CC BY
57
Российский журнал менеджмента 1S (1): 5-28 (2020)
Russian Management Journal 1S (1): 5-28 (2020)
теоретические и эмпирические исследования
E-COMMERCE ADOPTION IN RUSSIA: MARKET- AND STORE-LEVEL PERSPECTIVES
V. A. REBIAZINA
Faculty of Business and Management,
National Research University Higher School of Economics, Russiaa M. M. SMIRNOVA
Graduate School of Management, St. Petersburg University, Russia0 A. O. DAVIY
St. Petersburg School of Economics and Management,
National Research University Higher School of Economics, Russiac
E-commerce market development depends on a configuration of factors which can both enable its further development and hinder its adoption by consumers. In particular, emerging markets provide a range of opportunities for e-commerce; however, they are also associated with specific barriers, limiting the potential for fully exploiting these opportunities. With an Internet audience of 93 mln people, the Russian emerging market represents the largest online audience in Europe, allowing it to stimulate substantial e-commerce growth over the last decade. The main objective of this paper is to explore consumer perception of e-commerce adoption factors at two levels — macro-level factors associated with the overall environment, institutional factors and trust; and store-level factors associated with real consumer experiences. This multi-level approach reflects the complexity of consumer thinking about the market — both in terms of the evolving environment, which offers consumers opportunities to make decisions and purchases, and real experience, where product factors influence particular consumer decisions and are weighed by consumers as pros and cons. Our study is based on a survey of 3 387 respondents that represented the consumer perspective. The findings reveal the structure of the driving and limiting factors, highlighting the core role of the trustworthiness and transparency of the e-commerce market players, delivery conditions, and store-related risks.
Keywords: e-commerce, adoption, emerging markets, factor analysis. JEL: M31.
Postal Addresses: a 20, Myasnitskaya ul., Faculty of Business and Management, National Research University Higher School of Economics, Moscow, 101000, Russia; b 3, Volkhovskiy per., Graduate School of Management, St. Petersburg State University, St. Petersburg, 199004, Russia; c 3, Kantemirovskaya ul., St. Petersburg School of Economics nad Management, National Research University Higher School of Economics, St. Petersburg, 194100, Russia.
© V. A. Rebiazina, M. M. Smirnova, A. O. Daviy, 2020 https://doi.org/10.21638/spbu18.2020.101
INTRODUCTION
Over the past twenty years, active development of electronic markets has attracted the increasing attention of researchers from different subject areas [Hoffman, Novak, Peralta, 1999; Schafer, Konstan, Riedl, 2001; Childers et al., 2002; Pavlou, Dimoka, 2003; Gibbs, Kraemer, 2004; Kim, Urunov, Kim, 2016; Casado-Aranda, Martínez-Fiestas, Sánchez-Fernández, 2018; Sukhodolov, Popkova, Kuz-laeva, 2018]. An innovation for the retail market in the 2000-s, [Kendall et al., 2001; Santarelli, 2004] offering certain advantages over offline formats [Yadav, Varadarajan, 2005], e-commerce is now a vital channel for consumers and businesses alike, and it is growing.
While an increasing number of consumers and firms are getting involved in e-commerce, a significant proportion of retail market participants have not adopted it yet, because they don't know it exists. The context of emerging markets offers more opportunities for e-commerce development due to the inefficiencies of traditional markets [Sheth, 2011] and, thus, higher readiness to find cost- and time-effective solutions. However, due to the diversity of the emerging markets [Burgess, Steenkamp, 2006], adoption of e-commerce might be connected to transforming the values of consumers and firms, diverse behavioural strategies and consequences, in the form of the trust and involvement of consumers. In other words, whereas the overall high pace of technology integration, in commerce, is a global phenomenon, its adoption in a particular market is subject to the specifics of consumer behaviour, underlying the motives and concerns, and existing behavioural patterns. Researchers are calling for more evidence from emerging markets, providing insights and generating new understandings of what is required for successful management and marketing practices [Pham, 2013].
The Russian e-commerce market demonstrated a rapid development, particularly while compensating for the recession in the economy in 2014-2016 and the slow recovery
starting from 2016. Despite the negative trends in all the economic areas, including retail sales and real consumer income dynamics, e-commerce has steadily grown, attracting substantial business interest and providing more choice and options for consumers. With 93 mln people in Russia, over the age of twelve, 76% of the population, who, at least once a month, use the Internet [Internet audience in Russia, 2019], the Russian market has the largest online audience in Europe. Russian consumers are increasingly engaged in using the Internet and shop prolifically online.
The current study aims to explore consumer perception of e-commerce adoption factors from a multi-level perspective: firstly, the public attitude and perception of market-level factors, and, secondly, the level of consumer behavior and perception of particular stores. The multi-level approach allows us to identify both macro- and institutional-level factors, shaping consumer behaviour, as well as firm-level factors, as perceived by consumers — leading to consumption patterns. As in any market, it is a balance between demand and supply that contributes to the market evolution. In the case of the Russian economy, adoption of e-commerce has been embedded in the overall process of transition to the market economy, whereas its current stage of development characterised as an emerging market. Existing studies in Russia have insufficiently studied the role of consumer behaviour and perception as a market force, that both reflects existing market practices and contributes to further market development.
The paper is organised in the following way. The first part examines the existing research on e-commerce adoption factors in developed and developing markets. The second part describes the e-commerce market development in Russia and identifies e-commerce adoption and the development factors in the Russian market. The third section is concerned with the methodology used for this study and the sample description. The fourth section presents the findings of the research,
focusing on the data obtained from the survey, the fifth section presents the discussion of the results and subsequent conclusion.
1. theoretical framework
1.1. E-commerce development in emerging markets
A large and growing body of literature has investigated the development of B2C e-commerce [Wymer, Regan, 2005; Iglesias-Pradas et al., 2013; Lian, Yen, 2014; Chaparro-Peláez, Agudo-Peregrina, Pascual-Miguel, 2016]. Because of the highly fragmented nature of existing research on e-commerce, the definition of e-commerce is blurred: it can be understood as a distribution channel [Rodríguez-Ardura, Meseguer-Artola, 2010], static or interactive websites, capacity of processing electronic transactions, or even as an electronic integration [Abou-Shouk, Megicks, Lim, 2013]. In this research e-commerce is defined as any type of purchasing or selling of goods and services, by large- and small-size firms via the Internet [Solaymani, Sohaili, Yazdinejad, 2012].
As e-commerce implies integration of this retail phenomenon in the market economy, the choice of country where study is conducted plays an important role at the empirical investigation of factors developing e-commerce market. The country and market specifics determine what drives e-commerce adoption, which results in two important remarks. The first one is that the role of some factors, examined in the different studies, are found to be rather ambivalent. For example, the role of trust can have diverse effects, depending on the research context. In the USA context [Pavlou, Dimoka, 2006] trust was considered to be a driving factor of e-commerce adoption, but, in Spain, this factor is marked as a problematic one: in particular Chaparro-Peláez and co-authors have discovered that trust of the Internet channel and to the vendor inhibits the consumers' decision to buy online [Chaparro-
Peláez et al., 2016]. The second point refers to the fact that some factors, for instance, resource constraints (finance, technology and managerial expertise) are common limiting factors for companies operating in the developed and emerging markets [Anckar, Walden, 2001; Hsiao, 2003; MacGregor, Vrazalic, 2005; Abou-Shouk, Megicks, Lim, 2013]. However, studies conducted in the emerging markets identified some additional limiting e-commerce factors such as state regulation [Abou-Shouk, Megicks, Lim, 2013; Abualrob, Kang, 2016], infrastructure [Abou-Shouk, Megicks, Lim, 2013; Abualrob, Kang, 2016], and consumer behaviour [Wresch, Fraser, 2011; Abou-Shouk, Megicks, Lim, 2013]. Thus, it can be proposed that emerging markets create specific conditions for e-commerce adoption and development [Oreku, Mtenzi, Ali, 2013; Molla, Licker, 2005] and, thus, contributing to diversity in potentially relevant research factors [Wymer, Regan, 2005]. By classifying the factors, influencing e-commerce development and adoption, in existing studies, researchers have applied categorisation — e.g., technical and non-technical factors [Oreku, Mtenzi, Ali, 2013] technical, product- and service-related factors [Lin, Fu, 2012], or technical, organisation, individual and environmental factors [Valmohammadi, Dashti, 2016]. In line with existing research and the conceptualisation of consumer's digital competences and skills, some studies pay closer attention to the classification of relevant skills (e.g., computer skills, Internet skills, Internet trust, and online opinions seeker [Valarezo et al., 2018]. On the contrary, some researchers preferred to focus on the psychological side of e-commerce adoption — e.g., different facets of perceived risk — financial, privacy, and performance [Casado-Aranda, Martínez-Fiestas, Sánchez-Fernández, 2018].
Taking the diversity of factors into consideration, the current study aims to provide a systematized approach to the existing research on e-commerce adoption factors based on the type of factors considered (e.g. fostering e-commerce adoption vs. limiting its potential), method applied in the study, research
Key research on
N Source Factor classification Type of factor
Driving Limiting
1 [Wymer, Regan, 2007] • Environmental • Organizational • Knowledge-related • Technological + +
2 [Kshetri, 2007] • Economic • Sociopolitical • Cognitive - +
3 [Ho, Kauffman, Liang, 2007] • Economic • People-related • Environmental • Technological + -
4 [Rodríguez-Ardura, Meseguer-Artola, 2010] • Technological external and internal context • Nontechnological external context • Nontechnological internal context + -
5 [Wresch, Fraser, 2011] n/a - +
6 [Solaymani, Sohaili, Yazdinejad, 2012] n/a - +
7 [Lin, Fu, 2012] • Technical • Service-related • Product-related + -
8 [Abou-Shouk, Megicks, Lim, 2013] • Internal • External + +
9 [Iglesias-Pradas et al., 2013] n/a + +
10 [Oreku, Mtenzi, Ali, 2013] • Technical • Non-Technical - +
11 [Lian, Yen, 2014] n/a + +
12 [Clemes, Gan, Zhang, 2014] n/a + +
13 [Chaparro-Peláez, Agudo-Peregrina, Pascual-Miguel, 2016] n/a + +
14 [Abualrob, Kang, 2016] • External • Internal - +
15 [Valmohammadi, Dashti, 2016] • Technical • Individual • Organizational • Environmental - +
16 [Zaidan, 2017] n/a + +
17 [Rahayu, Day, 2017] n/a + -
18 [Valarezo et al., 2018] n/a + -
19 [Zhang et al., 2019] • Attitude • Perceived convenience • Perceived revenue disadvantages • Subjective norms • Habit + +
20 [Nathan et al., 2019] n/a + +
21 [Huang, Chang, 2019] • Perceived trustworthiness • Perceived value • Cost • Benefits • Attachment styles • Information index • Information signals + +
Note: "+" and "-" indicate the presence and absence of a certain attribute, "n/a" — not applicable.
object (e.g. firm vs. consumers), focus on emerging vs. developed market, and finally, whether users vs. non-users were addressed. We suppose that the combination of these criteria can help systematize existing research
and offer an agenda for the next steps. The search strategy was based on the Scopus database, using the keywords "e-commerce", "barriers" and "drivers", with the focus on publications in the English language, published
Table 1
B2c e-commerce market development
Method Research object Market Adoption
Qualitative Quantitative Firm consumer Developed Emerging user Non-user
- + + - + - + +
+ - + + - + + +
- + n/a n/a + - n/a n/a
- + + - + - + +
+ - + - - + + -
- + + - - + n/a n/a
- + - + n/a n/a + -
- + + - - + + +
- + - + + - - +
+ - + + - + n/a n/a
+ + - + - + + +
- + - + - + + +
- + - + + - + +
- + + - - + + +
+ - + - - + - +
- + + - - + + +
- + + - - + + +
- + - + + - + -
- + - + - + + +
- + + - + + + -
- + - + + + + +
in peer-reviewed journals. The search resulted in 290 articles with the focus on e-commerce driving factors, and 225 articles with the focus on e-commerce development barriers. After careful analysis of the selected articles,
we could stress 21 key studies in the field of e-commerce market development (Table 1).
Two main conclusions can be inferred from Table 1. First, while most studies mix the focus on the driving factors and barriers of
e-commerce adoption, only few studies (e.g., [Abou-Shouk, Megicks, Lim, 2013; Abualrob, Kang, 2016]) identify internal and external factors of adoption. At the macro-level, existing studies do classify the major factors, which we labeled as environmental, economic and sociopolitical ones. Internal factors are mostly associated with cognitive, knowledge-related, people-related, product-related/ service-related groups of factors. Additionally, several groups of factors, in particular technological and technical, are dualistic as they can be both internal and external ones according to different research. Division of all the groups of factors into two general categories raises two important questions: (1) what categories are studied in more detail, especially in the case of specific markets; and (2) what is the balance between internal and external factors in a certain market? Second, researchers examine various factors in their studies, that, in turn, leads to classification variety. To structure all groups of factors, we can combine them into three enlarged and more frequently used groups of factors — environmental, organizational/store-related and product-related/service-related ones.
We found out that the environmental factors have a very large variation and include governmental policy and standards (such an institutional factors) — as lack of trust in the suppliers of technology, rather that the market-factor of a lack of available specialists. As a result, this category looks very heterogeneous and broad and requires further clarification. We propose that the environmental category should reflect the business environment. This understanding of the environmental category corresponds with [Abou-Shouk, Megicks, Lim, 2013] points of view. Organizational or store-related category reflects the side of the firm and the consumer simultaneously. If we consider factors from the firm perspective, this category will include factors related to the organization and its processes or resources (e.g., financial resources or infrastructure readiness); on the other hand, the consumer perspective will fill this category with a different meaning, for example, some technical
peculiarities concerning consumer-e-shop interactions, like product search, order tracking, or identity verification. Product-related and service-related groups of factors incorporate such factors as policies, pricing, product variety and so on. So, although the product-related and service-related groups of factors reflect only the consumer perspective and are stressed in one research, that category seems to have more potential and can be investigated from the firm's perspective too.
As stated above, the focus on emerging markets offers space for the verification of what factors maintain their importance first, and what factors should be additionally taken into consideration, second. An additional constraint is related to identifying the role of each factor, as existing classifications are not aligned, and depend on the selected approach, which can vary from a very general-istic to a very specific approach. For instance, trust is established as one of the central factors in e-commerce adoption and consumer behavior in the e-commerce market [McKnight, Choudhury, Kacmar, 2002; Gefen, Karahanna, Straub, 2003]. But as distinct factors it's highlighted in [Chaparro-Peláez, Agudo-Peregrina, Pascual-Miguel, 2016; Iglesias-Pradas et al., 2013] and as part of technical and environmental group of factors, it is found only in [Valmohammadi, Dashti, 2016] study.
Thus, the overview of the factors, derived from the theoretical sources and studies in other markets, reveals a classification of drivers and barriers, influencing the development of e- commerce: economic, sociopolitical, and cognitive factors. The context of the study, as an example of an emerging market, generates an expectation of a particular role of the factors, highlighted in the previous studies. The changing nature of the Russian market and interactions between market actors, representing both the supply and demand sides, is calling for a more active investigation of how these developments are shaping the structure of the driving and limiting forces of the e-commerce growth in the country.
The current study is positioned as a study focusing on the internal factors of consum-
Table 2
comparison of e-commerce perspectives
E-commerce actors E-commerce consumers
The factors driving the development of e-commerce in Russia
• Low cost of market entry • Bankruptcy of competitors, increasing the market share of existing players • Using multi-channel and omni-channel approach • Improvement and changes in a firm's business processes • Russians becoming increasingly keen on the Internet • Standardization of the internal processes of e-stores • Implementation of more advanced technologies into the internal processes of e-stores • E-stores develop their own logistics • Availability due to territorial expansion of online stores • Availability of new products and services • Convenience • Reviews • Time-saving factors • Superior service • Detailed information about the product
Barriers of e-commerce adoption and market development in Russia
• Weak involvement of the regions in e-commerce • Weak saturation of the market for internet services in Russian regions • The underdevelopment of the Russian logistics system • Low margin business on the internet and the prevalence of price competition • Pressure of cross-border players on Russian Internet companies • Lack of optimal and coherent legislative framework to regulate the industry • Bureaucracy • Heavy taxation and tax saving schemes, grey imports and parallel importation of goods, the complexity of export, fraud • Shortage of staff and lack of qualified management • Involuntary prepaid payment • Popularity of payment by card or by cash when the order is delivered • The complexity of product return and exchange policy • Fear of sharing confidential information • Low perceived quality of products and shops • Low level of confidence in online shops • Low public confidence in the internet as a sales channel • Online shopping is difficult and unusual • The products cannot be touched or tried o • Extended delivery times • Fear that the order can be lost during delivery
Based on: [Virin, 2014; Lukina, 2014; Kulikov, 2014; The Russian E-commerce Report, 2019; GfK Rus..., 2019; Euromonitor, 2019; Data Insight, 2019; Alekseev, 2019].
ers perception of various factors, which can be grouped by the levels of perception of the market in general, under the influence of spreading e-commerce practices, and particular store perception — whereas the focus is shifted from institutional and macro-level aspects, to transactional and relational interactions with a particular store. By following this approach, we also aim to answer the research question of how does the existing structuring of the revealed e-commerce adoption factors fit the perception of consumers in the emerging market within Russia?
1.2. Factors affecting e-commerce adoption in Russia
Rapid development of the e-commerce market and its growing importance for the Russian economy has attracted the attention of both researchers [Kim, Urunov, Kim, 2016; Sukhodolov, Popkova, Kuzlaeva, 2018] and consulting companies (e.g., GfK Rus; Data-Insight). Current studies on e-commerce market development, described various factors which drive e-commerce adoption in Russia (Table 2), from the perspectives of e-commerce actors and e-commerce consumers. From the
Table 3
Russian e-commerce market, year-on-year growth (%), (retail value RSP excluding sales tax)
Industry 20102011 20112012 20122013 20132014 20142015 20152016 20162017 20172018
E-commerce 33.0 35.9 30.0 31.0 7.0 21.1 15.7 19.1
Store-based Retailing 14.0 12.4 9.1 11.3 4.4 3.0 5.3 5.1
Source: [Euromonitor, 2019].
perspective of e-commerce actors, existing research highlights the cost advantages, opportunities to build multiple- and omni-chan-nel strategies, opportunities of operational improvements, thus having operational advantages when running an online business. From the consumer perspective, e-commerce offers increased availability — both geographical by and with regard to a wider selection of products and service. Additionally, consumers highlight convenience, time-saving factors and superior service compared to offline stores.
The changes in the market dynamics and the market development, offer a call for businesses to re-evaluate their strategies and approaches. The call for a strategic approach should be primarily aimed at overcoming the major barriers to further development and the penetration of e-commerce in the Russian market, suggested by previous studies (Table 2). The barriers from the side of the e-commerce actor perspective, comprise general market factors, whereas e-commerce specific factors are related to the logistics as one of the most crucial factors for customer service in ecommerce. From the consumer perspective, the issue of payment is reflected by the preference to pay by cash or card on delivery, thus hedging the risks of late or non-delivery, and minimizing potential risks for the consumer. The role of trust-related factors is evident as various aspects of both institutional, market-, store- and product-related trust are revealed in previous studies.
Comparison between the results of theoretical analysis and the analysis of secondary
data concerning the Russian market, enable us to reveal some meaningful distinctions. Firstly, it's notable that environmental factors play an important role from the perspective of both e-commerce actors and consumers, while previous studies described this factor only from the firm's perspective. Secondly, existing research highlighted the structure of factors and their meta-groups, as environmental, market and cognitive factors; however, a deeper analysis of existing sources on Russian market specifics offer additional market-specific factors, which have to be integrated when studying Russian e-commerce practices. The changing nature of the Russian market and interaction between market actors, representing supply and demand sides, is calling for a more active investigation of how these developments are shaping the structure of forces driving, or hindering e-commerce adoption and development.
2. THE RUSSIAN E-COMMERCE MARKET OVERVIEW
The changing nature of the Russian e-commerce market requires an in-depth market overview in order to describe the context of the study and includes an analysis of the driving and limiting forces of e-commerce growth in the country. According to the Federal Service for State Statistics, the e-commerce market accounted for only 1.7% of the total retail market in Russia at the end of 2018 [Federal Statistics, 2019]; however, the increasing importance of e-commerce for the
2 588 340.6
2 229 298.7
1 900 990.1
1 604 947.8
1 341 076.1
1 107 854.1
2018 2019 2020 2021 2022 2023
V_J
Fig. 1. The growth rate of the Russian e-commerce market (retail value RSP excluding sales tax, mln rub), 2018-2023 Source: [Euromonitor, 2019].
Russian economy as a whole is evident. According to [Euromonitor, 2019], online trading accounts for approximately 77% of Russia's non-store retail. In 2010, the volume of the Russian e-commerce market ranged from 190 to 250 bln rub, and at the end of 2016, the overall size of the market accounted for 1.6 trn rub [AKIT, 2019].
From 2010 to 2012, the e-commerce market increased constantly in absolute numbers. Economic instability, along with the financial crisis, which started in 2014 but affected the Russian economy till the end of 2016 [World Bank, 2015], slowed down the growth of the retail market (Table 3). The tendency towards growth continued only after 2016, although at a slightly slower pace than in the previous years. Nevertheless, comparing store-based retailing with e-commerce, the growth of e-commerce in 2018 was significant, while store-based retailing witnessed modest growth.
In the middle of 2019, Russia ranked eighth worldwide in terms of its growth of Internet users, ahead of such countries as China, India, the USA, Brazil, Indonesia, Nigeria, and Japan [Data Insight, 2019]. The average an-
nual growth of internet users has exceeded 10% over the last few years. In September 2011, Russia overtook Germany as the market with the largest number of Internet users in Europe [Wauters, 2012]. Looking at the whole of Europe combined, 16% of all Internet users, are from Russia [Internet World Stats, 2019]. However, Russia still lags behind most other European countries in terms of penetration, with more than 80% of the adult population connected to the Internet [Levada-Center, 2019]. Thus, e-commerce as a market, still has significant potential for growth over the coming years (Figure 1).
Experts predict a promising future for the e-commerce market [Data Insight, 2019] and suggest that growth will be provided by certain factors, among which are the customer's habit to buy online, improvement of the customer experience and the standardization of the routine processes of the e-stores. Additional incentives that contribute to the markets development are the improvement of the online platforms which provide a mixed range of products and services, and the progress towards omni-channel strategy which retailers made during the last years.
On the other hand, despite the significant potential for a future growth, there are some limiting factors which impact the positive dynamics of the e-commerce market. The major disadvantage is that the Russian ecommerce market still feels the effect of the crisis, which is manifested in the low domestic demand and discouraged household consumption. Moreover, the e-commerce market growth is ensured by the increase in the number of orders; however, the average transaction value has fallen for the third year in a row. In addition, while the number of online buyers is rising, the new customers do not provide sustainable growth to the market as they do not purchase frequently.
New market realities create boundaries to the extensive growth of the e-commerce market, which was typical for the early 2010s. The current economic situation requires companies to develop a more reflective approach to the attraction and retention of clients, and build with them long-term relationships. On the one hand, companies should be focused on removing the barriers consumers face when making purchases via the Internet, and help them to overcome these barriers; on the other hand, companies should benefit from the drivers that stimulate Internet users to shop online. Such systematic activity can have a positive impact not only on online stores' performance, but on the whole market in general. Thus, the Russian e-commerce market requires further explorative empirical investigation to reveal its specific e-commerce adoption factors.
3. RESEARCH DESIGN AND SAMPLE DESCRIPTION
The empirical stage of the study has an explorative nature, as its main research objective is to identify, systemize and rank the factors, affecting adoption and development of the e-commerce market in Russia from the perspective of e-commerce consumers.
Research design. To achieve the set objectives two-stage quantitative study was con-
ducted: 3400 Russian consumers took part in the first survey, investigating the e-commerce adoption factors, and 1093 respondents in the second survey with the focus on the factors, influencing the choice of the particular online-shop. Thus, the empirical study methodology comprises three steps: Step one: consumer perception of macro-level factors of e-commerce market development was investigated. This step is in line with the studies, identified at the theoretical phase of research (Table 1).
Step two: consumer perception of the firm-or online-shop level factors was at the focus, reflecting the results of the Russian e-commerce market analysis. The difference between the levels of analysis represents perception of the market evolution and e-commerce as a part of this evolutionary process and — at the step three — perception of the specific purchase experience where the consumer does not think about the market in general, but applies their own experience and service encounters.
How authors [Hitt et al., 2007, p. 1385] define, "most management problems involve multilevel phenomena, yet most management research uses a single level of analysis". Due to specialization, increased requirements of the methods of analysis and research designs, the scope of research in each of the management domains has reduced. As a result, the boundaries between these specialized fields were established, and the fields and levels of analysis became increasingly isolated. We can differentiate between the environmental, network, organization, subunits, groups and individual levels of analysis in management, whereas knowledge, skills and abilities are being transferred between the levels. In any managerial phenomenon, including e-commerce, transformation and adoption also happens at various levels, resulting in macro-level evolution, that affects the environment as a whole, changes at the level of particular organizations, or e-commerce actors, and finally individuals. In this study we measure the perceptions of individual e-commerce consumers, whereas the objects of consumer perception represent two other levels: the
macro-level of e-commerce and market environment and the organization-level, whereas consumers experience encounters with a particular e-commerce actor, or store.
Data collection process. The quantitative data reflecting consumer perspective for both of the surveys was collected from a survey of Internet users that participated in the online course "Marketing" held on The Russian National Educational Platform "Open Education" [The Russian National Platform...]. The study involves respondents from this business course, which is a common practice in consumer research (for example, it is common for the papers published in the Journal of Consumer Research, one of the best marketing journals in the world). The online version of the questionnaire was developed on a platform "Survey monkey", a survey development cloud-based software [SurveyMonkey]. About 20 000 Internet users took part in the course, and the survey link was sent to all participants of the course. The survey link was sent in April, 2019 and the course participants were given two months to complete the questionnaire. Completing the questionnaire was voluntary and free of charge. A total of 3387 respondents from 63 regions of the Russian Federation participated in the survey, with 17 incomplete responses, so the response rate was about 17%, and almost a third of the whole respondents (1093) agreed to participate in the second stage of the study.
Operationalization. As was mentioned, quantitative research consists of two studies: the study of e-commerce adoption factors, and the second study with the focus on the factors, influencing the choice of the particular online-shop. To conduct the quantitative online survey, two questionnaires were developed and operationalized. The questionnaires for both the studies were based on the existing research and scales, operationalizing factors, affecting the adoption and development of e-commerce through the lens of consumer perception.
The first questionnaire (study 1) includes 37 questions on e-commerce adoption factors grouped in the following five main blocks:
1) benefits of purchasing online [Chaparro-Peláez, Agudo-Peregrina, Pascual-Miguel, 2016; Tsai, Huang, Jaw, Chen, 2006];
2) personal information sharing risks [Kor-gaonkar, Wolin, 1999];
3) trust in e-commerce / online stores [Kim, Ferrin, Rao, 2019; Tsai et al., 2006];
4) influence of the social norms and reference groups [Ajzen, 1991];
5) easiness of purchasing in the online store [Korgaonkar, Wolin, 1999].
The second questionnaire (study 2) includes five blocks of questions to determine how consumer choose online store:
1) quality of the online store delivery [Tsai et al., 2006; Corbitt, Thanasankit, Yi, 2003; Wresch, Fraser, 2011];
2) range of assortment provided by the online store [Chaparro-Peláez, Agudo-Peregrina, Pascual-Miguel, 2016; Clemes, Gan, Zhang, 2014; Lin, Fu, 2012];
3) online store payment options [Clemes, Gan, Zhang, 2014; Chaparro-Peláez, Agudo-Peregrina, Pascual-Miguel, 2016];
4) quality of online store services [Lin, Fu, 2012; Chaparro-Peláez, Agudo-Peregrina, Pascual-Miguel, 2016; Clemes, Gan, Zhang, 2014; Corbitt, Thanasankit, Yi, 2003; Kim, Ferrin, Rao, 2009; Kshetri, 2007; Wymer, Regan, 2007];
5) reasons for mistrust in online shopping [Kshetri, 2007; Chaparro-Peláez, Agudo-Peregrina, Pascual-Miguel, 2016; Corbitt, Thanasankit, Yi, 2003].
In order to avoid leading questions and prompting respondents to a definite answer, all formulations were tested for neutrality. Additional socio-demographic questions were added to the last blocks of both surveys to classify the respondents. The present study measures all items on a seven-point Likert scale ranging from 1 "completely disagree" to 7 "completely agree".
Sample descriptions. To filter the respondents on their online shopping experience and assess the respondents' frequency of online purchases the following screening question was developed: "How many times have you made purchases from online shops
Table 4
Online surveys sample descriptions
Selection criterion Characteristic Study 1 "E-commerce adoption factors" Study 2 "Factors influencing online-shop choice"
Number of respondents Share, % Number of respondents Share, %
Sex Male 882 26.0 361 33.6
Female 2 498 73.8 707 65.9
Age Under 18 22 0.6 5 0.5
18-25 1911 56.4 346 32.2
26-30 587 17.3 318 29.6
31-35 381 11.2 154 14.4
36-40 231 6.8 118 11.0
41-50 206 6.1 104 9.7
51-60 36 1.1 26 2.4
Older 60 9 0.3 0 0
Education Incomplete secondary education 15 0.4 6 0.6
Secondary education 183 5.4 27 2.5
Vocational secondary education 72 2.1 30 2.8
Associate degree 1 206 35.6 165 15.4
Higher education 1 559 46.0 650 60.6
Two and more degrees (bachelor) 271 8.0 142 13.2
PhD / MBA 78 2.3 50 4.7
Income level Not enough money to purchase food 21 0.6 7 0.7
Enough money only to purchase food 72 2.1 36 3.4
Enough money to purchase necessary food and clothing, but postponing buying larger purchases 1 460 43.1 474 44.2
Enough money to purchase most of the durable goods (e.g., refrigerator and TV), however can't buy a car 1 089 32.2 340 31.7
Enough money to purchase a car, but can't buy an apartment 544 16.1 175 16.3
Enough money to purchase all goods 182 5.4 32 3.0
Marital status Married 2 352 69.5 497 46.3
Divorced 1 001 26.7 71 6.6
Single 105 3.1 501 46.7
Table 4 (end)
Selection criterion Characteristic Study 1 "E-commerce adoption factors" Study 2 "Factors influencing online-shop choice"
Number of respondents Share, % Number of respondents Share, %
Children Have a child/children 722 21.3 378 35.2
Have no children 2 646 78.1 687 64.0
Region Moscow 1 332 41.6 391 37.9
St. Petersburg 480 15.0 185 17.9
Other regions 1 391 43.4 454 44.2
during the last year?" Analysis of the obtained results revealed that 20 respondents from the sample had never bought anything from online shops, thus the total sample was 1 093 respondents. Table 4 presents the demographic information of respondents.
While both the samples were determined to the online users, the main respondents' characteristics are consistent with the Russian online consumers' profile: according to the report "How Do Russians Shop Online" [Antonov, 2019], a third of all the orders are made by residents of Moscow and St. Petersburg, women buy more on the Internet than men (55% versus 45%), and more than half of all purchases are made by Russians aged 25 to 44 years [Antonov, 2019].
Most of the respondents in the samples are those aged between 18-35 years old, this is 84.9% and 76.2% in the first and the second surveys, respectively. This distribution is also quite adequate to the total population of e-commerce users, since according to the 2019 Internet trade survey in Russia, the majority of e-commerce users are consumers aged 20-40.
Respondents with a higher education (including those online consumers who have an associate degree) constitute the majority of online consumers in the sample. The sample of the current study was represented by students of the online course, and it had a certain influence on the sampling bias in favor of consumers with higher education. However,
in general, it is consistent with the market data that the share of online consumers with a higher education prevails over the online consumers with other types of education.
Analyzing the number of online consumers in Russia's regions it was noted that in 2019, about a half of Moscow and St. Petersburg residents using the Internet made an online purchase [Internet audience in Russia, 2019]. According to another report, Moscow and St. Petersburg generated a third of total sales proceeds [Antonov, 2019]. Respondents taking part in the survey represent the main cities of the e-commerce market in Russia: Moscow (41.6% and 37.9%, respectively), St. Petersburg (15.0% and 17.9%, respectively), and 64 other regions of Russia that are represented in the sample (43.4% and 44.2%, respectively).
4. RESEARCH RESULTS AND THE KEY FINDINGS
In order to reveal the e-commerce adoption factors from the consumer perspective, the exploratory factor analysis (EFA) with the IBM SPSS Statistics has been applied to explore the factor structure behind the scale items, representing potential e-commerce adoption factors. EFA has been applied without an initial assumption about the number of factors in order to avoid the bias. Principal
Table 5
E-commerce adoption factor analysis results (study 1, N = 3387)
Item in English Component
1 2 3 4 5 6 7 8
F1: Trust in e-commerce/online stores [Kim, Ferrin, Rao, 2019; Tsai et al., 2006]
Online stores do not inflate prices during the purchase 0.798
Online stores behave truthfully towards consumers 0.772
Online stores behave honestly to consumers 0.735
Online stores meet commitments to consumers 0.656
In general, prices in online stores are not overpriced 0.625
Overall, online shopping can be trusted 0.587
F2: Personal information sharing risks [Korgaonkar, Wolin, 1999]
I am concerned that my personal financial information may be shared with businesses without my consent 0.898
I am concerned over the security of personal information on the web 0.87
I am uncomfortable giving my credit card number on the web 0.856
I am worried about the security of financial transactions on the web 0.845
When I send a message over the web, I feel concerned that it may be read by some other person or company without my knowledge 0.711
F3: Benefits of purchasing online [Chaparro-Peláez, Agudo-Peregrina, Pascual-Miguel, 2016; Tsai, Huang, Jaw, Chen, 2006]
Quick online search of the needed product 0.774
Ease of product options comparison before the purchase 0.756
Time saving 0.726
Increase of the overall shopping performance 0.685
Money saving 0.652
F4: Comfort of purchasing online [Korgaonkar, Wolin, 1999; Tsai, Huang, Jaw, Chen, 2006]
It is easy for me to purchase online
0.801
Component Analysis and Varimax rotation with Kaiser normalization have been used, resulting in identifying eight e-commerce
adoption factors. EFA was applied to the initial pool of 44 items, that were then reduced to 31 in the final version of the factor
Table 5 (end)
Item in English Component
1 2 3 4 5 6 7 8
It is easy for me to interact with most of online stores during purchasing 0.801
It is easy for me to fulfill any operations during purchasing online 0.779
Overall, online purchasing is easy 0.761
F5: High customer focus of online stores [Tsai, Huang, Jaw, Chen, 2006]
Most online stores are responsible for their customers 0.806
Most online stores understand and respect their customers' needs 0.779
Most online stores prove the statement that a customer is always right 0.778
F6: High technical competences of online stores [Corbitt, Thanasankit, Yi, 2003]
Most online stores have necessary technology knowledge to carry out the online transaction 0.769
Most online stores have the necessary skills and ability to carry out the online transaction 0.736
Technology obstacles should not be a major concern when conducting online transactions 0.645
The chance of having a technical failure in an online transaction is quite small 0.521
F7: Positive influence of the social norms [Ajzen, 1991]
Most of the people who are important to me would support me if I bought online 0.876
Most of the people who are important to me would support my decision to buy online 0.876
F8: Easiness of online purchasing [Korgaonkar, Wolin, 1999]
It is easier for me to make the decision to buy online than offline 0.848
Online purchasing is the most effective method of shopping 0.767
Cronbach's Alpha 0.879 0.896 0.845 0.921 0.876 0.747 0.906 0.78
Average variance, % 32.42 11.72 8.69 4.18 4.09 3.64 3.49 3.32
Mean 4.772 4.807 5.703 5.547 4.583 4.728 4.674 4.338
Standard deviation 0.171 0.112 0.201 0.115 0.211 0.365 0.148 0.067
model after deleting the items with the cross- Eight factors explain 71.6% of total vari-
loadings and the items with low factor loadings ance. Factors obtained as a result of factor according to the EFA procedure (Table 5). analysis in study 1 show a very high level of
scale reliability as Cronbach's Alpha for each of the factors exceeds 0.7. The first factor combines the items from various blocks of the questionnaire, resulting in an overall assessment of the perceived and trustworthiness of the e-commerce and the online store activities.
A factor analysis of selection criteria by consumers of an online store (study 2) was carried out using the same methods as in study 1: 40 indicators were initially selected for the analysis. After conducting the factor analysis, the total number of revealed factors is seven, while the number of items is 31. The reliability coefficient of Cronbach's Alpha is 0.876, which indicates the high reliability of the scale valid-
ity used in the survey. The results of the factor analysis of indicators characterizing the consumer's online store choice can be found in Table 6.
Seven factors explain 58.9% of variance in total. As is seen from the table, Cronbach's Alpha is meeting the required level of the minimum 0.7. The resulting structure of factors of e-commerce adoption and development is presented in Table 7.
The structure of market-level factors, identified as core building stones of consumer perception, is centered around the leading factor of trust. Indeed, trust is the main institutional factor that drives development of any business phenomenon in emerging markets.
Table 6
Factor analysis results of online store choice (study 2, N = 1 093)
Item in English Component
1 2 3 4 5 6 7
F1: Benefits of shopping in the particular online store, including online store payment options [Clemes, Gan, Zhang, 2014; Chaparro-Peláez, Agudo-Peregrina, Pascual-Miguel, 2016]
Product quality 0.811
No online store fraud 0.776
Compliance of the delivered goods with their description on the site 0.765
The price of the product 0.74
Relevance of information on the site 0.661
The presence of a convenient method of payment for goods 0.656
No hidden fees when paying for goods 0.652
Accuracy of order execution 0.632
Shipping cost 0.571
Return and exchange availability 0.504
F2: Mistrust in the online store [Kshetri, 2007; Chaparro-Peláez, Agudo-Peregrina, Pascual-Miguel, 2016; Corbitt, Thanasankit, Yi, 2003]
I do not trust non-cash methods of payment for goods/services in an online store 0.854
I do not trust this online store 0.805
I do not trust payment systems in the online store 0.787
I am not ready to pay for a product / service until it is received 0.729
Table 6 (end)
Item in English Component
1 2 3 4 5 6 7
F3: Online store reputation [Virin, 2014]
Online Store Rating 0.806
Reputation / fame of the online store 0.76
Online customer reviews 0.733
Shopping experience at this online store 0.549
Online store advertising 0.463
F4: Quality of the online store delivery [Tsai, Huang, Jaw, Chen, 2006; Corbitt, Thanasankit, Yi, 2003; Wresch, Fraser, 2011; Tsai, Huang, 2007]
Delivery to the specified address 0.707
Delivery time 0.69
Availability of express delivery 0.665
Delivery of goods on time 0.659
F5: Range of assortment provided by the online store [Chaparro-Peláez, Agudo-Peregrina, Pascual-Miguel, 2016; Clemes, Gan, Zhang, 2014; Lin, Fu, 2012]
Unique/Exclusive Product Availability 0.833
The availability of new products that have just entered the market 0.751
Wide range of products 0.638
F6: Quality of online store services [Lin, Fu, 2012; Chaparro-Pelaez, Agudo-Peregrina, Pascual-Miguel, 2016; Clemes, Gan, Zhang, 2014; Corbitt, Thanasankit, Yi, 2003; Kim, Ferrin, Rao, 2009; Kshetri, 2007; Wymer, Regan, 2007]
Customer orientation and individual approach 0.768
Quality of service in the online store 0.761
Confidentiality and guarantee of personal data protection 0.581
F7: Cross-border financial advantages [Virin, 2014]
Existence of taxes when ordering goods in a foreign online store 0.864
Exchange rate when ordering goods in a foreign online store 0.843
Cronbach's Alpha 0.886 0.839 0.758 0.708 0.707 0.738 0.775
Average variance, % 24.60 11.18 7.01 5.43 4.40 3.76 3.65
Mean 6.652 3.34 5.389 5.538 5.089 6.019 5.835
Standard deviation 0.15 0.827 0.755 0.924 0.663 0.4 0.201
Table 7
Structure of e-commerce adoption and development factors
E-commerce adoption factors
Market-level e-commerce adoption factors
Store-level e-commerce adoption factors
Trust / Mistrust in the online purchasing Benefits of online purchasing Quality of online store service
Personal information sharing risks Online store reputation
Positive influence of the social norms Quality of the online store delivery
Easiness of online purchasing Range of assortment provided by the online store
Technical competences of the online store Cross-border financial advantages
In the case of our study, trust is associated with a fair price attitude, truthful and honest behavior, and commitment — thus representing the institutional norms applied to the ecommerce market. This perspective can also be associated with customer focus and care as an institutional norm — which is identified as a separated factor, as social norms are now supporting the adoption of e-commerce. The next three factors can mirror the factors as identified by [Rogers, 2010]: (1) benefits or superior performance — benefits of e-commerce as a mode of business; (2) comfort that can be associated with the perceived ease of using e-commerce; as well as (3) ease of decision-making — in other words, considering e-commerce as a facilitator for consumer decision making. These three factors reflect the perspective of adopting e-commerce as a new technology. Consumer risks are represented by factors related to the risks of sharing personal information and the risk of technical failure of e-commerce as a service. The latter one is associated with the widely studied technical side of e-commerce as equipment, competencies and knowledge of e-commerce actors. Thus, reflecting on identified macro-factors results in three institutional factors: trust,
customer care, and social norms; three factors of adoption according to the innovation diffusion theory — benefits, comfort and ease of making decisions; as well as one factor relating to consumer risks.
At the level of consumer perceptions of store-level factors the structure of factors reveals the following overarching angles: the most general and the strongest in terms of variance explained factor that mirrors the customers journey logic and combines the elements of service encountered in e-commerce. It combines the focus on quality, information required, price and payment options, commissions and delivery. Besides this general factor, the factors of delivery and assortment were highlighted as separate drivers of e-commerce adoption. The latter is also distinguished as assortment in general, and the benefits of buying from abroad, whereas consumers face availability of a wider choice and different models. The factor of trust repeats the same institutional macro-level trust, applied to a specific consumer experience. Again, the main risks are perceived as relating to payment options and the usage of credit cards vs. cash, typical for emerging markets practices. Finally, two factors stand out reflecting marketing practices and norms of managing customer relationships — the role of reputation and customer focus. The reputation factor includes both paid, owned and earned channels. However, customer focus represents entirely the company-level capability, as perceived by customers.
5. DISCUSSION AND CONCLUSION
The main objective of this paper is to explore consumer perception of e-commerce adoption factors on two levels — the first are the macro-level factors, associated with the overall environment, institutional factors and trust; the second one is store-level factors, or factors associated with real consumer experiences. Combining these two levels of analysis, especially when the source of data refers to the same individual level of e-commerce con-
sumers, provides a contribution to existing research where researchers are mostly focusing on one level only. As the result of the existing research on e-commerce adoption factors analysis, the following conclusions can be done:
1) previously, studies described environmental factors only from the firm perspective, whereas current studies are more focused on both e-commerce actors and consumers;
2) there are some factors, which are common for different countries (such as environmental, market and cognitive factors) whereas different countries also have factors, specific to them. This assumption could be used when studying marketing practices in different countries;
3) there is more research that needs to be done to determine the consumers behaviour in emerging markets, including the Russian e-commerce market.
Before conducting the empirical research, the secondary data describing the Russian e-commerce market were analysed, revealing several interesting findings. Firstly, environmental/market factors are the largest group of limiting factors from the actor perspective. At the same time, organizational/store-related and product/service-related factors are the main driving factors from the same perspective. Secondly, while the general theoretical structure can capture most of the identified factors, some groups of factors don't fit in the current theoretical classification. We have discovered such additional groups of factors such as individual and market related. In general, we can state that the extent to which the topic is studied depends on the selected perspective of factors' examination. Thus, theoretical background of the study on e-commerce adoption factors together with the secondary data analysis on the Russian ecommerce market has resulted in the main research question for the empirical study: How does the existing structuring of the revealed e-commerce adoption factors fit the perception of consumers in the Russian emerging market?
As the study is based on existing literature in terms of conceptualizing and operational-izing potential factors, the next empirical step aimed to reveal the e-commerce adoption factors was rather exploratory in nature. Empirical study is based on a survey, using a sample of 3 387 respondents representing the consumer perspective. The findings reveal the structure of the driving and limiting factors, highlighting the core role of the trustworthiness and transparency of the e-commerce market players, delivery conditions and store-related risks.
Generally, understanding barriers and drivers of the e-commerce market is important both from a managerial and academic point of view. The findings of this study make several contributions to existing literature. Consumer perception of e-commerce on market- and store- levels helped us add to the existing research both on a global scale and specifically within Russia in order to close the gap in the consumer perception of the ecommerce adoption factors. Additionally, we contributed to applying multi-level methodology to e-commerce research following the call for a more systematic and wider investigation of managerial phenomena [Hitt et al., 2007]. The gap in empirical research describing the barriers and drivers of the e-commerce market in Russia was filled. This research extends our knowledge of e-commerce adoption factors and might serve as a base for future studies in this field.
From a managerial perspective, these findings help to answer the following question: what e-commerce adoption factors should be taken into account when a company is planning further action in the field of online retailing? The changes in the market dynamics and development, offer a call for businesses to re-evaluate their strategies and approaches. This call for a strategic approach should be primarily aimed at overcoming the major barriers to further development. The empirical data allows companies operating in the e-commerce market to formulate the main direction of their future actions more specifically. Also, it gives guidelines for compa-
nies who are at the beginning of entering into the e-commerce market, or want to adopt new e-commerce practices. Moreover, at the present time there are debates concerning the development of Runet [The Formation of a Long-term Program..., 2015], and the experts and active members of this process can take into account the obtained results and utilise them in the discussion about issues related to current and long-term e-commerce projects.
Russian companies need to develop a more effective approach and focus on removing the barriers consumers face when making purchases via the Internet, and help them to overcome these barriers. At the same time, companies need to know how to use e-commerce adoption driving factors to stimulate Internet users to shop online to increase benefits. While companies operating in the e-commerce market are often small businesses, to be competitive in this dynamic industry they are constantly innovating their existing business models and bringing about changes which help companies to improve the seller and customer experience. As the results that the quantitative study demonstrate, all e-commerce businesses have the opportunity to learn, grow and develop their business, in terms of the improvements of service quality, providing innovative service offerings like one-day delivery, 30-day replacement warranty, cash on
delivery, the introduction of products and services that can be not only new for the company, but also for the market.
Future research should concentrate on the specific questions such as the customers' role in e-commerce development. From the business perspective, customers are one of the major drivers of the e-commerce market and their preferences and behavior can affect the way this market evolves. Another possible area for future research would be to assess the influence of existing e-commerce adoption factors to identify best practices of companies that help them to overcome difficulties and develop dynamically.
The present study is based on data collected in the Russian market only. As noted in [Sheth, 2011], marketing concepts developed in established markets may change when brought to emerging markets, a notion well aligned with our findings. However, to explore the differences between the e-commerce limiting and driving factors in developed and emerging markets further, comparative research should be conducted. The Russian data may not be representative of emerging markets in general, as the findings of the present study may be specific to the Russian market environment only. This calls for future studies in the field to combine data from different markets.
REFERENCES
Abou-Shouk M., Megicks P., Lim W. M. 2013. Perceived benefits and e-commerce adoption by SME travel agents in developing countries: Evidence from Egypt. Journal of Hospitality & Tourism Research 37 (4): 490-515.
Abualrob A. A., Kang J. 2016. The barriers that hinder the adoption of e-commerce by small businesses: Unique hindrance in Palestine. Information Development 32 (5): 1528-1544.
Ajzen I. 1991. The theory of planned behavior. Organizational Behavior and Human Decision Processes 50 (2): 179-211.
AKIT. 2019. E-commerce Market Analytics in Russia 2018. [Electronic resource]. https://yadi.sk/i/0fF9iSibAkpUnw (accessed: 09.01.2020). (In Russian) Alekseev V. 2019. The e-commerce market has grown by 26% in six months amid a reduction in the average receipt. Delovoy Peterburg 1. [Electronic resource]. https://www. dp. ru/subscribe/newspaper/2019/01/14 (accessed: 09.01.2020). (In Russian) Anckar B., Wal den P. 2001. Introducing web technology in a small peripheral hospitality organization. International Journal of Con-
temporary Hospitality Management 13 (5): 241-250.
Antonov S. 2019. How the Russians buy in the Internet. Tinkoff Journal. [Electronic resource]. https://journal.tinkoff.ru/e-commerce-stat/ (accessed: 12.12.2019). (In Russian)
Burgess S. M., Steenkamp J. B. E. 2006. Marketing renaissance: How research in emerging markets advances marketing science and practice. International Journal of Research in Marketing 23 (4): 337-356.
Casado-Aranda L. A., Martínez-Fiestas M., Sánchez-Fernández, J. 2018. Neural effects of environmental advertising: An fMRI analysis of voice age and temporal framing. Journal of Environmental Management 206: 664-675.
Chaparro-Peláez J., Agudo-Peregrina Á. F., Pascual-Miguel F. J. 2016. Conjoint analysis of drivers and inhibitors of e-commerce adoption. Journal of Business Research 69 (4): 1277-1282.
Childers T. L., Carr C. L., Peck J., Carson S. 2001. Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing 77 (4): 511-535.
Clemes M. D., Gan C., Zhang J. 2014. An empirical analysis of online shopping adoption in Beijing, China. Journal of Retailing and Consumer Services 21 (3): 364-375.
Corbitt B. J., Thanasankit T., Yi H. 2003. Trust and e-commerce: A study of consumer perceptions. Electronic Commerce Research and Applications 2 (3): 203-215.
Data Insight. 2019. E-commerce in Russia 2019. [Electronic resource]. http://www. datainsight.ru / ecommerce_2019 (accessed: 08.01.2020). (In Russian)
Dynamics of Internet Usage. 2019. Levada-Center. [Electronic resource]. https://www. levada.ru/2019/12/05/dinamika-polzovani-ya-internetom/ (accessed: 19.12.2019). (In Russian)
Euromonitor. 2019. Digital Commerce in Russia. [Electronic resource]. https://www.eu-romonitor.com/digital-commerce-in-russia/ report (accessed: 10.01.2020).
Federal Statistics. 2019. Share of Internet Sales in Total Retail Turnover. [Electronic
resource]. https://www.fedstat.ru/indica-tor/50236 (accessed: 15.12.2019). (In Russian)
Gefen D., Karahanna E., Straub D. W. 2003. Inexperience and experience with online stores: The importance of TAM and trust. IEEE Transactions on Engineering Management 50 (3): 307-321.
GfK Rus, Yandex.Market Research. 2019. Development of Online Trade in Russia. [Electronic resource]. https://yandex.ru/compa-ny/researches/2019/market-gfk (accessed: 07.12.2019). (In Russian)
Gibbs J. L., Kraemer K. L. 2004. A cross country investigation of the determinants of scope of e-commerce use: An institutional approach. Electronic Markets 14 (2): 124137.
Hitt M. A., Beamish P. W., Jackson S. E., Mathieu J. E. 2007. Building theoretical and empirical bridges across levels: Multilevel research in management. Academy of Management Journal 50 (6): 1385-1399.
Ho S. C., Kauffman R. J., Liang T. P. 2007. A growth theory perspective on B2C e- commerce growth in Europe: An exploratory study. Electronic Commerce Research and Applications 6 (3): 237-259.
Hoffman D. L., Novak T. P., Peralta M. A. 1999. Information privacy in the mar-ketspace: Implications for the commercial uses of anonymity on the Web. The Information Society 15 (2): 129-139.
Hsiao R. L. 2003. Technology fears: Distrust and cultural persistence in electronic marketplace adoption. The Journal of Strategic Information Systems 12 (3): 169-199.
Iglesias-Pradas S., Pascual-Miguel F., HernáNdez-García Á., Chaparro-Peláez J. 2013. Barriers and drivers for non-shoppers in B2C e-commerce: A latent class exploratory analysis. Computers in Human Behavior 29 (2): 314-322.
Huang S. L., Chang Y. C. 2019. Cross-border e-commerce: Consumers' intention to shop on foreign websites. Internet Research 29 (6): 1256-1279.
Internet audience in Russia. 2019. Mediascope. [Electronic resource]. https://mediascope.
n et/upload/iblock/423/17.04.2019_Media-scope_KceHHH%20A^KacoBa_PH®+KHB 2019.pdf (accessed: 20.04.2020). (In Russian)
Internet in World Stats. 2019. Internet world stats. [Electronic resource]. https://www. internetworldstats.com/stats4.htm#europe (accessed: 21.12.2019).
Kendall J. D., Tung L. L., Chua K. H., Ng C. H. D., Tan S. M. 2001. Receptivity of Singapore's SMEs to electronic commerce adoption. The Journal of Strategic Information Systems 10 (3): 223-242.
Kim D. J., Ferrin D. L., Rao H. R. 2009. Trust and satisfaction, two stepping stones for successful e-commerce relationships: A longitudinal exploration. Information Systems Research 20 (2): 237-257.
Kim E., Urunov R., Kim H. 2016. The effects of national culture values on consumer acceptance of e-commerce: Online shoppers in Russia. Procedia Computer Science 91: 966-970.
Korgaonkar P. K., Wolin L. D. 1999. A multivariate analysis of web usage. Journal of Advertising Research 39 (2): 53-53.
Kshetri N. 2007. The Adoption of e-business by organizations in China: An institutional perspective. Electronic Markets 17 (2): 113-125.
Lian J. W., Yen D. C. 2014. Online shopping drivers and barriers for older adults: Age and gender differences. Computers in Human Behavior 37: 133-143.
Lin S. W., Fu H. P. 2012. Uncovering critical success factors for business-to-customer electronic commerce in travel agencies. Journal of Travel & Tourism Marketing 29 (6): 566-584.
MacGregor R. C., Vrazalic L. 2005. A basic model of electronic commerce adoption barriers: A study of regional small businesses in Sweden and Australia. Journal of Small Business and Enterprise Development 12 (4): 510-527.
McKnight D. H., Choudhury V., Kacmar C. 2002. Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research 13 (3): 334-359.
Molla A., Licker P. S. 2005. Perceived e-read-iness factors in e-commerce adoption: An
empirical investigation in a developing country. International Journal of Electronic Commerce 10 (1): 83-110.
Nathan R. J., Victor V., Gan C. L., Kot S. 2019. Electronic commerce for home-based businesses in emerging and developed economy. Eurasian Business Review 9 (4): 463483.
Oreku G. S., Mtenzi F. J., & Ali A. D. 2013. A viewpoint of Tanzania e-commerce and implementation barriers. Computer Science and Information Systems, 10 (1): 263-281.
Pavlou P. A. 2003. Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce 7 (3): 101-134.
Pavlou P. A., Dimoka A. 2006. The nature and role of feedback text comments in online marketplaces: Implications for trust building, price premiums, and seller differentiation. Information Systems Research 17 (4): 392-414.
Pham M. T. 2013. The seven sins of consumer psychology. Journal of Consumer Psychology 23 (4): 411-423.
Rahayu R., Day J. 2017. E-commerce adoption by SMEs in developing countries: Evidence from Indonesia. Eurasian Business Review 7 (1): 25-41.
Rodríguez-Ardura I., Meseguer-Artola A. 2010. Toward a longitudinal model of e- commerce: environmental, technological, and organizational drivers of B2C adoption. The Information Society 26 (3): 209-227.
Rogers E. M. 2010. Diffusion of Innovations. The Free Press: N.Y.
Santarelli E. 2004. The nature of e-business: Do transaction costs matter? Rossiiskii Zhurnal Menedzhmenta 2 (3): 35-46. (In Russian)
Schaf er J. B., Konstan J. A., Riedl J. 2001. E-commerce recommendation applications.
Data Mining and Knowledge Discovery 5 (12): 115-153.
Sheth J. N. 2011. Impact of emerging markets on marketing: Rethinking existing perspectives and practices. Journal of Marketing 75 (4): 166-182.
Solaymani S., Sohaili K., Yazdinejad E. A. 2012. Adoption and use of e-commerce in
SMEs. Electronic Commerce Research 12 (3): 249-263.
Sukhodolov A. P., Popkova E. G., Kuzlaeva I. M. 2018. Internet Economy vs Classic Economy: Struggle of Contradictions; 63-70. Springer: Cham.
SurveyMonkey. Official website. [Electronic resource]. https://www.surveymonkey.com (accessed: 11.01.2020).
The Formation of a Long-term Program of the Russian Segment of the Internet and Related Industries Development. 2015. Institute for the Internet Development. [Electronic resource]. http://xn--h1aax.xn--p1ai/ projects/ (accessed: 08.01.2020). (In Russian)
The Russian E-commerce Report. 2019. East-West Digital News. [Electronic resource]. http://www.ewdn.com/e-commerce/in-sights.pdf (accessed: 14.01.2020).
The Russian National Educational Platform "Open Education". [Electronic resource]. https://openedu.ru (accessed: 11.01.2020). (In Russian)
Tsai H. T., Huang H. C., Jaw Y. L., Chen W. K. 2006. Why on-line customers remain with a particular e-retailer: An integrative model and empirical evidence. Psychology & Marketing 23 (5): 447-464.
Tsai H. T., Huang H. C. 2007. Determinants of e-repurchase intentions: An integrative model of quadruple retention drivers. Information & Management 44 (3): 231-239.
Federal Statistics. 2019. Share of e-commerce sales in total retail turnover. [Electronic resource]. https://www.fedstat.ru/indica-tor/50236 (accessed: 15.12.2019). (In Russian)
Valarezo Á., Pérez-Amaral T., Garín-Muñoz T., García I. H., López R. 2018. Drivers and barriers to cross-border e-commerce: Evidence from Spanish individual behavior. Telecommunications Policy 42 (6): 464-473.
Valmohammadi C., Dashti S. 2016. Using interpretive structural modeling and fuzzy
analytical process to identify and prioritize the interactive barriers of e-commerce implementation. Information & Management 53 (2): 157-168.
Virin F. 2014. Internet Trading in 2014: New Data from the Huge Market.
Wauters R. 2012. Already Europe's Largest Internet Market and Still Growing Astound-ingly Fast: Russia by the Numbers. The Next Web. [Electronic resource]. https:// thenextweb.com/eu/2012/04/20/already-eu-ropeslargest-internet-market-and-still- grow-ing-astoundingly-fast-russia-by-the-num-bers/#.tnwJfwT4m3z (accessed: 11.01.2020).
World bank. 2015. The dawn of a new economic era? Russia Economic Report 33. [Electronic resource]. https://www.world-bank.org/content/dam/Worldbank/docu-ment/eca/russia/rer33-eng.pdf (accessed: 12.01.2020).
Wresch W., Fraser S. 2011. Persistent barriers to e-commerce in developing countries: A longitudinal study of efforts by Caribbean companies. Journal of Global Information Management (JGIM) 19 (3): 3044.
Wymer S. A., Regan E. A. 2005. Factors influencing e-commerce adoption and use by small and medium businesses. Electronic Markets 15 (4): 438-453.
Yadav M. S., Varadarajan R. 2005. Interactivity in the electronic marketplace: an exposition of the concept and implications for research. Journal of the Academy of Marketing Science 33 (4): 585-603.
Zaidan E. 2017. Analysis of ICT usage patterns, benefits and barriers in tourism SMEs in the Middle Eastern countries: The case of Dubai in UAE. Journal of Vacation Marketing 23 (3): 248-263.
Zhang M., Huang G. Q., Xu S. X., Zhao Z. 2019. Optimization based transportation service trading in B2B e-commerce logistics. Journal of Intelligent Manufacturing 30 (7): 2603-2619.
Initial Submission: January 15, 2020 Final Version Accepted: March 30, 2020
Факторы внедрения электронной коммерции в России: анализ на уровне рынка и на уровне интернет-магазина
В. А. Ребязина
Факультет бизнеса и менеджмента, Национальный исследовательский университет «Высшая школа экономики», Россия
М. М. Смирнова
Институт «Высшая школа менеджмента», Санкт-Петербургский государственный университет, Россия
А. О. Давий
Санкт-Петербургская школа экономики и менеджмента, Национальный исследовательский университет «Высшая школа экономики», Россия
Развитие рынка электронной коммерции зависит от конфигурации факторов, которые либо способствуют, либо препятствуют его дальнейшему функционированию. Российский рынок электронной коммерции представляет самую большую онлайн-аудиторию в Европе (93 млн человек), что стимулирует значительный рост электронной коммерции. Цель статьи состоит в изучении восприятия российскими потребителями факторов внедрения электронной коммерции на макроуровне (развитие рынка электронной коммерции в целом) и на микроуровне (функционирование конкретного интернет-магазина, принимая во внимание опыт потребителей в момент покупки). Предложенный двухуровневый подход отражает сложность восприятия потребителями рынка электронной коммерции с учетом как меняющейся бизнес-среды, так и реального индивидуального опыта совершения покупок в Интернете. Исследование основано на количественном опросе 3 387 респондентов. Полученные результаты способствуют раскрытию движущих и сдерживающих факторов развития рынка электронной коммерции в России.
Ключевые слова: электронная коммерция, факторы внедрения электронной коммерции, развивающиеся рынки, факторный анализ.
JEL: M31.
For citation: Rebiazina V. A., Smirnova M. M., Daviy A. O. 2020. E-commerce adoption in Russia: Market- and store-level perspectives. Russian Management Journal 18 (1): 5-28.
Статья поступила в редакцию 15 января 2020 г. Принята к публикации 30 марта 2020 г.
