The Impact of the Sharing Economy on Traditional Product Sales:
The Case of Bicycle Sharing in China
Shi Yiyi
a
Broadcasting, College of Arts & Media, Tongji University, Shanghai, China
Keywords: Bike Sharing, Consumer Perceived Value, Sharing Economy, Product Sales.
Abstract: The sharing economy, exemplified by bike-sharing services, has transformed urban mobility while raising
questions about its impact on traditional product sales. This study examines how bike-sharing affects
consumers' intention to purchase private bicycles in China. Using the Perceived Value Theory, a conceptual
model is developed to explore the relationships between the perceived value of shared products, the intention
to use shared products, and the purchase intention of private products. Through empirical analysis based on
survey data, findings indicate that higher perceived value of shared bicycles positively influences the intention
to use them, which in turn negatively impacts the purchase intention of private bicycles. Among the four
perceived value dimensions—economic, social, emotional, and functional—economic, emotional, and
functional values significantly influence consumer behavior, while social value plays a less decisive role.
These results suggest that the sharing economy does not merely complement traditional consumption patterns
but actively reshapes them, potentially reducing demand for private ownership. The study contributes to
understanding consumer decision-making in the sharing economy and provides insights for businesses and
policymakers in managing the coexistence of shared and private product markets.
1 INTRODUCTION
As an emerging economic model, the sharing
economy has gained significant traction across
various industries, offering solutions for economic
development that differ from those of traditional
microeconomic frameworks (Zhou et al, 2022).
Among its many applications, bike-sharing systems
have been widely adopted in cities worldwide,
serving as a convenient commuting option (Li et al., ,
2015). Fu et al. (2024) examined the impact of bike-
sharing on urban public transportation ridership,
revealing that in larger cities with well-developed
public transit networks, shared bicycles effectively
complement rail transit while acting as substitutes for
bus services. Furthermore, policies encouraging bike-
sharing have been shown to enhance overall public
transportation usage.
In addition, existing literature has explored factors
influencing bike-sharing demand (Eren & Uz, 2020)
and proposed methods for evaluating and balancing
rental pricing and return rates in bike-sharing systems
(Li et al., 2015).
a
https://orcid.org/0009-0003-0637-6607
Despite the increasing maturity of bike-sharing
systems and their recognition as an environmentally
friendly transportation alternative, the impact of
shared bicycles (as shared products) on the sales of
traditional bicycles (as upstream products) remains
unclear. Empirical research on this topic is limited,
highlighting the need for further investigation.
Perceived Value Theory (Zeithaml, 1988), by
integrating economic and psychological elements,
places consumers in a decisive position within
transactions, emphasizing a consumer-oriented
approach. This theoretical framework provides a
valuable lens for analyzing the impact of product
sharing on sales,, identifying key variables and
dimensions relevant to the study.
Against this backdrop, this study investigates the
impact of product sharing on traditional bicycle sales,
drawing on Perceived Value Theory and situating the
analysis within the Chinese bike-sharing industry
under the sharing economy.
110
Yiyi, S.
The Impact of the Sharing Economy on Traditional Product Sales: The Case of Bicycle Sharing in China.
DOI: 10.5220/0013834700004719
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 2nd International Conference on E-commerce and Modern Logistics (ICEML 2025), pages 110-118
ISBN: 978-989-758-775-7
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

2 THEORETICAL ANALYSIS
AND RESEARCH
HYPOTHESES
2.1 Customer Perceived Value
The concept of perceived value was first
systematically introduced by Zeithaml (1988). Given
the ambiguity and lack of consensus regarding related
concepts within the academic community, Zeithaml
developed a theoretical framework to elucidate the
interrelationships among perceived value, perceived
quality, and perceived price. According to Zeithaml's
definition, consumer perceived value is a holistic
assessment derived from weighing the utility
obtained from a product against the costs incurred.
Subsequent researchers have refined this
theoretical model. Kantamneni and Coulson (1996)
developed a multidimensional measurement model
for consumer perceived value, validated its
effectiveness through surveys, and identified key
dimensions of perceived value. Similarly, Bourdeau,
Chebat, and Couturier (2002), based on an analysis of
internet consumer behaviour, categorized university
students' perceived value of internet usage into five
core dimensions, including social and hedonic value.
Their study represents an extension of CPV theory in
the digital economy, offering new insights into
experiential consumption and online consumer
behaviour.
CPV has since been widely adopted as a theoretical
tool across different contexts, from the examination
of service quality in fast-food restaurants in the real
economy (Slack et al., 2021), to the purchase of sleep
products (Kuncoro et al., 2021), and even to food
delivery robots (Hong et al., 2023).
In the context of the sharing economy, numerous
studies have leveraged CPV theory to explore
consumer behavior and market dynamics Zhang
(2020) applied the CPV framework to examine the
impact of product sharing—specifically truck and
bicycle sharing—on product sales. Similarly, Ding
and others(2021) constructed a CPV-based model to
examine factors influencing users' willingness to pay
for shared power banks.
Since its introduction, CPV theory has undergone
continuous refinement and empirical validation
across a broad spectrum of research domains and
practical application. From traditional product
consumption to the digital economy and, more
recently, the sharing economy, CPV has remained a
crucial tool for understanding consumer decision-
making processes. Building upon existing research,
this study further explores the applicability of CPV
iwith the sharing economy, using the bike-sharing
industry as a case study, to enrich the theoretical and
practical understanding of CPV in emerging business
models.
2.2 Variables and Hypotheses
The primary objective of this study is to explore the
impact of consuemrs' intention to use shared products
—specifically, shared bicycles— on their purchase
intention for corresponding private products, namely
private bicycles. However, when constructing the
analytical model, it is essential to consider the
complex interrelationships among variables,
particularly the factors influencing consuemrs'
intention to use shared bicycles. As outlined in
previous literature, CPV theory provides a theoretical
foundation for understanding the determinants of
consumers' adoption of shared bicycles.
Figure 1: Theoretical Model
Therefore, the proposed model (see Figure 1) in
this study incorporates three key variables: Perceived
Value of Shared Products (PV), Intention to Use
Shared Products (PIS) and Purchase Intention of
Private Products (PIP).
Traditional consumer behavior models have
predominantly centered on purchase intention
because ownership was historically regarded as the
primary consumption goal. However, in the sharing
economy, consumers may actively engage with and
derive utility from products or services without actual
ownership. Consequently, measuring usage intention
offers a more precise reflection of how consumers
adopt shared products based on perceived value.
The proliferation of internet technologies
underpinning the current sharing economy model has
facilitated a temporary separation between ownership
and usage rights, blurring the boundaries between the
two (Zheng, 2017). In light of this shift, Hypothesis 1
of this study is grounded in the Substitution Effect,
positing that a strong intention to use shared products
may reduce consumers' demand for private bicycles
ownership, thereby exerting a negative influence on
The Impact of the Sharing Economy on Traditional Product Sales: The Case of Bicycle Sharing in China
111

their purchase intention. This phenomenon has been
empirically validated in related fields, such as shared
mobility (e.g., Uber vs. private car ownership) (He,
2022) and shared accommodation (e.g., Airbnb vs.
traditional hotel stays) (Xu, 2020). Moreover, an
analysis of the ecosystem of a taxi-hailing app
revealed that user satisfaction and trust in the
platform are significantly influenced by both hedonic
and utilitarian value (Tumaku et al., 2023).
H1: The Perceived Value of Shared Products (PV)
affects the Purchase Intention of Private Products
(PIP).
H1a: The Economic value (PVE) negatively
affects the Purchase Intention of Private Products
(PIP).
H1b: The Social value (PVS) negatively affects the
Purchase Intention of Private Products (PIP).
H1c: The Emotional value (PVe) negatively
affects the Purchase Intention of Private Products
(PIP).
H1d: The Functional value (PVF) negatively
affects the Purchase Intention of Private Products
(PIP).
The measurement of Perceived Value is complex.
Sweeney and Soutar (2001) proposed the PERVAL
(Perceived Value Scale) model in their study,
emphasizing that consumers take multiple types of
value into account when making decisions.
Economic Value (PVE): Consumers consider
whether shared products offer better cost-
effectiveness compared to private products. For
example, they may evaluate whether using shared
bicycles is more cost-saving than purchasing a private
bicycle.
Social Value (PVS): Using shared products may
provide social recognition, such as reinforcing
environmental consciousness or fostering a sense of
identity associated with the sharing economy.
Emotional Value (PVe): Consumers may
experience positive emotions when using shared
products, such as convenience, excitement from
exploring new things, or a sense of enjoyment.
Functional Value (PVF): The actual performance
and usability of shared products, such as whether
using a shared bicycle is more efficient than owning
one.
The model proposed by Sweeney and Soutar was
originally designed to assess consumers' evaluations
of durable goods. However, its core framework
exhibits considerable adaptability, enabling its
application across diverse consumption contexts.
Within the sharing economy, consumers similarly
evaluate economic, social, emotional, and functional
values when deciding whether to engage with shared
bicycles.
Among these dimensions, economic value is
particularly significant in the sharing economy, as
consumers frequently compare the relative cost of
purchasing private products versus utilizing shared
alternatives. Additionally, social value applies to
shared bicycles, as the adoption of shared products
may enhance consumers' social identity, particularly
by aligning with environmental values and
sustainable consumption practices. Emotional value
reflects the degree of convenience, enjoyment, and
novelty derived from shared bicycle usage, whereas
functional value directly affects the actual user
experience, including aspects like riding comfort and
accessibility.
Therefore, drawing on Sweeney and Soutar's
perceived value measurement framework, this study
classifies the perceived value of shared products into
four key dimensions—economic, social, emotional,
and functional—to develop a more comprehensive
understanding of consumers' intention to use shared
bicycles and its subsequent impact on their purchase
intention for private bicycles.
According to the definition of perceived value, it
essentially represents a trade-off between perceived
benefits and perceived costs (Zeithaml, 1988). Since
purchasing behavior is driven by consumer needs,
individuals tend to choose the option that maximizes
perceived value (Feng et al., 2006). Additionally,
previous research has confirmed a positive
correlation between perceived value and purchase
intention (Wood & Scheer, 1996).
Furthermore, prior studies indicate that when
consumers attribute high perceived value to shared
products, they demonstrate a stronger inclination to
use them. This finding aligns with the hypothesis that
perceived value serves as a key determinant of
consumers' adoption of shared products, reinforcing
the positive relationship between perceived value and
usage intention.
Based on the above analysis, this study proposes
H2: The perceived value of shared products affects
the Intention to Use Shared Products (PIS).
H2a: The Economic value (PVE) positively affects
the Intention to Use Shared Products (PIS).
H2b: The Social value (PVS) positively affects the
Intention to Use Shared Products (PIS).
H2c: The Emotional value (PVe) positively affects
the Intention to Use Shared Products (PIS).
H2d: The Functional value (PVF) positively
affects the Intention to Use Shared Products (PIS).
The widespread adoption of shared products
reduces the necessity of private product ownership,
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112

allowing consumers to satisfy their needs through the
sharing model without acquiring the corresponding
private products. Building on this premise, this study
proposes H3: The Intention to Use Shared Products
(PIS) negatively affects the intention to purchase
private products (PIP).
3 METHODS
3.1 Survey Instrument
This study employed a questionnaire survey to collect
the necessary data. The questionnaire consisted of
three sections and was administered online.
The first section gathered basic demographic
information. The second section measured the
perceived value of shared products, while the third
section assessed relevant behavioral intentions. Both
sections utilized a seven-point Likert scale (Likert,
1932) for scoring. The questionnaire items were
designed with reference to well-established
measurement scales from existing literature
(Sweeney & Soutar, 2001; Bourdeau et al., 2002;
Zhong, 2013; Zhang, 2023), incorporating the
characteristics of bike-sharing as a shared product.
Specifically, three items were designed for each
dimension of perceived value—economic, social,
emotional, and functional value. Additionally, three
items were used to measure the intention to use shared
products, and four items were developed to assess the
purchase intention of private products.
Upon data collection, SPSS software was used for
data analysis.
3.2 Study Participants
Since this study focuses on the sharing economy in
China, all respondents are Chinese consumers. No
specific restrictions were imposed regarding age,
gender, occupation, or region, ensuring that the
sample possesses broad and generalizable
characteristics.
4 STATISTICS ANALYSIS
4.1 Descriptive Analysis
A total of 285 valid questionnaires were collected in
this survey. Among the respondents, 41.75% were
male, and 58.25% were female. The sample covers a
wide range of age groups and income levels.
Most respondents were in the 20-29 age group
(29.47%) and the 30-39 age group (27.02%),
followed by those aged 40-49 (22.11%). Respondents
under 20 years old and over 50 years old accounted
for 11.93% and 9.47%, respectively.
In terms of educational background, 54.74% of
respondents held a bachelor's degree or higher,
including 50.88% with a bachelor's degree and 3.86%
with a master's or doctoral degree. Additionally,
32.63% had an associate degree, while 12.63% had a
high school diploma or below.
Regarding disposable income, respondents earning
7,001-10,000 RMB per month constituted the largest
group (35.79%), followed by those earning 3,001-
7,000 RMB (30.18%). Respondents with a monthly
income of 1,001-3,000 RMB and above 10,000 RMB
accounted for 13.68% and 12.63%, respectively,
while 7.72% earned 1,000 RMB or less.
All respondents had prior experience using shared
bicycles. Among them, 28.42% used shared bicycles
almost daily, 51.23% used them two to three times per
week, and 20.35% had used them but infrequently.
This usage distribution ensures that respondents were
able to answer the survey questions based on their
actual consumption experiences, rather than relying
on speculation or assumptions.
4.2 Validity Analysis
The validity analysis helps determine whether the
design of the questionnaire items is reasonable.
Firstly, the communalities for all research items are
above 0.4, indicating that the information from these
items can be effectively extracted. The KMO value
for all scale items in the questionnaire is 0.887. Since
the KMO value is greater than 0.8, it suggests that the
data is highly suitable for information extraction,
reflecting good validity. Additionally, the variance
explanation rates for the six factors are 15.338%,
12.189%, 12.184%, 12.150%, 12.039%, and
11.692%, with the cumulative variance explanation
rate after rotation being 75.593%, which is greater
than 50%. This means that the information from the
research items can be effectively extracted.
Furthermore, the correspondence between the factors
and research variables aligns with the expected
results.
The Impact of the Sharing Economy on Traditional Product Sales: The Case of Bicycle Sharing in China
113

Table 1: Descriptive Statistical Analysis of the Sample
Category Subcategory
Frequency
(n)
Percentage
(%)
Gender
Male 119 41.75
Female 166 58.25
Age
Below 20
years
34 11.93
20-29 years 84 29.47
30-39 years 77 27.02
40-49 years 63 22.11
50 years and
above
27
9.47
Education
Level
High school or
below
36 12.63
Junior college 93 32.63
Bachelor's
degree
145 50.88
Master's or
Ph.D.
11 3.86
Monthly
Income
1000 or below 22 7.72
1001-3000 39 13.68
3001-7000 86 30.18
7001-10000 102 35.79
Above 10000 36 12.63
Frequency
of Shared
Bike Usage
Almost daily 81 28.42
2-3 times per
week
146 51.23
Used but rarely 58 20.35
4.3 Reliability Analysis
Reliability analysis is used to assess the accuracy and
consistency of responses in quantitative research,
particularly for attitudinal scale items. In the
proposed model of this study, economic value, social
value, emotional value, and functional value
represent the four dimensions of Perceived Value of
Shared Products. The analysis results indicate that the
Cronbach's Alpha coefficients for these dimensions
range from 0.833 to 0.849, demonstrating strong
internal consistency.
Additionally, the Intention to Use Shared Products
(α = 0.837) and the Purchase Intention of Private
Products (α = 0.869) also meet high reliability
standards. The overall reliability coefficient for all
attitudinal scale items is 0.743, which exceeds the
commonly accepted threshold of 0.7, indicating that
the research data exhibits good reliability.
5 RESULTS AND DISCUSSION
5.1 Correlation Analysis
The correlation analysis examines the relationships
between PIP, PIS, and the four dimensions of
perceived value (PVE, PVS, PVe, PVF) using
Pearson correlation coefficients to measure the
strength of these relationships.
PIP and PVE, PVS, PVe, PVF: The correlation
coefficients are -0.433, -0.472, -0.477, -0.429,
separately, significant at the 0.01 level, indicating a
significant negative correlation.
PIS and PVE, PVS, PVe, PVF: The correlation
coefficients are 0.426, 0.434, 0.411, and 0.377,
respectively, all significant at the 0.01 level,
indicating a significant positive correlation.
PIP and PIS: The correlation coefficient is -0.413,
significant at the 0.01 level, showing a noticeable
negative correlation.
These results support the hypothesized
relationships between the variables examined.
5.2 Regression Analysis
5.2.1 The Validation of H1
Table 2: Results of Linear Regression Analysis (n=285)
Unstandardiz
ed
Coefficients
Standardized
Coefficients
t p
Collinearity
Statistics
B
Std.
Error
Beta VIF Tolerance
Constant 6.695 0.290 - 23.081
0.000*
*
- -
PVF -0.170 0.055 -0.178 -3.077
0.002*
*
1.414 0.707
PVe -0.238 0.060 -0.243 -3.990
0.000*
*
1.560 0.641
PVE -0.153 0.062 -0.160 -2.476 0.014* 1.762 0.567
PVS -0.170 0.075 -0.159 -2.276 0.024* 2.062 0.485
R
2
0.336
Adjusted
R²
0.327
F F (4,280) =35.497,p=0.000
D-W
Value
2.010
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Table 2: Results of Linear Regression Analysis (n=285)
Unstandardiz
ed
Coefficients
Standardized
Coefficients
t p
Collinearity
Statistics
B
Std.
Error
Beta VIF Tolerance
Dependent Variable = PIP
* p<0.05 ** p<0.01
By conducting a linear regression analysis, we use
PVF, PVe, PVE, and PVS as independent variables
and PIP as the dependent variable. The resulting
regression equation is:
PIP = 0.695 − 0.170 × PVF − 0.238 × PVe − 0.153 × PVE
− 0.170 × PVS
(1)
The R-squared value of the model is 0.336,
indicating that PVF, PVe, PVE, and PVS collectively
explain 33.6% of the variance in PIP. The F-test result
(F = 35.497, p = 0.000 < 0.05) confirms the overall
significance of the model, meaning that at least one
of the independent variables significantly influences
PIP.
Additionally, multicollinearity diagnostics reveal
that all VIF values are below 5, indicating the absence
of multicollinearity issues. The Durbin-Watson (D-
W) statistic is close to 2, suggesting that the model
does not suffer from autocorrelation, confirming the
independence of observations and the model's
robustness.
PVF: β = -0.170, t = -3.077, p = 0.002 < 0.01
PVe: β = -0.238, t = -3.990, p = 0.000 < 0.01
PVE: β = -0.153, t = -2.476, p = 0.014 < 0.05
PVS: β = -0.170, t = -2.276, p = 0.024 < 0.05
The results indicate that PVF, PVe, PVE, and PVS
all have significant negative effects on PIP, providing
strong empirical support for H1.
While the current model can predict PIP to some
extent, there is still considerable room for
improvement. Given that the model explains 33.6%
of the variance in PIP, additional factors likely
contribute to consumers' purchasing decisions.
Introducing more variables, such as consumer
psychology, market trends, brand influence, and price
sensitivity, may enhance the model's explanatory
power.
5.2.2 The Validation of H2
Table 3: Results of Linear Regression Analysis (n=285)
Unstandardiz
ed
Coefficients
Standardized
Coefficients
t p
Collinearity
Statistics
B
Std.
Error
Beta VIF Tolerance
Constant 1.282 0.317 - 4.041
0.000*
*
- -
PVF 0.155 0.082 0.138 1.897 0.059 2.062 0.485
PVe 0.146 0.060 0.146 2.411 0.017* 1.414 0.707
PVE 0.190 0.065 0.185 2.922
0.004*
*
1.560 0.641
PVS 0.204 0.068 0.203 3.016
0.003*
*
1.762 0.567
R
2
0.278
Adjusted
R²
0.268
F F (4,280)=26.933,p=0.000
D-W
Value
2.043
Dependent Variable = PIS
* p<0.05 ** p<0.01
By conducting a linear regression analysis, we use
PVS, PVF, PVe, and PVE as independent variables
and PIS as the dependent variable. The resulting
regression equation is:
PIS = 1.282 + 0.155 × PVS + 0.146 × PVF + 0.190 × PVe
+ 0.204 × PVE
(2)
The R-squared value of the model is 0.278,
indicating that PVS, PVF, PVe, and PVE collectively
explain 27.8% of the variance in PIS.
The F-test result (F = 26.933, p = 0.000 < 0.05)
confirms the overall significance of the model,
meaning that at least one of the independent variables
significantly influences PIS.
Additionally, multicollinearity diagnostics reveal
that all VIF values are below 5, indicating the absence
of multicollinearity issues. The Durbin-Watson (D-
W) statistic is close to 2, suggesting that the model
does not suffer from autocorrelation, confirming the
independence of observations and the model's
robustness.
PVS: β = 0.155, t = 1.897, p = 0.059
PVF: β = 0.146, t = 2.411, p = 0.017 < 0.05
The Impact of the Sharing Economy on Traditional Product Sales: The Case of Bicycle Sharing in China
115

PVe: β = 0.190, t = 2.922, p = 0.004 < 0.01
PVE: β = 0.204, t = 3.016, p = 0.003 < 0.01
The results indicate that PVF, PVe, and PVE all
have significant positive effects on PIS, providing
empirical support for Hypotheses H2a, H2c, and H2d.
However, H2b is not supported, as PVS does not
reach the required significance level.
The result for H2b (PVS → PIS) exhibits a
different pattern compared to the other perceived
value dimensions. While PVF, PVe, and PVE
significantly influence PIS, PVS (Perceived Social
Value) only shows a marginal effect (p = 0.059),
failing to reach conventional significance thresholds.
Regarding this unexpected result, several points
merit discussion. First, we need to consider the
statistical tools and p-value threshold settings.
Although PVS (Perceived Social Value) shows a
marginal effect (p = 0.059), it does not meet the
conventional significance threshold, which may be
influenced by sample size or model specification.
Moreover, social value differs from the other three
dimensions of perceived value in its source.
Economic value, emotional value, and functional
value rely primarily on an individual's personal
judgment, whereas social value is closely tied to
broader societal and environmental contexts. In
certain economic products or services, usage or
purchase can provide an opportunity to join a
community or establish interpersonal relationships
and social recognition—such as gym memberships.
However, bike-sharing does not fall into this
category.
Although shared bicycles are environmentally
friendly and cost-effective, these characteristics alone
may not be prominent enough to make bike-sharing a
behaviour with strong interpersonal significance or
moral value. Consequently, users may not perceive
social value as a key driver in their decision to use
shared bicycles, explaining why its effect on PIS was
weaker than expected.
5.2.3 The Validation of H3
Table 4: Results of Linear Regression Analysis (n=285)
Unstandardiz
ed
Coefficients
Standardized
Coefficients
t p
Collinearity
Statistics
B
Std.
Error
Beta VIF Tolerance
Constant 5.111 0.242 - 21.091
0.000*
*
- -
PIS -0.394 0.052 -0.413 -7.635
0.000*
*
1.000 1.000
R
2
0.171
Table 4: Results of Linear Regression Analysis (n=285)
Unstandardiz
ed
Coefficients
Standardized
Coefficients
t p
Collinearity
Statistics
B
Std.
Error
Beta VIF Tolerance
Adjusted
R²
0.168
F F (1,283)=58.288,p=0.000
D-W
Value
2.046
Dependent Variable = PIP
* p<0.05 ** p<0.01
A linear regression analysis was conducted using PIS
as the independent variable and PIP as the dependent
variable. The resulting regression equation is:
PIP = 5.111 − 0.394 × PIS
(3)
The model's R-squared value is 0.171, indicating
that PIS explains 17.1% of the variance in PIP. The
F-test result (F = 58.288, p = 0.000 < 0.05) confirms
the overall significance of the model, demonstrating
that PIS has a significant effect on PIP. The
regression coefficient for PIS is -0.394 (t = -7.635, p
= 0.000 < 0.01), suggesting that PIS has a significant
negative impact on PIP. Therefore, H3 is supported.
This outcome is consistent with prior discussions
in this study, reinforcing the idea that the sharing
economy does not merely supplement traditional
consumption patterns but actively reshapes them.
However, the explanatory power of the model (R² =
0.171) suggests that other factors beyond PIS also
influence PIP. Future research should explore
additional variables, such as consumer trust in shared
services, product categories, and long-term
behavioral shifts, to further clarify the dynamics
between shared product adoption and private product
purchasing decisions.
6 CONCLUSION
This study adopts an empirical research approach
from the consumer perspective, based on perceived
value theory, to examine the extent to which the
perceived value of shared products negatively affects
consumers' intention to purchase private alternatives.
The findings reveal that the sharing economy
functions as a disincentive for upstream product
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purchases. Contrary to the prevailing assumption that
the sharing economy primarily stimulates
consumption, this study highlights its suppressive
effect on market demand for traditional private
products.
From this perspective, several important questions
warrant further exploration. While bike-sharing
serves as a representative case within the sharing
economy, do other shared products and services
exhibit unique characteristics that may lead to
different consumption patterns? To what extent can
the conclusions drawn in this study be generalized
across other sectors of the sharing economy?
Furthermore, how does the sharing economy interact
with upstream product sales from a macroeconomic
standpoint?
Additionally, this study finds that while economic,
emotional, and functional values significantly impact
consumers' intention to purchase private products, the
role of perceived social value remains ambiguous.
Unlike the other value dimensions, social value is
intricately linked to broader societal and
environmental contexts, rendering its influence more
complex and potentially variable across different
settings. Future research should further investigate
the role of perceived social value in shaping
consumer behavior across various shared product
categories and access whether its impact varies across
cultural and market conditions.
A deeper exploration of these issues will provide
policymakers with valuable insights into the
macroeconomic implications of the sharing
economy's rapid expansion..Such an understanding is
essential for designing informed policy interventions
and regulatory frameworks that promote sustainable
and balanced economic development in an era
increasingly shaped by shared consumption models.
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