Investigating digital marketing readiness among tourism firms: an emerging economy perspective

Figures & data

Figure 1. Proposed conceptual framework.

Table 1. Description of demographic data (n = 191).

Variable	Category	N	%
Gender	Male	137	72%
	Female	54	28%
Level of education	Diploma holder	61	32%
	Bachelor’s degree holder	105	55%
	> Bachelor’s holder	25	13%
Age	< 21 years old	21	11%
	21–30 years old	47	25%
	31–40 years old	76	40%
	> 40 years old	47	25%
Year of experience in the tourism industry	< a year	7	17%
	Between 1 and 3 years	34	46%
	> 3 years	150	37%
Position	Manager	102	53%
	Marketing officer	89	47%

Table 2. Summary of items, descriptive statistics, and reliability test results (n = 191).

Factor (Item Source)	Item code	Item	Mean	S.d	Loading	CR	Rho-A	Cronbach’s Alpha
Convenience (Jiang et al., 2013; Shankar & Rishi, 2020)	CON1	Digital marketing helps us to provide our travel and tourism services at any time.	4.031	0.870	0.886	0.940	0.905	0.904
	CON2	I can offer travel and tourism services at any place via digital marketing.	4.136	0.913	0.943
	CON3	Using digital marketing will improve the convenience of our tour and travel services.	4.037	0.861	0.919
Security concern (Abed, 2020; Molla & Licker, 2005)	SEC1	Use of digital marketing is trustworthy.	3.304	1.219	0.94	0.970	0.978	0.963
	SEC2	Use of digital marketing provides enough security during information exchange.	3.330	1.170	0.965
	SEC3	Digital marketing maintains the privacy of the organisation.	3.440	1.267	0.927
	SEC4	Digital marketing provides enough data confidentiality.	3.419	1.211	0.946
	SEC5	Security concerns are not issues in using digital marketing.	3.314	1.221	0.85
Managerial commitment (Khayer et al., 2020)	MGC1	Management is willing to take the risks associated with adopting digital marketing.	3.921	0.814	0.874	0.930	0.899	0.899
	MGC2	Management is seriously considering the adoption of digital marketing.	4.079	0.911	0.902
	MGC3	The management understands the benefits of using digital marketing.	3.984	0.837	0.875
	MGC4	Management is willing to provide the necessary resources in using digital marketing.	4.079	0.787	0.854
Resource availability (Wang et al., 2019)	RES1	Our organisation has the financial resources to adopt digital marketing.	3.775	0.898	0.822	0.924	0.901	0.898
	RES2	Our organisation has the technical readiness to adopt digital marketing.	4.047	0.823	0.814
	RES3	We have high connectivity to the internet.	3.937	1.009	0.822
	RES4	Most of our employees have unrestricted access to computer.	4.068	0.929	0.869
	RES5	Most of our employees are computer literate.	4.005	0.954	0.884
Government regulations (Ali & Xia, 2022)	GOV1	Government effectively promotes the use of digital marketing.	3.864	0.803	0.89	0.930	0.888	0.887
	GOV2	Data protection is well regulated by the government.	3.942	0.872	0.904
	GOV3	Government regulations create a conducive environment to adopt digital marketing.	3.885	0.922	0.916
Customer pressure (Lin & Lin, 2008)	CUS1	Most of our customers and clients use digital marketing platforms.	3.880	0.877	0.87	0.929	0.886	0.885
	CUS2	Most of our trading partners recommended us to use digital marketing.	3.906	0.859	0.917
	CUS3	Our customers are generally knowledgeable about digital marketing.	3.895	0.820	0.918
Competitors pressure (Lin & Lin, 2008)	COM1	Most of our competitors have already adopted or at least seriously considering adopting digital marketing.	3.990	0.775	0.9	0.910	0.856	0.852
	COM2	Most of our competitors use digital marketing as a competitive dimension.	3.958	0.813	0.882
	COM3	I fear that unless we adopt digital marketing, our sales and market share will decrease.	3.958	0.839	0.853
PU (Daragmeh et al., 2021)	PU1	I believe using digital marketing increases the performance of our organisation.	4.000	0.754	0.937	0.965	0.952	0.951
	PU2	I can achieve things in a quicker way using digital marketing practices.	4.058	0.789	0.918
	PU3	Digital marketing helps achieve our tasks in a cheaper way.	4.031	0.787	0.889
PEOU (Daragmeh et al., 2021)	PEOU1	The process of using digital marketing is easy to understand.	4.031	0.787	0.912	0.939	0.905	0.903
	PEOU2	It is easy for an organisation to adopt and use digital marketing practices.	4.031	0.794	0.926
	PEOU3	I will be able to understand and perform digital marketing activities easily.	4.031	0.774	0.959
	PEOU4	I am sure that all the responsible employees can easily understand and perform digital marketing activities.	4.037	0.757	0.949
Intention to adopt digital marketing (Mathew & Soliman, 2021)	DMA1	We intend to use digital marketing in the future.	4.026	0.721	0.925	0.944	0.914	0.911
	DMA2	We predict that our business will adopt digital marketing in the future.	4.094	0.762	0.937
	DMA3	Our organisation is ready to adopt digital marketing in the near future.	4.010	0.775	0.901

Table 3. Discriminant validity using AVE² Versus Correlation (F&L criterion) (n = 191).

Factor	AVE	1	2	3	4	5	6	7	8	9	10
(1) CON	0.839	0.916
(2) SEC	0.865	0.026	0.93
(3) MGC	0.768	0.684	−0.03	0.876
(4) RES	0.710	0.497	−0.098	0.57	0.843
(5) GOV	0.816	0.548	−0.075	0.639	0.756	0.903
(6) CUS	0.814	0.539	0.002	0.595	0.696	0.558	0.902
(7) COM	0.771	0.496	−0.024	0.617	0.505	0.59	0.584	0.878
(8) PU	0.873	0.69	−0.031	0.771	0.594	0.585	0.638	0.696	0.915
(9) PEOU	0.837	0.625	−0.084	0.7	0.545	0.71	0.671	0.586	0.762	0.934
(10) DMA	0.849	0.514	−0.1	0.637	0.52	0.679	0.574	0.45	0.719	0.777	0.921

Table 4. R^{2 and} Q² indices (n = 191).

Endogenous variable	R^2	R^2 Adjusted	Q^2
PU	0.791	0.782	0.598
PEOU	0.674	0.661	0.496
DMA	0.721	0.713	0.566

Table 5. Hypothesis testing (n = 191).

Relationship	Coefficient	p – values	Decision
H1a: CON → PU	0.143	0.031	Supported
H1b: CON → PEOU	0.119	0.083	Supported
H2a: SEC → PU	0.02	0.598	Not Supported
H2b: SEC → PEOU	−0.042	0.313	Not Supported
H3a: MGC → PU	0.223	0.008	Supported
H3b: MGC → PEOU	0.179	0.019	Supported
H4a: RES → PU	0.048	0.359	Not Supported
H4b: RES → PEOU	−0.009	0.877	Not Supported
H5a: GOV → PU	0.21	0.003	Supported
H5b: GOV → PEOU	0.319	0.000	Supported
H6a: CUS → PU	0.113	0.074	Supported
H6b: CUS → PEOU	0.237	0.002	Supported
H7a: COM → PU	0.064	0.291	Not Supported
H7b: COM → PEOU	0.141	0.026	Supported
H8: PEOU →PU	0.269	0.001	Supported
H9: PU → DMA	0.264	0.004	Supported
H10: PEOU → DMA	0.342	0.000	Supported
H11a: Moderation on PU→DMA	0.070	0.431	Not Supported
H11b: Moderation on PEOU → DMA	−0.246	0.008	Not supported *

Table 6. Direct and indirect /mediation effects (n = 191).

Linkage	Direct effect		Indirect effects
	Coefficient	p – value	Coefficient	p – value
CON → DMA	0.027	0.736	0.087	0.006
SEC → DMA	−0.059	0.273	−0.012	0.522
MGC → DMA	0.260	0.000	0.133	0.003
RES → DMA	0.070	0.434	0.009	0.774
GOV → DMA	0.392	0.000	0.187	0.001
CUS → DMA	0.173	0.048	0.128	0.002
COM → DMA	−0.072	0.325	0.075	0.027
PEOU →DMA	0.470	0.000	0.071	0.000

Figure 2. (a) Moderation effect of LDS on the relationship between PU and intention to adopt digital marketing. (b) Moderation effect of LDS on the relationship between PEOU and intention to adopt digital marketing.

Figure 3. Importance – performance map analysis result.

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