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Article

Evolution of the Online Sales of Sustainable Products in the COVID-19 Pandemic

by
Magdalena Iordache Platis
1,*,
Cosmin Olteanu
1 and
Anca Luiza Hotoi
2
1
Department of Business Administration, University of Bucharest, 030018 Bucharest, Romania
2
Performance Target LTD, 023674 Bucharest, Romania
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(22), 15291; https://doi.org/10.3390/su142215291
Submission received: 29 October 2022 / Revised: 10 November 2022 / Accepted: 12 November 2022 / Published: 17 November 2022
(This article belongs to the Section Economic and Business Aspects of Sustainability)
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Versions Notes

Abstract

:
In the context of the COVID-19 pandemic, online sales have increased in recent years for many products. Responsible consumption has also been considered by households and individuals, and interest in sustainable products has positively evolved. Although sustainable products have more and more been considered by consumers and producers as appropriate alternatives, the results are still underwhelming. This study aims to demonstrate the relationship between the online sales of sustainable products and the online marketing costs expressed by the cost-per-click in Romania in the last three years. This quantitative research is a conclusive and descriptive study based on secondary data sets collected from the evidence registered in an online agency, which, in turn, was based on a sample of companies and products in three industries considered highly open to sustainable approaches: electronics; home and garden; clothing and footwear. The results show different relationships between the online marketing cost and the sales of sustainable products in the above-mentioned industries. In addition, online consumer purchasing intention is a mediator for the relationship between marketing cost and online sales in one industry only, namely electronics. The study reveals the development of the online transactions of sustainable products, considering the connection between marketing costs and subsequent sales.

1. Introduction

In the recent years, the commerce of products and services through the internet expanded, with the pandemic having greatly contributed. E-commerce evolution made companies consider new strategies exploring innovative information on specific platforms to better understand consumer needs and to support their decision-making [1,2]. In some cases, consumer income was also affected by the pandemic and therefore, at least impulse shopping was reduced as consumers considered a potential risk, while in the case of scarce products, consumers understand the value of the products and perceive them as unique thus their willingness to buy increases [3,4]. During the pandemic, online sales have proven to be an effective sales channel for companies to maintain their sales, including agro-food products, despite several businesses having had to shut down, especially in the second quarter of 2020. Studies have demonstrated that small businesses need a lot of support from government to better adapt to the online environment [5,6,7].
The COVID-19 pandemic affected several aspects of social wellbeing, including food security and global trade, which created a context of uncertainty regarding the sustainability of food placement, and products became more sensitive to costs and price changes. The need to support agrobusinesses in the process of implementing eco-efficient actions has proven critical and post-pandemic strategies are required [8,9,10]. The same awareness of sustainability regarding the need for responsible consumption and social responsibility is demonstrated by studies pertaining to conscious consumption incorporated in a behavioral change, as well as those considering the social learning towards social change [11,12]. Moreover, several studies, show that COVID-19 not only increased the interest in online sales and responsible consumption, but also affected the online purchasing behavior of the consumer segments, including the green product purchase attitude and the attitude towards certified food providers. Some studies show that purchase intention is related to perceived risk, wherein reduced risk would increase the purchase intention [13,14,15,16].
The current post-pandemic context is still affecting online businesses; both small and medium enterprises, as well as large companies, must re-evaluate their options and marketing strategies according to the type of industry they are in. Recent research demonstrated that satisfaction is one factor influencing customer online purchase intention in the small business environment. Brand trust is still impacting online purchasing intention and business management should invest in their corporate social responsibility image to increase customer trust [17,18,19].
The online marketing cost is reflected in the pricing of the online advertising, measured as cost-per-click, which is typically considered by the brand retailer. Cost-per-click are charges for displaying advertising but in some cases losses are inevitable, as click behavior is unpredictable [20,21,22]. However, what the role of online purchasing intention in the sale of the sustainable products is, and how the online advertising cost relates to sales, is still unclear. This gap between the declared action on one hand and concrete behavior on another hand is observed in the 2022 report on the progress of the sustainable development goals (SDG) which includes 12 goals dedicated to responsible consumption and the production of relevant statistics; for example, 83 new and a total of more than 400 policy instruments, over 60% of large companies having a sustainability report published in 2021, and a national preoccupation to stimulate the acquisition of environmentally sound and energy-efficient products [23].
The aim of the current study is to demonstrate the relationship between the online sales of sustainable products and the online marketing costs in Romania in the last three years, considering the role of the online purchasing intention of customers. The study is based on a sample of companies and products in three industries considered highly open to sustainable approaches, namely electronics, home and garden, and clothing and footwear. Two research questions are defined: 1. How can online advertising costs relate to sales of sustainable products and the customer purchasing intention? 2. How can online purchasing intention for sustainable goods relate to online sales? The originality of the study is generated from the three areas of sustainable products and the online consumers’ purchase intention in Romania covering the last three years which mean before, during, and after the pandemic period. The results will provide adequate understanding of this variable relationship for the near-future decision-making of entrepreneurs in the sustainable production field, as well as for marketers to support future budget allocations for online advertisement. Moreover, the paper can support policy makers in their decisions to stimulate responsible consumption of sustainable products in Romania. Lastly, the research provides a literature development in the field of online marketing and responsible consumption and production.
The next sections will describe the theoretical background of the concepts used in the paper, starting from recent approaches covering the pandemic period (Section 2), followed by the research methodology, including the hypothesis generated from the literature, the research structure model, and the statistical approach (Section 3). In the end, results will be particularized to clarify how online advertising costs relate to sales of sustainable products and customer purchasing intention and how online purchasing intention for sustainable goods relates to online sales in Romania (Section 4). Conclusions to the theoretical approach and the results achieved will be also outlined (Section 5).

2. Literature Review and Research Model

2.1. Sustainable Products

At the European Commission, several regulations which apply to the internal market, industries which stimulate entrepreneurship, and small and medium enterprise development include a sustainable product approach; for example, regulation on eco-design of sustainable products, or specific communications making sustainable products the norm with strategies for sustainable textiles, circular business models and new rules for consumers. In this context, sustainable products are those which reduce energy and resource consumption and sometimes they are defined as eco-friendly products [24,25].
Research relates sustainable products to the electric energy consumption management system, meaning the IT industry, with housing and service-infrastructure being areas where sustainable products can be developed, whilst also being important solution to reduce the impact on the environment. Other studies associate sustainable products to cleaner production and express the need to support companies in developing new sustainable product design approaches [26,27,28]. At the same time, for the fashion industry, sustainable fashion is defined as local production and sustainable garments with practices that are still unclear [29,30].
In Romania, a recent survey showed that people declared themselves interested in sustainable food, and aware of the impact of production and consumption on the environment and health. However, in practice, they did not purchase these products. Moreover, energy efficiency was considered an important factor by companies willing to make operational decisions, but which required significant investment [31,32].

2.2. Cost-per-Click and Online Campaigns

Retailers in e-commerce are aware of the importance of the cost-per-click scheme. This is the cost they pay when a customer clicks on the product page of their website and generates sales for them. In the case of a click of no value or an accidental click, a cost is generated which causes dissatisfaction to the company and sometimes the cost is increased by mistargeting customers in the advertisements [33,34,35]. Moreover, competition has a huge effect on the cost-per-click and therefore advertising profitability is based on its dynamics; it is important for long term strategy and economic cost monitoring [36,37].
Understanding the role of the cost-per-click in marketing campaigns is useful for companies which seek to diminish this cost without affecting their online traffic or conversion level; every click is the interaction of a potential customer with a product offered, meaning his or her attention/interest in shopping. The cost-per-click is specific to every industry and business type [38].
The online marketing campaigns include a variety of categories and their effectiveness is reflected in variables, for example click per rate, or conversion rate [39]. Google Ads include search campaigns (potential customers are reached while they are searching on Google), display campaigns (potential customers are reached when they browse different websites, applications etc.), shopping campaigns (in the case of selling the product inventory), video campaigns (dedicated to video ads on websites), app campaigns (in the case of retailers using applications to sell products), local campaigns (referring to the customers willing to shop in physical areas), smart campaigns (to design the posted advertisement), and performance max campaigns (having specific conversion goals). The frequency at which these campaigns appear is called impression and their effectiveness can also be assessed as a percentage of clicks in the total number of impressions [40,41]. Research shows that ad impressions have an impact on both own-channel and cross-channel click-through rates despite optimization returns to advertising being a real challenge [42].

2.3. Online Purchase Intention

The purpose of any advertisement is to help potential consumers to proceed in their shopping interest and satisfy their purchase intention. Research demonstrates that after a click on an ad, a common experience is to search for the products, but at the same time, online ads affect the product search. Records of clicks also support buying behavior models and the contention that predicting users’ preferences can be achieved [43,44]. The impact of ad clicks on behavioral intention has been demonstrated with respect to the purchasing process and on spreading positive impressions by the power of word of mouth. In addition, the display efforts reflected in visuals affect consumer clicks and even sales [45,46].
Online shopping has significantly taken off, especially as a result of the pandemic; for example; in grocery shopping, where online shopping intentions after the pandemic have been demonstrated as being positively influenced by the pandemic aftermath, both due to positive online shopping experiences and the risk perception being strongly reduced [47,48]. Moreover, in the fashion industry, and taking sustainable apparel products as an example, consumer attitude is positively related to purchase intention, and price-per-click has a moderating effect between these two variables [49,50]. In the case of shopping decision for a luxury product, sustainable consumption behavior plays a mediating role, while in general, the environmental concern can be translated into behavior [51,52].

2.4. Consumer Differences towards Sustainable Products

Generational analysis of consumers of green products is based on understanding purchasing behavior and environmental thinking, and supporting companies to better promote their sustainable products towards specific targets. A positive relationship between environmental behavior and brand awareness has been demonstrated and the role of social marketing in generating a behavioral change has increased [53,54,55]. Therefore, it is important to create a proper message, targeted at the specific audience, which admits that green marketers should increase their communications skills to influence consumers’ purchase intentions; in the fashion industry, it has been proven that nature-connection among other factors influences the intention to purchase green products, and that environmental concern, green image, and knowledge are the predictors of the purchase intention [56,57,58].
A multi-group analysis demonstrates that generations of customers mediate the relationships between the value co-creation and overall customer satisfaction and, in the case of sustainable services, that consumer engagement in green practices is important, but companies have a responsibility to create more opportunities towards this [59]. Generational differences have been considered in studying sustainable consumption and local practices [60,61].

2.5. Research Model and Hypotheses

In Romania, interest over time in sustainable issues in general has been continuously increasing since 2018. An exploratory study in Romania from 2021 showed that a sustainable ecosystem had a potential to be developed with the engagement of different interested parties (entrepreneurs, innovators, universities, investors, government organizations etc.) [62]. A recent study proved that half of Romanians defined sustainability in relationship to environmental issues, pollution reducing efforts, health systems, and the protection of endangered species. Moreover, generational differences have been noticed, with the youngest one entering the labor market showing a more positive attitude towards a potentially sustainable world [63]. A Google search on key words considered relevant in the literature review for sustainability issues such as “durable”, “sustainable”, “eco-friendly”, “green energy” and “recycling” demonstrated a clear discrepancy of interest at a national level compared to the global level. The period considered in Google Trends to explore the word interest is July 2018 to June 2022 which covers the pre-pandemic, during mid-pandemic, and the post-pandemic contexts, is shown in Figure 1 [64].
The research model is based on the literature review and represented in Figure 2. The independent variable is sales of sustainable products in Romania during the 2018–2022 period in three industries, namely electronics, house and garden, and clothes (shoes and clothes), and the dependent variables are the online marketing cost the companies pay based on the cost-per-click, as well as the purchasing intention of the Romanian consumers in relationship to several online campaigns, including the following types: search, shopping, display, smart, and performance max, which have previously been described.
The following hypotheses were formulated:
Hypotheses 1 (H1).
Online campaign costs have a positive direct effect on online transactions in the electronics industry.
Hypotheses 2 (H2).
Online campaign costs have a positive direct effect on online transactions in the house and garden industry.
Hypotheses 3 (H3).
Online campaign costs have a positive direct effect on online transactions in the clothing industry.
Hypotheses 4 (H4).
Online campaign costs have a positive direct effect on online purchasing intentions in the electronics industry.
Hypotheses 5 (H5).
Online campaign costs have a positive direct effect on online purchasing intentions in the house and garden industry.
Hypotheses 6 (H6).
Online campaign costs have a positive direct effect on online purchasing intentions in the clothing industry.
Hypotheses 7 (H7).
Online purchasing intentions have a positive direct effect on online transaction in the electronics industry.
Hypotheses 8 (H8).
Online purchasing intentions have a positive direct effect on online transactions in the house and garden industry.
Hypotheses 9 (H9).
Online purchasing intentions have a positive direct effect on online transactions in the clothing industry.
Hypotheses 10 (H10).
Online purchasing intentions mediate the effect of online campaign costs on the online transactions in the electronics industry.
Hypotheses 11 (H11).
Online purchasing intentions mediate the effect of online campaign costs on the online transactions in the house and garden industry.
Hypotheses 12 (H12).
Online purchasing intentions mediate the effect of online campaign costs on the online transactions in the clothing industry.

3. Materials and Methods

3.1. Data Collection

The research is based on secondary data extracted both from the Performance Target agency’s MCC (Manage Multiple Google Ads Clients Accounts/My Client Center) and from the company’s internal CRM (Customer Relationship Management software) where data from Google Ads campaigns, Google Analytics, and e-commerce platforms are collected. The data collection can be described in more details as follows:
  • Each client of the Performance Target agency, in the e-commerce area, has both a presentation site and an e-commerce platform for the online sale of products. Both on the website and the e-commerce platform (generally a version of Prestashop, OpenCart, WordPress with WooCommerce, Magento, etc.), codes (tracking codes) are installed for the collection of visitor data, which is then added to several databases for interpretation and analysis;
  • One database is meant for the agency’s Google Ads MCC and another for the agency’s internal CRM. This visitor data (device, operating system, display resolution, browser, demographic data—age, gender, location, number of sessions, number of pages viewed, average time spent on the site, on a page or on a product, etc.) is collected and recorded in Google Analytics 4, but also used in the promotion campaigns carried out by the agency;
  • Some campaigns (depending on the contract and the price established and signed by the client and the agency) are carried out with custom targeting (email addresses are used directly, collected according to data protection regulations from the data bases, to give just one of several examples) on each type of promotion campaign in order to achieve as many conversions as possible in a short period of time;
  • It should be mentioned that following the implementation of promotional campaigns, the “conversion tracking” type code, generated by the Google Ads platform, counts in different databases, each conversion (each product addition to the virtual shopping basket or completion of the shopping basket order) to follow the promotion campaigns in real time and follow the promotion budget established for the optimization of the campaign and a cost-per-click as optimal as possible;
  • It should be mentioned that following the implementation of promotional campaigns, the “conversion tracking” type code, generated by the Google Ads platform, counts in different databases, each conversion (each product addition to the virtual shopping basket or completion of the shopping basket order shopping) to follow the promotion campaigns in real time and follow the promotion budget established for the optimization of the campaign and a cost-per-click as optimal as possible;
  • The data in the spreadsheets was generated from the MCC of the agency for each client, and for each period, by exporting data to CSV files, which were later processed in Excel. Moreover, the data was correlated at the client level with the promotion campaigns and the agency’s CRM by running a SQL (Structured Query Language) script for the retrieval of products, monthly searches and age categories, and real data recorded in the e-commerce platforms related to the agency’s platform. Moreover, this data was exported in CSV files, which were also later processed in Excel.
The data collection considered several online advertisement types and several companies in three industries, namely electronics, house and garden, and clothing, which have been characterized by several indicators: number of clicks for the campaigns; number of impressions; click through rate; average cost-per-click; cost of campaign from the company’s budget allocated for the campaign; conversions; conversion rate. The companies’ advertisement campaign’s categories and the industry are presented in Table 1.
The variables used are continuous and have been extracted for the period between July 2018 and June 2022. Eleven companies have been selected for being the most active in online marketing and sales and most engaged in promoting sustainable products and energy efficiency.

3.2. Research Method

The research method is based on a four-step analysis considering the simple and multiple regression and therefore significance coefficients are exposed. The regression analysis (simple and multiple) and a causal mediation analysis were chosen for the data analysis after calculating the correlation coefficient to prove if there might be a strong relationship between the variables or not [65,66,67,68]. The follow the phases are described below:
  • Testing the relationship between the online campaign costs (OCC) and the online transactions (OT) in the selected industries used a simple regression method. OCC refers to the costs the selected companies pay for the online campaigns, while OT expresses the online transactions the companies received from the online clients;
  • Testing the relationship between the online campaign costs (OCC) and the online purchasing intentions (OPI) in the selected industries used a simple regression method. OPI refers to the number of clicks the clients generated based on the campaigns run by the companies, namely search, display, shopping, smart and performance max;
  • Online purchasing intentions of sustainable products have a positive direct effect on online transactions in the selected industries using a simple regression method;
  • Online purchasing intentions mediate the effect of online campaign costs on the online transactions in the selected industries using a multiple regression method, as this is frequently considered in social sciences to test moderation hypotheses [69].
In proceeding, the analysis considered the following rules [70,71,72]:
  • Variables are independent. The data collection considered the same period of time, but the observations were independent from one company to another in each industry;
  • There are no missing variables to be added. The data collection was based on a clear extraction of the data, but of course, the research has its limitations which will be considered in the end;
  • The relationship between the independent and dependent variables is linear and was observed using a scatter plot for the variables;
  • Residuals follow a normal distribution and were tested using a QQ Plot (plot of the quantiles).
The steps following are briefly described in Table 2:
Step 4 will be conducted if the first three reveal a significant relationship between the variables; otherwise, the mediation effect is not possible.

4. Results

The results are described for every step.

4.1. Step 1. OCC–OT Relationship

The linear regression showing the relationship between OCC–OT is presented for the first industry, electronics, in Table 3; it demonstrates that at an R Square (R2) which equals 0.6253, 62.5% of the variability of OT is explained by OCC. In addition, correlation R equals 0.7908 which shows that there is a strong direct relationship between OCC and OT in the electronics industry. ANOVA results are shown in Table 3.
For the other two industries, the results show a very weak reversed relationship with an R Square (R2) which equals 0.2022 and a correlation (R) of −0.1422, meaning that only 2% of the variability of the OT is explained by the OCC in the home and garden industry, and a very weak direct relationship with an R Square (R2) which equals 0.3406 and a correlation (R) of 0.1846, which means that only 3.4% of the variability of the OT is explained by the OCC in the clothing industry, as shown below in Table 4 and Table 5.

4.2. Step 2. OCC–OPI Relationship

The linear regression showing the relationship between OCC and OPI is presented for the first industry, electronics, in Table 6, and for the other two industries in Table 7 and Table 8.
The linear regression showing the relationship between OCC and OPI in the case of the first industry, electronics, demonstrates that at an R Square (R2) which equals 0.8382, 83.8% of the variability of OPI is explained by OCC. In addition, correlation R equals 0.9155 which shows that there is a very strong direct relationship between OCC and OPI in the electronics industry.
In addition, for the other two industries, the results show a strong direct relationship with an R Square (R2) which equals 0.582 and a correlation (R) of 0.7629, meaning that 58.2% of the variability of the OT is explained by the OPI in the home and garden industry, and a moderate direct relationship with an R Square (R2) which equals 0.1733 and a correlation (R) of 0.4163, meaning that 17.3% of the variability of the OT is explained by the OPI in the clothing industry.

4.3. Step 3. OPI–OT Relationship

The linear regression showing the relationship between OPI and OT is presented for the first industry, electronics, in Table 9, and for the other two industries in Table 10 and Table 11.
The linear regression showing the relationship between OPI and OT in case of the first industry, electronics, demonstrates that at an R Square (R2) which equals 0.7291, 72.9% of the variability of OT is explained by OPI. In addition, correlation R equals 0.8539, which shows that there is a very strong direct relationship between OPI and OT in the electronics industry.
In addition, for the other two industries, the results show a moderate direct relationship with an R Square (R2) which equals 0.1792 and a correlation (R) of 0.4234, which means that 17.9% of the variability of the OT is explained by the OPI in the home and garden industry, and a very strong direct relationship with an R Square (R2) which equals 0.7827 and a correlation (R) of 0.8847, which means that 78.3% of the variability of the OT is explained by the OPI in the clothing industry.

4.4. Step 4. OCC–OPI–OT Relationship

This phase is conducted if the first three relations reveal significant relationship between the variables: OCC–OT, OCC–OPI, OPI-OT. If not, the mediation effect is not possible. Although steps 1–3 were conducted for all industries, only electronics showed a strong or very strong relationship between the variables. Consequently, step 4 will be considered for the electronics industry only. The results of the multiple regression are shown in Table 12, Table 13 and Table 14 below.
The multiple regression indicates that there is a very strong collective significant effect between OCC, OPI, and OT in the electronics industry for the sustainable products.

5. Discussion

The results of the data analysis are synthetized in Table 15.
Sustainable products in Romania have generated a clear relationship between the variables influencing the online transactions. However, there is a relationship difference in the selected industries, as consumers’ intention demonstrates a different relationship in the electronics industries from the home and garden and clothing industries. The regression analysis has shown a clear mediation model of the online purchase intention of the relationship between online campaigns costs and online transactions only in the electronics industry.
Considering the hypotheses, strong or very strong relationships have been confirmed, as following:
  • OCC–OPI only in the electronics industry (H1 confirmed);
  • OCC–OPI in two industries: electronics and home and garden (H4 and H5 confirmed);
  • OPI–OT in the electronics industry and the clothing industry (H7 and H9 confirmed);
  • OCC–OPI–OT only in the electronics industry (H10 confirmed).
The results are in line with other studies for the electronics industry. Firstly, the costs in the electronics industry are considered a primary element of a transaction, because they do not include the cost of production or of shipment, as has been demonstrated in the electronics market. In addition, digital products represent the main interest of men (35.7%), while utilitarian products represent the main interest for women (30.8%), which might be associated with the electronics and home and garden industries [73,74]. Secondly, most studies on sustainable products include analyses of products directly and demonstrate an interest of the consumers to buy sustainable products; however, research has demonstrated that the consumer interest on sustainable products in physical store is more declarative, i.e., consumers expressing their intention without effective purchase following it. Moreover, predictive analytics and testing 3D digital samples prior to production are used by companies to improve their sustainability targets [75,76]. Thirdly, the intention to purchase sustainable products with sustainable packaging is a reality which shows the interest of the young generation to buy not only sustainable, but to buy online; this is also in line with the findings showing clear evidence in the electronics industry [77,78,79]. Fourthly, the online purchase intention in the electronics industry is also strongly influenced by the online campaign cost. This is like the findings in other studies analyzing online shopping behavior and revealing the importance of marketing strategies and the intention to use internet marketing [80,81].
Regarding the other two industries, where the mediation effect of the online purchasing power has not been confirmed, further research should be conducted. However, in the case of the clothing industry, it has been demonstrated, based on empirical evidence, that there is a strong positive relationship between online reviews and consumer purchasing intention and not the cost of the online campaigns, as well as a moderately positive relationship between trust and the consumer purchasing intention [82].
The results show a type of behavior that is different in the case of electronic products from the behaviors specific to the other two industries, with a clear effect of the online campaign costs on the online transactions mediated by the online purchasing intention. The mediation effect is not confirmed in the case of the other two industries since consumers might still enjoy trying or touching physical products [83].

6. Conclusions

6.1. Theoretical and Practical Implications

The research contributes to the theoretical knowledge of the main concepts, namely sustainable products, online campaign costs, online purchase intention, and online transaction, and reveals the importance of online purchase intention in the context of online trends regarding sustainability issues in Romania.
The pandemic supported the online environment in all kinds of transactions, but moreover, it increased the awareness of sustainable products. The findings presented in this paper show that online transactions are more influenced by the online costs and the online purchasing intention in the electronics industry, whereas, in the case of the home and garden and clothing industries, a moderate and weak relationship was shown.
Managers of marketing campaigns in advertising agencies can improve their efforts in better supporting the brands by accepting the differences in the industries and focusing not only on product characteristics communicated to potential clients but also on creating different campaigns based on the differences in the relationships between the parameters in each industry.
Brand managers of sustainable products in electronics can also consider the role of the online purchasing intention and pay attention to the campaign cost level which directly and strongly determines the online purchasing intentions of their customers. Therefore, the higher the marketing cost allocation becomes, the higher the online purchase intention gets.
Managers in the home and garden industry and the clothing industry can benefit from the findings in the sense that the relationship between online costs and their online sales are not strongly related and they should be aware of the fact that their online efforts might become profitless.

6.2. Limitations of the Research and Future Research Development

The study has a few research limitations: it does not consider the online cost per type of the campaigns, so relationships between parameters might be different in the case of search, shopping, display, and other types of campaigns. Another limitation is that the study applies to the eleven companies selected in the research, and the number of the companies could change the relationship between parameters. Thirdly, the study covers the pandemic period and some of the behaviors (such as the online one) had been displayed during this unique worldwide crisis situation. The considered period of time relates to the period of 2018–2022 which includes a mixture of data: The period before the outbreak of the COVID-19 pandemic, the peak period of the pandemic, and the period after the peak of the pandemic to the present. A more detailed analysis could be conducted in the future by dividing this period from a behavioral point of view and analyzing the consumers’ purchase intention and the online sales before, during, and after the pandemic. In addition, the pandemic period forced people to react differently from a non-pandemic period and to become more open to online sales, in general. Once the pre-pandemic context returns, it is more likely that consumers will return to previous behaviors, but this cannot happen entirely, since the online shopping has already been more and more integrated in their regular transactions.
Further research might include a generational analysis of the online purchase intention of sustainable products, an empirical study of sustainability awareness in Romania, an online campaign-based approach of the purchase intention regarding the sustainable products, and a comparative study between different countries, so as to better understand the characteristics and determinants of the purchasing intentions of sustainable products.

Author Contributions

Conceptualization, M.I.P. and C.O.; methodology, M.I.P.; software, C.O. and A.L.H.; resources, A.L.H.; writing—original draft preparation, M.I.P.; writing—review and editing, M.I.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 14 15291 g001 550
Figure 1. Interest over time at global and national level (author’s own research).
Figure 1. Interest over time at global and national level (author’s own research).
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Sustainability 14 15291 g002 550
Figure 2. Research model.
Figure 2. Research model.
Sustainability 14 15291 g002
Table
Table 1. The companies’ advertisement campaign’s categories in the three industries 1.
Table 1. The companies’ advertisement campaign’s categories in the three industries 1.
CompanyCampaign TypeIndustry
Electronics industry
Company 12 Search + 1 ShoppingElectronics
Company 21 Search + 1 Display + 1 ShoppingElectronics
Company 32 Search + 1 ShoppingElectronics
Company 42 Search + 1 ShoppingElectronics
House and garden industry
Company 52 Search + 1 ShoppingHouse and garden
Company 62 Search + 1 Display + 1 performance MaxHouse and garden
Company 71 Search + 1 Display + 1 Shopping + 1 SmartHouse and garden
Company 81 Search + 1 Display + 1 ShoppingHouse and garden
Clothing industry
Company 91 Search + 2 Display + 1 ShoppingClothing
Company 101 Search + 1 Display + 1 ShoppingClothing
Company 111 Search + 1 DisplayClothing
1 Authors’ own research.
Table
Table 2. Steps and analysis method 1.
Table 2. Steps and analysis method 1.
StepRelationsHypothesesMethod
Step 1OCC-OTH1, H2, H3Linear regression
Step 2OCC-OPIH4, H5, H6Linear regression
Step 3OPI-OTH7, H8, H9Linear regression
Step 4OCC–OPI–OTH10, H11, H12Multiple regression
1 Authors’ own research.
Table
Table 3. OCC–OT relationship in the electronics industry—Regression ANOVA results 1.
Table 3. OCC–OT relationship in the electronics industry—Regression ANOVA results 1.
SourceDFSum of SquareMean SquareF Statistic (df1, df2)p-Value
Regression142,464.003742,464.003716.6886 (1, 10)0.002196
Residual1025,444.91292544.4913
Total1167,908.91676173.5379
1 Authors’ own research.
Table
Table 4. OCC–OT relationship in the home and garden industry—Regression ANOVA results 1.
Table 4. OCC–OT relationship in the home and garden industry—Regression ANOVA results 1.
SourceDFSum of SquareMean SquareF Statistic (df1, df2)p-Value
Regression11,004,521.631,004,521.630.2477 (1, 12)0.6277
Residual124,866,368.794,055,307.316
Total1349,668,209.423,820,631.494
1 Authors’ own research.
Table
Table 5. OCC–OT relationship in the clothing industry—Regression ANOVA results 1.
Table 5. OCC–OT relationship in the clothing industry—Regression ANOVA results 1.
SourceDFSum of SquareMean SquareF Statistic (df1, df2)p-Value
Regression1960,675.3554960,675.35540.2468 (1, 8)0.6345
Residual727,245,527.113,892,218.158
Total828,206,202.463,525,775.398
1 Authors’ own research.
Table
Table 6. OCC–OPI relationship in the electronics industry—Regression ANOVA results 1.
Table 6. OCC–OPI relationship in the electronics industry—Regression ANOVA results 1.
SourceDFSum of SquareMean SquareF Statistic (df1, df2)p-Value
Regression11,233,466,0521,233,466,05251.8057 (1, 10)0.00002934
Residual10238,094,772.723,809,477.27
Total111,471,560,824133,778,256.8
1 Authors’ own research.
Table
Table 7. OCC–OPI relationship in the home and garden industry—Regression ANOVA results 1.
Table 7. OCC–OPI relationship in the home and garden industry—Regression ANOVA results 1.
SourceDFSum of SquareMean SquareF Statistic (df1, df2)p-Value
Regression1235,902.9237235,902.923716.7053 (1, 12)0.001506
Residual121,694,570,185141,214,182.1
Total134,053,599,422311,815,340.1
1 Authors’ own research.
Table
Table 8. OCC–OPI relationship in the clothing industry—Regression ANOVA results 1.
Table 8. OCC–OPI relationship in the clothing industry—Regression ANOVA results 1.
SourceDFSum of SquareMean SquareF Statistic (df1, df2)p-Value
Regression155,803,299.7355,803,299.731.4672 (1, 7)0.2651
Residual7266,230,181.838,032,883.12
Total8322,033,481.640,254,185.19
1 Authors’ own research.
Table
Table 9. OPI-OT relationship in the electronics industry—Regression ANOVA results 1.
Table 9. OPI-OT relationship in the electronics industry—Regression ANOVA results 1.
SourceDFSum of SquareMean SquareF Statistic (df1, df2)p-Value
Regression149,514.825249,514.8252269,189 (1, 10)0.0004081
Residual1018,394.09141839.4091
Total1167,908.91676173.5379
1 Authors’ own research.
Table
Table 10. OPI-OT relationship in the home and garden industry—Regression ANOVA results 1.
Table 10. OPI-OT relationship in the home and garden industry—Regression ANOVA results 1.
SourceDFSum of SquareMean SquareF Statistic (df1, df2)p-Value
Regression18,901,997.4228,901,997.4222.6204 (1, 12)0.1315
Residual1240,766,2123,397,184.334
Total1349,668,209.423,820,631.494
1 Authors’ own research.
Table
Table 11. OPI-OT relationship in the clothing industry—Regression ANOVA results 1.
Table 11. OPI-OT relationship in the clothing industry—Regression ANOVA results 1.
SourceDFSum of SquareMean SquareF Statistic (df1, df2)p-Value
Regression122,076,472.4822,076,472.4825.2108 (1, 7)0.001529
Residual76,129,729.987875,675.7124
Total828,206,202.463525,775.308
1 Authors’ own research.
Table
Table 12. OCC–OPI–OT relationship in the electronics industry. Multiple regression results—Correlation matrix 1.
Table 12. OCC–OPI–OT relationship in the electronics industry. Multiple regression results—Correlation matrix 1.
Correlation Matrix (Pearson)
YX1X2
Y10.7907640.853895
X10.79076410.915534
X20.8538950.9155341
1 Authors’ own research.
Table
Table 13. OCC–OPI–OT relationship in the electronics industry. Multiple regression results 1.
Table 13. OCC–OPI–OT relationship in the electronics industry. Multiple regression results 1.
SourceDFSum of SquareMean SquareF Statistic (df1, df2)p-Value
Regression149,514.8252449,514.8252426.918875 0.00040812
Residual1018,394.091421839.4091420.915534
Total1167,908.91426173.537879
1 Authors’ own research.
Table
Table 14. OCC–OPI–OT relationship in the electronics industry. Multiple regression results—Coefficient Table Iteration 1.
Table 14. OCC–OPI–OT relationship in the electronics industry. Multiple regression results—Coefficient Table Iteration 1.
Coefficient Table Iteration 1 (Adjusted R-Squared = 0.67)
CoeffSEt-StatLower t0.025 (9)Upper t0.975 (9)Stand Coefp-Value VIF
b−2.37085321.660237−0.109456−51.36971446.62800900.915242
X10.0006120790.004744090.129019−0.01011980.0113440.05559320.900186.180568
X20.005454920.002927131.863573−0.001166710.01207650.8029970.09526956.180568
b−0.61724716.014336−0.0385434−36.29941135.06491700.970013
X20.005800670.001118025.188340.003309570.008291780.8538950.000408121
1 Authors’ own research.
Table
Table 15. Relationships between variables 1.
Table 15. Relationships between variables 1.
VariablesIndustryRelationship
Electronics industry
OCC-OTstrongdirect
OCC-OPIvery strongdirect
OPI-OTvery strongdirect
OCC–OPI–OTvery strongcollective significant effect
House and garden industry
OCC-OTvery weakinverse
OCC-OPIstrongdirect
OPI-OTmoderatedirect
OCC–OPI–OTNANA
Clothing industry
OCC-OTvery weakdirect
OCC-OPImoderatedirect
OPI-OTvery strongdirect
OCC–OPI–OTNANA
1 Authors’ own research.
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Iordache Platis, M.; Olteanu, C.; Hotoi, A.L. Evolution of the Online Sales of Sustainable Products in the COVID-19 Pandemic. Sustainability 2022, 14, 15291. https://doi.org/10.3390/su142215291

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Iordache Platis M, Olteanu C, Hotoi AL. Evolution of the Online Sales of Sustainable Products in the COVID-19 Pandemic. Sustainability. 2022; 14(22):15291. https://doi.org/10.3390/su142215291

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Iordache Platis, Magdalena, Cosmin Olteanu, and Anca Luiza Hotoi. 2022. "Evolution of the Online Sales of Sustainable Products in the COVID-19 Pandemic" Sustainability 14, no. 22: 15291. https://doi.org/10.3390/su142215291

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