Enhancing CSR activities to increase bank attractiveness

Figures & data

Figure 1. Proposed model research.

Table 1. Contructs and items

Constructs	Variable Code	Items	Sources
CSR for Employees	CSE1	This bank pays reasonable salaries and benefits.	Nybakk and Panwar (2015)
	CSE2	Bank employees are treated fairly.
	CSE3	This bank is concerned with its employees safety and health.
CSR for Customers	CSC1	This bank provides high-quality products and services.	Nybakk and Panwar (2015)
	CSC2	This bank appears to be a trustworthy partner for clients.
	CSC3	This bank treats its customers fairly.
	CSC4	This bank can effectively handle customer complaints and requests for compensation.
CSR for social public welfare	CSS1	This bank uses a portion of its budget to fund disadvantaged groups and community programs.	Maignan et al. (1997); Pérez et al. (2013)
	CSS2	This bank makes donations to various social and cultural activities.
	CSS3	This bank contributes to society in addition to profit.
	CSS4	This bank is concerned with environmental stewardship and protection.
Bank Reputation	AFR1	This bank is well-known for providing financial services.	Muflih (2021)
	AFR2	This bank is extremely prosperous.
	AFR3	This bank has a long history
	AFR4	This bank gives off a positive vibe
Bank Attractiveness	BAT1	This is a great bank to collaborate for.	Zhang et al. (2020)
	BAT2	This bank is the preferred bank.
	BAT3	This is a very appealing bank to me.
	BAT4	I find this bank attractive to customers

Table 2. Descriptive statistics

Characteristic	Value	Frequency	Ratio (%)
Gender	Male	689	59
	Female	478	41
Age	18–25	455	39
	25–45	467	40
	45–60	222	19
	>60	23	2
Income	10 mVND − 20 mVND	432	37
	20 mVND − 30 mVND	350	30
	>30 mVND	385	33
Type of studies	High school	175	15
	Bachelor	759	65
	Master	233	20

*Demographic data from customers who are using the bank’s services.

Table 3. Results from the measurement model estimation

Latent variable	Manifest variable	Outer weight	Outer loading	rho_A	CR value	Cronbach’s alpha (α)	AVE
CSR for employees	CSE1	0.458	0.862	0.843	0.895	0.826	0.739
	CSE2	0.339	0.844
	CSE3	0.365	0.873
CSR for customers	CSC1	0.279	0.771	0.851	0.897	0.846	0.686
	CSC2	0.312	0.845
	CSC3	0.324	0.876
	CSC4	0.291	0.817
CSR for social public welfare	CSS1	0.281	0.810	0.883	0.819	0.783	0.740
	CSS2	0.305	0.869
	CSS3	0.289	0.894
	CSS4	0.287	0.869
Bank reputation	AFR1	0.285	0.812	0.823	0.845	0.822	0.811
	AFR2	0.282	0.821
	AFR3	0.276	0.807
	AFR4	0.267	0.860
Bank attractiveness	BAT1	0.397	0.840	0.735	0.849	0.733	0.652
	BAT2	0.394	0.809
	BAT3	0.449	0.775

*Processed using SmartPLS 3.0.

Table 4. Discriminant validity (Fonell-Larker)

	AFR	CSC	CSE	BAT	CSS
AFR	0.901
CSC	0.388	0.828
CSE	0.339	0.546	0.860
BAT	0.301	0.317	0.212	0.809
CSS	0.391	0.444	0.456	0.226	0.861

*Processed using SmartPLS 3.0.

Table 5. Discriminant validity (Heterotrait – Monotrait)

	AFR	BAT	CSC	CSE	CSS
AFR
BAT	0.364
CSC	0.438	0.396
CSE	0.381	0.271	0.650
CSS	0.433	0.279	0.514	0.525

*Processed using SmartPLS 3.0.

Table 6. Results of the structural model assessment

R^2EFL = 0.805
				Confidence interval 95%
	Path coefficients	t-value	p-value	2,5%	97,50%	Significant
Direct effect
AFR→ BAT	0.301	9.999	0.000	0.243	0.361	Yes
CSC → AFR	0.221	5.709	0.000	0.144	0.296	Yes
CSE → AFR	0.107	2.713	0.007	0.030	0.186	Yes
CSS → AFR	0.244	6.613	0.000	0.173	0.320	Yes
Indirect effect
CSC → AFR → BAT	0.066	4.534	0.000	0.040	0.097	Yes
CSE → AFR → BAT	0.032	2.604	0.009	0.009	0.057	Yes
CSS → AFR → BAT	0.073	5.410	0.000	0.049	0.102	Yes
Total effect
CSC → BAT	0.064	4.427	0.000	0.038	0.096	Yes
CSE → BAT	0.034	2.728	0.006	0.012	0.060	Yes
CSS → BAT	0.238	5.349	0.000	0.168	0.100	Yes
AFR → BAT	0.302	10.324	0.000	0.247	0.360	Yes

*Processed using SmartPLS 3.0.

Figure 2. Output bootstrapping.

Note: *Processed using SmartPLS 3.0.

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