Exploring the sources of competitive advantage through customer satisfaction and customer loyalty: Case study of Vietnamese securities companies

Abstract:

There is a strong relationship among customer satisfaction, customer loyalty and competitive advantage. This study is to find out the competitive advantages of securities companies in Vietnam via their customer satisfaction and customer loyalty factors. A PLS-SEM model with 8 latent variables and 28-item questionnaires is proposed. There are 47,418 valid responses out of more than 200,0000 questionnaires delivered to customers of 31 securities companies in Vietnam, then the study indicates the relationship among those latent variables. The study’s results show that the most impactful variables on the customer satisfaction are perceived quality, information quality, technology quality and brand image. Meanwhile, the price policy of company has just a minor impact on the customer satisfaction, and it has more influence on the customer loyalty. The information quality and the brand image has favorable impacts on the customer loyalty. This study’s results are expected to help Vietnamese securities companies improve their competitive advantages and enhance their competitive strategies.

Keywords: competitive advantage, customer satisfaction, customer loyalty, securities companies, information quality, brand image.

1. Introduction

Over the past 25 years of development, Vietnam's stock market has been developing strongly, but along with it has been many fluctuations. The number of stock companies increased from just 14 in 2005 to 103 in 2009 and 2010. This number have reduced to 79 in since 2018.

When a company's profitability exceeds the industry average profit of all other companies, it has a competitive advantage over its rivals. When a business can outperform its rivals over an extended period of time with a higher average profit margin, it has a sustainable competitive advantage. A competitive advantage, which leads to superior profitability and profitable growth, is at the core of all these strategies. Two key components for a successful business are customer satisfaction and loyalty.

Competitive advantage can be achieved by investing in customer satisfaction and loyalty. Companies must respond to customer needs, enhancing the company's competitive advantage by providing customers with superior service experiences (Ihalainen, 2011). Making sure your customers are happy with the service your business offers is the best way to gain a sustainable competitive advantage. Understanding customer expectations and having efficient customer feedback collection mechanisms are crucial for businesses. When customers use a company's services, businesses should pay attention to their perceptions and feelings (Massawe, 2013).

The author develops a research model to analyze the effect of competitive advantage on the performance of securities companies in Vietnam, where firm performance is expressed through customer satisfaction and loyalty, based on the premise that there is a close relationship between customer satisfaction, loyalty, and competitive advantage. To evaluate the impact of competitive advantages on customer satisfaction and loyalty of Vietnamese securities companies and the relationship between those factors, the article proposes a structural equation model with 8 latent variables and 13 hypotheses.

2. Literature review and hypothesis development

2.1. Quality of technology, brand image, tangible attributes and perceived quality

Information and communication technology is undoubtedly one of the key drivers of the economy's explosive growth (Yeh, 2015). Information and communication technology has been extensively utilized in the service industries. Service businesses strive to differentiate their brand by providing excellent customer experiences. To achieve this, an increasing number of businesses are adopting technology. A business with strong technology will be able to establish and keep a competitive position in the market thanks to its positive brand image (Sasmita & Suki, 2015).

H1: Quality of technology is positively related to brand image

Brands act as a bridge between companies and their customers. Consumer trust is influenced by brand image, claims Afsar (2014). Numerous studies have demonstrated that a company's brand image has a positive impact on how well customers perceive the services and goods the business offers. As a result, businesses should concentrate on developing their brand image to improve how consumers view the caliber of their services (Alhaddad, 2015).

H2: Brand Image is positively related to Perceived Quality

One of the key elements influencing how customers perceive quality is tangible characteristics. Customers' perceptions of quality are significantly influenced by elements like facilities, buildings, equipment, vehicles, and level of sanitation (Wakefield & Blodgett, 1999). According to Barber and Scarcelli (2010), customers always favor places that are tidy, secure, and hygienic.

H2: Tangibles Attributes are positively related to Perceived Quality

2.2. Factors affecting Customer Satisfaction and Customer Loyalty

Since the early 1970s, researchers in the field of marketing have begun to extensively study the factor of customer satisfaction. In it, focus on researching consumer satisfaction with products and services of companies or organizations. Kotler (2012) defines satisfaction as the customer's experience gained during the use of goods or services, this satisfaction occurs when comparing expectations with the value received from using goods or services.

Table 1. Several previous studies of factors affecting customer satisfaction

From above analysis and literature review, the authors put forward following hypotheses:

H4: Technology Quality is positively related to Customer Satisfaction

H5: Perceived Quality is positively related to Customer Satisfaction

H6: Brand Image is positively related to Customer Satisfaction

H7: Price Policy is positively related to Customer Satisfaction

H8: Tangibles Attribute is positively related to Customer Satisfaction

H9: Information Quality is positively related to Customer Satisfaction

The researchers also looked into the relationship between customer loyalty and service quality, price, brand image, and information quality (see: Liu & Lee, 2016; Yi et al., 2018; Kim & Niehm, 2009). Therefore, in this study, the thesis author also proposes that customer loyalty is positively impacted by brand image, information quality, pricing policy, and customer satisfaction.

H10: Customer Satisfaction is positively related to Customer Loyalty

H11: Brand Image is positively related to Customer Loyalty

H12: Price Policy is positively related to Customer Loyalty

H13: Information Quality is positively related to Customer Loyalty

3. Research model and data collection

3.1. Proposed research model and hypothesis

The authors put forwards a PLS-SEM model with 8 latent variables and 13 hypotheses as follow:

Figure 1. PLS-SEM model to determinants of customer satisfaction and customer loyalty

3.2. Research data

Customers who have used the services of 31 securities companies operating in Vietnam are the subject of this study. By using the service of Vietstock Companies, a finance-securities platform service, the authors send the surveys to more than 200,000 customers of the 31 selected companies. After purification, the number of valid responses is 47,418, these responses are collected during nearly 18 months from 2020 to 2021. The surveys are made up of 28 items on a five-level Likert scale: (1) Strongly disagree, (2) Disagree, (3) Neutral, (4) Agree, (5) Strongly Agree. The authors perform the SEM evaluation in this work using SMARTPLS 3.3.3. Regarding to research subjects, males account for 74.4 percent of the study's participants. In this survey, more than half of the consumers (50.2%) had used the firm's services for 1 to 5 years, with 29.3 percent having used the service for less than 1 year and 20.5 percent having used the service for more than 5 years.

4. Results and discussions

4.1. Evaluation of measurement models

To assess the measurement models in this study, the researchers used SMARTPLS to calculate the PLS Algorithm and then chose from a list of criteria such as Outer Loadings, Cronbach's Alpha, Composite Reliability, Average Variance Extracted (AVE), and Heterotrait-Monotrait Ratio (HTMT).

- Quality of observed variables

Outer Loadings evaluation is a set of metrics that measures the degree of correlation between the observable and latent variables (Hair et al., 2019), The square root of the absolute value of R2 for the linear regression from the latent variable to the observable variable is the outer loading in SMARTPLS. According to Hair et al. (2016), the outer loading factor should be larger than or equal to 0.708 quality observed variables. Because 0.708²= 0.5, the latent variable accounted for half of the variation in the observed variable. When using the SMARTPLS software, we will run the Algorithm calculation function, and see if there are any unqualified variables needed to be eliminated from the original proposed model. The Outer Loadings of all 28 items are greater than 0.7, then in terms of outer loadings, the measurement model is appropriate for further study. The outer loading of CS is equal to 1 because there is just one observed variable.

- Construct Reliability and Validity

Cronbach's Alpha and Composite Dependability are two major markers for assessing the scale's reliability on SMARTPLS. Many academics favor composite Dependability (CR) over Cronbach's Alpha because the former represents reliability that is less dependable than the latter. The CR index threshold of 0.7 is the acceptable level for confirmatory research (Henseler and Sarstedt, 2013). Many additional researchers, agreed that 0.7 is the acceptable criterion in the majority of instances. The following are the criteria used in this study: Cronbach's Alpha ≥ 0.7 and Composite Reliability ≥ CR 0.7 are two measures of reliability. In the model, the Cronbach’s Alpha and Composite Reliability of latent variables in the model are greater than 0.7. Therefore, the construct of the model is reliable and suitable for further analysis. The CS variable’s Cronbach ‘Alpha and Composite Reliability is equal 1 because there is only one observed variable for CS.

- Convergence assessment

The Average Variance Extracted (AVE) is used to assess convergence on SMARTPLS. According to Hock and Ringle (2010), a scale reaches convergent value when the AVE is 0.5 or greater. This threshold of 0.5 (50%) implies that the average latent variable will account for at least half of the variation in each observable variable. All the latent variables in the model 4 have AVE values greater than 0.5. As a result, the model's convergence level is eligible for future investigation.

- Discrimination assessment

When compared to other structures in the model, discriminant value reflects how distinct a structure is (Fornell and Larcker, 1981). Henseler et al. (2015) utilized simulation experiments to show that the Heterotrait-Monotrait Ratio (HTMT) index is superior at assessing discriminant validity. When assessing HTMT, we must use the SMARTPLS bootstrapping function. The number of subsamples used by the research team in this study is 5000. If we choose a 95 percent confidence level for the bootstrap test, we will see if the 2.5 percent to 97.5 percent percentile includes the number 0.85. Discriminability is ensured if the percentile does not contain the value 0.85. (Kline, 2015). In the model, the Heterotrait-Monotrait Ratio (HTMT) values are less than 0.85, indicating that all effect relationships’ confident intervals are within acceptable range.

4.2. Evaluation of structural model

This study examines collinear/multicollinearity, the impact link between latent variables, the level of explanation of the independent factors for the dependent variables. According to Hair et al. (2019), the model has a particularly high probability of multicollinearity if the value of the Variance Inflation Factor (VIF) is greater than 5. All value of the Variance Inflation Factor (VIF) in the model are less than 5, showing that the model does not have multicollinearity.

Table 2. Relationships of latent variables

Table 2 shows that all of the model's all effect connections have P-values less than 0.05. It indicates that the structural model's direct impacts are statistically significant. The R-square and Adjusted R-square values range from 0 to 1, with the closer they are to 1, the more independent factors explain the dependent variable. The intricacy of the model and the topic of research make it difficult to come up with an empirical rule that accepts the R-squared value. Both of these indicators are present in this investigation. However, it is preferable for most researchers to use the modified R-squared index.

Table 3. Explanation level of the model

Table 3 indicates that the model's explanatory power is very good, as it explains 76 percent of the variation in customer satisfaction (CS) and 59.8 percent of the variation in job satisfaction (CL). It also explains 65.1 percent of the variation in perceived quality (PQ) and 60.2 percent of the variation in brand image (BI).

- Effect size value (f Square)

The f-Square coefficient shows whether the independent variable has a strong or weak effect on the dependent variable (Cohen, 1988). The f-Square index was proposed by Cohen (1988) to measure the effect of independent factors on dependent variables in the following way: f Square < 0.02: extremely small or no effect, 0.02 ≤ f Square < 0.15: small effect, 0.15 ≤ f Square < 0.35: medium effect and f Square ≥ 0.35: strong effect.

Table 4. Summary of model’s effect relationships by effect size

These findings are impressive and in line with previous research. Levitt (2000) confirms that information is the lifeblood of securities markets. Customers of securities are always in high demand of quality information. Duh et al. (2006) claim that technology can be a source of competitive advantage and its impact can be either direct or indirect. According to Duh et al. (2006), technology can be a source of competitive advantage and can have a direct or indirect influence. According to West et al. (2015) the advantages of having a strong brand image are creating a great impression, grabs your customers’ attention; setting you apart from your competitors; enabling consumers to make an easier, quicker using service decision, which equals improved profits for your brand. A recognizable brand with a positive image is more easily trusted and gives confidence to customers. The ability to leverage the brand identity that has already been built to release future services onto the market that can make a bigger impact in less time.

5. Conclusions

Thus, it can be seen that all 6 factors of perceived quality, technology quality, brand image, information quality, tangible attributes and pricing policy all create competitive advantages of Vietnam securities. However, the impact of each of these factors on business results expressed in customer satisfaction and loyalty is different. It can be seen that quality perception has the strongest impact on customer satisfaction and information quality has the strongest impact on customer loyalty. An interesting result is that brand image affects both customer satisfaction and loyalty, while the price policy factor has more impact on customer loyalty.

Another point to consider is that information quality and brand image have a favorable impact on customers’ loyalty. One implication of this study is for service providers develop their strategy to enhance their competitive advantage through customer satisfaction and loyalty. With securities companies, it is necessary to build up information quality, and brand image and might be necessary to invest more in technology. However, it could be better if this study contains an in-depth interview with customers who have more than five years of using the firm service to understand the factors keeping them loyal to the firm.

REFERENCE:

Afsar, B. (2014). Effect of perceived price, brand image, perceived quality and trust on consumer’s buying preferences. Journal of Economics and Business Research, 20(1), 7-20.
Albayrak, T., Caber, M., & Aksoy, S. (2010). Relationships of the Tangible and Intangible Elements of Tourism Products with Overall Customer Satisfaction. International Journal of Trade, Economics and Finance, 1(2), 140.
Alhaddad, A. (2015). Perceived quality, brand image and brand trust as determinants of brand loyalty. Journal of Research in Business and Management, 3(4), 01-08.
Ayyash, M. M. (2017). Scrutiny of Relationship between E-Banking Information Quality Dimensions and Customer Satisfaction. J. Comput. Sci., 13(4), 78-90.
Badwan, J. J., Al Shobaki, M. J., Abu-Naser, S. S., & Abu Amuna, Y. M. (2017). Adopting technology for customer relationship management in higher educational institutions.
Barber, N., & Scarcelli, J. M. (2010). Enhancing the assessment of tangible service quality through the creation of a cleanliness measurement scale. Managing Service Quality: An International Journal.
Basir, M., Modding, B., Kamase, J., & Hasan, S. (2015). Effect of service quality, orientation services and pricing on loyalty and customer satisfaction in marine transportation services. International Journal of Humanities and Social Science Invention, 4(6), 1-6.
DeVellis, R.F. (2012). Scale development: theory and applications (ed.) Thousand Oaks: SAGE.
Duh, R. R., Chow, C. W., & Chen, H. (2006). Strategy, IT applications for planning and control, and firm performance: The impact of impediments to IT implementation. Information & Management, 43(8), 939-949
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
Hair Jr, J. F., Sarstedt, M., Matthews, L. M., & Ringle, C. M. (2016). Identifying and treating unobserved heterogeneity with FIMIX-PLS: part I–method. European Business Review. https://doi.org/10.1108/EBR-09-2015-0094
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European business review. https://doi.org/10.1108/EBR-11-2018-0203
Hair, J.F., Black, W.C., Babin, B.J. and Anderson, R.E. (2010). Canonical correlation: A supplement to multivariate data analysis. Multivariate data analysis: a global perspective. 7th ed. Upper Saddle River: Pearson Prentice Hall Publishing,
Haming, M., Murdifin, I., Syaiful, A. Z., & Putra, A. H. P. K. (2019). The application of SERVQUAL distribution in measuring customer satisfaction of retails company. Journal of Distribution Science, 17(2), 25-34.
Henseler, J., & Sarstedt, M. (2013). Goodness-of-fit indices for partial least squares path modeling. Computational statistics, 28(2), 565-580. DOI 10.1007/s00180-012-0317-1
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
Hock, M., & Ringle, C. M. (2010). Local strategic networks in the software industry: an empirical analysis of the value continuum. International Journal of Knowledge Management Studies, 4(2), 132-151.
Ihalainen, M. (2011). Competitive advantage through customer satisfaction. (Business Management Bachelor’s Thesis, Laurea University of Applied Sciences, Finland).
Kaura, V., Prasad, C. S. D., & Sharma, S. (2014). Impact of service quality, service convenience and perceived price fairness on customer satisfaction in Indian retail banking sector. Management and Labor Studies, 39(2), 127-139.
Kim, H., & Niehm, L. S. (2009). The impact of website quality on information quality, value, and loyalty intentions in apparel retailing. Journal of interactive marketing, 23(3), 221-233.
Kline, R.B. (2015). Principles and practice of structural equation modeling. New York: Guilford publications.
Kotler, P. (2012). Kotler on marketing. New York: Simon and Schuster.
Levitt, A. (2000). Quality information: The lifeblood of our markets. Quality Assurance, 7(2), 65-75
Li, S. (2020). The impact of service quality, self-service technology, and the corporate image on customer satisfaction and customer revisit intention among luxury hotels in Kuala Lumpur, Malaysia. International Journal of Services, Economics and Management, 11(1), 48-70.
Liu, C. H. S., & Lee, T. (2016). Service quality and price perception of service: Influence on word-of-mouth and revisit intention. Journal of Air Transport Management, 52, 42-54.
Malik, M. E., Ghafoor, M. M., & Hafiz, K. I. (2012). Impact of Brand Image, Service Quality and price on customer satisfaction in Pakistan Telecommunication sector. International journal of business and social science, 3(23).
Massawe, D. (2013). Customer satisfaction and complaints as a means of gaining a competitive advantage in hospitality industry. The Eastern African journal of hospitality, leisure and tourism, 1(1), 1-10.
Neupane, R. (2015). The effects of brand image on customer satisfaction and loyalty intention in retail super market chain UK. International Journal of social sciences and management, 2(1), 9-26.
Nguyen, Q., Nisar, T. M., Knox, D., & Prabhakar, G. P. (2018). Understanding customer satisfaction in the UK quick service restaurant industry: The influence of the tangible attributes of perceived service quality. British Food Journal.
Panda, T. K., & Das, S. (2014). The role of tangibility in service quality and its impact on external customer satisfaction: A comparative study of hospital and hospitality sectors. IUP Journal of Marketing Management, 13(4), 53.
Sasmita, J., & Suki, N. M. (2015). Young consumers’ insights on brand equity: Effects of brand association, brand loyalty, brand awareness, and brand image. International journal of retail & distribution management. https://doi.org/10.1108/IJRDM-02-2014-0024
Tamwatin, U., Trimetsoontorn, J., & Fongsuwan, W. (2015). The effect of tangible and intangible service quality on customer satisfaction and customer loyalty: a SEM approach towards a five-star hotel in Thailand. Journal for Global Business Advancement, 8(4), 399-419.
Wakefield, K. L., & Blodgett, J. G. (1999). Customer response to intangible and tangible service factors. Psychology & Marketing, 16(1), 51-68.
West, D.C., Ford, J.B. and Ibrahim, E. (2015). Strategic marketing: creating competitive advantage. USA: Oxford University Press.
Yeh, H. (2015). Effects of ICT's innovative applications on brand image and customer's purchase intention. International Journal of Organizational Innovation, 7(4).
Yi, S., Zhao, J., & Joung, H. W. (2018). Influence of price and brand image on restaurant customers’ restaurant selection attribute. Journal of foodservice business research, 21(2), 200-217. https://doi.org/10.1080/15378020.2017.1368808

KHÁM PHÁ LỢI THẾ CẠNH TRANH THÔNG QUA SỰ HÀI LÒNG VÀ TRUNG THÀNH CỦA KHÁCH HÀNG: NGHIÊN CỨU VỀ CÁC CÔNG TY CHỨNG KHOÁN TẠI VIỆT NAM

Kim Manh Tuan^1* - Kim Hương Trang²

¹ Trường Đại học Quốc gia Hà Nội

² Trường Đại học Ngoại Thương

Tóm tắt:

Tồn tại mối quan hệ chặt chẽ giữa sự hài lòng và lòng trung thành của khách hàng và lợi thế cạnh tranh. Mục đích của nghiên cứu này là tìm ra lợi thế cạnh tranh thông qua sự hài lòng và lòng trung thành của khách hàng đối với trường hợp của các công ty chứng khoán Việt Nam. Nghiên cứu này đề xuất mô hình PLS-SEM với 8 biến tiềm ẩn, 13 giả thuyết với 28 quan sát. Nghiên cứu thu được 47.418 câu trả lời hợp lệ trong tổng số hơn 200.0000 bảng câu hỏi được chuyển đến khách hàng của 31 công ty chứng khoán tại Việt Nam, nghiên cứu đã chỉ ra tác động mối quan hệ giữa các biến tiềm ẩn trong mô hình. Kết quả của nghiên cứu này cho thấy các biến có tác động mạnh nhất đến sự hài lòng của khách hàng là chất lượng cảm nhận, chất lượng thông tin, chất lượng công nghệ và hình ảnh thương hiệu. Chính sách giá của công ty ảnh hưởng nhiều hơn đến lòng trung thành của khách hàng. Chất lượng thông tin, hình ảnh thương hiệu đều có tác động thuận lợi đến sự trung thành của khách hàng. Các công ty chứng khoán Việt Nam có thể sử dụng kết quả của nghiên cứu này để nâng cao lợi thế cạnh tranh và thực hiện chiến lược cạnh tranh của doanh nghiệp.

Từ khóa: lợi thế cạnh tranh, sự hài lòng của khách hàng, công ty chứng khoán, chất lượng thông tin, hình ảnh thương hiệu.