INTRODUCTION
Problem thesis
Grab, a technology company based in Singapore, offers transportation services and has a strong presence in Vietnam Unlike its taxi services in Malaysia and Singapore, Grab has adapted to Vietnam's traffic conditions by promoting motorbike transportation This innovative approach positions Grab as a refreshing alternative in Southeast Asia App-based transportation services like Grab are effectively competing with traditional methods by providing enhanced security, comfort, and convenience.
Grab is experiencing robust growth across 8 countries and 195 cities in Southeast Asia, with an estimated 90 million device users and over 5 million daily active users The platform dominates the motorbike taxi market, with more than 2 million drivers contributing to 95% of the global market share (Pradhan, 2019) In recent years, Vietnam has witnessed a surge in smartphone-ordered rides, fueled by technology companies like Grab and Uber Notably, in 2018, Uber sold its Southeast Asian operations to Grab, exchanging shares that amounted to 20-30%, effectively withdrawing from the region As a result, Grab now commands nearly 80% of the market share in Vietnam, leaving competitors with a mere 20% (Nguyễn, 2018).
Launched in Vietnam in 2014 as GrabTaxi, the service initially struggled to gain traction However, the introduction of GrabBike in November 2014, aimed at meeting the demand for safe and affordable motorbike taxis, significantly improved its appeal, especially in Ho Chi Minh City, with expansion to Hanoi by May 2015 According to ABI Research, Grab maintained its status as the market leader in Vietnam's ride-hailing sector, completing 62.5 million rides and capturing 74.6% of the market share in the first half of 2020, a slight decline from 73% in the same period of 2019 Its dominance is particularly notable in major cities like Ho Chi Minh City, where it commands an impressive 82% market share, making it an attractive opportunity for both domestic and international companies.
Since its emergence in December 2019 in Wuhan, China, the Coronavirus has posed significant challenges to global health systems and economies, with Vietnam facing substantial impacts as well The fourth outbreak, which began in late April 2021, resulted in 97,370 recorded infections by July 2021, particularly affecting Ho Chi Minh City, the epicenter, with 62,139 cases linked to the Delta variant In response, local authorities implemented Directive 15, which prohibits social events and limits gatherings to no more than 20 people indoors and 10 outdoors, while maintaining a minimum distance of two meters in public spaces Even stricter, Directive 16 restricts public gatherings to just two people, allowing residents to leave their homes only for emergencies or to purchase essential goods and services like food and medicine.
During the citywide social distancing period in Ho Chi Minh City, authorities mandated the suspension of motorbike taxi services, including both traditional and app-based options, for passenger transport and food delivery However, goods delivery services such as GrabMart and GrabExpress remained operational to ensure a steady food supply for residents.
Customer satisfaction is shaped by individual perceptions of pleasure or disappointment when comparing received products or services to expectations (Kotler and Keller, 2012) While previous research by Pham & Nguyen (2020) focused on GrabBike and GrabCar services in Ha Noi and Ho Chi Minh City, Grab's super app model encompasses additional functions like delivery, bill payments, and bookings, as noted by Nguyen-Phuoc et al (2020) Despite Grab's significant market lead, prioritizing customer satisfaction is crucial for maintaining its competitive edge, particularly in Ho Chi Minh City, where it holds the largest market share in Vietnam Given the context of the Covid-19 pandemic, this study explores "The impact of service quality on Grab's customer satisfaction in Ho Chi Minh City: An Empirical Study during the Covid-19 epidemic."
Aims and objective of the research
The aims and objectives are to identify service quality factors that impact on customer satisfaction of Grab's users Therefore, the thesis aims to analyze the following purposes:
- To verify and explore service quality factors impacting on customer satisfaction of users using Grab
- To evaluate service quality factors affecting customer satisfaction
- To propose implications to enhance the satisfaction of Grab's users
Based on aims and objectives, the thesis gives three questions below:
- Which are the factors of service quality affecting customer satisfaction on Grab's users?
- How do service quality factors affect customer satisfaction on Grab's users?
- What are the implications for enhancing customer satisfaction of Grab's users?
Subject and scope of the research
The subject of the study analyzes factors of service quality affecting customer satisfaction on Grab's users
The scope of the study includes users who have used Grab in Ho Chi Minh City, Vietnam
Time of the research: Carry out from 07/2021 to 09/2021
Research method
This research employs a qualitative method to identify the problem of customer satisfaction through a review of related studies, ultimately proposing a model for improvement It is grounded in a comprehensive literature review and an analysis of relevant factors influencing customer satisfaction Subsequently, a pilot questionnaire will be administered to randomly selected individuals aged 18 to 30 who have utilized Grab services.
This study employs a quantitative approach, utilizing survey questionnaires directed at customers who have used Grab services in Ho Chi Minh City to gather data The collected information will be analyzed using SPSS Statistics 20, which will help describe the sample and eliminate inconsistencies in observed variables through Cronbach's Alpha Additionally, Exploratory Factor Analysis (EFA) will be conducted to identify underlying elements and assess the internal reliability of the measurement scale Finally, regression analysis will be used to investigate the factors influencing changes in independent variables.
Structure of thesis
The study is structured in 5 chapters as follows:
This chapter will provide the broad research background, aims, and objectives of the research, the scope of the study, and the research method
This chapter examines the theories and research findings regarding the influence of brand equity on consumer purchasing behavior Additionally, it presents a theoretical framework along with concepts related to service quality and customer satisfaction.
This chapter will present the detail of the current study's methodological research framework, including research design, methodology, data sources, qualitative analysis, and quantitative study
This chapter will analyze and discuss the data collected by the customers who used the Grab service in Ho Chi Minh City
This chapter will discuss the essential findings and fundamental problems relevant to factors affecting the customer satisfaction of Grab's users in Ho Chi Minh
It has also provided the limitation of the study and recommendations for future research.
Summary
This chapter outlines the current study, detailing its research background, context, and structure It highlights the key issues prompting this research and identifies its main objective: to explore the factors influencing the satisfaction of Grab users in Ho Chi Minh City The following chapter will delve into the theoretical perspectives surrounding customer satisfaction.
LITERATURE REVIEW
The basic concepts
Customer satisfaction is a multifaceted concept that varies in definition among researchers, leading to ongoing debate Many believe that it arises from the gap between customer expectations and their actual experiences.
Customer satisfaction reflects a customer's overall attitude towards a service provider and is influenced by the gap between their expectations and the actual experience According to Hansemark and Albinsson (2004), this emotional response arises from how well a service meets the customer's needs and desires Kotler and Armstrong (2012) further define customer satisfaction as the sensory state resulting from the comparison between the outcomes of consuming a product or service and the customer's expectations The degree of satisfaction varies based on this comparison: when expectations exceed actual results, dissatisfaction occurs, while alignment between expectations and results leads to customer satisfaction.
When perceived quality exceeds customer expectations, it leads to delighted customers who positively impact a company's performance Satisfied customers tend to be loyal and often share their positive experiences with others, enhancing marketing efforts through word-of-mouth Expectations arise from personal needs, past experiences, and external influences like advertising and social interactions Ultimately, individuals seek satisfaction in their communication and food needs, shaping their overall expectations.
Previous experience plays a crucial role in shaping consumer expectations, as increased knowledge and experience lead to heightened anticipations Additionally, external information can further elevate these expectations Consumer satisfaction is determined by the perceived gap between prior expectations and the actual performance of a product, ultimately influencing their acceptance of it (Tse & Wilton, 1988) Alternatively, satisfaction can also be viewed as the fulfillment of consumer wants (Oliver, 2010).
Customer satisfaction consists of two key elements: the fulfillment derived from acquiring quality products or services at a fair price, and the trust and rapport built through ongoing business interactions This includes the professionalism of employees and the overall service attitude, which contribute to a positive customer experience over time.
According to Zeithaml and Bitner (2000), service quality and customer satisfaction are two concepts; while service quality focuses specifically on service components, the customer's satisfaction level is a general concept
Despite ongoing debates among researchers regarding the relationship between service quality and customer satisfaction, a general consensus suggests a significant connection (Cronin & Taylor, 1992; Spreng et al., 1996; Indra & Ibrahim, 2017; Bismo et al., 2018; Nguyen et al., 2020; Hayder, 2020) However, many studies have overlooked the detailed examination of how specific components of service quality influence customer satisfaction across various service industries Therefore, further investigation is essential to explore the intricate relationship between service quality factors and customer satisfaction within distinct sectors.
Since the 1930s, quality in the manufacturing sector has been identified as one of the competitive factors, while service quality has only developed in recent decades
Quality in the service sector, as defined by the International Organization for Standardization (ISO), refers to the ability of a process, system, or product to meet customer and stakeholder requirements A product may be deemed low quality if it fails to meet customer acceptance, regardless of its advanced manufacturing technology Unlike tangible products, where customers can evaluate aspects like style and packaging before purchasing, service quality lacks concrete evidence for assessment, relying instead on factors such as physical facilities, equipment, and personnel Therefore, service quality is crucial for determining how well services align with customer expectations consistently.
(1982) conducted a model that presented consumers comparing the perception of received service with their expectations
Services differ from tangible products in that they require customers to evaluate their experiences before, during, and after consumption The intangible nature of services presents challenges for providers in gauging customer satisfaction and assessing service quality During the consumption of a service, the quality is perceived by both the customer and the provider (Svensson, 2002) Research by Parasuraman et al (1985, 1988, 1991) has highlighted that service quality is defined as the gap between consumer expectations and their perceptions of the actual service received.
2.1.2.1 Functional and technical service quality model
According to Gronroos (1984), service quality is assessed by comparing the value that customers expect before using the service and the value that customers receive when using the service
Research problem: How do technical quality and functional quality affect service delivery and customer perception of service? What are those factors?
Gronroos offers three criteria to measure service quality: technical quality, functional quality, and image quality
(1) Technical quality describes what the service is provided with and what quality the customer receives
(2) Functional quality describes how the service is delivered, or the customer receives the technical quality outcome
(3) Image is a critical factor, built mainly on the service's technical and functional quality and other factors such as tradition, word of mouth, pricing policy, PR)
2.1.2.2 Service Quality Gap Analysis model (SERVQUAL)
Service quality, as defined by Parasuraman et al (1985), is the gap between consumer expectations and their actual perceptions of service outcomes This concept has led to the creation of a measurement tool designed to assess this difference effectively.
The SERVQUAL model utilizes 22 observed variables to effectively assess service quality, enabling researchers to identify the disparity between actual service performance and customer expectations This gap analysis serves as a crucial tool for understanding and improving service delivery.
GAP 1 highlights the disparity between customer expectations and tourism service managers' perceptions A significant gap indicates a lack of understanding among managers regarding what customers truly desire Therefore, grasping customer expectations is essential for delivering high-quality service in the tourism industry.
GAP 2 highlights the discrepancy between a service manager's understanding of customer expectations and how those insights translate into actual service quality standards Meanwhile, GAP 3 addresses the difference between the established service quality standards of a tourism enterprise and the actual quality of the primary services delivered, focusing on whether the service performance meets the defined standards.
GAP 4 represents the disparity between the actual quality of service delivered and the information, advertising, or promises made by a travel service to its customers This gap highlights the importance of service managers in managing and fulfilling service promises effectively.
GAP 5 represents the discrepancy between expected service and perceived service, highlighting that customer ratings of service quality are influenced by their expectations and the actual results experienced.
Parasuraman suggested that at the point where the GAP 5 is zero, service quality will be perfect
2.1.2.3 The performance-based measure of service quality (SERVPERF)
Cronin and Taylor (1992) explored the concepts and methodologies for assessing service quality, highlighting its correlation with customer satisfaction and purchase intention Their findings indicate that perceived factors serve as more accurate predictors of service quality.
Theoretical research
The study by Indra and Ibrahim (2017) investigates the relationship between service quality in ride-sharing and customer satisfaction in Malaysia It identifies five key factors—tangible aspects, reliability, pricing, comfort, and promotion—that positively impact user satisfaction, with comfort emerging as the most significant contributor.
A study by Horsu and Yeboah (2015) identified five key factors influencing customer satisfaction among minicab taxi users in Cape Coast, Ghana: reliability, continuous service, safety, comfort, and affordability While these elements positively affected user satisfaction, the behavior of drivers had a negative impact Notably, these six factors accounted for only 53% of the variations in overall customer satisfaction.
Mai Ngoc Khuong and Ngo Quang Dai (2016) investigated the factors influencing customer satisfaction and loyalty to enhance profitability for local taxi companies in Ho Chi Minh City Their research utilized the SERVQUAL model, which includes assurance, tangibility, reliability, empathy, and responsiveness, alongside the IDCTP model, focusing on dignity, information, comfort, price, and trip time The findings revealed that both comfort and price have a direct impact on user satisfaction.
Nashid Bintey Hayder's research (2020) examined the factors influencing customer satisfaction with online taxi services in Dhaka city, highlighting that the majority of users are female Key independent variables, including reliability, price, comfort, and service quality, were found to significantly impact customer satisfaction, with price demonstrating the strongest positive correlation.
A study by Mohsina Jahan (2019) examined various factors influencing ride-sharing user satisfaction in Bangladesh, including ride speed, cost, user security, convenience, waiting time, and driver behavior The findings revealed that ride speed is a primary factor affecting satisfaction, with cost and convenience also playing significant roles.
Service quality significantly influences passenger satisfaction and loyalty between Surabaya and Jakarta, as highlighted by Sachro and Pudjiastuti (2013) The study identifies five key factors—tangible, empathy, responsiveness, reliability, and assurance—that play a crucial role in enhancing customer satisfaction Notably, service quality accounts for 72.9% of the variables related to customer satisfaction.
Bismo, Sarjono, and Ferian (2018) proved that service quality with five factors: tangibles, reliability, responsiveness, assurance, and empathy has a significant impact on customer satisfaction of GrabCar's users in Jakarta, with 0.764
A study by Zhi-gang Yao and Xiao-dong Ding (2011) identified five key dimensions affecting taxi service quality: reliability, assurance, responsiveness, empathy, and tangibles In Hangzhou, the taxi service exhibited the highest assurance and lowest responsiveness, while the significance of these dimensions showed the highest reliability and lowest empathy These findings suggest that enhancing service quality in these areas can significantly improve customer satisfaction with taxi services.
By doing this, the management of driving operations should be the main task
A study conducted by Pham Van Hau and Nguyen Thi Hai Hanh (2020) investigated the factors affecting user satisfaction with Grab services in Hanoi, Vietnam The research identified six key factors: reliability, information, responsiveness, dignity, tangibles, and price The findings revealed that information and reliability emerged as the most significant contributors to user satisfaction, with influence scores of 0.426 and 0.417, respectively.
Murad et al (2019) utilized the SERVQUAL model to evaluate service quality through five key factors: tangibles, responsiveness, empathy, assurance, and reliability, focusing on customer satisfaction among Uber and Careem users Their findings revealed that reliability has a significant impact on user satisfaction, with 89.2% of respondents expressing a preference for using Uber and Careem over traditional taxis.
Satisfaction on Ride- sharing service in Malaysia
Tangible, Reliability, Price, Promotion and redemption, comfort
Five independent factors have a significant impact, especially with comfort, the most influential factor
Influence of Service Quality on Customer Satisfaction: A Study of Minicab Taxi Services in Cape Coast, Ghana
Safety, Comfort, Continuous, Service, Affordability, Driver behavior, Reliability
Most factors influence customer satisfaction, except for safety and driver behavior
The Factors Affecting Customer Satisfaction and Customer Loyalty –
A Study of Local Taxi Companies in
Ho Chi Minh City, Vietnam
Reliability, responsiveness, comfort, price, and information
Only comfort and price impact customer satisfaction
Satisfaction of Online Taxi Services in Dhaka City
Price, comfort, reliability, and quality
Price, reliability, and quality positively affect, with 0.342, 0.338, 0.198, respectively
Satisfaction of the Ride-sharing Industry in Bangladesh
Speed of ride, cost of the ride, security of users’, convenience of ride, waiting time for the car, and drivers’ behavior
Only speed of the ride, cost of ride, and convenience of ride positively impact customer satisfaction, with the speed of ride is the most influential factor
The Effect of Service Quality on Customer Satisfaction and Customer Loyalty of Argo Bromo
Anggrek Train Jakarta-Surabaya in Indonesia
Factors of service quality: tangible, empathy, responsiveness, reliability, and assurance
Factors of service quality explain 72.9% of the variables of customer satisfaction
The Effect of Service Quality and
Customer Satisfaction on Customer Loyalty: A Study of Grabcar Services in Jakarta
SERVQUAL models with five dimensions: reliability, responsiveness, assurance, empathy, and tangibles
Service quality has a significant effect on customer satisfaction
Measuring Passenger's Perceptions of Taxi Service Quality with Weighted
Tangibles, responsiveness, reliability, assurance, empathy
All dimensions were a significant positive correlation with customer satisfaction
Satisfaction: A Case Study of Grab in Vietnam
All factors positively impact Grab users’ satisfaction, with information is the most decisive influence
The Correlation between Customer Satisfaction and Service Quality in Jordanian Uber &
SERVQUAL model consists of five dimensions: tangibility, reliability, responsiveness, assurance, and empathy
The empirical result showed that all factors have a positive effect on customer satisfaction
Source: Compiled by the author
Conceptual model and hypothesis
2.3.1.1 The relationship between reliability and customer satisfaction
Reliability is essential for businesses, as it reflects their ability to deliver accurate, timely services while maintaining a positive reputation This involves consistent service implementation, honoring commitments, and fulfilling promises to customers (Parasuraman, Zeithaml, & Berry, 1988) For transportation services, reliability encompasses factors such as journey length, timely arrival, adherence to scheduled routes, and effective communication (McKnight et al., 1986) Research indicates that reliability is a critical factor in evaluating taxi services (Yao & Ding, 2011) and significantly influences customer satisfaction (Hayder, 2020) Furthermore, a comparative study highlighted that reliability impacts the overall service quality of both public buses and mini-bus taxis (Govender, 2014).
H1: There is a relationship between reliability and customer satisfaction
2.3.1.2 The relationship between responsiveness and customer satisfaction
Responsiveness is defined as a company's willingness to assist customers by providing high-quality and prompt service (Parasuraman et al., 1988) Customers feel valued when they receive exceptional service that includes employees demonstrating passion for their work and effectively managing emergencies (Masrurul, 2019) The assessment of a company's responsiveness is based on the speed and attention given to customer requests, questions, complaints, and issues Quick responses to inquiries or complaints significantly enhance customer satisfaction ratings Research indicates that responsiveness is a crucial factor in taxi service customer satisfaction (Hussein, 2016), further supported by studies from Yao and Ding (2011) and Mensah and Ankomah.
(2018) found that responsiveness is the lowest point of appraisal of taxi service
H2: There is a relationship between responsiveness and customer satisfaction
2.3.1.3 The relationship between tangibles and customer satisfaction
Physical evidence plays a crucial role in conveying service quality through tangible elements such as facilities, staff appearance, and equipment (Parasuraman, Zeithaml, & Berry, 1985) These tangibles serve as visual representations that customers, particularly newcomers, use to assess the quality of a service Industries that prioritize tangibles include restaurants, hotels, retail stores, and entertainment venues, where customers visit to receive services While service companies leverage tangibles to enhance their image and signal quality, they often integrate these elements with other dimensions to develop a comprehensive service quality strategy (Zeithaml, Bitner, & Gremler).
H3: There is a relationship between tangibles and customer satisfaction
2.3.1.4 The relationship between empathy and customer satisfaction
Empathy in service quality refers to a company's ability to understand and respond to individual customer needs, making them feel valued and unique (Parasuraman, Zeithaml, & Berry, 1988) Small service firms often excel in this area by building personal relationships with customers, knowing them by name, and being attuned to their preferences This empathetic approach provides a competitive edge over larger companies (Zeithaml, Bitner, & Gremler, 2017) Ultimately, prioritizing human connections and demonstrating genuine care for customers significantly enhances empathy and fosters business success.
H4: There is a relationship between empathy and customer satisfaction
2.3.1.5 The relationship between assurance and customer satisfaction
Assurance, as defined by Parasuraman et al (1988), is the level of knowledge and courtesy displayed by employees, which fosters customer trust, particularly in high-risk services like ride-hailing Research by Yao & Ding (2011) indicates that assurance is a critical factor in the performance evaluation of taxi services, a finding echoed by Mensah and Ankomah (2018) However, Hussein (2016) noted that assurance has a minimal impact on passenger satisfaction.
H5: There is a relationship between assurance and customer satisfaction
2.3.1.6 The relationship between price policy and customer satisfaction
Price serves as a crucial indicator of quality and significantly influences customer value (Parasuraman, Zeithaml, & Berry, 1988) Research by Turel et al (2006) highlights that varying prices of products or services can impact brand standards In the transportation sector, Button and Hensher (2001) emphasize that price is vital for balancing service quality with fares Additionally, Khuong and Dai (2016) found that price has a direct effect on customer satisfaction and loyalty, with respective impacts of 0.363 and 0.296 Furthermore, Ilma Khairani and Sri Rahayu Hijrah Hati (2017) assert that perceived value for money, along with service quality and e-service quality, positively and significantly influences customer satisfaction.
H6: There is a relationship between price and customer satisfaction
Table 2.2: Synthesize the hypothesis of the current research
Source: Compiled by the author 2.3.2 Research model
According to the hypotheses above and SERVQUAL (Parasuraman, Zeithaml,
& Berry, 1988), the thesis suggests a research model including six factors reliability, responsiveness, tangibles, empathy, assurance, and price policy
H1 There is a relationship between reliability and customer satisfaction Positive
H2 There is a relationship between responsiveness and customer satisfaction Positive
H3 There is a relationship between tangibles and customer satisfaction Positive
There is a relationship between empathy and customer satisfaction Positive
H5 There is a relationship between assurance and customer satisfaction Positive
H6 There is a relationship between price policy and customer satisfaction Positive
Summary
Chapter 2 presented theories related to customer satisfaction and built a theoretical model to represent the impact of factors on customer satisfaction when using Grab service Specifically, these factors are reliability, responsiveness, empathy, assurance, tangibles, and price The next chapter will present the research method carried out to build and evaluate the measurement scales and test the suitability of the theoretical model with market information.
METHODOLOGY
Design of research
The research process involves clearly defining the research problem and objectives, reviewing existing studies to grasp the current scientific knowledge, and identifying relevant theories Subsequently, a research design is developed, which includes creating measurement scales and analyzing the data collected.
Conclusion and implications from research findings
Regression analysis and hypothesis testing
The differences between group by One-way ANOVA
Cronbach’s Alpha 0.6 Correlated with total variable 0.3
KMO: 0.5 KMO 1 Test Barlett: Sig < 0.05 Total variance 50%
The preliminary research employed qualitative methods, including group discussions and in-depth interviews, to identify the key factors influencing customer satisfaction with Grab This approach allowed for the collection of detailed insights from targeted participants, facilitating a comprehensive understanding of user experiences The study involved three distinct groups of users, who engaged in discussions to provide valuable data for the survey.
Based on the literature review, this study gives some questions in a focused group outline:
– How many times do you use the service of Grab?
– What kind of service did you use in the Grab app?
– Why do you choose to use the service in the Grab app?
– What are factors that impact customer satisfaction? (Reliability, responsiveness, empathy, assurance, tangibles, and price policy) – Do you have any other comments?
Two focus groups, each consisting of five participants, were conducted to assess user satisfaction with Grab services The discussions revealed that key factors influencing satisfaction included reliability, responsiveness, empathy, assurance, tangibles, and pricing policies.
This thesis utilized group discussions and in-depth interviews to refine the suitability scales Subsequently, a survey was conducted with 40 users to evaluate the questionnaires, revealing that all participants comprehended and endorsed the content of the questionnaires.
Building the scale
Previous studies have explored customer satisfaction, but the existing scales do not accurately reflect the experiences of Grab users in Ho Chi Minh City Therefore, the components of the customer satisfaction scale will be modified to address the specific issues identified in the preliminary study.
3.2.1 The scale of reliability (RE)
Reliability refers to the capacity to provide exceptional service consistently and punctually, ensuring that employees fulfill their commitments accurately As a critical component of service quality, reliability has been extensively researched, including studies by Indra and Ibrahim.
(2017), Mai & Ngo (2016), Hayder (2020), Sachro & Pudjiastuti (2013), Bismo, Sarjono, & Ferian (2018), Yao & Ding (2011), and Pham & Nguyen (2020)
Table 3.1: Encode the reliability scale
RE1 Grab provides its services at the time it promises to do so (Parasuraman,
Zeithaml, & Berry, 1988) RE2 When I have a problem, Grab is sympathetic and reassuring
RE3 Grab services are completely error-free when I use them
RE4 The information that Grab provides to me is always complete and accurate
RE5 I feel reliable when using the Grab service
Source: Compiled by the author 3.2.2 The scale of responsiveness (RS)
Responsiveness in service quality reflects the eagerness and commitment of staff to deliver prompt assistance to customers This crucial component has been extensively researched, with studies conducted by Hoàng (2018), Mai & Ngo (2016), and Yao & Ding highlighting its significance in enhancing customer satisfaction.
Table 3.2: Encode the responsiveness scale
RS1 Employees of Grab tell me exactly when services will be performed
RS2 I received prompt service from Grab's employees and drivers
RS3 Employees and drivers of Grab are always willing to help me
RS4 Employees and drivers of Grab are not too busy to respond to my request promptly
Source: Compiled by the author 3.2.3 The scale of empathy (EM)
Empathy is understood as showing concern and care for each customer Empathy as a service quality component has been studied, such as Sachro & Pudjiastuti (2013), Bismo, Sarjono, & Ferian (2018), Yao & Ding (2011)
Table 3.3: Encode the empathy scale
EM1 Grab provides various services, catering to customers' demands
EM2 Grab has operating hours convenient to all their customers
EM3 Employees and drivers of Grab know what customers' needs are
EM4 Grab has customers' best interests at heart
Source: Compiled by the author 3.2.4 The scale of tangibles (TA)
Tangibles, which encompass the appearance and attire of service staff as well as the equipment used in service delivery, play a crucial role in assessing service quality Numerous studies, including those by Indra & Ibrahim (2017), Hoàng (2018), Sachro & Pudjiastuti (2013), and Pham & Nguyen (2020), have explored the significance of tangibles in enhancing customer perceptions of service quality.
Table 3.4: Encode the tangibles scale
TA1 Grab's application interface is clear and easy to use
TA2 Grab's uniforms are comfortable and easy to identify
TA3 Employees and drivers of Grab are well dressed and appear neat
TA4 Drivers of Grab are fully equipped with related services: helmets, seat belts, raincoats, delivery boxes
Source: Compiled by the author 3.2.5 The scale of assurance (AS)
Assurance in service quality encompasses the knowledge and politeness of employees, which fosters trust among customers Research on this component has been conducted by scholars such as Bismo, Sarjono, and Ferian (2018), Sachro and Pudjiastuti (2013), Yao and Ding (2011), and Murad et al (2019).
Table 3.5: Encode the assurance scale
AS1 Employees and drivers of Grab are polite
AS2 Employees and drivers get adequate support from Grab to do their jobs well
AS3 I can trust employees and drivers of Grab
AS4 I feel safe when using the Grab service
Source: Compiled by the author 3.2.6 The scale of price policy (PR)
Price serves as a crucial indicator of quality and significantly influences customer value The relationship between pricing strategy and service quality has been explored in various studies, including those by Indra & Ibrahim (2017), Horsu & Yeboah (2015), Mai & Ngo (2016), Hayder (2020), Jahan (2019), and Pham & Nguyen (2020).
Table 3.6: Encode the price policy scale
PR1 Grab's fare is not too high compared to other carriers during off-peak hours
PR2 Grab's fare during peak hours is acceptable
PR3 The fare is in line with the service that Grab offers
Source: Compiled by the author 3.2.7 The scale of customer satisfaction (CS)
Table 3.7: Encode the customer satisfaction scale
CS1 I am satisfied with the service quality of Grab that always meets my needs
CS2 I would continuously use Grab service frequently
CS3 I will recommend that my friends and family use the
CS4 I would continue to use Grab service because I expect its service quality will be further improved in the future
Source: Compiled by the author
Sample and data
This research, conducted in Ho Chi Minh City from July to August 2021, aims to gather data to evaluate the hypotheses outlined in the proposed model.
A formal quantitative research study was conducted to analyze and test hypotheses within the model, utilizing a 5-point Likert scale questionnaire for data collection According to Hair et al (1998), the minimum sample size required for exploratory factor analysis is five times the total number of observed variables (n=5*m), which is deemed appropriate for factor analysis research (Comrey, 1973; Roger, 2006).
The minimum sample size needed for multivariate regression analysis is calculated using the formula: n = 50 + 8 * m (m: number of independent variables) (Tabachnick and Fidell, 1996)
The survey questionnaire aims to gather information aligned with the research objectives, distributed both personally and through a Google Form link It comprises two sections: the first collects personal information using closed-ended questions, while the second assesses factors influencing Grab customer satisfaction through a five-point Likert scale, ranging from 1 (completely dissatisfied) to 5 (completely satisfied) The selected variables are based on a comprehensive literature review and consultations with the guiding lecturer.
The survey questionnaire outlines the participant profiles and examines the key differences and relationships among the factors influencing user satisfaction with the Grab service.
Profile questionnaire: it is the first part of the survey questionnaire It askes for the participant's background, including gender, age, and jobs
Survey questionnaire: the survey type is the second part of the questionnaire
This study aims to identify participants' preferences concerning the factors influencing their satisfaction with Grab services It encompasses six key elements that are crucial to customer satisfaction: reliability, responsiveness, assurance, empathy, tangibles, and pricing.
The survey questionnaire is crafted in Vietnamese to ensure participants fully comprehend the questions Key demographic information, including gender, age, and occupation, is collected at the end of the survey To safeguard personal privacy, the survey does not request respondents' email addresses or phone numbers.
A quantitative approach is selected as the main method for data collection, focusing on objective measurements and statistical analysis, as outlined by Babbie (2010) This method utilizes tools such as polls, questionnaires, and surveys, or leverages existing statistical data through computational techniques The advantages of quantitative research include its ability to gather data from a large number of respondents, making it particularly suitable for this thesis.
Descriptive research effectively addresses problems by collecting data that accurately describes the situation (Ethridge, 2004) This method is particularly useful for comparing various factors influencing customer satisfaction Consequently, the gathered information can provide insights to help Grab improve user satisfaction.
This study analyzes data from 259 urban survey participants in Ho Chi Minh City using the Statistical Package for Social Sciences (SPSS) software The analysis focuses solely on descriptive statistics, which include cross-tabulation, frequencies, percentages, and means, to effectively summarize and present the data The use of descriptive statistics simplifies the interpretation of large datasets, allowing for clearer insights (Berenson, Levine, and Krehbiel, 2006).
Data analysis
After collecting the data, each questionnaire is coded, and the scores are entered into SPSS version 20 Once data entry is complete, the generated frequencies are verified for accuracy, ensuring that there are no missing data or out-of-range responses.
3.4.1 Test reliability by Cronbach's Alpha
Cronbach's Alpha evaluates the reliability of the scale Using Cronbach's Alpha before EFA to exclude inappropriate variables as these variables may produce fake factors (Nguyen & Nguyen, 2009)
Cronbach's Alpha assesses the reliability of measurements but does not specify which observation variables to retain or discard To refine the analysis, calculating the correlation coefficient among total variables can help identify and exclude those that contribute minimally to the overall concept (Hoang & Chu, 2008).
Standard for scale selection when Cronbach’s Alpha is more significant than 0.6 (the greater Cronbach's Alpha, the higher internal consistency will be) (Nunally
& Burnstein 1994, Nguyen & Nguyen, 2009) However, this is not entirely accurate
If Cronbach's Alpha is too large, about 0.95 or higher, that means there are no variations in the scale This phenomenon is called duplication in the scale (Nguyen
Value levels of Cronbach's Alpha:
Cronbach's Alpha is greater than or equal to 0.8: excellent measurement scale; Cronbach's Alpha from 0.7 to 0.8: good measurement scale;
Cronbach's Alpha values ranging from 0.6 to just below 0.7 indicate that a scale may still be applicable, particularly when the research concept is novel or relatively unexplored in its context (Nunally, 1978; Peterson, 1994; Slater, 1995; Hoang Trong and Chu Nguyen Mong Ngoc, 2005).
Observation variables exhibiting a total correlation of 0.4 or higher, along with a Cronbach's Alpha below 0.7, will be excluded from the model Conversely, variables that have a total correlation of less than 0.3 but possess a Cronbach's Alpha of 0.7 or above will be retained in the analysis.
3.4.2 Test Validity by Exploratory factor analysis (EFA)
Exploratory factor analysis is a quantitative technique that simplifies a large number of interrelated observed variables into a smaller set of significant factors, retaining the essence of the original data (Hair et al.).
In exploratory factor analysis (EFA), variables with a factor loading below 0.50 will be excluded, utilizing principal components extraction with varimax rotation, ceasing when factors reach an eigenvalue of 1 A scale is deemed acceptable when it accounts for at least 50% of the total variance (Gerbing & Anderson, 1988) Additionally, the KMO index must be 0.5 or higher, indicating the suitability of EFA, with a range of 0.50 to 1 considered appropriate for factor analysis.
According to Hair et al (1998), factor loading is an indicator that ensures the practical significance level of EFA (ensuring practical significance) Factor loading
In factor analysis, a loading of 0.30 is deemed minimal, while a loading greater than 0.40 is considered important, and a loading above 0.50 is regarded as practically significant According to Hair & ctg (1998), when the factor loading criterion is set at over 0.30, a sample size of at least 350 is required For a sample size of approximately 100, it is advisable to use a factor loading threshold of greater than 0.50, and for a sample size around 50, the factor loading should exceed 0.75.
Bartlett's test evaluates the null hypothesis (H0) that there is no correlation among the observed variables in the population A statistically significant result (Sig < 0.05) indicates that the observed variables are indeed correlated within the population (Truong & Ngoc, 2005).
Linear regression is a straightforward method used in research monitoring, based on the assumption that the relationship between the dependent variable Y and the independent variables X1, X2, …, Xp is linear This supervised learning technique is widely employed for predicting numerical outcomes and is essential for quantification in various fields.
Linear regression aims to identify linear relationships between variables by developing a model that describes the connection between input variable X and output variable Y It involves transforming input variables to achieve linearity, interpreting the relationships between these variables, and is commonly applied in inference problems.
Multivariable linear regression is a regression model with many explanatory variables and is widely used in practice
Suppose the relationship between the dependent variable (response variable)
Y and k independent variables (regressor) k x1 , , xk given by the model:
Where 𝛽0, 𝛽1, …, 𝛽𝑘 are unknown parameters, called regression coefficients, 𝛽0 is called intercept coefficient, 𝛽1, … 𝛽𝑘 , , are the slopes; is a random error with zero expectation and variance 𝛼2
Indicators commonly used to evaluate the model:
SSE (Sum of Squared Errors):
SST(Sum of Square Total):
With “n” is the total sample
The relationship between R-squared and adjusted R-squared :
The coefficient of the model is represented by "q." A research model is deemed effective if the R squared or adjusted R squared values exceed 0.8, while values below 0.5 indicate that the linear model may not be appropriate for use.
The difference in determining the influence of each factor on customer satisfaction between user groups was examined using analysis of variance (ANOVA)
If Sig < 0.05, it can be concluded that there will be a statistically significant difference between the two means
If Sig ≥ 0.05, it can be concluded that there will not be a statistically significant difference between the two means
When the significance level (Sig.) is below 0.05, it indicates a violation of the homogeneity of variance assumption among the groups of variable values This suggests that the variances across the different work unit groups are not equal.
RESULT AND FINDING
Company overview
4.1.1 History of Grab Holding Inc
Founded in 2012 as My Teksi in Malaysia, Grab quickly gained traction with 11,000 app downloads on its launch day By June 2013, it achieved a remarkable milestone of 10,000 daily car orders The same year, GrabTaxi expanded into the Philippines, followed by entries into Singapore, Thailand, Vietnam, and Indonesia In January 2016, the company rebranded to Grab, solidifying its presence in Southeast Asia By 2014, Grab had relocated its headquarters to Singapore and experienced exponential growth across eight countries A pivotal moment came in 2018 when Grab acquired Uber's Southeast Asia operations, elevating its valuation to nearly $14 billion and earning the status of a "decacorn" after securing $1.46 billion in Series H funding from SoftBank's Vision Fund.
Grab offers a convenient mobile app that allows users to rent various wheeled vehicles effortlessly With over ten on-demand ride-hailing services, including taxis, private cars, car sharing, bike sharing, shuttle services, and motorbike taxis, Grab boasts a network of more than 2.8 million drivers handling over 6 million requests daily The company is committed to expanding its offerings and enhancing consumer services across the board.
Grab, marketed as a super-app, has expanded its services to include hotel bookings, video-on-demand, ticket purchasing, food delivery, and grocery shopping, while also offering financial services This innovative model, originally pioneered by Chinese giants like Alipay and Tencent's WeChat, has positioned Grab as a leading player in the super-app landscape, generating over $1 billion in sales.
In January 2016, Grab introduced GrabPay, a QR code-based mobile payment service, aiming to enhance its financial services business By 2018, the company aspired to double its user base by the following year Currently operational in six countries—Singapore, Indonesia, Philippines, Malaysia, Thailand, and Vietnam—GrabPay facilitates not only payments for Grab rides but also in-store purchases, food delivery, and money transfers.
As part of its "Grow with Grab" roadmap, Grab has expanded into loans and insurance services, offering financial support to small and medium-sized businesses and providing microinsurance for drivers in Singapore The company boasts a network of over 600,000 merchants, tapping into Southeast Asia's insurance market, which is the sixth largest globally.
GrabPay, a key player in the e-commerce landscape, facilitates online payments for consumers on platforms such as Qoo10 and 11Street, contributing to a market valued at $100 billion In Singapore, GrabPay is enhancing its financial services by introducing 'Postpaid' and installment payment options, enabling customers to settle their Grab service charges without incurring additional costs at the month's end.
Grab's co-founder, Hooi Ling Tan, attributes the company's success to its deep understanding of the local market and innovative problem-solving approaches By offering services that are tailored, distinct, and appropriate for each request, Grab effectively navigates the diverse landscape of Southeast Asia, which is characterized by a rich variety of languages, cultures, and religions.
Grab made its debut in the Vietnamese market in 2014, establishing its first legal entity as GrabTaxi Co., Ltd, which later evolved into Grab Co., Ltd In October of the same year, the company introduced its GrabBike service and began recruiting motorcycle drivers to enhance its integrated transport business model.
In its early days, GrabTaxi Co., Ltd had total assets amounting to nearly 4.4 billion VND During the same year, the company generated net revenue of 1.5 billion VND from sales and service providers, yet it faced a significant challenge, reporting a post-tax loss of nearly 4.4 billion VND.
In 2015, Grab received official licensing from the Ministry of Transport to initiate operations in major Vietnamese cities including Hanoi, Ho Chi Minh City, and Da Nang This legalization allowed the company to significantly expand its business model, resulting in a remarkable revenue of 32 billion VND that year.
Sales of it then increased continuously year by year, reaching 188 billion VND
In 2018, Grab experienced a significant surge in revenue following its acquisition of Uber's Southeast Asian operations, marking the largest merger and acquisition deal in the region's startup history.
In the same year, Grab Vietnam's revenue reached 2,194 billion VND, an increase of nearly three times in 2017 and almost 1,500 times of sales achieved in
In the fiscal year 2019, Grabs, the parent company, achieved a remarkable revenue of 3,382 billion VND, marking a 54% increase compared to the previous year and representing the highest growth since the company's entry into the Vietnamese market.
Despite experiencing exponential revenue growth, Grab has faced a consistent decline in profits, reporting annual losses in the billions In 2015, the company's revenue surged over 20 times, yet its after-tax profit plummeted nearly ninefold, resulting in a loss of VND 442 billion This downward trend continued in 2016 and 2017, with losses of VND 445 billion and VND 789 billion, respectively.
In 2019, Grab experienced a significant financial setback, reporting a loss of 1.67 trillion VND, which marks an 89% increase from the previous year's losses This substantial annual deficit highlights the ongoing financial challenges the company has faced since entering the Vietnamese market.
After over six years in Vietnam, Grab has diversified its business model beyond transportation services The company now fully owns Grab Vietnam Co., Ltd., having made an initial investment of 2 billion VND, and also holds complete ownership of GPay Network Vietnam Co., Ltd., with a reserve of 45 billion VND.
Sample description
From 2 to 4 times per month 29 93 122 47.1%
5 times and more per month 28 56 84 32.4%
Are Grab service essential during
The data reveals that the majority of respondents are female, with 131 individuals aged 18 to 24, 41 aged 25 to 34, and 6 aged 35 to 44, while only 3 respondents are over 55 Among the occupations, the largest group comprises 124 female students, followed by 48 officers and 8 freelancers, with unskilled laborers being the smallest group at 4 Notably, a significant majority, 173 respondents, believe that Grab services are essential during the COVID-19 pandemic.
A survey conducted among 75 men revealed that 41 respondents were aged 18 to 24, with 38 identifying as students Notably, 39 of these men reported spending less than 500,000 VND monthly on Grab services, while 29 used the service 2 to 4 times each month Remarkably, almost all respondents acknowledged the importance of Grab services during the COVID-19 health crisis.
Table 4.2: The frequency of Grab services
Mobile and data top-up 55 7,3% 21,2%
Booking attractions and hotels 3 0,4% 1,2% Electricity and water billing payments 47 6,3% 18,1%
Source: Result of data processing
During the COVID-19 pandemic's fourth outbreak in Ho Chi Minh City, GrabFood emerged as the most utilized service, capturing 24.5% of user engagement, followed by GrabExpress at 20.4% Surveys conducted in June and July 2021 revealed that the city's transport services, including GrabCar and GrabBike, saw limited use at just 8.8% and 17.4% respectively, due to the implementation of Directives 15 and 16, which halted operations in early July Additionally, 14.9% of respondents used GrabMart, while mobile and data top-up services accounted for 7.3%, and electricity and water billing payments made up 6.3% Notably, only 0.4% of users booked attractions and hotels through Grab, reflecting the significant impact of the pandemic on consumer behavior in Ho Chi Minh City and beyond.
Reliability test by Cronbach’s Alpha
Table 4.3: Result of the reliability assessment of reliability scale
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
The analysis revealed a Cronbach's Alpha reliability coefficient of 0.754 for the scale, indicating satisfactory reliability All correlation coefficients for the observed variables exceeded 0.3, and no variables were eliminated, ensuring the scale's reliability remains above 0.754 Consequently, all observed variables are deemed acceptable and will be utilized in the subsequent factor analysis.
Table 4.4: Result of the reliability assessment of responsiveness scale
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
Source: Result of data processing
The Cronbach's Alpha result shows a reliability coefficient of 0.828 for the responsiveness scale, with all correlation coefficients of the observed variables exceeding 0.3 Since there are no observation variables to eliminate, the scale's reliability remains above 0.828 Consequently, all observed variables are accepted for the subsequent factor analysis.
Table 4.5: Result of the reliability assessment of tangibles scale
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
Source: Result of data processing
The Cronbach's Alpha results show a reliability coefficient of 0.779 for the tangibles scale, with all correlation coefficients of the observed variables exceeding 0.3 Since no observation variables were eliminated, the scale's reliability remains above 0.779, confirming that all observed variables are valid and will be included in the subsequent factor analysis.
Table 4.6: Result of the reliability assessment of empathy scale
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
Source: Result of data processing
The reliability of the empathy scale was confirmed with a Cronbach's Alpha coefficient of 0.825, indicating strong reliability All correlation coefficients of the observed variables exceeded 0.3, and no variables were eliminated, further enhancing the scale's reliability Consequently, all observed variables are deemed acceptable for subsequent factor analysis.
Table 4.7: Result of the reliability assessment of assurance scale
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
Source: Result of data processing
The Cronbach's Alpha results demonstrated a reliability coefficient of 0.861 for the assurance scale, with all correlation coefficients of the observed variables exceeding 0.3 Since no observation variables were deemed necessary to eliminate, the scale's reliability remains robust at over 0.861 Consequently, all observed variables are accepted for further factor analysis.
Table 4.8: Result of the reliability assessment of price policy scale
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
Source: Result of data processing
The Cronbach's Alpha reliability coefficient for the price policy scale is 0.832, indicating strong reliability All correlation coefficients of the observed variables exceed 0.3, and there are no variables that need to be eliminated, reinforcing the scale's reliability Consequently, all observed variables are deemed acceptable and will be utilized in subsequent factor analysis.
Table 4.9: Result of the reliability assessment of customer satisfaction scale
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
Source: Result of data processing
The Cronbach's Alpha results show a reliability coefficient of 0.818 for the customer satisfaction scale, with all correlation coefficients of observation variables exceeding 0.3 Since no observation variables need to be eliminated, the scale's reliability remains robust at over 0.818, allowing for the acceptance of all observed variables for subsequent factor analysis.
Table 4.10: Synthesize Cronbach’s Alpha of each variable
Source: Compiled by the author
Results of exploratory factor analysis (EFA)
4.4.1 Factors analysis for independent variables
The factor analysis revealed a KMO coefficient of 0.925, indicating a strong suitability of the data for analysis, supported by Bartlett’s test result of 3335.745 at a significance level of 0.000 The exploratory factor analysis (EFA) identified five factors through varimax rotation, with an eigenvalue greater than or equal to 1, explaining 64.793% of the total variance, which is above the satisfactory threshold of 50% Out of the 24 variables analyzed, 23 exhibited loading factor coefficients exceeding 0.5, while EM4 was excluded due to its near-zero loading.
The second factor analysis yielded a KMO coefficient of 0.920, indicating excellent suitability of the data, as it exceeds the threshold of 0.5 Additionally, Bartlett’s test result was 3117.306 with a significance level of 0.000, which is well below the 0.05 mark, confirming that the data is highly appropriate for factor analysis.
Table 4.11: The result of KMO and Bartlett's Test for independent variables
Kaiser-Meyer-Olkin Measure of Sampling Adequacy ,920
Source: Result of data processing
Table 4.12: Eigenvalue and covariance deviations for independent variables
The exploratory factor analysis (EFA) revealed five factors extracted from the observation variable using the varimax rotation method, with an eigenvalue of ≥ 1, which is one less than the initial model The total variance explained by these factors is 65.166%, exceeding the satisfactory threshold of 50%.
Table 4.13: Result of independent factor analysis with principal varimax rotation
Extraction Method: Principal Component Analysis
Rotation Method: Varimax with Kaiser Normalization a Rotation converged in 6 iterations
Source: Result of data processing 4.4.2 Factors analysis for the dependent variable
Table 4.14: The result of KMO and Bartlett’s Test for the dependent variable
Kaiser-Meyer-Olkin Measure of Sampling Adequacy ,792 Bartlett's Test of Sphericity
The factor analysis results indicate a KMO coefficient of 0.792, confirming the suitability of the data for analysis, as it falls within the acceptable range (0.5 < KMO 0.05), which means that constant is no statistical significance
The variable SC shows a statistically significant impact at 19.7% (t = 3.167, significance level = 0.002), indicating that when other factors are held constant, changes in SC will lead to a 19.7% variation in CS.
"SC" is a sensitive factor that directly impacts customer satisfaction on Grab services Therefore, the H1 was accepted: Service Competence has a positive impact on customer satisfaction
The variable RE shows a statistically significant effect, with a coefficient of 0.151 (t = 2.987, significance level = 0.003, which is less than 0.05) This indicates that, when other factors are held constant, a change in RE will result in a 15.1% change in the CS.
"RE" is a sensitive factor that directly impacts customer satisfaction on Grab services
Therefore, the H2 was accepted: Reliability has a positive impact on customer satisfaction
The variable RS shows a statistically significant impact at 16.5%, with a t-value of 2.688 and a significance level of 0.008, which is below the 0.05 threshold This indicates that, when other factors are held constant, a change in RS will result in a 16.5% change in CS.
"RS" is a sensitive factor that directly impacts customer satisfaction with Grab services Therefore, the H3 was accepted: Responsiveness has a positive impact on customer satisfaction
PR= 0.215 (t = 5.882, significance level = 0.00 < 0.05) Therefore, the variable
The analysis reveals that PR has a statistically significant impact of 21.5% on customer satisfaction (CS) when other factors are held constant This indicates that changes in PR directly influence customer satisfaction levels with Grab services Consequently, the hypothesis H4 is validated, demonstrating that price policy positively affects customer satisfaction.
AS = 0.226 (t = 3.758, significance level = 0.00 < 0.05) Therefore, the variable
The analysis reveals that Assurance (AS) significantly influences customer satisfaction (CS) at a rate of 22.6% This indicates that when other variables are constant, any change in Assurance directly affects customer satisfaction with Grab services Consequently, the hypothesis (H5) is accepted, confirming that Assurance positively impacts customer satisfaction.
Table 4.26:Synthesize the result of the hypothesis test
Source: Compiled by author Unstandardized linear regression equation:
CS = 0.197*SC + 0.165*RS + 0.226*AS + 0.215*PR + 0.151*RE
CS = 0.164*SC + 0.154*RS + 0.222*AS + 0.287*PR + 0.155*RE
The price policy exhibits the highest beta value among all independent variables, indicating its significant impact on customer satisfaction for Grab users Following the price policy, the factors of assurance, service competence, reliability, and responsiveness also contribute to enhancing user satisfaction.
Testing of the sample
A comparative customer satisfaction test assesses the differences among specified groups, with meaningful results indicated by a significance level below 0.05.
Hypothesis Relationship Hypothesis test result
H1 Service competence has a positive impact on customer satisfaction Accepted
H2 Reliability has a positive impact on customer satisfaction Accepted
Responsiveness has a positive impact on customer satisfaction Accepted
H4 Price policy has a positive impact on customer satisfaction Accepted
H5 Assurance has a positive impact on customer satisfaction Accepted
Table 4.27: One-way ANOVA by gender
The results from the Levene test indicated a significance level of 0.926, which is greater than the 0.05 threshold, allowing us to accept the hypothesis of equal variance and proceed with ANOVA analysis The ANOVA results revealed a significance level of 0.079, also exceeding 0.05, suggesting that there is no statistically significant difference in customer satisfaction across different gender groups In summary, customer satisfaction levels do not vary significantly among groups defined by gender.
Table 4.28: One-way ANOVA by age
Source: Result of data processing
The results from Table 4.28 indicate that the Levene test yielded a significance level of 0.683, which is greater than the 0.05 threshold, allowing us to accept the hypothesis of equal variance, making the data suitable for ANOVA analysis Consequently, the ANOVA analysis revealed a significance level of 0.286, also exceeding 0.05, suggesting that there is no statistically significant difference in customer satisfaction across different age groups In summary, customer satisfaction remains consistent regardless of varying age levels.
Table 4.29: One-way ANOVA by occupations
The results from Table 4.29 indicate that the Levene test yielded a significance level of 0.920, which is greater than the 0.05 threshold, allowing us to accept the hypothesis of no difference in variance Consequently, ANOVA analysis was conducted, revealing a significance level of 0.097, also exceeding 0.05 This outcome suggests that there is no statistically significant difference in customer satisfaction among various occupation groups.
In other words, there is no difference in customer satisfaction between groups with varying levels of occupation
4.7.4 The difference between the frequency
Table 4.30: One-way ANOVA by frequency
From 2 to 4 times per month 122 4,1270 ,63737
5 times and more per month 84 4,1577 ,67192
The results from Table 4.30 indicate that the Levene test yielded a significance level of 0.440, which is greater than 0.05, allowing us to accept the hypothesis of no difference in invariance, thus making the data suitable for ANOVA analysis The ANOVA analysis revealed a significance value of 0.014, indicating a statistically significant difference in customer satisfaction across various frequency groups Notably, customer satisfaction was highest among those who engaged five times or more, while it was lowest for those who participated once per month To further clarify the differences between frequency groups, a post-ANOVA test using the Tukey method was conducted.
Table 4.31: Post Hoc test by frequency
Once per month From 2 to 4 times per month -,28743 * ,10894 ,009
5 times and more per month -,31812 * ,11617 ,007 From 2 to 4 times per month
5 times and more per month -,03069 ,09389 ,744
5 times and more per month
Once per month ,31812 * ,11617 ,007 From 2 to 4 times per month ,03069 ,09389 ,744
Source: Result of data processing There is a statistically significant difference between each group: once per month, from 2 to 4 times per month, and 5 times and more per month
4.7.5 The difference between essential service during COVID-19
Table 4.32: One-way ANOVA by advocates and opponents of Grab services
The results from Table 4.32 indicate that the Levene test yielded a significance level of 0.008, which is less than the 0.05 threshold, leading to the rejection of the hypothesis of equal variances Consequently, the Robust test was employed in place of the ANOVA analysis The data reveals that Welch's test produced a significance value of 0.003, also below 0.05, indicating a statistically significant difference in customer satisfaction among various groups Therefore, it can be concluded that there is a statistically significant disparity in perceptions between advocates and opponents regarding essential Grab services during COVID-19.
Summary
Chapter 4 of this thesis presents an analysis of the sample's descriptive statistics and assesses the scale's reliability through Cronbach’s alpha, exploratory factor analysis (EFA), and regression analysis The findings derived from the data processing and analysis are detailed in this chapter.
4, the next chapter presents the conclusion of the current study, the implications of these findings It identifies limitations and contributions for future research.
