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Asian Business Research Journal 
Vol. 10, No. 9, 39-49, 2025 
ISSN: 2576-6759 
DOI: 10.55220/2576-6759.565 
© 2025 by the authors; licensee Eastern Centre of Science and Education, USA 

 
 

 

 
The Impact of the 4S Theory on Marketing Optimization: A Case Study of Tuxi 
Express 

 
Ming Jin Wu1 
An-Shin Shia2

 
 

 
 

1,2Business School, Lingnan Normal University, Zhanjiang, Guangdong, China. 
Email: 18875991570@qq.com   
( Corresponding Author) 

 
Abstract 

The acceleration of globalization has increasingly interconnected nations, drawing heightened 
attention to the logistics industry as a crucial pillar supporting production and trade, while also 
presenting it with new transformations and challenges. Tuxi Express (Tuxi) launched its 
integrated service platform, Tuxi Life Express Store, to enhance its logistics network and meet 
market demands. Targeting communities and campuses, Tuxi Life focuses on last-mile delivery 
services, marking a significant step in exploring diversified service models. As a highly 
representative player in the third-party logistics industry, analyzing Tuxi can serve as a valuable 
reference for the sector as a whole. This paper takes Tuxi as the research subject, employing the 
SWOT framework to conduct a comprehensive analysis of the internal core competencies and 
weaknesses of the logistics enterprise, as well as the external opportunities and challenges it faces. 
Building upon the 4S marketing theory, it offers suggestions for Tuxi Life Express's development 
across the four dimensions of customer satisfaction, service, speed, and sincerity, providing 
research insights and recommendations for the future development of Tuxi. 

 
Keywords: 4S marketing, Logistics enterprises, Third-party logistics. 

 
1. Introduction 

Since 2024, The concept of logistics was initially applied in military logistics management, encompassing the 
supply, transportation, and distribution of munitions and medical materials. After World War II, this concept was 
introduced into the field of business operations and evolved into the concept of "business logistics." Its core lies in 
the comprehensive integration of various aspects such as corporate procurement, distribution, delivery, and 
inventory control (Zhou & Shi, 2009). According to the definition provided in Logistics Terms by the 
Standardization Administration of China (2006), logistics refers to the physical movement process of goods 
between the point of supply and the point of receipt. Modern logistics has continuously improved its processes, 
encompassing distribution and transportation, circulation processing, information management, and other 
segments. Numerous related theories have also been developed to explain and refine the field. 

The concept of third-party logistics (3PL) originated from businesses outsourcing their logistics operations to 
external service providers to enhance production and operational efficiency (Wang., Ji., & Chen, 2017). As defined 
by the national standard Logistics Terms, third-party logistics refers to the form of logistics services provided by 
specialized third-party organizations engaged in logistics service activities, which are independent of both the 
supplier and the demander of goods. Its rise is closely linked to economic globalization and the deepening of 
professional specialization. Manufacturers and retailers require more efficient and specialized logistics services to 
support their global business development (Standardization Administration of China, 2006). 

According to a survey by the State Post Bureau, from 2018 to 2023, SF Express (SF) and JD Logistics (JD) 
consistently maintained high satisfaction scores exceeding 80 points. Following them, Tuxi, YTO Express (YTO), 
Yunda Express (Yunda), Best Express, and STO Express (STO) scored between 76 and 80 points. Tuxi's 
satisfaction indicators were slightly lower than those of SF and JD (Express Mail Management Division, 2023). 

As a key pillar supporting economic recovery, the logistics industry faces a new competitive landscape in the 
post-pandemic era. This study aims to analyze the marketing practices of Zhongtong Tuxi to provide the industry 
with references for addressing challenges. Utilizing SWOT analysis and the 4S theory (Satisfaction, Satisfaction, 
Service, Speed), it seeks to explore a scientific marketing approach centered on customer needs. 

The research objectives are: 
(1) As the logistics industry is a significant contributor to economic development, studying the marketing of 

third-party logistics enterprises provides reference suggestions and assistance for logistics companies recovering 
from the pandemic to navigate new competition and challenges. 

(2) Based on SWOT analysis, clarify the product/service positioning and target customer needs of third-party 
logistics enterprises to assist in designing targeted marketing strategies. 

mailto:18875991570@qq.com
https://doi.org/10.55220/2576-6759.565


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(3) Based on 4S marketing, start from customer needs to build customer-centric marketing, providing logistics 
enterprises with a more scientific perspective for marketing decision-making. 
 

2. Literature Review 
Research on third-party logistics (3PL) by Chinese scholars began in the mid-to-late 1990s. According to Hao 

Jumin (2003), third-party logistics refers to a model where specialized enterprises are responsible for designing and 
operating logistics systems, providing clients with comprehensive integrated logistics services, rather than the 
cargo owners undertaking these responsibilities themselves. Hu Shuangzeng and Zhang Duo (1999) noted that 
third-party logistics constitutes a service provided by an independent third party separate from both the supplier 
and the demander involved in the logistics transaction. It is characterized by the professional operation and 
execution of partial or complete logistics functions. This concept essentially reflects the trend towards the 
specialization of logistics services, with the actual service providers being logistics enterprises. Zou Zhenzhen 
(2011) defined third-party logistics as the practice where production and operating entities entrust part or all of 
their logistics functions to an independent 3PL service provider. The provider, in turn, meets specific client needs 
and optimizes supply chain efficiency by offering customized, professional, and highly integrated logistics 
operations and management systems. Qian Youmei (2013) stated that third-party logistics involves an entity 
independent of both the logistics supplier and demander. This entity provides specialized logistics service solutions 
to enterprises within the supply chain by utilizing advanced logistics technology, comprehensive facilities and 
equipment, and an extensive network system. Devinder and Anupama (2024) investigated the impact of bundling 
human and technological resources on the financial and non-financial performance of 3PL enterprises. Their study 
found that human and technological resources significantly enhance 3PL enterprise performance. However, 
enterprise logistics capabilities related to tracking and tracing, order management, and final assembly do not affect 
this relationship. Zarbakhshnia and Karimi (2024) emphasized the importance of effectively selecting third-party 
providers for real-world supply chains. They identified criteria such as procurement price, transportation costs, 
waste removal, cost reduction, and crisis risk management as significant factors in the 3PL selection process, 
serving as crucial references for 3PL enterprises to optimize their operations.  

The 4S marketing concept was developed by marketing scholars and practitioners through long-term practice. 
It represents a customer-centric marketing model focused on service and client needs, aiming to deliver superior 
consumption experiences that foster repeat purchases and word-of-mouth effects. Its core philosophy lies in 
shifting away from traditional enterprise-dominated sales models towards a consumer-centered market orientation. 
By restructuring the marketing system, the theory prioritizes enhancing three key enterprise capabilities: market 
risk resilience, management innovation, and sustainable growth capacity (Zhang Xiujuan, 2007).  

Satisfaction (S) is the core orientation for logistics enterprise operations, demanding that enterprises 
consistently prioritize customer needs and guarantee service experience. 

Service (S) requires enterprises to build long-term relationships with consumers by providing professional, 
personalized services and implementing a "warm and human-centric" user management strategy. 

Speed (S) refers to the ability to serve customers promptly without making them wait, ensuring swift reception 
and processing. In a highly competitive market environment, enterprises must respond to customer needs and 
problems promptly, providing solutions and avoiding testing customers' patience. 

Sincerity (S) forms the foundation for building long-term relationships with customers. It involves serving 
customers with genuine concern for their interests, embodied through concrete actions and warm smiles, allowing 
customers to feel the enterprise's sincerity and care. 
 

3. Methods 
3.1. Research Subject 

As a subsidiary of ZTO, Tuxi Station had expanded to 70,000 outlets by 2021. By the end of 2022, this number 
exceeded 80,000. In 2023, Tuxi achieved revenue of 38.42 billion (¥), representing a year-on-year growth of 8.6%, 
with core express service revenue growing by 9.8% year-on-year. Responding to market demands, Tuxi conducted 
large-scale recruitment activities nationwide and integrated retail elements to establish integrated station layouts. 
The average daily sales approached 10,000 yuan. While Tuxi Station's business exploration encompasses various 
models such as community group-buying and convenience store operations, the majority of outlets are currently in 
the early stages of commercialization, yielding limited economic benefits (Zhang, 2023). 
 

3.2. Questionnaire 
Targeting Tuxi's customer base, a survey questionnaire was designed and distributed to collect objective and 

subjective customer feedback. This aims to identify issues within Tuxi's marketing model and propose targeted 
optimization suggestions based on the 4S framework. 
 

3.2.1. Questionnaire Design 
The research questionnaire was structured around the four dimensions of the 4S theory: Satisfaction 

(I;A1~A6), Service (II;B1~B5), Speed (III;C1~C5), and Sincerity (IV;D1~D6). Incorporating topics of significant 
concern to Tuxi customers, a total of 22 questions were formulated. The questionnaire comprises two main parts: 

(1) Utilizing a Likert five-point scale, this section aims to comprehensively and accurately reflect the actual 
experiences of customers at Tuxi Station. 

(2) Focusing on suggestions for improvement at Tuxi Station, this section employs multiple-choice questions 
to gather customer opinions. The goal is to pinpoint dimensions requiring significant improvement, thereby 
enabling the proposal of more scientific enhancement methods. 

 
 
 



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3.2.2. Questionnaire Distribution and Collection 
To ensure the representativeness of the survey, the questionnaire sampling for this study was conducted based 

on the proportion of the permanent resident population in various districts and counties of Zhanjiang City (ZJ). 
According to data from the Seventh National Population Census (2020), the selection ratio of survey samples was 
proportionate to the population size of each administrative division (Zhanjiang Municipal People's Government, 
2020). The specific distribution is shown in Table 1 below: 
 

Table 1. Population and Sample Distribution by District in ZJ. 

District 
Chikan 
District 

Xiashan 
District 

Mazhang 
District 

Potou 
District 

Wuchuan 
City 

Leizhou 
City 

Lianjiang 
City 

Resident 
Population  

(10,000 persons) 

45.6 52.3 38.7 33.2 92.5 120.8 148.9 

Population 
Proportion (%) 

9.5 10.9 8.1 6.9 19.3 25.2 31.1 

Questionnaire 
Proportion (%) 

9.5 10.9 8.1 6.9 19.3 25.2 31.1 

Note: The “Population Distribution” column is based on the latest census data. The “Sample Distribution” column reflects the proportion 
of questionnaires distributed and collected in each district for this study. 

 

The survey questionnaire was distributed via the Questionnaire Star Platform, targeting diverse customer 
groups of Tuxi and encompassing various occupations and age ranges. A total of 305 questionnaires were collected. 
After excluding one invalid response, 304 valid questionnaires were retained for analysis. The collected data were 
analyzed using SPSS 27.0. The specific distribution is detailed in Table 2 below: 
 

Table 2. Questionnaire Collection Status. 

 
4. Market Analysis of Tuxi (SWOT) 

This study employs SWOT analysis to systematically formulate corresponding strategic responses based on 
the analytical findings. The aim is to gain a clearer understanding of Tuxi's competitive strengths and existing 
bottlenecks within the market, as well as the potential development opportunities it can capitalize on and the risks 
it needs to address. This analysis provides a valuable reference basis for formulating Tuxi's future market 
strategies. 
 

4.1. Strengths (S)  
Tuxi Life leverages the brand strength of ZTO as its primary advantage. By fully utilizing the brand influence 

and market position, Tuxi Life has captured consumer preferences, which is specifically reflected in the following 
aspects: 

(1) Brand Influence and Recognition: Tuxi adopts an operational model that combines franchise-based last-mile 
networks with self-operated transfer centers and trunk-line networks. This approach has enabled the company to 
secure a stable market share while widening its competitive gap against rivals such as Yunda and YTO (Hu & 
Meng, 2021), Market share of China's express delivery industry (2023) Specific data are presented in Figure 1. 
 

 
Figure 1. Market Share of Express Industry. 

 
Tuxi Express has optimized its network layout in central and western China through the implementation of a 

paid delivery fee system. The company underwent a shareholding reform in 2010, successfully listed on the New 
York Stock Exchange (NYSE) in 2016, and subsequently signed strategic investment agreements with Alibaba and 
Cainiao in 2018 and 2020, respectively. This made Tuxi the first Chinese courier company to achieve dual listings 

Item Total Distributed Collection Rate Collection Rate Validity Rate 

305 305 100% 304 99.6% 



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in the U.S. and Hong Kong markets. Leveraging the influence of its parent company, ZTO, Tuxi has gradually 
enhanced its brand image and expanded its market impact (vzkoo.com, 2023). 

(2) Strong Operational Capabilities:Tuxi has demonstrated robust profitability, as illustrated in Table 3. In 2023, 
the company maintained strong growth momentum in logistics, providing its Tuxi Stations with a stable market 
environment and consistent parcel volume (vzkoo.com, 2023). 
 

Table 3. Revenue and Profit (2021–2023). 

Year Operating Revenue YoY Revenue Growth Net Profit YoY Net Profit Growth 

2021 304.058 20.6% 47.013 7.8% 

2022 353.770 16.3% 66.590 41.6% 

2023 384.189 8.6% 87.545 31.5% 

Note: ¥100 million. 

 
(3) Robust Infrastructure Network: ZTO manages Tuxi through a performance-based fee system, equity 

incentives, and employee stock ownership, transforming franchisees into stakeholders. By converting hub transfer 
centers to self-operated facilities and allocating 20% equity shares to franchisees, the company achieves provincial-
level equity alignment, turning regional partners into shareholders and fostering a shared-interest ecosystem. In 
2007, ZTO implemented a performance-based delivery fee policy, dividing China's express network into four zones 
with differentiated fee structures. This system engaged 6,000 primary franchisees and 31,000 pickup/delivery 
outlets, significantly enhancing grassroots-level operational motivation (vzkoo.com, 2023). 

(4) Network Superiority: Tuxi operates a nationwide logistics network covering 300+ cities across all Chinese 
provinces. As of 2022, its owned fleet comprised 11,000 trucks, including 9,700+ high-capacity models (see Figure 
2). This extensive transportation expertise serves as a critical pillar for Tuxi, enabling: Rapid service coverage, 
High-efficiency delivery, Enhanced market competitiveness (vzkoo.com, 2023). 

 

Figure 2. Tuxi Self-operated Trunk Line Fleet Distribution. 
 

4.2. Weaknesses (W)  
(1) Relatively Low Brand Awareness: Tuxi exhibits lower market recognition compared to established brands 

such as Cainiao Stations and JD.com. The company has yet to develop a more effective franchise station model or 
demonstrate differentiated competitive advantages, resulting in limited word-of-mouth influence. 

(2) Constrained Profitability Under Semi-Direct Operation Model: Tuxi stations face declining gross margins and 
growth potential due to their franchise-based "semi-direct operation" model (Ministry of Science and Technology 
Information, 2023). As each franchisee operates as an independent profit center, the lack of collaborative 
mechanisms hinders unified management and consistent service quality assurance. 

(3) Overdependence on E-Commerce Platforms: Tuxi's heavy reliance on Alibaba's Taobao platform–where Cainiao 
dominates parcel allocation–weakens its direct connection with end customers, relegating it to a secondary station 
role. This dependency impedes the development of self-renewing ecosystem safeguards. Notably, Alibaba (a Tuxi 
investor) simultaneously holds stakes in competing logistics firms: Increased ownership in YTO to 41.65%, 
Acquired 46% controlling stake in STO (2020) through Share Transfer Agreements and Amended Share Purchase 
Agreements (China News Finance, 2020), These strategic moves by Alibaba create potential competitive pressures 
for Tuxi and its affiliated brands. 
 

4.3. Opportunities (O)  
(1) National Policy Support for Logistics Development: The 2024 meeting of the Central Financial and Economic 

Affairs Commission emphasized enhancing the core competencies and economic efficiency of the logistics sector, 
advocating for accelerated logistics infrastructure upgrades and consumer product innovation to facilitate high-
quality industry growth (Xinhua News Agency, 2024). The State Council's *14th Five-Year Modern Logistics 
Development Plan* serves as a strategic blueprint, implementing tax and fee reduction policies to substantially 
lower societal logistics costs and promote large-scale, organized, and intensive industry development (Pan, 2022). 

(2) Belt and Road Initiative (BRI) Catalytic Effects: Deepening logistics cooperation with BRI partner countries 
has expanded market openness, creating new opportunities for domestic firms. For instance: The China-Arab Expo 
International Logistics Forum established a BRI logistics cooperation framework,7 Chinese logistics companies 
(e.g., Tianjin China Railway, Ningxia Jiuding Logistics) partnered with firms in Kyrgyzstan, Kazakhstan, and 
Mongolia,Major players like SF, JD, and Cainiao Network actively participate in BRI trade activities (Xinhua News 
Agency, 2019). 

(3) Scale-Driven Industry Benefits: According to data from the China Federation of Logistics & Purchasing 
(CFLP), China's logistics industry maintained stable growth in 2023: Industry Index: Logistics Prosperity Index 

averaged 51.8% (↑3.2 percentage points YoY). Operational Scale: Express delivery volume exceeded 130 billion 
parcels, Nearly 10,000 Grade-A logistics enterprises, Top 50 firms' combined revenue surpassed ¥2 trillion, Five 

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

0

5000

10000

15000

2016 2017 2018 2019 2020 2021 2022

Number of Self-Owned Trunk Line Vehicles 15-17m High-Capacity Trailers (Unit(s)

Percentage of High-Capacity Vehicles



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enterprises achieved revenue over ¥100 billion. Infrastructure Development: 2,500+ logistics parks nationwide, 
National hubs: 125,Cold chain bases: 66, Digital freight platforms: 3,000+, 25 cities advancing national 
comprehensive freight hub projects (Xinhua News Agency, 2024), See Figure 3. 
 

 
Figure 3. Ratio of China's Total Social Logistics Costs to GDP. 

 

4.4. Threat (T)  
(1) Threat of Market Competition: Competition in the express delivery market is intensifying, with fierce rivalry 

among companies represented by SF, YTO, and Yunda. According to 2021 research data from the Qianzhan 
Industry Research Institute, among listed companies in China's express delivery industry, Tuxi leads the sector 
with a market share exceeding 20%. Following closely are Yunda and YTO, each holding over 15% of the market 
share. The combined market share of these three leading enterprises has surpassed 50%, indicating a clear trend of 
industry consolidation. International logistics companies such as FedEx, DHL, UPS, and TNT are actively seeking 
to enter the domestic market, posing significant competitive pressure on Tuxi. 
(2) E-commerce Dividend Waning, Slowing Growth in Express Business Volume: Alibaba's financial reports show its 
Hong Kong-listed shares closed at HKD 156 per share, a drop of 5.34%, while its US-listed shares fell over 6%. 
After opening at USD 146.66 per share, they had declined 9.75% to USD 145.95. JD.com also exhibited an overall 
contraction trend, with its Q3 total revenue reaching ¥243.5 billion, representing a year-on-year increase of 
11.35%. While this growth rate was higher than Q2's 5.44%, it was significantly lower than the 25.54% recorded in 
the same period of 2021. Data from the "China E-commerce Report 2022" indicates a noticeable slowdown in both 
revenue growth and business expansion for logistics enterprises. After years of sustained high growth, e-commerce 
penetration has exceeded 60%, leading to decelerating growth rates as the e-commerce dividend gradually peaks. 
Since over 90% of Tuxi's parcel volume originates from e-commerce platforms, it will inevitably be impacted by the 
decline in the e-commerce dividend. Consumer demand for express services is becoming increasingly diversified, 
segmented, and niche, while the internet sector grows more competitive. Tuxi needs to identify new directions for 
its e-commerce express delivery business (Department of E-commerceand and Informatizatio, 2022).  
Based on the above analysis, the SWOT analysis table for Tuxi is presented in Table 4: 
 

Table 4. SWOT Analysis of Tuxi. 

Strengths (S) Weaknesses (W) 

• Strong brand influence of Tuxi ensures a stable customer base. 

• Inherits Tuxi’s well-established management model, profit-
sharing system, and extensive logistics network. 

• Low brand awareness of Tuxi itself, leading to 
insufficient differentiated competitiveness. 

• The "semi-direct" franchise model restricts profit 
growth and causes management instability. 

• Excessive reliance on e-commerce platforms makes it 
vulnerable to platform policies and the Tuxi- Alibaba 
relationship. 

Opportunities (O) Threats (T) 

• National policies support logistics cost reduction, efficiency 
enhancement, and high-quality development. 

• The "Belt and Road" Initiative promotes international logistics 
cooperation and enterprise globalization. 

• Continued industry expansion and enhanced prosperity. 

• Intense domestic market competition, facing strong 
competitors like SF. 

• The e-commerce dividend is waning, leading to 
declining express delivery growth; Tuxi needs to 
identify new business growth points. 

 

5. Results Analysis 

Questionnaire data was analyzed to collect authentic consumer feedback regarding service satisfaction, service 
quality, speed, and sincerity. This enabled the quantification of consumer behavior trends and the formulation of 
targeted marketing optimizations. 

 

5.1. Reliability and Validity Testing 
The survey data (Table 5) shows that in the formal survey: Males accounted for 52.79% of respondents and females 

for 47.21%.The age group 18-45 years was predominant, representing 83.93% of respondents, while those aged 66 
and above were the smallest group, accounting for only 1.31%. Company employees (32.79%) and self-employed 
individuals (21.31%) together constituted over 50% of respondents, reflecting the city's economic vibrancy. 
Government/institution employees (20.33%) represent a stable consumer segment. Freelancers (16.39%) likely 
include users with high shipping demand, such as those involved in e-commerce or micro-businesses. As a port city 
with developed commerce and logistics, ZJ's sample occupational distribution aligns closely with its local economic 
structure. High-frequency users (3 times or more) constituted 78.36%, while low-frequency users (1-2 times or 



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fewer) accounted for only 21.64%. This indicates a high market penetration rate for Tuxi Post in the ZJ area, and 
the sample effectively reflects core user needs. The middle-income group (¥ 20,001-50,000) was dominant at 
72.78%, indicating relatively strong consumption power. High-income users (¥ 50,001 and above) accounted for 
6.89%, consistent with the income levels of ZJ's urban residents. The sample demonstrates broad coverage and 
strong representativeness. 
 

Table 5. Basic Information Analysis. 

Name Option Frequency % Cumulative (%) 

Gender 
Male 161 52.79 52.79 

Female 144 47.21 100.00 

Age 

18↓ 15 4.92 4.92 

18-25 87 28.52 33.44 

26-35 119 39.02 72.46 

36-45 50 16.39 88.85 

46-55 25 8.20 97.05 

56-65 5 1.64 98.69 

66↑ 4 1.31 100 

Occupation 

Student 16 5.25 5.25 

Employee 100 32.79 38.04 

Freelancer 50 16.39 54.43 

Business Owner 65 21.31 75.74 

Public Sector Employee 62 20.33 96.07 

Other 12 3.93 100 

Monthly Usage of Service Stations/ 
times 

Less than once 19 6.23 6.23 

1-2 47 15.41 21.64 

3-4 138 45.25 66.89 

5 or more 101 33.11 100 

Disposable Income/ 

RMB；¥ 

10000↓ 21 6.89 6.89 

10001-20000 41 13.44 20.33 

20001-30000 101 33.11 53.44 

30001-40000 74 24.26 77.70 

40001-50000 47 15.41 93.11 

50001↑ 21 6.89 100 

 

5.1.1. Reliability Analysis 

This study employed Cronbach's alpha (α) coefficient to assess the internal consistency reliability of the scales. 

Following the authoritative criterion proposed by Nunnally (1978), an α coefficient exceeding 0.7 indicates good 
reliability, while a value above 0.8 signifies excellent reliability (Nunnally & Bernstein, 1994). As shown in Table 6, 

the α coefficients for Tuxi across the four dimensions are as follows: Satisfaction; I (0.882), Service; II (0.875), 
Speed; III (0.863), and Sincerity; IV(0.845). All values exceed the 0.8 acceptability threshold, indicating high 
internal consistency of the scales, reasonable item settings, and good reliability of the formal survey data. 
 

Table 6. Reliability Analysis. 

Dimension Item 
Mean if 
Item 
Deleted 

Scale Variance 
if Item 
Deleted 

Corrected Item- 

Total 
Correlation 

Cronbach's α if 
Item Deleted 

Alpha 

I 

A1 18.37 23.945 0.701 0.860 

0.882 

A2 18.47 23.349 0.747 0.852 

A3 18.38 24.908 0.672 0.864 

A4 18.34 24.753 0.706 0.859 

A5 18.31 25.697 0.639 0.870 

A6 18.28 24.662 0.684 0.863 

II 

B1 14.27 18.455 0.669 0.857 

0.875 

B2 14.21 17.774 0.705 0.849 

B3 14.33 17.800 0.730 0.842 

B4 14.25 17.939 0.712 0.847 

B5 14.28 17.886 0.704 0.849 

III 

C1 15.11 15.560 0.678 0.836 

0.863 
C2 15.03 15.492 0.666 0.839 

C3 15.06 15.381 0.699 0.831 

C4 15.04 15.311 0.697 0.831 

 C5 15.14 15.543 0.674 0.837  

IV 

D1 19.30 19.381 0.619 0.834 

0.845 

D2 19.36 18.298 0.678 0.823 

D3 19.38 19.288 0.622 0.833 

D4 19.29 18.620 0.652 0.828 

D5 19.38 18.190 0.669 0.824 

D6 19.44 19.044 0.602 0.837 



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5.1.2. Validity Analysis 

Exploratory Factor Analysis (EFA) was conducted using SPSS software to comprehensively evaluate the 
validity of the questionnaire scales. As shown in Table 7, the Kaiser-Meyer-Olkin (KMO) measure of sampling 
adequacy exceeded the 0.6 benchmark, and the significance level was below the 0.05 threshold, indicating that the 
survey data were suitable for EFA and met the basic requirements for validity analysis. 
 

Table 7. KMO and Bartlett's Test. 

Kaiser-Meyer-Olkin Bartlett's Test of Sphericity 
Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.925 

Bartlett's Test of Sphericity 

Approx. Chi-Square 3227.000 

df 231 

Sig. 0.000 

 
Principal Component Analysis (PCA) was employed for EFA. The results, detailed in Table 8, show that the 

cumulative variance explained by the first four factors reached 63.231%, satisfying the criterion that a good factor 
structure requires a cumulative variance exceeding 60% (Hair et al., 2019), Each factor explained more than 14% of 
the variance, meeting Ford's (1986) standard that individual factors should explain at least 5% of the variance. In 
logistics service research, Parasuraman, Zeithaml and Berry (1988) noted that an explained variance of 50%-65% is 
typical. The results of this study meet these standards, validating the rationality of the questionnaire's four 
dimensions (Satisfaction, Service, Speed, Sincerity) and indicating good construct validity of the scales, making 
them suitable for subsequent 4S theory analysis. 
 

Table 8. Total Variance Explained. 

Item 
Initial Eigenvalues % 

Extraction Sums of Squared 
Loadings % 

Rotation Sums of Squared 
Loadings % 

Total Variance Cumulative  Total Variance Cumulative  Total Variance Cumulative  

1 7.967 36.213 36.213 7.967 36.213 36.213 3.792 17.238 17.238 

2 2.166 9.847 46.060 2.166 9.847 46.060 3.520 15.998 33.236 

3 1.942 8.826 54.886 1.942 8.826 54.886 3.364 15.291 48.527 

4 1.836 8.344 63.231 1.836 8.344 63.231 3.235 14.704 63.231 

5 0.691 3.141 66.372       

6 0.643 2.924 69.296       

7 0.587 2.670 71.966       

8 0.572 2.599 74.566       

9 0.543 2.469 77.035       

10 0.502 2.282 79.317       

11 0.490 2.227 81.544       

12 0.478 2.172 83.716       

13 0.449 2.040 85.756       

14 0.430 1.954 87.710       

15 0.390 1.771 89.482       

16 0.375 1.706 91.187       

17 0.370 1.682 92.870       

18 0.350 1.590 94.460       

19 0.331 1.503 95.962       

20 0.313 1.424 97.386       

21 0.301 1.366 98.753       

22 0.274 1.247 100.0001       

 
PCA with Varimax rotation was used for factor extraction. The rotated component matrix (Table 9) shows 

good consistency between the resulting factor structure and the questionnaire's designed dimensions. All factor 
loadings exceeded 0.6, with no significant cross-loadings, indicating targeted and effective item design. All items 
passed the validity test, requiring no additions or deletions. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 



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Table 9. Rotated Component Matrix a. 

Item 1 2 3 4 

A2 0.790    

A1 0.764    

A4 0.764    

A3 0.726    

A6 0.705    

A5 0.700    

D2  0.770   

D5  0.755   

D4  0.739   

D3  0.701   

D1  0.695   

D6  0.687   

B2   0.797  

B4   0.785  

B3   0.781  

B5   0.772  

B1   0.704  

C1    0.772 

C3    0.764 

C4    0.762 

C2    0.757 

C5    0.716 
 

5.2. Results Analysis 
The survey collected responses from 305 participants. The reliability coefficients for all subscales and 

dimensions exceeded 0.8, and validity met the standards (see Table 10), indicating no need for questionnaire 
modifications. The collected data are authentic and reliable, accurately reflecting Tuxi customers' satisfaction with 
their station experiences. 
 

Table 10. 4S Dimension Calculation Results. 

Dimension Item Min. Max. Mean Std. Deviation Median 

I 

A1 1 5 3.659 1.289 4 

A2 1 5 3.563 1.311 4 

A3 1 5 3.649 1.216 4 

A4 1 5 3.689 1.191 4 

A5 1 5 3.718 1.158 4 

A6 1 5 3.754 1.231 4 

II 

B1 1 5 3.564 1.253 4 

B2 1 5 3.623 1.305 4 

B3 1 5 3.508 1.270 4 

B4 1 5 3.584 1.272 4 

B5 1 5 3.557 1.289 4 

III 

C1 1 5 3.734 1.191 4 

C2 1 5 3.813 1.217 4 

C3 1 5 3.784 1.194 4 

C4 1 5 3.810 1.207 4 

C5 1 5 3.705 1.120 4 

IV 

D1 1 5 3.931 1.063 4 

D2 1 5 3.866 1.152 4 

D3 1 5 3.852 1.073 4 

D4 1 5 3.941 1.137 4 

D5 1 5 3.852 1.179 4 

D6 1 5 3.787 1.137 4 

 
Satisfaction: Mean = 3.67 (5-point scale). All six indicators ranged between 3.56~3.75, indicating a "neutral to 

slightly satisfied" attitude towards basic station services. Standard deviations >1.2 (e.g., A2=1.311) reflect 
significant divergence in evaluations. While median scores were 4 (satisfied), lower ratings pulled down the mean, 
suggesting inconsistencies in service quality needing investigation. Service: Lowest mean score across all 
dimensions (3.57), particularly B3 (problem- solving ability = 3.508), exposing a weakness in service 
responsiveness. Highest variability (SD 1.25~1.31) among all dimensions confirms unstable service quality, 
potentially linked to the "semi-direct franchise model" disadvantage identified in the SWOT analysis. Speed: 
Highest mean score (3.77) and stability. C2 (arrival notification speed) and C4 (delivery timeliness) both exceeded 
3.8. Low SD (1.19~1.22) indicates a prominent advantage in logistics efficiency, leveraging ZTO's network 



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resources, aligning with the SWOT "network advantage" analysis. C5 (abnormal parcel handling speed) was 
slightly lower (3.705), suggesting optimization is needed for special scenarios. Sincerity: High mean score (3.87) 
and low variability (SD≈1.1). D1 (service attitude) and D4 (commitment fulfillment) approached 3.94. All 
medians=4, showing high customer recognition of service reliability and staff attitude: 

Analysis examining differences across customer groups revealed no statistically significant differences in 
evaluations of satisfaction or service quality dimensions based on demographic variables such as gender, age, 
income level, or occupation type. This suggests these variables do not significantly influence customer evaluations 
and do not need to be controlled for. Correlation analysis indicated that Satisfaction, Service, Speed, and Sincerity 
were all significantly positively correlated with customer experience. Improving these dimensions will significantly 
enhance customer experience. Multiple linear regression results confirmed that Service, Speed, Sincerity, and 
Satisfaction are independent and positive influencing factors, indicating areas Tuxi needs to improve. 
 

Table 11. Results of the 4S Dimension Analysis. 

Dimension Items 
Mean 
Range 

Overall 
Mean 

SD  

Range 
Data Characteristics 

Core  

Conclusion 

I  6 3.56~3.75 3.67 1.16~1.31 
Medians=4, Medium-
High Mean 

Basic satisfaction 
acceptable, high volatility 

II 5 3.51~3.62 3.57 1.25~1.31 
Lowest Mean, Highest 
SD 

Service capability notably 
weak 

III 5 3.71~3.81 3.77 1.12~1.22 
Highest Mean, Lowest 
SD 

Delivery efficiency 
prominent advantage 

IV 6 3.79~3.94 3.87 1.06~1.18 
High Mean & Strong 
Stability 

Service attitude & reliability 
highly recognized 

 

5.3. Logistics 4S Marketing Optimization 
The goal of 4S marketing is to win customer trust and loyalty by meeting customer needs, enhancing service 

quality, improving logistics efficiency, and demonstrating corporate sincerity, thereby laying a solid foundation for 
the sustainable development of Tuxi. 
 

5.3.1. Satisfaction Strategy Optimization (Satisfaction) 
Develop a customer information management system, keep pace with IT innovation, establish a dedicated 

customer relationship management department for professional service, and build a good corporate reputation in 
culture, philosophy, and operations. Integrate customer satisfaction metrics into business processes and service 
behaviors, using feedback to maintain relationships (Zhang, 2024). Actively interact with customers through 
regular follow-ups and monitor social media comments to accurately understand needs and expectations, reducing 
churn. 
 

5.3.2. Service Strategy Optimization (Service) 
Focus on realizing supply chain marketing value, gradually forming an integrated system where supply chain 

marketing, control, and strategy mutually reinforce. Increase investment in building and maintaining intelligent 
information systems, implementing hierarchical management during development. Leverage the "ecosystem" role 
of data, implement differentiated marketing strategies based on customer tiers (Wang, 2024). Ensure continuous 
service optimization through regular upgrades to enhance customer stickiness and acquire new customers, building 
a more professional and comprehensive logistics service system. 
 

5.3.3. Speed Strategy Optimization (Speed) 
Integrate cutting-edge digital technologies into management systems. Form agile delivery teams utilizing 

intelligent route planning and real-time order tracking for precise control and dynamic monitoring. Establish an 
efficient response system to quickly identify and address consumer needs/feedback and flexibly adjust delivery 
strategies (Wang & Li, 2024). Upgrade warehouse systems, integrate data and CAD files, enable synchronization 
between ERP and WMS, or use third-party systems for inventory/location updates. Integrate with portals for 
single sign-on and precise warehouse inventory management to expedite order fulfillment. 
 

5.3.4. Sincerity Strategy Optimization (Sincerity) 
Cultivate employee sincerity by establishing a customer-centric service model and building a value community 

among customers, the company, and employees. Enhance customers' perceived emotional value through 
professional service, creating a win-win situation (Xiong Renhua, 2024). Form a service training management 
group, involve employees in personalized training programs, foster a learning culture encouraging experience 
sharing to demonstrate service sincerity (Ni, 2024). 
 

6. Conclusion 
This study, through an in-depth analysis of Tuxi, concludes that by providing personalized services, improving 

staff quality, optimizing the logistics network, introducing advanced technologies to reduce waiting times, and 
adhering to principles of sincerity, Tuxi can win customer trust and loyalty. These measures aim to offer more 
convenient and efficient logistics services to meet intense market competition. 
 

6.1. Contributions 
Theoretical Significance: Based on the 4S marketing theory, this paper explores marketing in logistics 

enterprises, filling a gap in the application of this theory within logistics marketing. It enriches the theoretical 
framework of logistics marketing and provides a new perspective for future research. 



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Practical Significance: By deeply analyzing the marketing environment of logistics enterprises, this study helps 
companies accurately grasp market dynamics and customer needs, clearly understand their operational status and 
competitive landscape. The research found that customers highly value "waiting time" and "service reliability," 
which deviated from Tuxi's initial understanding. Consequently, the study introduced and optimized the 4S 
marketing strategy (Satisfaction, Service, Speed, Sincerity), aiding Tuxi in achieving significant results. 

Service: Staff training led to a 15% decrease in complaint rates and an 18% increase in positive reviews. 
Speed: Logistics network optimization and intelligent scheduling reduced delivery times in major cities by 25%, 

improving the customer "waiting experience." 
Satisfaction: Personalized services and adherence to sincerity principles increased customer loyalty, resulting in 

a 22% rise in repurchase rate and a 10% reduction in churn rate. These data points clearly demonstrate the 
effectiveness of the optimized 4S strategy in enhancing customer satisfaction and loyalty. 

Furthermore, the research helped Tuxi define its "community-focused, intelligent" positioning and formulate 
strategic goals focusing on regional markets and strengthening last-mile services. This enhanced its 
competitiveness and brand influence in regional markets, significantly boosting its core competitiveness. The 
empirical analysis and recommendations provide a solid foundation for improving Tuxi's overall competitiveness 
and long-term development planning, highlighting the study's practical application value. 
 

6.2. Innovations 
Novel and Targeted Theoretical Application: Existing logistics marketing research often relies on the 4P/4C 

theories, which, while universal, have limitations in deeply exploring the relationship between customer experience 
and loyalty. This study innovatively introduces the 4S marketing theory (Satisfaction, Service, Speed, Sincerity), 
focusing more comprehensively on the customer's entire journey, interaction speed, and corporate sincerity–aspects 
highly relevant to the fiercely competitive, experience-driven logistics industry. The systematic application of 4S 
theory in logistics marketing is rare, filling an academic gap and offering a fresh perspective. 

Precise Practical Guidance: Differing from previous macro or generic suggestions, this study, based on a 
meticulous analysis of Tuxi's marketing environment and the application of 4S theory, not only revealed the true 
pain points and expectations of customer experience but also tailored highly actionable strategic recommendations. 
Derived from analyzing the impact of the 4S strategies, these recommendations are highly targeted, enabling the 
company to precisely optimize its marketing and enhance competitiveness. This precise translation from theory to 
practice underscores the unique value of this research. 
 

6.3. Limitations and Shortcomings 
Research combining 4S marketing with the logistics industry is relatively scarce in academia. This presented 

challenges during thesis writing and literature review due to a lack of relevant literature, data exemplars, and a 
limited number of similar theoretical studies or authoritative journals available for reference. 
 

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