The relationship between architecture, social, law and market in determine challenges of big data analysis for Malaysia SMEs

Figures & data

Table 1. Summary of challenges of big data analytics faced by Malaysia SMEs

Challenges	Author (Date)
Legal	Coleman et al. (2016), Iqbal et al. (2018), Del Vecchio et al. (2018), Ardagna et al. (2016), Vajjhala and Ramollari (2016), Kızıltan (2018), Kalan and Unalir (2016), Karim et al. (2017), Sivarajah et al. (2017), and Makhele (2018).
Architecture	Coleman et al. (2016), Shah et al. (2017), Del Vecchio et al. (2018), Soroka et al. (2017), Ardagna et al. (2016), Vajjhala and Ramollari (2016), Kızıltan (2018), Moyne and Iskandar (2017), Kalan and Unalir (2016), Karim et al. (2017), Mbassegue et al. (2016), Seseni and Mbohwa (2018), Sivarajah et al. (2017), Gupta et al. (2018), Limpeeticharoenchot et al. (2020), Makhele (2018), and Rajabion (2018).
Social	Coleman et al. (2016), Shah et al. (2017), Iqbal et al. (2018), Ardagna et al. (2016), Kızıltan (2018), Kalan and Unalir (2016), Karim et al. (2017), Mbassegue et al. (2016), Sivarajah et al. (2017), Baharuden et al. (2019), Limpeeticharoenchot et al. (2020), and Makhele (2018).
Market	Coleman et al. (2016), Shah et al. (2017), Iqbal et al. (2018), Soroka et al. (2017), Kızıltan (2018), Kalan and Unalir (2016), Limpeeticharoenchot et al. (2020), Makhele (2018), and Nemati and Khajeheian (2018).

(Developed by: Authors)

Table 2. Type of industries

Type	Percentage	Frequency
Financial services	27.9%	12
Oil and gas processing	7.0%	3
Telecommunication	2.3%	1
Manufacturing	14%	6
Government	2.3%	1
Hotel and restaurants	4.7%	2
Health care and life sciences	4.7%	2
Others	37.2%	16
Total	100%	43

Table 3. Company’s size distribution

Company’s size	Percentage	Frequency
Micro (0–9 person employed)	14%	6
Small (10–49 person employed)	9.3%	4
Medium (50–250 person employed)	76.7%	33
Total	100%	43

Figure 1. PLS-SEM structure.

Table 4. Loading, composite reliability and AVE value for latent variables

Latent variables	Indicators	Loadings	Composite reliability	AVE
Architecture	A1	0.527	0.894	0.634
	A2	0.879
	A3	0.847
	A4	0.893
	A5	0.778
Law	law1	0.711	0.880	0.712
	law2	0.886
	law3	0.919
Market	m1	0.761	0.830	0.554
	m2	0.901
	m3	0.628
	m4	0.658
Social	s1	0.785	0.892	0.676
	s2	0.889
	s3	0.868
	s4	0.738

Table 5. Discriminant validity

	Architecture	Law	Market	Social
Architecture	1.000
Law	0.510	1.000
Market	0.653	0.699	1.000
Social	0.616	0.728	0.746	1.000

Table 6. R squared value

Latent variables	R^2
Market	0.410
Social	0.143

Table 7. The path coefficients

	T statistics (|O/STDEV|)
architecture -> market	3.69
architecture -> social	5.01
law -> market	2.52
social -> market	3.57

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