Factors affecting the successful implementation of customer relationship management in 3-to-5-star hotels in Viet Nam

Figures & data

Table 1. List of factors affecting the successful implementation of CRM.

Authors	Factors affecting the successful implementation of CRM
Marianna Sigana	Managing intellectual, information technology and communication; marketing relationships (including internal marketing: corporate culture, external marketing: customer orientation, and customer interaction)
Mendoza et al. (2007)	Care of senior leaders; diversity in team principles; target identification; unity between departments; strategies to communicate CRM to staff; commitments of staff; customer information management; customer service, automation sales; automation marketing; support management; customer contact management; united information system
King and Burgess (2008)	Support of senior leaders; CRM communication strategies; intellectual management capacity; data sharing; technology; changes in customer culture/ orientations; abilities to change procedures; unity in the system
Arab et al. (2010)	Technology; procedures; humans
Ahmad et al. (2012)	Customers, interactions with customers, customer database, technology and humans in organizations
Abdul Alem Mohammed (2012)	Customer orientations, CRM organization, intellectual management, technology-based CRM
Budiono Hardjono and Lai Pooi San (2017)	Customer orientations, CRM organization, intellectual management, technology-based CRM
Rakiki (2018)	Customer orientations, senior management support; CRM training orientations, customer database, customer informing procedures, customer interaction
Bukola et al. (2019)	Customer orientation, customized services, service quality

Source: summarized by the research team.

Table 2. Selecting factors affecting the successful implementation of CRM in hotels.

No.	Affecting factors	Reasons for selection
1	Commitments of senior leaders	Widely accepted by researchers Related to reasons for failure of CRM implementation: lack of commitments from top leaders (Sherif and Newby, 2007; Chalmeta, 2006)
2	CRM strategies	Widely accepted by researchers Related to reasons for failure of CRM implementation: CRM strategies and visions are not specific (Sherif and Newby, 2007; Chalmeta, 2006)
3	Business procedures	Widely accepted by researchers Related to reasons for failure of CRM implementation: management of structural changes and restructuring remains poor (Kale, 2004; Forsyth, 2001)
4	Corporate culture	Widely accepted by researchers Related to reasons for failure of CRM implementation: corporate culture has not been built to support CRM implementation (Chalmeta, 2006; King and Burgess, 2008)
5	Technology infrastructure	Widely accepted by researchers Related to reasons for failure of CRM implementation: CRM is only considered as a technological software to manage customer data (Arab et al., 2010)
6	Database management	Widely accepted by researchers Related to reasons for failure of CRM implementation: poorly managed and inadequately integrated (Meyer and Kolbe, 2005)

Source: Summarized by the research team.

Table 3. Linking affecting factors on the successful implementation of CRM with the main CRM components.

Affecting factors	Humans	Technology	Procedures
Commitments of senior leaders	x
CRM strategies	x
Business procedures			x
Corporate culture	x		x
Technology infrastructure		x
Database management		x	x

Source: Summarized by the research team.

Table 4. Variables and measurements.

No.	Observation variables	Source
1	Commitments of senior leaders	Mendoza et al. (2007); King and Burgess (2008); Rakiki (2018); Arab et al. (2010); Helen M. Dah et al (2021)
	Senior leaders direct the CRM implementation process
	Senior leaders are aware of the relations between visions, missions and CRM strategies
	Senior leaders provide necessary resources and budgets for CRM implementation
	Senior leaders remove barriers to CRM implementation
2	CRM strategies	Mendoza et al. (2007);Abdul Alem Mohammed (2012); Rakiki (2018); Bukola et al. (2019)
	CRM strategies are customer-oriented
	CRM strategies establish clear and sufficient goals
	CRM strategies are built up and developed on the basis of business strategies
	CRM strategies are communicated to all divisions
3	Business procedures	King and Burgess (2008); Arab et al. (2010)
	Business procedures are changed towards placing customers at the centre
	Business procedures are automated
	Business procedures are designed consistently in all customer interaction channels
	Business procedures related to customers are analyzed and evaluated
4	Corporate culture	Chalmeta (2006), King and Burgess (2008),
	Staff are aware of the importance of CRM implementation
	Divisions in the hotels work towards the common goals when implementing CRM
	Collaboration procedures between divisions in the hotels are clearly stipulated
	Staff of different divisions cooperate when implementing CRM
	Staff are willing to participate in CRM implementation
	Hotels have staff evaluation and remuneration systems
5	Technology infrastructure	King and Burgess (2008), Arab et al. (2010), Ahmad et al. (2012), Abdul Alem Mohammed (2012), Rakiki (2018); Sofi et al. (2020); Lonashree Sanasam et al (2022).
	Hardware system is appropriate
	Customization software is available
	Data archive is highly security
	Data exploiting system is consistent
	Data analyzing tools are modern
	The system updates technological advancements
6	Database management	King and Burgess (2008), Ahmad et al. (2012), Rakiki (2018),
	The data collected is complete and appropriate
	Data is constantly updated to database
	Data is synchronously shared to different divisions
	Database management modes are effective
7	Successful implementation of CRM	Urdziková et al. (2012), Mohammadhossein and Zakaria (2012); King and Burgess (2008); Bhattacharya (2011) Vijay Pal Dhaka and Pooja Nahar (2014)
	Reducing costs
	Increasing customer satisfaction
	Increasing profits from customers
	Interacting more effectively
8	Loyalty	Bowie and Buttle (2007), Litvin et al. (2008), Goyette et al. (2010), Lu et al. (2014)
	Repeated Purchase
	Word of Mouth
	Customer Lifetime Value

Figure 1. Proposed research model.

Table 5. Personal information of survey respondents.

Features	Indicators	Frequency	Percentage (%)
Gender	Male	192	48.4
	Female	204	51.6
Qualification	Vocational school	22	5.7
	College	46	11.5
	University	263	66.4
	Post-graduate	65	16.4
Working experience	Less than 5 years	198	50.0
	5–10 years	65	16.4
	10–15 years	62	15.6
	More than 15 years	71	18.0
Income	Under 10 million	78	19.7
	10–15 million	146	36.9
	Tù 15–20 million	88	22.1
	Trên 20 million	84	21.3
Grade	3 stars	123	31.1
	4 stars	104	26.2
	5 stars	169	42.6

Source: Data processed by the research team.

Table 6. Results of evaluating reliability of measurements.

Measurements	No. of observation variables	Cronbach alpha	Lowest Item-Total Correlation	Conclusion
Commitments of senior leaders (COL)	4	0.743	0.421	Meet requirements
CRM strategies (CS)	4	0.811	0.560	Meet requirements
Business procedures (BP)	4	0.765	0.476	Meet requirements
Corporate culture (CC)	6	0.792	0.430	Meet requirements
Technology infrastructure (IT)	6	0.796	0.404	Meet requirements
Database management (DM)	4	0.851	0.590	Meet requirements
Successful implementation of CRM (SIC)	4	0.673	0.347	Meet requirements
Loyalty (LOY)	3	0.664	0.348	Meet requirements

Source: Data processed by the research team.

Table 7. Descriptions of methods and variables in linear regression analysis.

Lists of variables inserted/ deleted^a
Model	Variables inserted	Variables deleted	Method
1	F6_COL, F5_BP, F3_IT, F4_DM, F1_CC, F2_CS^b		Enter

^a Dependent variable: F7_SIC.

^b All variables inserted at request.

Source: Data processed by the research team.

Table 8. Results of research hypothesis testing.

Hypotheses		Conclusion
H1	Senior leaders’ commitments have positive and significant impacts on enterprises’ success	Accepted
H2	CRM strategies have positive and significant impacts on enterprises’ success	Accepted
H3	Business procedures have positive and significant impacts on enterprises’ success	Accepted
H4	Hotel culture has positive and significant impacts on enterprises’ success	Accepted
H5	Technology infrastructure has positive and significant impacts on enterprises’ success	Accepted
H6	Database has positive and significant impacts on enterprises’ success	Accepted

Source: summarized by the research team.

Table 9. Results of evaluating conformity of regression model and autocorrelation statistics.

Summary of model^b
Model	R	R square	Adjusted R square	Standard error of estimate	Durbin-Watson
1	.688^a	.473	.465	.529	1,911

^a Explanatory/ independent variables: (Constant), F6_COL, F5_BP, F3_IT, F4_DM, F1_CC, F2_CS.

^b Dependent variable: F7 _ SIC.

Source: Data processed by the research team.

Table 10. Regression coefficients.

Model		Unstandardized regression coefficients		Standardized regression coefficients	T-statistics	Statistical significance level	Multicollinearity statistics
		B	Standard error	Beta			Inflation level	Variance Inflation Factor
1	(Constant)	.989	.160		6.188	.000
	F7_SIC	.598	.043	.571	13.816	.000	1.000	1.000
Dependent variable: F8_LOY

Source: Data processed by the research team.

Table 11. Summary of model^b.

Model	R	R square	Adjusted R square	Standard error of estimate
1	.571^a	.326	.325	.622

^a Explanatory variable: (Constant), F7_SIC.

^b Dependent variable: F8_LOY.

Source: Data processed by the research team.

Figure 2. Frequency histogram of standardized residuals.

Table 12. Regression coefficients.

Model		Unstandardized coefficients		Standardized coefficients	T-statistics	Statistical significance level	Multicollinearity statistics
		B	Standard error	Beta			Inflation level	Variance Inflation Factor
	(Constant)	−.341	.261		−1.308	.192
	F1_CC	.309	.053	.285	5.866	.000	.573	1.744
	F2_CS	.257	.054	.230	4.728	.000	.573	1.747
	F3_IT	.165	.039	.176	4.203	.000	.774	1.292
	F4_DM	.089	.043	.093	2.068	.039	.666	1.502
	F5_BP	.152	.055	.122	2.750	.006	.687	1.457
	F6_COL	.158	.043	.137	3.687	.000	.977	1.024
Dependent variable: F7_SIC

Source: Data processed by the research team.

Figure 3. Scatter plot of standardized residuals and standardized predicted values.

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