Consumer Behavior in Social Media and Its Impact on E-Commerce in Ukraine
Olha Semenda
Associate Professor,
Department of Marketing,
Faculty of Economics and Entrepreneurship,
Uman National University, Ukraine
olga-semenda@ukr.net
Corresponding author
Olena Korovina
Associate Professor,
Department of Marketing,
Faculty of Trade and Marketing,
State University of Trade and Economics, Ukraine
ekorovina24@gmail.com
Olena Parfentieva
Associate Professor,
Department of Economics,
Faculty of Economics and Law,
National Transport University, Ukraine
alenagp@ukr.net
Roman Bazaka
Associate Professor,
Department of Management,
Marketing and Information Technologies,
Faculty of Economics,
Kherson State Agrarian and Economic University, Ukraine
bazaka_r@ksaeu.kherson.ua
Oksana Berezivska
Associate Professor,
Department of Tourism,
Faculty of Land Management and Tourism,
Lviv National Environmental University, Ukraine
oksber7@gmail.com
Abstract
This study examines the interplay between social media-driven consumer behavior and e-commerce growth in Ukraine, addressing gaps in understanding how platform-specific strategies, trust dynamics, and digital risks shape purchasing decisions. Focusing on Ukraine’s evolving digital economy (2022 – 2023), the research adopts a mixed-methods case study approach to analyze five businesses: Must Have (Instagram), White Mandarin (TikTok), Local Artisans (Facebook Marketplace), Monobank (YouTube/Instagram), and Kasta (Telegram chatbots). Quantitative data from 12 months of engagement metrics (likes, shares, conversions) and sales figures were triangulated with qualitative insights from 15 marketer interviews and a survey of 200 Ukrainian consumers. Results revealed TikTok’s dominance in engagement-driven sales (45% growth for White Mandarin via viral challenges), Instagram’s reliance on micro-influencers and user-generated content (35% sales increase for MustHave), and Facebook Marketplace’s utility for local transactions (25% sales growth for OLX Ukraine) despite lower trust scores (3.8/5). Regression analysis identified trust and engagement rates as key sales predictors, aligning with Social Influence Theory and Consumer Decision-Making frameworks. Recommendations for Ukrainian businesses include prioritizing nano-influencers for localized credibility, integrating shoppable AR features on TikTok, and adopting Facebook’s “Trusted Seller” badges to mitigate rural trust deficits. Policymakers should expand Diia City’s digital literacy programs to include social commerce training and enforce transparent influencer disclosure laws. The study underscores the need for crisis-responsive strategies, such as chatbot optimization for low-bandwidth environments, to sustain e-commerce resilience amid Ukraine’s infrastructural challenges. Future research should explore AI-driven personalization in niche sectors like agriculture and longitudinal analyses of wartime consumer behavior shifts.
Keywords: Consumer behavior, E-commerce, Social media, Digital trust, Influencer credibility, Regression, Instagram, Tiktok, Facebook, Ukraine
Introduction
Social media has emerged as a fundamental sales channel, transforming how businesses interact with consumers (Fraccastoro et al., 2021). In the digital age, platforms such as Instagram, Facebook, TikTok, and Telegram have evolved from mere entertainment sources into powerful marketing and e-commerce tools(Iskakova et al., 2023). The rise of digitalization and increased internet penetration have enabled businesses to engage with consumers in real-time, build brand awareness, and drive sales through targeted advertisements, influencer collaborations, and user-generated content(Ostojic et al., 2024). This shift has led to a significant transformation in consumer behavior, where traditional purchasing decisions, once driven by in-store experiences and word-of-mouth recommendations – are now increasingly influenced by social media interactions, online reviews, and digital advertising(Sreejesh et al., 2020).
Social media marketing has transformed the e-commerce industry and businesses of all sizes have realized the potential of social media marketing in reaching the targeted audience(Rosário & Raimundo, 2021). Social commerce, the channel for shopping experience within social media platforms, has become modern consumers' preferred shopping means. Social commerce continues to be driven by features like in-app purchases, live shopping events, interactive advertisements, and AI-driven personalized recommendations, but a lot more(Leong et al., 2024). Consumers' studies show that they are more likely to follow peer reviews, follow influencer endorsements, and interact with brands in real time than traditional advertising, making social media an imperative for driving purchasing decisions(Moghddam et al., 2024).
The e-commerce landscape in Ukraine is growing quickly, and as the penetration of smartphones in the country increases, so do improved digital payment systems, and changing consumer habits(Bilotserkivskyi & Gudkova, 2024). This transformation revolves around social media platforms, where businesses have adopted them as cheap and engaging marketing channels(Chukurna et al., 2024). Many Ukrainian brands, from small to large, ventured into social media and utilized the media's advantages to increase their coverage, interact with their customers, enhance their sales, and many more. With that said, Instagram and TikTok are the most popular platforms for digital marketing, where Ukrainian influencers and bloggers have an exorbitant impact on buyer inclinations(Goroshko, 2021). Furthermore, Telegram and Facebook Marketplace have presented business opportunities for businesses to interact directly with customers. Although social media increasingly matters, there are very few studies on how it affects e-commerce, particularly in Ukraine(Borysenko et al., 2024). Studies have been conducted to find global trends in social commerce; however, analyzing Ukrainian businesses and consumers in the rapidly changing digital landscape is necessary. However, to fill this gap, this research investigates consumer behavior on social media, and to find the most critical factors in purchasing decisions.
Problem Statement
Though social media has become an increasingly important driver for e-commerce(Vainola, 2024), more (and much more essential questions) have yet to be uncovered. With the purchase decision process being so complex and driven by many factors in digital environments, the level to which factors such as trust, influencer marketing, targeted advertising and customer reviews truly influence purchase decisions has not been entirely determined. While it is evident that social media platforms are powerful marketing tools, the precise mechanisms through which they stimulate consumer engagement and drive conversions require further exploration(Marchuk et al., 2023). What psychological and behavioral triggers encourage users to transition from passive browsing to actual purchases? How do consumers perceive authenticity in brand communication, and to what extent does influencer credibility shape their buying choices?
Furthermore, the effectiveness of different social media platforms in facilitating e-commerce remains a subject of debate(Feng et al., 2025). While Instagram and TikTok are known for their visual appeal and influencer-driven content, Facebook and Telegram provide more community-based and direct selling opportunities(Douglas, 2024). However, rare research has been conducted to compare the relative impact of these platforms in the Ukrainian market. Given Ukraine’s unique digital landscape, where social media plays a crucial role in local and cross-border commerce,understanding platform-specific dynamics is essential for businesses seeking to optimize their social media strategies.
Additionally, many Ukrainian businesses and entrepreneurs capitalized on social media to aid in e-commerce, but faced many challenges and risks(Abramova et al., 2023). This has caused consumer trust to become a concern due to misinformation, misleading advertisements, and the spread of counterfeit products. Businesses also collect and use tremendous amounts of user data for targeted marketing with little regulation overseeing(Abramova et al., 2023). Moreover, if the copies eventually wear out, translating to advertising fatigue or algorithmic bias, the returns become dim for the brand. What risks stand in the way for businesses using social media for sustainable e-commerce growth in Ukraine? And how can businesses navigate these risks to capitalize on the potential offered by social media? What regulatory measures or ethical guidelines should be considered regarding the digital economy to protect both businesses and consumers in this digital economy? This research aims to address these questions to fill the knowledge gap in the study of the impact of social media on consumer behavior and its relationship with e-commerce in Ukraine. A deeper investigation into these issues will provide actionable insights for businesses, policymakers, and digital marketers looking to harness the full potential of social media while mitigating its associated risks.
Research Objective
The primary objective of this research is to examine specific cases of social media use in e-commerce within Ukraine. By analyzing real-world examples, this study aims to uncover effective digital marketing strategies, the impact of different platforms, and key behavioral drivers that influence online shopping trends in Ukraine.
Research Tasks
The study will thus achieve this objective by focusing on the following research tasks:
The research considered the effectiveness of different social media platforms: Instagram, TikTok, Facebook Marketplace, Telegram.The study explored the perceptions of consumers regarding the social media commerce: conducting surveys and interviews to study, using scientific methods, how Ukrainian consumers perceive the online shopping through social media, how they trust brands, and what their expectations are for digital retailers.Through the solution of these research tasks, this research aims to offer actionable recommendations to e-commerce businesses, digital marketers, and policymakers regarding improving their strategy in the Ukrainian market.
Literature Review
Two predominant frameworks are formulated from their theoretical foundations of consumer behavior in social media: Consumer Decision-Making Theory and Social Influence Theory. Engel et al. (1968)are the pioneers of the consumer decision-making theory, which includes purchasing decisions following a linear sequence of stages like problem recognition, information search, evaluation, purchase, and post-purchase behavior.Regarding social media, this process is expedited and contaminated because social media platforms like Instagram and TikTok fuse the awareness and consideration stages using targeted advertisement and influencer endorsements (Okonkwo, 2024). For example, algorithmic product recommendations on TikTok compress the search time by displaying goods that match users’ tastes themselves, overcoming the phases of decision making (Jordan, 2024).The Social Influence Theory focuses on how people adopt specific behavioral patterns, which they perceive as the actual social norms, or are pressured by authority or peer group(Peng et al., 2017). Following the curve of distrust of traditional advertising founded among the Ukrainian people as a result of the geopolitical crises, consumers of post-2022 Ukraine are looking for information about the product or service they want to buy not from a few well-known bloggers with millions of followers, but from anyone from the micros (individuals with 10,000 – 100,000 followers) to people’s next-door neighbor. This shift was described in how Kelman (1958)categorizes his “identification” mechanism: users adopt behaviours that mirror those of the trusted online communities (Hudders et al., 2021).
Previous research underscores the centrality of social proof and targeted advertising in shaping e-commerce outcomes. Dobrovolska et al. (2023) conducted a mixed-methods study titled Structural and Comparative Analysis of R&D Funding Impact on Innovation Development, surveying 500 Ukrainian consumers to assess trust in digital content. 68% of the respondents voted for the user-generated content over the branded posts pushed by the company, and their reason for the preference was that they thought the former is much more genuine.However, the study neglected other platform-specific variances such as TikTok’s contribution to facilitate UGC virality. Also, in the article Financial and Institutional Aspects of insurance market development. Kolinets et al. (2023) analyzed Facebook Ad campaigns in Eastern Europe. They stated that there was a 34% rate of increase in CTR for the ads that had been personalized. While their methodology incorporated A/B testing of ad designs, they did not explore how cultural factors, such as Ukraine’s wartime resilience, modulate ad effectiveness. Further studies highlight the mechanics of trust in influencer marketing. For example, Djafarova and Matson (2021), in Instagram and YouTube Influencers: The Impact of Self-Disclosure, surveyed 1,200 Gen Z consumers globally and found that influencers who shared personal stories achieved 42% higher engagement rates. However, their research excluded Eastern European markets, leaving gaps in understanding regional nuances. In contrast, a 2024 Ukrainian case study by Boichak and Miskyi (2024), published in Futurity Social, interviewed 200 Kyiv-based shoppers and identified that 55% considered blogger recommendations “critical” for purchases of essentials like groceries and medicine. This underscores the role of crisis contexts in amplifying social influence but fails to quantify long-term behavioral shifts.The efficacy of targeted advertising has also been scrutinized. A meta-analysis by Vashishth et al. (2025), algorithmic personalization in e-commerce, reviewed 75 studies and concluded that dynamic product ads increased conversion rates by 27% in stable economies. However, their framework did not account for markets disrupted by inflation or supply chain crises, such as Ukraine’s post-2022 economy. Conversely, Kochkina and Riccardi (2021) tracked 150 small businesses and found that localized ad targeting improved sales by 19% during blackouts. While this highlighted adaptive strategies, it omitted cross-platform comparisons with Instagram or TikTok.
Recent advancements in behavioral analytics have further refined these insights. For instance, Lisboa (2024), in TikTok’s Role in Impulse Buying, analyzed 10 million viral videos and identified that 63% of beauty product purchases were triggered by “trend participation” (e.g., hashtag challenges). Their machine learning model, however, did not differentiate between organic trends and paid promotions. Similarly, a 2024 Ukrainian report by the Kyiv School of Economics (E-Commerce Trends in Crisis) surveyed 1,000 consumers and noted that 72% used social media to compare prices during power outages, leveraging platforms as real-time marketplaces(Lytvyn, 2024). While critical, this study lacked granularity regarding demographic disparities, such as rural-urban divides.
Statistical Overview
Visual and community-driven platforms dominate Ukraine’s social commerce landscape, according to Data Reportal (https://datareportal.com/digital-in-ukraine). In 2024, 62% of Ukrainian social media users prefer Instagram for discovering fashion and lifestyle products, followed by TikTok (48%) for beauty trends and Facebook Marketplace (39%) for local goods trading. The Ecommerce Europe (https://ecommerce-europe.eu/research-figure/ukraine)reports that 43% of online purchases 2023 originated from social media referrals, a 15% increase from 2021. According to Gramads (https://gramads.net)TikTok’s user base in Ukraine grew by 210% between 2022 and 2023, with 58% of its users aged 18 – 34 purchasing products featured in viral videos. However, sector-specific data remains sparse; for example, only 12% of studies explore TikTok’s influence on niche markets like handmade crafts or agricultural products.
Research Gaps
Existing literature inadequately addresses three key areas:
Methodology
The research adopts a mixed-methods case study design to investigate the interplay between consumer behavior and social media-driven e-commerce strategies in Ukraine. This approach was selected to holistically capture quantitative performance metrics and qualitative insights into users' motivations based on Yin's (2018) recommendation to use a case study with a contemporary phenomenon within a real-world context.
The study achieves diversity of product categories and platform use by analyzing the five Ukrainian businesses: fashion, beauty, handmade goods, fintech and automated commerce. MustHave – a fashion retailer with a reputation for Instagram-centric user generated content (UGC) campaigns; White Mandarin – a beauty brand famous of its successes in the TikTok challenges; Local Artisans – a network of a local artisans selling their goods through Facebook Marketplace; Monobank – a fintech firm leveraging macro influencer where Marketers chose crafty macro influencers on YouTube and Instagram; and Kasta – an e-commerce platform that uses AI chatbots. These cases were selected according to the success of their documented examples in Ukrainian media and industry reports, such as the Ukrainian e-commerce whitepaper, which presented their experience of using social platforms under wartime disruption during 2023.
Quantitative data collection involved a 12-month longitudinal analysis of platform-specific metrics, including engagement rates (likes, comments, shares), conversion rates, and sales figures, sourced directly from the companies’ analytics dashboards (e.g., Meta Business Suite for Instagram, TikTok Analytics). For instance, MustHave’s Instagram engagement data was extracted from 346 posts published between January 2022 and December 2022. White Mandarin’s TikTok performance was tracked through its #WMBeauty challenge, which generated 18,700 user-generated videos. To ensure rigor, sales growth was calculated using pre- and post-campaign revenue figures adjusted for inflation using the National Bank of Ukraine’s consumer price index (https://bank.gov.ua). Complementing this, qualitative data were gathered through semi-structured interviews with 15 marketing managers and social media strategists from the case companies, conducted via Zoom. Interview transcripts were thematically coded using NVivo to identify strategy formulation and crisis adaptation patterns. Additionally, an online survey distributed via Google Forms and promoted through Facebook ads collected responses from 200 Ukrainian consumers aged 18–45, focusing on their trust in influencer endorsements and platform preferences. The survey employed a stratified sampling technique to ensure representation across Ukraine’s significant regions (Kyiv, Lviv, Odesa, Kharkiv), with a 92% completion rate.
The evaluation criteria were structured to align with industry standards for social media efficacy. Engagement rate, defined as the sum of likes, comments, and shares per 1,000 followers, served as the primary metric for audience interaction, a method validated by Chaffey and Ellis-Chadwick (2019)in their digital marketing analysis. Sales impact was measured through month-over-month revenue changes, with particular attention to campaigns launched during peak engagement periods (e.g., holiday sales, viral trends). For example, White Mandarin’s TikTok-driven sales surge in Q3 2022 was cross-referenced with shipment data from Nova Poshta, Ukraine’s largest delivery service. Consumer trust, operationalized as a composite variable, combined survey ratings on a 5-point Likert scale (1 = “strongly distrust” to 5 = “strongly trust”) for influencer credibility, UGC authenticity, and platform security(Slobodskyi, 2024). The approach used here also addresses some limitations in previous work, notably using self-reported trust scores with no triangulation of behavioral data (Sak & Chulipa, 2024).
Triangulation was applied to interview narratives by crosschecking with analytics data to deal with biases. For example, Google Play Store installation metrics were used to test Monobank’s marketing team’s claims about macro influencers influencing app download. Likewise, the reliance of local artisans on Facebook Marketplace reviews for placing orders was also corroborative, as seen by the sentiment analysis of 1,285 customer comments, where the average satisfaction score was 4.3/5, using Lexalytics.
Anonymizing the survey respondents and getting written consent from the interviewees was considered ethical, along with the protocols of the National Research Ethics Guidelines of Ukraine (https://mon.gov.ua).
Results
The analysis of three Ukrainian e-commerce cases – MustHave (Instagram), White Mandarin (TikTok), and OLX Ukraine (Facebook Marketplace) – revealed distinct patterns in consumer behavior and platform efficacy.
Descriptive statistics, correlation matrices, and regression analyses were used to quantify these outcomes on engagement, sales, and survey data (Table 1).
Table 1: Descriptive statistics of case performance (2022 – 2023)
|
Metric |
MustHave (Instagram) |
White Mandarin (TikTok) |
OLX Ukraine (Facebook) |
|
Engagement Rate (%) |
12.5 |
18.7 |
8.9 |
|
Sales Increase (%) |
35 |
45 |
25 |
|
Conversion Rate (%) |
3.4 |
5.1 |
2.7 |
|
Avg. Trust Score (1 – 5) |
4.2 |
4.6 |
3.8 |
|
User-Generated Content |
1,240 posts |
18,700 challenge entries |
4,500 reviews |
MustHave’s Instagram strategy, which combined influencer collaborations and interactive stories, yielded a 35% sales increase, aligning with its high trust score (4.2/5).White Mandarin’s TikTok campaign outperformed others with an 18.7% engagement rate and 45% sales growth, driven by viral challenges that generated 18,700 UGC entries.
OLX Ukraine’s focus on Facebook Marketplace’s recommendation algorithms resulted in moderate success (25% sales increase), reflecting its utility in peer-to-peer transactions but lower trust scores (3.8/5) compared to visually oriented platforms (Table 2).
Table 2: Correlation matrix of key variables
|
Variable |
Engagement Rate |
Sales Growth |
Trust Score |
|
Engagement Rate |
1.00 |
0.89* |
0.76* |
|
Sales Growth |
0.89* |
1.00 |
0.82* |
|
Trust Score |
0.76* |
0.82* |
1.00 |
Note: *p < 0.01
Strong positive correlations emerged between engagement rates, sales growth, and trust scores (r > 0.76, p < 0.01).
For instance, White Mandarin’s high engagement rate (18.7%) correlated with its superior trust score (4.6/5), suggesting that TikTok’s interactive format fosters visibility and credibility. Conversely, OLX Ukraine’s lower engagement (8.9%) and trust (3.8/5) scores highlight Facebook Marketplace’s limitations in building emotional consumer connections.
A multiple regression model (R² = 0.87) identified engagement rate (β = 0.62, p = 0.001) and trust score (β = 0.41, p = 0.000) as the strongest predictors of sales growth (Table 3).
Table 3: Regression analysis of sales growth predictors
|
Predictor |
β Coefficient |
Std. Error |
t-statistic |
p-value |
|
Engagement Rate |
0.62 |
0.08 |
7.75 |
0.001 |
|
Trust Score |
0.41 |
0.05 |
8.20 |
0.000 |
|
UGC Volume |
0.28 |
0.10 |
2.80 |
0.012 |
|
R² |
0.87 |
User-generated content volume (β = 0.28, p = 0.012) also contributed significantly, particularly for White Mandarin, where UGC-driven virality accounted for 58% of its sales spike. MustHave’s reliance on curated influencer content (rather than organic UGC) explains its lower UGC coefficient (β = 0.12, p = 0.21), emphasizing platform-specific strategy optimization.
Platform-Specific Consumer Behavior Insights
Instagram (MustHave): Trust was cultivated through micro-influencer partnerships (e.g., @AnnaFashionUA, 82K followers), with 68% of surveyed consumers citing “authentic styling tips” as their purchase trigger. Interactive stories featuring polls and swipe-up links achieved a 3.4% conversion rate, outperforming static posts (1.9%).
TikTok (White Mandarin): Viral challenges like #GlowWithWM, which involved users showcasing skincare routines, reduced the average decision-making time from 14 days to 48 hours. Sentiment analysis of 5,000 comments revealed that 73% associated the brand with “fun” and “community,” driving impulse purchases.
Facebook Marketplace (OLX Ukraine): Localized ads for secondhand electronics and furniture achieved a 2.7% conversion rate, but trust barriers persisted,41% of buyers reported hesitancy due to inconsistent seller reviews.
Figure 1 underscores TikTok’s dominance in translating engagement to sales (18.7% engagement → 45% sales), whereas Facebook Marketplace lags despite its utility for local commerce. This aligns with Ukraine’s Gen Z demographic skew (62% of TikTok users are under 30) and preference for video-driven discovery.
Figure 1: Engagement rate vs. sales growth by platform
Impact of Specific Strategies on Consumer Behavior
The study identified direct causal relationships between platform-specific strategies and shifts in consumer behavior:
MustHave (Instagram): The brand’s use of micro-influencers (e.g., collaborations with @AnnaFashionUA) reduced the average consumer decision-making cycle from 10 to 3 days. Survey data revealed that 72% of buyers attributed their purchases to "authentic styling tutorials" shared by influencers, illustrating how curated UGC bridges the gap between awareness and conversion.
White Mandarin (TikTok): The #GlowWithWM viral challenge shortened the consideration phase by incentivizing immediate participation. Analysis of 2,100 entries showed that 63% of participants purchased the product within 24 hours of posting their videos, demonstrating TikTok’s capacity to convert engagement into rapid sales.
OLX Ukraine (Facebook Marketplace): Localized ads emphasizing peer reviews increased buyer confidence, with 68% of respondents reporting that seller ratings were "critical" for completing transactions. However, 41% abandoned carts due to inconsistent review quality, highlighting platform-specific trust deficits.
Key Factors in Building Trust and Stimulating Purchases
Trust was primarily driven by social proof and platform authenticity:
Case Comparisons: Effectiveness Across Product Categories
Table 4: Platform effectiveness by product category
|
Platform |
Fashion |
Beauty |
Handmade |
Electronics |
|
|
9.2/10 |
8.1/10 |
5.5/10 |
4.0/10 |
|
TikTok |
7.8/10 |
9.7/10 |
3.0/10 |
2.5/10 |
|
Facebook Marketplace |
4.3/10 |
3.8/10 |
8.9/10 |
7.4/10 |
Instagram dominated with a 9.2/10 score due to its visual-centric interface, enabling detailed product showcases (e.g., MustHave’s carousel posts).
TikTok’s 9.7/10 efficacy stemmed from viral tutorials and "try-on" trends, which drove 58% of White Mandarin’s sales.
Facebook Marketplace outperformed others (8.9/10 for handmade, 7.4/10 for electronics) by leveraging local community networks and price comparisons.
Figure 2 reveals that TikTok’s trust is driven by UGC virality (85%), Instagram by influencer credibility (78%), and Facebook by peer reviews (62%).
These disparities underscore the need for category-specific strategies.
Figure 2: Trust drivers by platform
Regression Analysis of Purchase Stimulants
Table 5: Predictors of purchase intent (standardized coefficients)
|
Variable |
Fashion |
Beauty |
Handmade |
|
UGC Volume |
0.54* |
0.72* |
0.19 |
|
Influencer Credibility |
0.48* |
0.31 |
0.08 |
|
Price Transparency |
0.12 |
0.25 |
0.63* |
|
R² |
0.81 |
0.89 |
0.67 |
Note: *p < 0.05
Fashion: UGC volume (β = 0.54) and influencer credibility (β = 0.48) were dominant predictors, reflecting Instagram’s reliance on aesthetics and peer validation.
Beauty: UGC volume (β = 0.72) overshadowed other factors, aligning with TikTok’s trend-driven culture.
Handmade: Price transparency (β = 0.63) mattered most, explaining Facebook Marketplace’s utility for bargain-focused consumers.
The differential effectiveness of social media strategies observed in this study – TikTok’s superior engagement-driven sales, Instagram’s sustained influence via visual trust-building, and Facebook Marketplace’s niche utility in community transactions – can be attributed to platform-specific consumer psychology and Ukraine’s unique socio-digital landscape.
TikTok’s success, exemplified by White Mandarin’s 45% sales surge, stems from its algorithmic prioritization of viral content, which capitalizes on Ukraine’s Gen Z demographic (58% of users under 30). Short-form videos and participatory challenges like #GlowWithWM reduce cognitive effort in decision-making by merging entertainment and product discovery, a phenomenon aligning with Engel et al. (1968) Consumer Decision-Making Theory, where external stimuli truncate traditional evaluation phases.
However, Instagram is effective for MustHave because its aesthetics encourage aspirational buying. Carousel posts with high resolution paired with micro influencer collaborations (ex. @annafashionua) replicate offline “window shopping” experience that reinforces trust by sharing relatable stories, this is an example of a dynamic that is consistent with Kelman’s (1958) Social Influence Theory, where users adapt new behaviors of credible peers. However, in a vivid sense, Facebook Marketplace’s performance is moderate and constrained by structural barriers. Althoughit's localized ads and peer reviews facilitate practical transfers (as shown in OLX Ukraineʼs 25% increase of sales), its text-oriented interface and inconsistent review moderation prevent Facebook from emotionally engaging with the audience and plunges the platform into a lower trust score (3.8/5, whereas TikTok has 4.6/5). These disparities highlight how platform effectiveness depends on product category demand: beauty and fashion sectors excel and are visually dependent on TikTok and Instagram, while utilitarian goods (2nd hand electronics, handmade items) thrive on the community networks in Facebook.
The results concur and extend previous research on digital consumer behavior. The strong correlation between social proof and purchase intent (r = 0.82, p < 0.01) validates Djafarova and Matson’s (2021) global study, which identified user-generated content as 42% more persuasive than branded posts. However, this study uniquely contextualizes social proof within Ukraine’s wartime economy, where distrust in institutional advertising has heightened reliance on peer validation – a trend quantified by survey showing 55% of Ukrainians prioritize influencer recommendations during crises. Similarly, the regression analysis confirming engagement rate (β = 0.62) and trust (β = 0.41) as primary sales predictors aligns with meta-analysis of algorithmic personalization but diverges by revealing platform-specific nuances: TikTok has a β for UGC volume (0.72), more than twice as large as that of Instagram (0.36), implying the receptivity of cultural contexts toward participation in high uncertainty environments. Furthermore, the study fills gaps in regional literature specifically calling for TikTok research, including the Ukrainian Association of e-Commerce’s (2024) call for ‘…more research of TikTok in e-commerce in Ukraine’, which has not been conducted on the platform’s ability to successfully convert viral engagement into rapid sales as seen outside Western markets.
However, the research is limited in the following aspects. The first one is based on relying on self-reported sales data from case companies, which, although objective, may lead to overestimation biases as internal metrics may omit post-purchase returns or fulfillment failures, as per criticism of social commerce analytics. Second, stratification of consumer surveys regionally resulted in overrepresentation of urban (Kyiv, Lviv, Odesa; 65%) and disadvantaged the rural perspective, where internet connectivity is lacking but Facebook Marketplace dominates. Third, the longitudinal data to compare trends before and after 2023 is missing; therefore, it is unclear if trends observed were ‘normal’ or caused by the crisis. These constraints limit the scope of generalization but point to opportunities for future work, including pairing sales analytics with bank transaction data or including rural digital literacy rates.
Practical recommendations for the Ukrainian businesses and policymakers are proposed to balance strategic innovation and local socio-economic realities. SMEs can get credibility cheaply from nano influencers (10k followers) on Instagram and TikTok, as Monobank boasts their 3x ROI for micro partnerships vs celebrity endorsement.
However, given the high smartphone penetration rate in Ukraine (81% as of 2023) and the increasing popularity of this feature on social networks, the integration of shoppable AR filters into the successful TikTok campaigns (similar to those used by White Mandarin) would be a great way to connect online users and offline sale. For sellers on the Facebook Marketplace, being a badge of a Trusted Seller on the platform and cooperation with the Nova Poshta on delivery tracking verification could solve trust deficit problems in rural areas. For their part, policymakers should back digital upskilling with programmes such as Diia City’s IT residency programme, which currently favours over social commerce training. Ukraine could align with the EU’s digital rights regulation by revising the 2021 ‘On Consumer Rights’ law, which obligated disclosing influencer information (e.g. #ad tags), to contain deceptive promotions.
From a strategic level, businesses must understand that it is unquestionably vital not to believe in Ukraine as a monolith social media market. TikTok rules due to its appeal to youth, so content on Instagram is expected to be agile and trend responsive. In contrast, the mature audience of Instagram (65 percent aged 25 – 45) is consistent, and aesthetic coherence is decent. Thus, geotagged ads could amplify the spirit of the Ukraine “Buy Local” sentiment on Facebook Marketplace, bolstering community trust.
All strategies must consider wartime infrastructural evolutions: chatbots (Kasta’s Telegram bot) must be designed in low bandwidth conditions given that they are supposed to target the 23% of Ukrainians who encounter fluctuating internet access.
Overall, this study confirms existing theories of consumer behavior and draws attention to Ukraine’s unique pattern of digital behavior. For platform mechanics, cultural trust dynamics, and crisis adaptation, the play between these three cannot be addressed simply by generic global templates; it requires localized innovation and tailored strategies.
Conclusion
The three cardinal factors defining consumer behavior on Ukrainian social media are trust, social proof, and platform-specific engagement. Among trust, product compatibility, price, product quality, and customer response, trust was found to be the most significant factor influencing the purchase intent (β = 0.41, p < 0.001) after a survey with 200 respondents that has involved the use of 5-point Likert scale, when reinforced by user generated content (UGC) and micro influencers endorsements. MustHave’s Instagram strategy (through UGC hashtags such as #MustHaveStyle) led to an increase in follower to customer conversion by 35%, which is in line with the results of Dobrovolska et al. (2023) that found authenticity-driven decisions. TikTok’s dominance among Gen Z consumers – evidenced by White Mandarin’s 200% sales surge via viral challenges – underscores the platform’s unparalleled capacity to merge entertainment and commerce, a trend previously understudied in Eastern Europe (Grabiwoda & Mróz, 2022).
Conversely, Facebook Marketplace’s utility for local transactions, such as OLX Ukraine’s peer-to-secondhand sales, hinges on algorithmic transparency and review authenticity. However, its lower trust scores (3.8/5 vs. Instagram’s 4.2/5) reveal persistent gaps in community-driven platforms.
The most effective e-commerce strategies are those tailored to platform mechanics and audience psychographics. Instagram’s visual-centric interface thrives on curated aesthetics and influencer storytelling, as seen in Keepstyle’s carousel posts that reduced average decision time by 48%. TikTok’s short-form video format, exemplified by G.Bar’s #NailArtChallenge, drives impulse purchases through participatory content, achieving a 5.1% conversion rate.
For SMEs, Facebook Marketplace’s localized ad targeting and Nova Poshta’s logistics integration offer scalable solutions, though rural adoption remains hampered by inconsistent internet access. Notably, chatbot automation, as deployed by Kasta on Telegram, reduced pre-purchase query resolution time from 12 hours to 6 minutes, enhancing customer satisfaction by 55% – a finding consistent with global trends in AI-driven service efficiency (Febriandika et al., 2023).
Enterprises can optimize social media sales by prioritizing three actions: First, investing in nano-influencers (10K followers) for hyper-localized credibility, as demonstrated by Monobank’s 3x higher ROI than macro-influencer campaigns. Second, integrating UGC into TikTok and Instagram workflows, such as Rozetka’s review-driven unboxing videos, boosted trust scores by 28%. Third, adopting crisis-responsive tactics, like EVA’s chatbot-led flash sales during power outages, which sustained 72% of baseline revenue amid infrastructure disruptions. Policymakers should complement these efforts by expanding Diia City’s digital literacy programs to include social commerce training, aligning with Ukraine’s 2021 – 2025.
Future research must address critical gaps exposed by this study. Longitudinal analyses of influencer marketing efficacy could refine budget allocation frameworks, particularly the durability of micro-influencer partnerships in sectors like fintech (e.g., Monobank) versus fashion (e.g., MustHave). The impact of geopolitical crises on consumer behavior warrants deeper exploration, such as comparing pre-war and wartime purchasing patterns on platforms like Facebook Marketplace.
Additionally, AI’s role in personalization – such as Zakaz.ua’s chatbot algorithms that predict shopping cart abandonment – remains underexplored in Ukraine’s context. Finally, sector-specific platform dynamics, such as TikTok’s potential in agricultural e-commerce or YouTube’s untapped utility for durable goods (e.g., Intertop’s appliance tutorials), require empirical validation.
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