Analyzing Supply Chain Uncertainty to Deliver Sustainable Operational Performance

Read this study, which surveyed supply chain managers to understand how they address supply chain uncertainty. Section 5 identifies the study's solutions to overcoming uncertainty.

Data Analysis and Findings

Evaluation of the Measurement Model

Table 1 presents the descriptive statistics for the latent constructs. The results of the measurement model reveal that the model meets all of the minimum requirements. Firstly, all of the first- and second order-constructs with reflective items suggest a good indicator reliability with few indicator loadings below 0.70 (Table 2). However, since the criteria for reliability and convergent validity were met, we decided to retain all original items, as suggested by Hair et al. Secondly, Cronbach's alpha and composite reliability values range from 0.70 to 0.91, thus meeting the commonly accepted threshold level. Thirdly, all of the average variance extracted (AVE) values of the first- and second-order constructs are above 0.50, supporting the construct measures' convergent validity.

Table 1. Descriptive statistics, correlations for the study constructs.

	M	S.D	ASC	ESC	RHSC	RSC	SCU	DU	SU	SCS	FLEX	SPD	OP
ASC	3.26	0.75	0.85
ESC	3.91	0.70	0.32 **	0.79
RHSC	3.25	0.79	0.27 **	0.31 **	0.84
RSC	3.76	0.62	0.60 **	0.11 **	0.52 **	0.71
SCS	3.14	0.51	0.50 **	0.50 **	0.27 **	0.47 **	0.60
DU	2.81	0.74	0.12	0.19 *	0.13	0.18 *	0.18 *	0.81
SU	3.47	0.65	0.10	0.32 **	0.16 *	0.23 **	0.23 **	0.077	0.84
SCU	3.54	0.59	0.15	0.36 **	0.20 *	0.28 **	0.28 **	0.600 **	0.50 **	0.66
FLEX	3.61	0.59	0.23 **	0.29 **	0.23 **	0.31 **	0.31 **	0.327 **	0.19 *	0.34 **	0.77
SPD	3.89	0.53	0.17 *	0.17 *	0.21 **	0.14	0.21 **	0.112	0.19 *	0.18 *	0.46 **	0.78
OP	3.75	0.48	0.24 **	0.28 **	0.26 **	0.27 **	0.31 **	0.26 **	0.22 **	0.31 **	0.57 **	0.64 **	0.66

M = Mean, S.D = Standard deviation. ASC = Agile supply chain. ESC = Efficient supply chain. RHSC = Risk-Hedging supply chain. RSC = Responsive supply chain. SCS = Supply chain strategy. DU = Demand uncertainty. SU = Agile supply chain. SCU = Supply chain uncertainty. FLEX = Flexibility. SPD = Speed. OP = Operational performance. Diagonal and italicized bold elements are the square roots of the AVE (average variance extracted). Off-diagonal elements are the correlation between constructs' variables. * |t| ≥ 1.65 at p = 0.10 level. ** |t| ≥ 1.96 at p = 0.05 level.

Table 2. Measurement Model.

Construct	Code	Items	FOL	SOL
Measurement model at first-order factors (First-stage)
ASC (reflective)
C.R = 0.89; α = 0.83; AVE = 0.67; VIF = 2.99	SSA1	Our supply chain always faces the volatile customer demand.	0.81	0.74
	SSA2	Our supply chain needs to maintain a higher capacity buffer in response to the volatile market.	0.85	0.75
	SSA3	Our supply chain provides the customer with personalized products.	0.81	0.69
	SSA4	Our supply chain structure often changes to cope with a volatile market.	0.80	0.76
ESC (reflective)
C.R = 0.84; α = 0.71; AVE = 0.64; VIF = 1.12	SSE1	Our supply chain supplies predictable products	0.83	0.57
	SSE2	Our supply chain reduces any waste as much as possible	0.83	0.64
	SSE3	Our supply chain reduces costs through mass production.	0.72	0.47
RHSC (reflective)
C.R = 0.88; α = 0.80; AVE = 0.71; VIF = 1.78	SSH1	Our supply chain partners are ready to share resources whenever necessary	0.89	0.75
	SSH2	Our supply chain reduces costs through sharing capacities/resources	0.86	0.69
	SSH3	Our supply chain partners are always ready to support and cooperate	0.78	0.76
RSC (reflective)
C.R = 0.80; α = 0.70; AVE = 0.50; VIF = 1.81	SSR1	Our supply offers wider product range	0.76	0.57
	SSR2	Our supply chain offers new products more frequently	0.77	0.64
	SSR3	Our supply chain offer more innovative products	0.64	0.47
	SSR4	Our supply chain provides fast deliveries	0.66	0.74
DU
C.R = 0.88; α = 0.81; AVE = 0.65; VIF = 1.56	SUD1	Our master production schedule has a high degree of variation in demand over time.	0.86	0.54
	SUD2	Our demand fluctuates drastically from week to week.	0.86	0.49
	SUD3	Our requirements for raw materials supply vary drastically from week to week.	0.84	0.51
	SUD4	Customer requirements/services for products change dramatically.	0.63	0.42
SU
C.R = 0.91; α = 0.87; AVE = 0.71; VIF = 2.84	SUS1	Our suppliers always provide us a correct lead time estimation	0.82	0.62
	SUS2	Our suppliers consistently meet our delivery requirements	0.87	0.73
	SUS3	Our suppliers provide us the input with consistent quality	0.88	0.77
	SUS4	Our suppliers consistently meet specified volume requirements	0.81	0.65
FLEX (reflective)
C.R = 0.86; α = 0.78; AVE = 0.60; VIF = 3.14	FPF1	Ability to customize products/services	0.79	0.60
	FPF2	Ability to respond to changes in delivery requirement	0.74	0.50
	FPF3	Ability to adjust production volumes	0.81	0.73
	FPF4	Ability to produce a range of products/services	0.75	0.72
SPD (reflective)
C.R = 0.86; α = 0.79; AVE = 0.61; VIF = 1.28	FPS1	On time delivery	0.80	0.71
	FPS2	Delivery Dependability	0.81	0.68
	FPS3	Delivery Speed	0.70	0.60
	FPS4	Time to Market	0.82	0.73
Measurement model at second-order factors (Second-stage) *
SCS		Supply Chain Strategy
C.R = 0.82; α = 0.74; AVE = 0.92	ASC	Agile supply chain	0.80
	ESC	Efficient supply chain	0.85
	RHSC	Risk Hedging supply chain	0.77
	RSC	Responsive supply chain	0.95
SCU		Supply Chain Uncertainty
C.R = 0.92; α = 0.90; AVE = 0.87	DU	Demand uncertainty	0.75
C.R = 0.92; α = 0.90; AVE = 0.87	SU	Supply uncertainty	0.76
OP		Operational performance
C.R = 0.86; α = 0.82; AVE = 0.89	FLEX	Flexibility	0.64
C.R = 0.86; α = 0.82; AVE = 0.89	SPD	Speed	0.96

Note: All loadings and weights are significant at 0.001 level (2-tailed). * Second order construct, two-stage approach. FOL = First-order loadings. SOL = Second-order loadings. C.R = Composite reliability. α = Cronbachs Alpha. AVE = Average variance extracted. VIF = Variance inflation factor.

To examine the discriminant validity, this study uses two approaches: First, the results of cross loading show that all of the items load is higher on their respective constructs than on the other constructs, and that the differences between loadings and cross loading are much higher than the suggested threshold of 0.1. Second, Table 1 shows that the square root of AVE is greater than the corresponding construct correlation on the diagonal.

Finally, the constructs in this study are operationalized in a reflective-reflective type 1 model based on theoretical considerations. This study applies a two-stage approach to evaluate the hierarchical second-order latent constructs. In the first stage, the repeated indicator approach is used to obtain the latent variable scores for all the first-order constructs, which, in the second stage, serve as manifest variables in the measurement model of second-order constructs.

Evaluation of the Structural Model

First, this study examines the model for collinearity. The results show minimal collinearity with the variance inflation factor (VIF) for two sets of (predictor) constructs because they are much less than the common threshold of 5. Therefore, collinearity among the predictor's constructs in the structural model is not a problem. Second, the model predictability is assessed using R2 values for the dependent latent variables. The R2 values of SCS (0.16) and OP (0.24) are in line with prior research, supporting the PLS-SEM model's in-sample explanatory power.

Third, the sizes and significance of the path coefficients that represent the derived hypotheses were examined. To obtain the significance levels, the bootstrapping procedure (with some 5000 bootstrap samples and 146 bootstrap cases, using no sign changes) was run. As shown in Table 3, the results indicate that SCU has a significant effect on SCS (β = 0.30 ***; t = 3.91; CI0.90: (0.22, 0.27) and OP (β = 0.27 **; t = 3.02; CI0.90: (0.26, 0.31). In addition, SCS has a significant effect on OP (β = 0.25 **; t = 3.20; CI0.90: (0.22, 0.27). These results confirmed that all three hypotheses were accepted.

Table 3. Mediation Analysis Results.

Mediation Model	Standardized Coefficient	t-Value	95% CI	VAF (%)	f^{2 †}	q^{2 †}	Conclusion
Direct effect
SCU on OP	0.27 **	3.02	(0.37, 0.43)		0.17
SCU on SCS	0.30 ***	3.91	(0.38, 0.44)		0.21	0.04	H1 supported
SCS on OP	0.25 **	3.20	(0.15, 0.19)		0.17	0.04	H2 supported
Indirect effect
SCU on OP (mediated by SCS)	0.07 ***	13.31	(0.06, 0.08)	22.15			H3 supported

Note: CI = Confidence interval; VAF (%) = Variance represented percentage. ^† = Effect size. The values of f² and q²; 0.02, 0.15, 0.35 for weak, moderate, strong effects. * |t| ≥ 1.96 at p = 0.05 level; ** |t| ≥ 2.58 at p = 0.01 level; *** |t| ≥ 3.29 at p = 0.001 level.

Fourth, the values of the effect size f² and q² of 0.02, 0.15, and 0.35 are regarded as small, medium, or large, respectively. The results of the f² and q² effect sizes concerning all of the relations in the model are provided in Table 3.

Finally, a blindfolding procedure was run to evaluate the model's predictive relevance. All of the Q² values are considerably above zero, thus providing support for the model's predictive relevance regarding out-of-sample prediction as shown in Figure 1.

Mediation Analysis

This study follows Zhao et al.'s recommendations to examine mediation effects in the model. Zhao et al. posit the key condition in showing mediation is that the indirect effect is significant. The bootstrapping procedure facilitates the exploration of the SCS (mediator) simultaneously in the association between SCU (independent variable) and OP (dependent variable). Based on Zhao et al., this study applies the recommended 5000 bootstrap samples at the 90% confidence level. The structural equation model is examined to determine whether SCS mediates the effect of SCU on OP. The results show the existence of a significant indirect effect of SCU on OP with the SCS mediator (β = 0.07 ****; t = 13.31; CI0.90: (0.06, 0.07), thus suggesting that SCS mediates the association between SCU and OP. However, the direct effect of SCU on OP shows partial mediation. The variance was (VAF) 22.15%; that is, there was a partial mediation or complementary mediation. Further, the meditation testing, using the Baron and Kenny's procedure, produced highly similar results to those attained using the Zhao et al.'s method, thus suggesting SCS mediates the association between SCU and OP.

fsQCA Results

fsQCA analysis requires a calibration of the conventional variables measured using Likert scales. First, this study measures each latent variable by calculating the average of the values of their items. This process results in six conditions and two variables reflecting the outcome. Second, the eight variables are then calibrated into fuzzy sets using the direct calibration method. Accordingly, the decision was made for the threshold of full membership in the sets (fuzzy score = 0.95). The threshold for full non-membership (fuzzy score = 0.05) and the cross-over point, which indicates a point of maximum ambiguity where respondents are not in or out of the sets (fuzzy-score = 0.50) (pp. 104–105). In line with extant studies, such as Fiss, this study employs the 75th percentile of each variable as an anchor for the full membership. For the full non-membership, the 25th percentile and the 50th percentile for the crossover point were used. This study does not use absolute anchors for two reasons. Firstly, it is the first exploration of a research incorporating SCU and an SCS in a single empirical frame. Secondly, concept measurements are based on subjective scales. However, due to the asymmetric nature of the distribution of responses associated with variable supply uncertainty, the percentile for the calibration process was not used; instead, the six-value fuzzy set was used. Because the outcome is originally measured by the flexibility and the speed of the supply chain, a so-called macro-variable was created by combining them. Ragin qualifies the macro-variable as a 'higher-order construct', which benefits from using the maximum of the values of the measures of which it is composed.

Table 4 exhibits the results of the fsQCA analysis of high OP. The notation in Table 4 is based on the extant literature.

Table 4. Solutions table indicating the configurations needed to achieve high OP.

Configuration	Solution
Configuration	1a	1b	2	3	4	5
SCS
ASC
ESC
RSC
RHSC
SCU
DU
SU
Consistency	0.99	0.93	0.88	0.80	0.87	0.86
Raw coverage	0.17	0.21	0.16	0.10	0.13	0.21
Unique coverage	0.00	0.03	0.08	0.02	0.06	0.11
Overall solution consistency	0.84
Overall solution coverage	0.56

Note: "

" indicates the presence of a condition, and " Sustainability 09 02217 i001

" indicate the absence. Framed signs (" Sustainability 09 02217 i004

" and "

") indicate core conditions; signs without frame indicate (" Sustainability 09 02217 i002

" and "

") peripheral conditions. Blank spaces indicate "don't care".

Table 4 shows the existence of five causal paths leading to high OP. They represent various combinations of SCS and SCU. Each part of the overall solution has a consistency greater than 0.80, which can be considered acceptable. These five solutions demonstrate the existence of a first-order equifinality (e.g., Fiss) in the analysis of the OP of the supply chain. In Solution 1 (1a and 1b), the SCS consists of a conjunction of lack of ASC, efficiency, responsiveness, and a lack of risk hedging, while the SCU is only based on a demand uncertainty. In Solution 1a, efficiency and a lack of risk hedging constitute the core conditions regarding the SCS. These conditions have a strong causal relation with high OP (the outcome). Conversely, responsiveness and lack of agility are peripheral or contributing conditions, which imply that they have a low causal relation with the high OP. Regarding SCU; demand uncertainty maintains a strong causal relationship with the high OP of the supply chain, regardless of the presence or absence of supply uncertainty. Solutions 1a and 1b provide evidence of a second-order equifinality (e.g., Fiss). Indeed, comparing Solutions 1a and 1b indicates that a high demand uncertainty and a high uncertainty in supply can be treated as substitutes. These results also demonstrate that companies can achieve a high OP even though they do not hedge against risks and do not encounter agility in their supply chain. Solution 2 indicates that certain companies may engage an SCS to a lesser extent, although they face an uncertainty context marked by high demand uncertainty and low supply uncertainty. In contrast, Solution 3 emphasizes that companies may deploy other massive supply chain strategies in the absence of high uncertainty in their supply chain. Solutions 4 and 5 suggest that companies encountering a high level of uncertainty may implement differentiated supply chain strategies.

To additionally examine the causal asymmetry assumption underlying fsQCA, a new outcome variable was created that represents the absence of the high OP of the supply chain. The new outcome is computed as the negation of the high OP of the supply chain examined in Table 4, and investigated with the same six conditions reflecting the SCS and uncertainty. The analysis was not conclusive in emphasizing the manner in which the asymmetrical nature of the causal relations led to either a high or low OP. Indeed, we obtain a solution only when lowering the cut-off of the truth table algorithm from "0.8" down to "0.75".

Testing for Predictive Validity

Previous PLS-SEM and fsQCA analyses demonstrate how well the model investigated fits the data. They are not indicative of how independent variables (conditions) predict dependent variables (result). Thus, a predictive analysis is performed in this study. This study follows the procedure in Cepeda-Carrión et al. to report the predictive validity of the PLS-SEM model as follows: first, two-thirds of the sample is randomly chosen, which is composed of 96 samples as the training set; the remaining 50 samples represent the holdout sample. The training set is used to estimate the parameters in the model. Using the holdout sample, each indicator is standardized, and the construct scores are formed as linear combinations of the respective indicators using the weights obtained from the training sample. The construct scores are standardized. For the OP of the endogenous construct in the holdout sample, the predictive scores are created by using the path coefficients obtained from the training sample. The correlation between the predictive scores and construct scores is 0.63 (p < 0.01), which suggests that the PLS-SEM model has acceptable predictive validity.

Furthermore, the predictive validity of fsQCA analysis is made following recent empirical studies. Table 5 highlights that the patterns of the complex combination of conditions are causally consistent indicators of a high level of supply chain OP. Furthermore, Figure 2 shows that the first part of the solution of the modeling subsample is causally relevant in predicting the high OP of the supply chain with a consistency higher than "0.80" (0.91). Predictive tests for the remaining four parts of the solution of the modeling subsample show the high consistency of the outcome under investigation. All of the results will be provided at the request of any interested readers.

Figure 2. Test of Part 1 of the solution from the modeling subsample using data from the holdout subsample.

Table 5. Complex configurations of SCU and strategy dimensions, which indicate a high level of OP for the modeling subsample.

Solution from the Modeling Subsample	Raw Coverage	Unique Coverage	Consistency
1. ~sudu~esc~asc~rsc*~rhsc	0.12	0.05	0.84
2. su~duesc~ascrsc*~rhsc	0.14	0.08	0.95
3. ~suduesc~ascrsc*~rhsc	0.07	0.01	0.98
4. sudu~escasc~rsc*rhsc	0.15	0.08	0.89
5. suduescascrsc*rhsc	0.19	0.12	0.87
Overall solution consistency:	0.87
Overall solution coverage:	0.47

Note: SU: Supply uncertainty, DU: Demand uncertainty, ASC: Agile supply chain, ESC: Efficient supply chain, RSC: Responsive supply chain, RHSC: Risk-hedging supply chain, and "~": the negation of the condition.