a significant level p-value ≤ 0.05. The alternate hypothesis is accepted, concluding that consumer habits are significatively related to electronic commerce in Lima millennials - Covid Pandemic - 19/2021.
Table 5 “Spearman rank correlation coefficient (rho)” for the general hypothesis
| Spearman rank | Variables | Definitions | Consumption habits | Electronic commerce |
|---|---|---|---|---|
| Spearman Rho | Consumption habits | Correlation coefficient | 1,000 | 0,371** |
| Sig. (bilateral) | 0,000 | |||
| N | 384 | 384 | ||
| Electronic commerce | Correlation coefficient | 0,371** | 1,000 | |
| Sig. (bilateral) | 0,000 | |||
| N | 384 | 384 |
Note: the value of the coefficient resulting from the Spearman rank correlation coefficient is described herein.
Source: Authors’ own elaboration.
4.2.2. The contrast of the first specific hypothesis 1.
The following is presented: null and alternate hypotheses.
H0 “The dimension consumer behavior it not related with electronic commerce in Lima millennials - Covid Pandemic -19/2021”.
H a “The dimension consumer behavior is related with electronic commerce in Lima millennials - Covid Pandemic -19/2021”.
About the results obtained in Table 6, it is determined that the Spearman rank correlation coefficient (rho) is (r=0,205) a low correlation, with a significant p-value level of ≤ 0.05. The alternate hypothesis is accepted; it is concluded that the consumer behavior dimension is related to e-commerce in Lima millennials - Covid Pandemic -19/2021.
Table 6 “Spearman rank correlation coefficient (rho)” for the first specific hypothesis
| Spearman rank | Variables | Definitions | Consumer behavior | Electronic commerce |
|---|---|---|---|---|
| Spearman’s Rho | Consumer behavior | Correlation coefficient | 1,000 | 0,205** |
| Sig. (bilateral) | 0,000 | |||
| N | 384 | 384 | ||
| Electronic commerce | Correlation coefficient | 0,205** | 1,000 | |
| Sig. (bilateral) | 0,000 | |||
| N | 384 | 384 |
Note: The rank correlation coefficient obtained through the Spearman test is described herein.
Source: Authors’ own elaboration.
4.2.3. The contrast of the second specific hypothesis 2.
The following is presented: null and alternate hypotheses
H0 “The dimension obtained is not related with electronic commerce in Lima millennials - Covid Pandemic - 19/2021”.
Ha “The dimension obtained is related with electronic commerce in Lima millennials - Covid Pandemic - 19/2021”.
About results obtained in Table 7, it is determined that the Spearman rank correlation coefficient is (r=0,435) a moderate correlation, with a significant level p-value ≤ 0.05. Consumer habits in Lima millennials in electronic commerce are significatively related - Covid Pandemic - 19/2021.
Table 7 Spearman rank correlation coefficient (rho) test for the second specific hypothesis
| Spearman rank | Variables | Definitions | Obtaining | Electronic Commerce |
|---|---|---|---|---|
| Spearman’s Rho | Obtaining | Correlation coefficient | 1,000 | 0,435** |
| Sig. (bilateral) | 0,000 | |||
| N | 384 | 384 | ||
| Electronic Commerce | Correlation coefficient | 0,435** | 1,000 | |
| Sig. (bilateral) | 0,000 | |||
| N | 384 | 384 |
Note: The rank correlation coefficient obtained through the Spearman test is described herein.
Source: Authors’ own elaboration.
4.2.4. The contrast of the third specific hypothesis 3.
Null and alternate hypotheses are used for this process.
H0 “The consumer dimension is not related with electronic commerce in Lima millennials - Covid Pandemic - 19/2021”.
Ha “The consumer dimension is related with electronic commerce in Lima millennials -Covid Pandemic -19/2021”.
Regarding the results obtained in Table 8, it has been determined that the Spearman rank correlation coefficient (rho) is (r=0,302) a low correlation, with a significant p-value ≤ 0.05. The alternate hypothesis is accepted, and it is concluded that the consumer dimension is related to electronic commerce in Lima millennials - Covid Pandemic - 19/2021.
Table 8 “Spearman rank correlation coefficient (rho) test” for the third specific hypothesis
| Spearman rank | Variables | Definitions | Consumption | Electronic commerce |
|---|---|---|---|---|
| Spearman’s Rho | Consumption | Correlation coefficient | 1,000 | 0,302** |
| Sig. (bilateral) | 0,000 | |||
| N | 384 | 384 | ||
| Electronic commerce | Correlation coefficient | 0,302** | 1,000 | |
| Sig. (bilateral) | 0,000 | |||
| N | 384 | 384 |
Note: The rank correlation coefficient obtained through the Spearman test is described herein.
Source: Authors’ own elaboration.
5. Analysis
According to the results, with scientific evidence, it has been shown that the general hypothesis obtained the value of Rho=0,371, p=0,000 < α=0,05. Likewise, the H0 (null hypothesis) was rejected, and the alternate hypothesis was accepted, affirming that consumer habits are significatively related to electronic commerce in Lima millennials - Covid Pandemic - 19/2021. These results agree with those obtained by Linero and Botero (2020), who explained the relevance of the millennial needs; that frequent brand use can define their buyer and consumer habits. In the same manner, the results of Salazar et al. (2018) affirmed that access to credit cards is a determinant factor and of high impact that influences positively the probability of shopping online.
For specific hypothesis 1, H0 (null hypothesis) may be rejected, and the alternative hypothesis is accepted. Likewise, this is showcased with statistical evidence as (Rho=0,205) p=0,000 < α=0,05. It is then concluded that the consumer behavior dimension is related to e-commerce in Lima millennials - Covid Pandemic -19/2021. In this regard, Mercado et al. (2019) studied the usual consumers of online purchases determining different variables that influence the preferences and purchase decisions, thus altering consumer behavior. Likewise, Casco (2020) indicated that change has to be observed in the consumer behavior acquired during the pandemic and if it will be maintained with time as a permanent and relevant change.
The results of the specific hypothesis 2, indicated a score, based on scientific evidence, of Rho=0,435, p=0,000 < α=0,05). H0 (null hypothesis) was rejected, having accepted the alternate hypothesis. It is concluded that the obtained dimension is related to electronic commerce in Lima millennials -Covid Pandemic -19/2021. Garrido argues in this respect (2019) that hedonic motivation has more influence on utilitarian motivation in decision making than the motivational factors that influence the intention of online shopping.
Finally, specific hypothesis 3, acquired the value of Rho=0,302, p=0,000 < α=0,05. This result enables the rejection of Ho (null hypothesis) and accepts the alternate hypothesis that indicates the consumer dimension is related to electronic commerce in Lima millennials - Covid Pandemic- 19/2021. The Mexican Internet Association (2018) showed that users changed how they consumed, as there are larger product catalogs and better offers in the use of online acquisition platforms.
6. Conclusions
C.G.: Millennials are different consumers now, most of the products are purchased by digital means because of the pandemic, and uncontrolled consumption has put aside savings as the first option, using credit cards as payment means.
C.E. 1. Millennials do not investigate or seek information regarding the products and services; they show a low reflective level of the purchase decision and immediateness in the decision, being inclined to new technologies.
C.E. 2. Millennials buy because of fashion; they follow trends and expect that these scarcely reflexive purchases bring them satisfaction or a well-being sensation. The usefulness of a product is not a decisive feature for buying such a product.
C.E.3 Millennials, despite managing technology and using social media, do not do thorough research before buying through the Internet. Of every ten individuals who buy through Internet, 3 seek information before purchasing. The brands must make post-sale evaluations since the relationship with the millennials improves if they feel a brand hears them and gives them value by responding to their needs.
