Exploring the determinants of electronic commerce adoption in a municipality with limited internet access

Figures & data

Figure 1. Conceptual model and hypotheses represented in SmatPLS software.

Source: Adapted from Davis (1989).

The figure depicts a conceptual model and accompanying hypotheses related to the subject matter. The model is analyzed using the SmatPLS software, a statistical tool commonly used for structural equation modeling.

Table 1. Characteristics of the study sample.

Sampling unit	Internet users older than 14
Geographical area of the study	Urban area of the municipality of Andes (Antioquia, Colombia)
Data collection method	Self-administered questionnaire
Sampling procedure	Convenience sampling using quotas
Sample size	400
Date of the field work	September 2017

Source: Own work.

Table 2. Constructs and items in the self-administered questionnaire.

Construct	Code	Item	Source
Perceived Ease of Use	EU1	Learning how to use the internet to make purchases/transactions is easy for me.	Davis (1989); Moore and Benbasat (1991)
	EU2	Overall, I think that making online purchases/transactions is easy.
	EU3	It would be easy for me to acquire the skill to make online purchases/transactions.
	EU4	I don’t need to make a mental effort to make online purchases/transactions.
	EU5	I find it easy to make online purchases/transactions the way I want to.
	EU6	My interaction with the internet when I make online purchases/transactions is clear and understandable.
Perceived Usefulness	U1	It is faster to make purchases/transactions online than at a brick-and-mortar store.	Davis (1989); Moore and Benbasat (1991)
	U2	Online purchases/transactions save me time.
	U3	Online purchases/transactions have benefits.
	U4	Overall, online purchases/transactions are very useful.
	U5	It is more efficient to make purchases/transactions online than in person.
	U6	It is easier to make purchases/transactions online than in person.
Attitude	A1	I like the idea of making online purchases/transactions.	Taylor and Todd (1995)
	A2	Making online purchases/transactions is a great idea.
	A3	Making online purchases/transactions is a good idea.
	A4	Making online purchases/transactions is a positive idea.
Behavioral Intention	BI1	I have the intention to make online purchases/transactions.	Taylor and Todd (1995); Coyle and Thorson (2001); Kim and Biocca (2006)
	BI2	I think I will make online purchases/transactions in the next few months.
	BI3	I would recommend online purchases/transactions to others.
	BI4	I would use my credit/debit cards to make online purchases/transactions.
	BI5	I am going to use websites to make online purchases/transactions frequently.

Source: Own work.

Table 3. External loadings. Own work.

Construct	Code	Original sample	Sample mean	Standard error	t-statistic	p value
Perceived Ease of Use	EU1	0.769	0.767	0.027	27.978	0.000
	EU2	0.842	0.842	0.018	45.833	0.000
	EU3	0.852	0.853	0.018	47.081	0.000
	EU4	0.751	0.753	0.035	21.460	0.000
	EU5	0.821	0.823	0.020	41.418	0.000
	EU6	0.829	0.830	0.020	42.109	0.000
Perceived Usefulness	U1	0.745	0.747	0.030	24.859	0.000
	U2	0.808	0.809	0.021	38.988	0.000
	U3	0.810	0.811	0.021	38.052	0.000
	U4	0.808	0.808	0.025	31.706	0.000
	U5	0.809	0.807	0.024	34.373	0.000
	U6	0.838	0.839	0.017	50.757	0.000
Attitude	A1	0.855	0.856	0.016	52.394	0.000
	A2	0.908	0.908	0.011	80.776	0.000
	A3	0.921	0.920	0.010	88.875	0.000
	A4	0.892	0.891	0.013	67.426	0.000
Behavioral Intention	BI1	0.908	0.908	0.011	80.776	0.000
	BI2	0.921	0.920	0.010	88.875	0.000
	BI3	0.892	0.891	0.013	67.426	0.000
	BI4	0.908	0.908	0.011	80.776	0.000
	BI5	0.921	0.920	0.010	88.875	0.000

Source: Own work.

Table 4. Stages and minimum criteria employed in the validation of the proposed model.

Validation stage	Indicators	Minimum criteria
Measurement model	Internal consistency: Cronbach’s Alph (CA)	= >0.7 (Nunnally & Bernstein, 1994)
	Reliability: Composite Reliability (CR)	= >0.7 (Fornell & Larcker, 1981)
	Convergent validity: Average Variance Extracted (AVE)	= >0.5 (Fornell & Larcker, 1981)
	Convergent validity: loadings’ size and significance	= >0.6 (Bagozzi & Yi, 1988) and p < 0.001; p < 0.05; p < 0.01
	Discriminant validity: Heterotrait-Monotrait (HT/MT) ratio of correlations	= < 0.9 (Helgesen, 2008)
Structural model	Coefficient of determination (R^2 value)	= > 0.75 and = > 0.51, substantial; = < 0.50 and = > 0.26, moderate; and < 0.25, weak (Hair et al., 2014)
	Significance of the structural relationships	p < 0.001

Source: Own work.

Table 5. Reliability and convergent validity of the constructs in the proposed model.

Construct	Items	Loadings	CA	CR	AVE
Perceived Ease of Use	EU1	0.769***	0.896	0.920	0.658
	EU2	0.842***
	EU3	0.852***
	EU4	0.751***
	EU5	0.821***
	EU6	0.829***
Perceived Usefulness	U1	0.745***	0.890	0.916	0.646
	U2	0.808***
	U3	0.810***
	U4	0.808***
	U5	0.809***
	U6	0.838***
Attitude	A1	0.855***	0.916	0.941	0.799
	A2	0.908***
	A3	0.921***
	A4	0.892***
	A5	0.908***
Behavioral Intention	BI1	0.921***	0.923	0.942	0.763
	BI2	0.892***
	BI3	0.908***
	BI4	0.921***
	BI5	0.769***

Source: Own work.

Table 6. Discriminant validity of the constructs.

	EU	U	A	BI
Perceived Ease of Use	0.811
Perceived Usefulness	0.697	0.804
Attitude	0.560	0.703	0.894
Behavioral Intention	0.584	0.560	0.528	0.874

Source: Own work.

Table 7. Hypotheses testing results.

Hypothesis	Description	Trajectory coefficient	t value	Result
H1	Perceived Ease of Use → Attitude	0.135*	1.984	Supported
H2	Perceived Usefulness → Attitude	0.609***	9.311	Supported
H3	Attitude → Behavioral Intention	0.528***	13.804	Supported

Source: Own work.

Figure 2. Structural model results.

Source: Own work.

The figure presents the outcomes of the structural model analysis. It illustrates the relationships between variables and their corresponding statistical significance, demonstrating the findings of the study or analysis.

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