E-supply chain coordination and performance impacts: An empirical investigation

Figures & data

Table 1. Example of the significant work done on E-SC by previous researchers.

Study	Journal	E-SC Internal & External	Main contributions & findings
Barratt, M., & Barratt, R. (2011)	Journal of Operations Management	Several propositions have been put up on the distinct roles played by internal and external factors.	This study yields many contributions: (1) It shows how information flows across a large supply chain via multiple external and internal linkages; (2) It suggests whether information flows, either individually or compositely, improve operational performance; (3) It illuminates the firm’s previous ‘black box’ in terms of how external information travels in, though, and out of a firm; and (4) It identifies specific roles and ramifications of different types of.
Wu, L., & Chuang, C. H. (2010)	Decision support systems	The technical framework is characterized by the inclusion of a single determinant, namely innovation. The dissemination of e-SCM encompasses both internal diffusion inside an organization’s functional units and exterior dispersion across a multitude of inter-organizational trade partners.	This study presents an innovative research framework that examines the connections between external factors, the dissemination of stage-based e-SCM, and company performance. Empirical research has yielded data that establish the relative impact of external antecedents on the various phases of diffusion, which subsequently influence business performance.
Wong et al. (2013)	International Journal of Production Economics	The study aims to enhance the understanding of the impacts of internal and external integration on product innovation, an area that has been relatively underexplored in prior research. The sustainability of internal and external exploitation and exploration processes is posited to stem from their capacity to mitigate the danger of obsolescence.	The study suggests that it is essential for a company to possess the ability to produce, acquire, and integrate both internal and external sources.
Gilaninia, S., Ganjinia, H., & Mahdikhanmahaleh, B. A. (2013)	Arabian Journal of Business and Management Review	The findings are shown in a probability-impact matrix, which differentiates between supply chain hazards of both internal and external nature.	Analysis shows no substantial difference between internal and external risk probability. However, external supply chain risks affect the supply chain more than internal ones. The average corporate performance will increase with level.
Akyuz and Rehan (2009)	International Journal of Production Research	Study about integration with legacy systems, ERP timing and presence, BPR needs of internal and external business processes, and business intelligence/decision support needs when forming an e-supply chain. The article indicates that fully connected web-based supply chains have complicated internal and external activities and need ERP system integration.	Creating an e-supply chain and web-based collaborative strategic management are significantly harder than implementing an ERP. Most of the problems are organizational and administrative, with technological ones include heterogeneous system network connection and web-based security. In today’s digital economy, companies who can overcome these web-based integration hurdles will succeed.
Alshurideh et al. (2023)	Uncertain Supply Chain Management	To increase the performance of e-supply chains and effectively manage supply chain risks, it is essential for communities of practice to develop information security policies both internally and externally. This will aid in making informed and improved information security choices within the framework of esupply chain.	The emergence of the e-supply chain necessitates the implementation of security measures in order to provide favourable outcomes for consumers and ensure the efficient functioning of organizations.
Adel and Younis (2021)	International Journal of Emerging Markets	This research examined the dissemination of banking supply chain management (SCM) in relation to its internal and external electronic SCM (eSCM) diffusion, while excluding other concurrent indicators of sustainable SCM practices.	The authors propose that more empirical studies should be conducted to examine the correlation between topic and other factors such as sustainable performance. Furthermore, they advocate exploring its association with other entrepreneurial dimensions, such as entrepreneurial experience or gender.

Figure 1. Model 1.

Figure 2. Model 2.

Figure 3. Model 3.

Table 2. List of items used to measure our constructs, descriptive statistics and measurement properties of the constructs.

Construct/Sources	Items^a	Factorial weight	T-value	Cronbachs α	Composite reliability	AVE
Internal E-SC/ (Lucia-Palacios et al., 2014; Soto-Acosta et al., 2015)	Electronically conducting the following activities:
	Manufacturing activities (InESC1)	0.825^__	^____	0.883	0.889	0.668
	Distribution activities (InESC2)	0.892***	13.33
	Inventory control activistes (InESC3)	0.799***	11.42
	Order processing and fulfulimemt activities (InESC4)	0.747***	10.38
External E-SC/ (Lucia-Palacios et al., 2014; Soto-Acosta et al., 2015; Wu et al., 2003)	Online after-sales service to customers (E-SC1)	0.773^__	^____	.0.950	0.953	0.782
	Responding to customer enquires (E-SC2)	0.913***	12.99
	Assisting customers with their online ordering and solving their problems (E-SC3)	0.976***	14.01
	Facilitating shipping and logistics activities (E-SC4)	0.841***	11.65
	Procurement activities of materials, products or services (E-SC5)	0.788**	8.69
	Online planning and coordination activities with customers and suppliers (E-SC6)	0.983***	14.84
Business Performance/ (Lucia-Palacios et al., 2014; Soto-Acosta et al., 2015)	Increase customers’ satisfaction (BusP1)	0.814^__	^____	0.914	0.916	0.732
	Increase sales volume (BusP2)	0.796***	11.45
	Increase overall earnings growth and business profitability (BusP3)	0.945***	14.74
	Enhance firm’s reputation and public image (BusP4)	0.859***	12.80
Operational Performance/ (Devaraj et al., 2007; Soto-Acosta et al., 2015; Benitez et al. 2018)	Enhance delivery speed and logistics efficiency (OpsP1)	0.620^__	^____	0.820	0.820	0.541
	Enhance the level of flexibility and responsiveness to market changes (OpsP2)	0.952***	8.998
	Enhance productivity (OpsP3)	0.688***	7.343
	Enhance quality of products and service as perceived by customers (OpsP4)	0.631***	6.744

Note: ^aAll items related to E-SC practices were assessed using a five point Likert scale (1 =  not considering it, 2 =  planning to consider it, 3 =  considering it currently, 4 =  carrying out to some degree, 5 =  carrying it out fully), and items related to performance were also assessed on a five point Likert scale (1 =  not at all, 2 =  a little bit, 3 =  to some degree, 4 =  relatively strong, 5 = very significant).

***p-value<.001, ** p-value<.01, ^__ Fixed parameter for scaling purposes.

Table 3. Correlation matrix and square root AVE and descriptive statistics of the constructs.

	IntESC	ExtESC	BusP	OpsP
IntESC	0.817
ExtESC	0.203	0.884
BusP	0.191	0.424	0.856
OpsP	0.304	0.361	0.608	0.736
Mean	3.78	3.96	3.40	3.47
S.D.	1.30	1.05	0.81	1.11

Note: Square root of AVE on the diagonal.

Correlation is significant at p < .01 for values greater than 0.180, and at p < .001 for values greater than 0.220.

Table 4. Results of structural equation modelling.

Direct Effects Results (Model 1)	Direct effects model	Mediation effect	Decision on Hypotheses?	Mediation results
IntESC→ BusP (H1a)	0.117*		Supported
IntESC →OpsP (H1b)	0.546***		Supported
ExtESC→ BusP (H2a)	0.214**		Supported Supported
ExtESC→ OpsP (H2b)	0.418**
Mediation Effects Results (Model 2)
IntESC→ BusP (H1a)	0.092		Not supported	Full Mediation
IntESC →OpsP (H1b)	0.415***		Diminished Supported	Partial mediation
ExtESC→ BusP (H2a)	0.207**		Supported
ExtESC→ OpsP (H2b)	0.331***	0.533***	Supported
IntESC→ExtESC (H3)			Supported
Mediation Effects Results (Model 3)
IntESC→ BusP (H1a)	0.111*		Supported
IntESC →OpsP (H1b)	0.394***		Supported
ExtESC→ BusP (H2a)	0.178*		Diminished Supported	Partial mediation
ExtESC→ OpsP (H2b)	0.370***	0.357***	Diminished Supported	Partial mediation
ExtESC→IntESC (H4)			Supported
Control Variables’ effects Size^a → BusP Size→ OpsP Years^b → BusP Years→ OpsP		0.152* 0.107 0.097 0.128*

Note: *Path is significant at p < .05 for values greater than 0.150, ** p < .01 for values greater than 0.220, at *** p < .001 for values greater than 0.250.

◊InESC → Internal ESC, ExtESC →External ESC with customers and suppliers, BusP→Business Performance, OpsP→Operational Performance.

^aTotal number of employees was used as to measure size as suggested by Wu et al., 2003; Zhu et al., 2006; Sanders, 2007; Popa et al., 2016), ^bTotal years of implementing E-SC.

Table 5. CFA models’ goodness of fit results.

Models\indices	χ2 (d.f)	Normed χ2	CFI	IFI	RMSEA	PCFI
Structural models
Model 1	504.76(224)	2.254	0.908	0.910	0.078	0.765
Model 2	478.82(231)	2.073	0.922	0.937	0.075	0.780
Model 3	478.82(231)	2.073	0.911	0.924	0.075	0.777
Suggested values (Hair et al., 2021)		<3.0	≥.9	≥.9	<.10	≥0.70

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