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Table Ill Path analysis standardised regression weights, C.R. (t-value), (n = 105)

Table 3 Ill Path analysis standardised regression weights, C.R. (t-value), (n = 105)

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Figure 1 Conceptual model of cloud adoption and performance relationship delivery status and stock level can occur when the logistics processes of key suppliers and customers are integrated, using cloud in this case. Logistics activities within a firm, mainly around inventory turnover, safety stock, order processing, labour cost, accurate pricing etc. are likely to be well supported via cloud services. Cloud adoption, similar to traditional ICT, will be likely to facilitate information flow (Lee ez al., 1997), material flow (Stevens, 1990), funds flow (Mabert and Venkataramanan, 1998) and demand information flow (Coyle et al., 2016) among partners in a supply chain. As information distortion remains a recurring issue in a supply chain, the cloud technology may be able to mitigate this effect, known as bullwhip effect (Lee et al., 1997; Shee and Kaswi, 2016), thereby improving the inventory situation at each partner along the chain. When demand-supply is in perfect sync, cloud services can manage a series of activities among chain partners to deliver efficient consumer response by replenishing inventory in more efficient way (Harris et al., 1999). Just-in- time is an effective replenishment programme between a manufacturer and suppliers. Cloud is believed to deliver better buyer-supplier collaboration by using the internet. Even ICT has the capability to do this currently through wide range of technologies (e.g. WMS, ERP, EDI) (Autry er al., 2010). Cloud-based services, however, are believed to do it in an improved way (e.g. reduced cost, scalability, flexibility, mobility and shared resources)(Marston et al., 2011; Zhang etal., 2010). Cland eenmicee are heljeaved tra eamnifiry the mived and analytics. Another perspective is that when the big businesses are resting on their legacy of a robust ICT platform and it appears to be self-reliant, they may also choose to adopt it for selective projects that go beyond their current capacity when it comes to timely delivery. However, small and medium enterprises (SMEs) are the potential adopters of the emerging cloud services (1.e. SaaS) because of easy access, low cost, pay- as-you-go perspectives for their information processing and decision-making. Regardless of the advantages, adoption of the “cloud” may depend on the information processing requirement and in-house capacity available in a firm (Wu et al., 2013) and compatibility with existing ICT and security issue both internal and external information flow (Gangwar et al., 2015). While a small proportion of SMEs have achieved SCI using conventional ICT applications on piece-meal basis, many are yet to realise the potential benefits of cloud-based technologies for a higher level of performance in logistics process integration.
Figure 1 Conceptual model of cloud adoption and performance relationship delivery status and stock level can occur when the logistics processes of key suppliers and customers are integrated, using cloud in this case. Logistics activities within a firm, mainly around inventory turnover, safety stock, order processing, labour cost, accurate pricing etc. are likely to be well supported via cloud services. Cloud adoption, similar to traditional ICT, will be likely to facilitate information flow (Lee ez al., 1997), material flow (Stevens, 1990), funds flow (Mabert and Venkataramanan, 1998) and demand information flow (Coyle et al., 2016) among partners in a supply chain. As information distortion remains a recurring issue in a supply chain, the cloud technology may be able to mitigate this effect, known as bullwhip effect (Lee et al., 1997; Shee and Kaswi, 2016), thereby improving the inventory situation at each partner along the chain. When demand-supply is in perfect sync, cloud services can manage a series of activities among chain partners to deliver efficient consumer response by replenishing inventory in more efficient way (Harris et al., 1999). Just-in- time is an effective replenishment programme between a manufacturer and suppliers. Cloud is believed to deliver better buyer-supplier collaboration by using the internet. Even ICT has the capability to do this currently through wide range of technologies (e.g. WMS, ERP, EDI) (Autry er al., 2010). Cloud-based services, however, are believed to do it in an improved way (e.g. reduced cost, scalability, flexibility, mobility and shared resources)(Marston et al., 2011; Zhang etal., 2010). Cland eenmicee are heljeaved tra eamnifiry the mived and analytics. Another perspective is that when the big businesses are resting on their legacy of a robust ICT platform and it appears to be self-reliant, they may also choose to adopt it for selective projects that go beyond their current capacity when it comes to timely delivery. However, small and medium enterprises (SMEs) are the potential adopters of the emerging cloud services (1.e. SaaS) because of easy access, low cost, pay- as-you-go perspectives for their information processing and decision-making. Regardless of the advantages, adoption of the “cloud” may depend on the information processing requirement and in-house capacity available in a firm (Wu et al., 2013) and compatibility with existing ICT and security issue both internal and external information flow (Gangwar et al., 2015). While a small proportion of SMEs have achieved SCI using conventional ICT applications on piece-meal basis, many are yet to realise the potential benefits of cloud-based technologies for a higher level of performance in logistics process integration.
Table | Correlation coefficients of all constructs, Cronbach alpha, CR and AVE (n = 105) Table II Discriminant validity test
Table | Correlation coefficients of all constructs, Cronbach alpha, CR and AVE (n = 105) Table II Discriminant validity test
Notes: **p < .05; ***p < .001; NS: non-significant
Notes: **p < .05; ***p < .001; NS: non-significant
Notes: *p < 0.05; **p < 0.01;Diagonal values are square root of AVEs. Values below the diagonal are correlation coefficients
Notes: *p < 0.05; **p < 0.01;Diagonal values are square root of AVEs. Values below the diagonal are correlation coefficients
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Supply Chain ManagementSupply Chain Performance Management
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