Services Redesign Strategies for Demand and Capacity Management and Impact on Company Performance

Contingency Theory and Structure–Conduct–Performance Paradigm

With the dynamism in market forces, the economies are increasingly exposed to these forces, and so it increases the relevance of the contingency theory (Lee & Miller, 1996). In a study by Ruekert et al. (1985) on contingency theory, relative significance held by different dimensions of performance and the environmental nature will contribute to the characterization of different activities. The performance of a firm is influenced by its strategy selection choices (Taylor & Taylor, 2014). With the aim of proposing a conceptual framework, Wadongo and Abdel-Kader (2014) have used the theory of contingency to clarify the impact of contingency variables on the measurement of performance. The theory has been applied to emphasize the range of related performance metrics by Gacenga (2011).

The structure–conduct–performance (SCP) model has been considered in the study to hypothesize and investigate how the use of SRS can influence firm’s performance in spite of the presence of SCR. Though the model was initially applied for the industrial sector (Ferguson & Ferguson, 1994), it is of good significance for the service sector too. Thus, research related to SRS suggests use of applying lean, agile principles, automation, process simplification, mass customization, postponement, modularization, and reorganizing front and back end. However, there is lack of empirical research on the relationship between SCR, use of SRS and the influence on company performance.

Hence, there is an urgent call to comprehend the firm’s performance not just with reference to the formulated and executed SRS but also to understand the impact of the presence of the SCR on the SRS. With such an understanding, the organizations will be able to focus on improvising the SRS while also exerting efforts in reducing the incidence of SCR.

Supply Chain Risks (SCR) and Company Performance

Expansion in recurrence and results of SC interruptions have expanded the interest with respect to SCR and in ways of tending to it (Heckmann et al., 2015). Powerful control of SC adaptability can work on hierarchical execution (Sahu et al., 2015). Key practices to upgrade SC strength further develop SC execution (Pettit et al., 2013). A study carried out by Qazi et al. (2018) explained how SCR are directly associated with performance of corporations. Chu et al. (2020) and Truong Quang and Hara (2018) talked about how risks can influence the performance of SCs. Further, Shang et al. (2018) did touch upon how uncertainty and risks hamper SC performance negatively. In recent studies, Munir et al. (2020), Jajja et al. (2018) and Liu et al. (2021) mentioned how SCR negatively affects the SC performance. Cerabona et al. (2020) talked about better management of SC keeping performance under consideration. In order to make SCs sustainable, Hossan Chowdhury and Quaddus (2021) mentioned about alleviating manageability hazard and further developing business sector execution with dynamic ability view. According to Bag et al. (2020), Big Data analytics is yet another tool for mitigating risks and to enhance SC performance. In addition, Singh (2020) discussed about the managing risks and the uncertainty of better decision support systems. Considering the ongoing COVID-19 outbreak, Sharma et al. (2020) argued on farming inventory network dangers and COVID-19: moderation techniques and suggestions for the experts. Further, Nandi et al. (2020) mentioned about how SCs enabled with blockchain technology are able to increase their performance. Xu et al. (2020) carried out a bibliometric analysis to exhibit how disruption risks mitigate to active performance.

Erratic interest in limit-compelled circumstances could prompt decreased help quality and longer holding up periods (Shugan, 2002). Provider limit imperatives prevent item conveyance to client and impact volume and blend adaptabilities. Motivating force to put resources into limit before request acknowledgement turns out to be more articulated when limit speculations can build the general interest as well as ensure piece of the pie (Perdikaki et al., 2016). Shukla and Naim (2017) exhibited the plausibility of naturally and rapidly identifying aggravations in SC which compel the limit at an SC echelon. Irregularity of produce along with longer development of activities is a critical test in various levelled SCs.

Upgrading competitive performance, expanding consumer loyalty and diminishing expenses are the key objectives of supply chain management (SCM) (Mentzer et al., 2001; Yerpude et al., 2019). The key components of cut-throat needs incorporate expense, quality, conveyance execution, adaptability and ingenuity (Kumar & Kansara, 2018; Ward et al., 1998). The relationship among cost, adaptability, quality, administration level and lead time focuses on SC coordination system (Shukla et al., 2018). A brief help, opportune satisfaction, accessibility of merchandize and timing of administration are the key determinants of value in administrations (Bandyopadhyay, 2018). Administration quality is dictated by the nature of client care, consumer loyalty and handiness of data and prompts authoritative execution (Yaghoubi & Rigi, 2017).

Alhawari et al. (2021) highlighted that demand variability influences business performance negatively. It may be an arduous task for businesses to accurately conduct a prediction of demand. The risk of demand variability is marked with variation in demand as compared to the planned levels. As per a study by Chen et al. (2017), unpredictability in demand could lead to reduced order quantity, increased prices and lower profits for the SC as well as the firm. Even Anning-Dorson (2017) recognized that high levels of variation in demand can dampen or reduce the positive impact of product innovation on the performance of a firm (adapted from Tseng, 2019). Imprecision in sales forecast can be used as proxy for uncertainty in demand; it negatively impacts the performance of the firm (Hançerlioğulları et al., 2016).

One of the key SCR to a firm is the mismatch of capacity against the demand. While capacity that is not adequate could lead to unmet demand, especially during periods of surge in demand influencing performance in a negative manner, capacity that is in excess could lead to its improper utilization resulting in cost of idle capacity during periods of lean demand. As per studies done by Park et al. (2021), in situations experiencing constraints in capacity augmented with unpredictability in demand, there could be increased waiting periods and reduced quality of service. The viability of automatic and fast detection of SC disturbances were demonstrated in a study by Shukla and Naim (2017).

Not being able to meet the quality levels as expected and demanded by the customers relates to the risk of service delivery quality. Certain customers have lowered their levels of tolerance and higher standards for service acceptance (Park et al., 2021).

In the situations involving service partners, when the standards of quality of service partners are not properly monitored, there could be risk of service delivery quality. According to Bandyopadhyay (2018), availability of merchandize, timely fulfilment, timing of service and prompt service determine service quality. In studies done by Asian et al. (2019) and Yaghoubi and Rigi (2017), customer service quality, usefulness of information and customer satisfaction not only determines service quality but also the results in organizational performance. Therefore, there is a negative influence on the performance of the company if the required service delivery quality is not met.

Based on the literature review, the key risks include demand variability, mismatch of capacity and demand, and quality of service delivery. Presence of these risks leads to companies finding opportunities in service redesign to address the risks and keep up the performance levels. The presence of existing literature on company performance, there is lack of holistic view with the dimensions of SC competitiveness, financial performance and customer satisfaction for the service industry. Thus, for leading this review, a stable approach has been taken by including customer satisfaction, financial performance and SC competitive performance.

Supply Chain Risks (SCR) and Services Redesign Strategies (SRS)

Kim and Pomirleanu (2021) worked on actual redesign strategies but for the tourism sector in a disaster situation. Likewise, Hasani and Mokhtari (2018), through redesigning strategies, contributed towards disaster management, whereas Farmanova et al. (2019) carried out similar kind of research in regard to population health. Chari et al. (2020) considered humanitarian operations domain and assessed SCR impacting the overall performance. Likewise, Birkel and Hartmann (2020) concluded that Internet of Things (IoT) will be a very helpful and easy-to-operate tool to manage SCR. Ivanov and Dolgui (2021) and Brink et al. (2020) focused on identifying SCR and managing disruptions caused due to these risks (Wang et al., 2020). In their study, Kim and Pomirleanu (2021) highlighted that redesign strategies have influence on customer perceptions and on image of the firm. Redesign interventions include innovative and creative ways of addressing issues and risks (adapted from Farmanova et al., 2019). The use of ‘agile’ was explored to optimize delivery in unpredictable demand environments, and this redesign strategy can help reduce service-related risks and increase responsiveness and performance (Rust et al., 2013). Aronsson et al. (2011) also focused on the significance of focusing on leanness and agility combined. Mass customization, as an effective redesign strategy, was defined by Hart in 1995 as ‘the use flexible processes and organization structures to produce varied and often individually customized products and services at the low cost of a standardized, mass-production system’ (adapted from Feitzinger & Lee, 1997), and postponement is a practical way of doing mass customization (Shao, 2008). As per a study by Davila and Wouters (2007), a powerful strategy to enhance SC is the postponement of the point of differentiation of a product.

In Table 1, we examine the presence of various SCR and whether the use of specific SRS—process simplification, applying factory principles, applying lean, agile principles, automation, mass customization, postponement, modularization, re-organizing front and back end, and matching SC to demand—is influenced by the presence of specific risks. A more thorough literature review of last 30 years and beyond on use of service redesign as a strategy influenced by the presence of SCR to address SC performance impact was carried out and has been presented in Table 1.

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Table 1.

Presence of SCR and Use of SRS

Presence of Risk/SRS Used (ST_SD)	Description of SRS	References
Process Simplification
Ch_Dvar	Adequate design, system change, time compression at all stages were suggested to build a customer-driven phase advance or predictive capability to improve flow of orders and products.	Towill (1996)
Ch_Qual	Service delivery quality is an outcome of strategic design choices, including structural—for example, facilities, layout—and infrastructural—people and other resources.	Roth and Menor (2003)
Ch_Cap	Reconfiguration/redesign of service and availability of risk management infrastructure were suggested to manage capacity.	Ambulkar et al. (2015)
Applying Factory Principles
Ch_Dvar	Service factory processes enable managing the demand at the point of service delivery.	Chase and Erikson (1989)
Applying Lean Principles
Ch_Dvar	Leaner organizations were found to have better handled demand variability.	Ali and Deif (2016)
Ch_Cap	Lean principles were applied in municipal services to upgrade limit in numerous stages at ideal expense.	Shrafat and Ismail (2019)
Applying Agile Principles
Ch_Dvar	Coordinated SCs help organizations confront fluctuating interest to create items rapidly by deciding ideal gathering grouping.	Sunil et al. (2018)
Mass Customization
Ch_Dvar	Varieties in the time strain to alter an assistance, combined with degree of customization required, determines the configuration needed; the configurations are classified as embellished, pre-determines, intuitive and prompt.	McCarthy et al. (2010)
Ch_Dvar	Services firms with high customization and customer contact are subject to process variation.	Avery and Swafford (2009)
Ch_Dvar	Mass customization (MC) limits enable firms to set up, make and pass on high volume of isolated things to address client issues in ideal way at large-scale manufacturing cost.	Tu et al. (2001)
Postponement
Ch_Dvar, Ch_Cap	Postponement is used as a strategy to handle demand surges.	Lee (2002)
Ch_Dvar	Stopping order batching of random, correlated orders, consolidating the orders by logistics provider, postponing delivery closer to delivery date and location were suggested to manage demand variation.	Lee et al. (1997); Burbidge (1961)
Ch_Cap	Inventorying the defined units of capacity for sale in advance of production and holding system backlog at stages closest to the point of end customer demand.	Anderson and Morrice (1999)
Modularization
Ch_Qual	Modularization of services on dimensions of service, process, organization and customer interface increased customers’ perception of service quality.	Chou (2009); Pekkarinen and Ulkuniemi (2008)
Front/Back-Office Coordination
Ch_Dvar, Ch_Cap	‘Chase’ strategy was suggested to control capacity in the case of varying demand and sharing of capacity.	Sasser (1976)
Ch_Dvar, Ch_Cap	The issue of limit and arranging techniques was joined with the back and front office tasks; certain blends of limit and arranging methodologies were connected to either front office or administrative centre activities.	Johansson (2006)
Ch_Dvar	Choice of service design layout (sequential, standard, reciprocal, sequential customized, pooled is influenced based on the dimensions of “Diversity of Demand” and “Customers’ Disposition to Participate” in the service production process. Each of these layouts have respective areas of front- and back-office.	Larsson and Bowen (1989)
Ch_Dvar	Front office can be managed with stationary queuing models even in non-stationary environments, where demands vary in a predictable pattern over the day.	Green et al. (2007)
Ch_Cap	Cross-training of employees (front and back office) was proposed to relieve task-specific bottleneck.	Shemwell and Cronin (1994)
Ch_Cap	Increased customer participation was suggested as non-price approach in the capacity constrained.	Shugan (2002)
Ch_Dvar, Ch_Cap	Customer participation in service production process, cross-training of employees between front and back offices were considered as key decisions modelled to have a balance of capacity and demand.	Klassen and Roehleder (2001)
Segmentation
Ch_Dvar	Use of segmentation, regionalization and decentralization in SCs was proposed to reduce losses.	Chopra and Sodhi (2014)
Ch_Qual	Segmenting of sourcing/multi-sourcing was suggested to reduce the risk of low-service levels.	Sawik (2014)
Automation
Ch_Cap, Ch_Dvar	Automation was considered as key capacity management option (CMO) and demand management option (DMO) to handle demand variations and capacity constraints.	Klassen and Rohleder (2001)
Self-Service
Ch_Cap, Ch_Qual	Self-service was proposed in ‘Chase’ model to have customers provide resources to better manage the capacity.	Sasser (1976)
Provide Off-Site Access
Ch_Cap	Off-site access to service mitigated capacity constraints with on-site service.	Klassen and Rohleder (2001)
Service Packaging
Ch_Cap	Service packaging is used by tour operators to match capacity with demand.	Kandampully (2000)
Ch_Dvar	Service packaging was proposed to address variations in demand.	Gilmour et al. (1994)
Service Clusters/Net Chains
Ch_Dvar	Better collaboration/integration with integrated manufacturing and service networks (IMSN) minimized the variability and improved the profits.	Viswanadham et al. (2005)
Ch_Dvar	Two-way flow of information in system minimizes the whiplash effect.	Viswanadham et al. (2005)
Design SC to Meet Customer Demand Chain
Ch_Dvar	Design of feedback loops was proposed to handle medium-term demand amplification and to reduce delays.	Forrester (1968)

Source: Authors’ composition.

Note: Ch_Dvar = demand variability risk; Ch_Qual = risk of quality of service delivery; Ch_Cap = risk of mismatch of capacity.

Use of Services Redesign Strategies (SRS) and Impact on Company Performance

The rearrangement, reconstitution or substitution of processes that constitute a service is referred to as service redesign. Bose et al. (2018) found the process flexible and plant profitable compared to the dedicated plant, specifically with high-capacity investment and constrained margins. Andreasson examined that administration configuration research that offered double worth further developed client experience and authoritative execution. Brozovic et al. (2016) conceptualized the linkages between supplier adaptability and client esteem creation. Advantages of information technology (IT) in SCM (Ohmori et al., 2021) along with process update incorporate further developed client support, effectiveness, data quality and nimbleness of the stock organization (Auramo & Ala-risku, 2005). Yap and Tan (2012) demonstrated that service SCM practices (demand management, customer relationship management (CRM), supplier relationship, capacity and resource management) had significant positive direct relationship with organizational performance (Silva et al., 2020). Berry and Naim (1996) had emphasized that redesign strategy could result in dynamic improvement in performance. The SC partners need to simplify their products and processes and reduce costs (Davila & Wouters, 2007). The studies presented in previous sections also highlight the significance of SCR and SRS with respect to company performance.

Company performance variables have been grouped as follows: (a) services supply chain competitive performance (R_SC); (b) customer satisfaction (R_CS); and (c) financial performance (R_FP). We consider the use of SRS and whether the use of these strategies mediates company performance (refer Table A.1).

The researchers have assessed the performance of a firm in response to the application of SRS in the presence of SCR by applying the contingency theory and the SCP paradigm as the theoretical premise. The implementation of proper service redesign strategy will result in improved performance. But the influence of SRS does not exist as stand-alone; so the presence of SCR can be a deterrent to the positive influence on performance, and the extent of influence depends upon various factors that have been discussed in the study. Therefore, this research has focused not just upon proper selection but proper implementation too of the SRS even in the presence of SCR and their impact on the firm performance. On the basis of the above discussion, Figure 1 presents the conceptual framework along with the hypothesized relationships.

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Figure 1.

Source: Authors’ composition.

Relationships Between Presence of SCR and Use of SRS

As discussed in the previous section, the SCR are demand variability risk, capacity mismatch with respect to demand and quality of service delivery. The SRS constitute simplification process; application of service; implementation of lean; implementation of agile; mass customization; postponement; modularization; re-organization; segmentation; automation; self-service; off-site service; packaging; clustering; and matching. There is an increase in the number and frequency of the SCR, which have a significant effect on the SCs operations and stability; hence, it is important that the SC becomes more robust to handle or tackle these risks and so requiring adequate redesign strategies (Carvalho et al., 2012). The following three hypotheses have been developed which shows the relationship of presence of SCR and SRS.

The first hypothesis has been proposed to explain the risk of demand variability (Ch_Dvar) influencing the use of SRS.

The second hypothesis has been proposed to explain the risk of mismatch capacity (Ch_Cap) against demand influencing the use of SRS.

The third hypothesis has been proposed in explaining the delivery quality risk (Ch_Qual) influencing the use of SRS.

Relationships Between Use of SRS and Company Performance

Firms need to examine their present service designs, that is, the way they are delivered and received, and also create innovative ways to serve clients. Therefore, it becomes imperative that the services are redesigned for improved customer service and firm performance. Based on the discussion presented in the previous section, the constituents of company performance are SC competitive performance, customer satisfaction and financial performance. The three hypotheses developed to show the use of SRS and its impact on company performance are as follows:

The use of SRS enhances the SC competitive performance, that is, (R_SC).

The use of SRS enhances the customer satisfaction, that is, (R_CS). There is hypothetical proof of further developed consumer loyalty using various SRS (except postponement, integration of back and front office, and provide off-site access to service) as shown in Table A.1.

The use of SRS enhances the financial performance, that is, (R_FP). There is proof of progress in monetary execution with utilization of SRS (except with use of factory principles, postponement, modularization, integration of back and front office, self-service and providing off-site access to service) (refer Table A.1).

Thus, the above hypotheses proved the influence of SCR and SRS on company performance and established the basis of the findings discussed above. Based on all the six hypotheses developed, Figure 1 indicates the conceptual model for conducting the empirical learning.

Measurement

In this study, a 5-point Likert scale (where 1 indicates strongly disagree and 5 indicates strongly agree) was adopted. The framework included the following

3 broad SCR covering demand variability (Ch_DVar), constrained capacity (Ch_Cap), and service quality (Ch_Qual) covering 10 items.

The construct has been introduced in Table A.1. All the things are estimated in 5-point Likert scale where 1 alludes to strongly disagree and 5 alludes unequivocally concur concerning the different aspects. Few reactions with missing qualities inquiries were supplanted with normal worth of the reactions for the separate inquiry.

Thinking about utilization of developmental estimations, a composite-based incomplete least square strategy is utilized to do dependability and legitimacy, factor examination, organize condition model, intervention investigation with circuitous and absolute impacts, and utilize Smart partial least squares (PLS). The utilization of PLS strategy is suggested explicitly when it is obscure that whether the idea of information is normal element or composite based (Hair et al., 2017; Rigdon et al., 2017).

Reliability and Validity of the Instrument

To guarantee non-attendance of legitimacy and dependability concerns, a progression of tests was led to kill any methodical mistakes. The factor examination test showed the dependability of the things in poll with stacking for the things was wilt >0.60 or the p-values was <0.1. Irrelevant stacking of things 2, 5, 9 has been eliminated from the intervention investigation. Table 3 displays corresponding values of the main factors, while the aftereffects of the component stacking are portrayed in Table 4.

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Table 3.

SCR, SRS Used and Outcomes Achieved

		Number of Items	Original Sample (O)	Sample Mean (M)	Standard Deviation (STDEV)	T-statistics O/STDEV	P-values
I	Ch_Cap	3	0.697	0.695	0.031	22.263	.000
II	Ch_Qual	3	0.741	0.738	0.026	28.336	.000
III	Ch_Dvar	4	0.743	0.740	0.025	29.524	.000
IV	R_CS	5	0.889	0.888	0.015	61.208	.000
V	R_FP	4	0.877	0.875	0.015	56.874	.000
VI	R_SP	11	0.922	0.921	0.009	107.756	.000
VII	ST_SD	15	0.922	0.921	0.008	112.206	.000

Source: Authors’ composition.

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Table 4.

Factor Analysis of Items (SCR, SRS Used, Outcomes Achieved)

	Original Sample (O)	Sample Mean (M)	Standard Deviation (STDEV)	T-statistics O/STDEV	P-values
SCR
Ch_Dvar (Demand Variability)
1. CH_Dvar_1	0.383	0.378	0.070	5.461	.000
2. CH_Dvar_2	0.145	0.122	0.126	1.154	.249
3. CH_Dvar_3	0.367	0.369	0.074	4.954	.000
4. Ch_DVar_4	0.409	0.417	0.078	5.210	.000
Ch_Cap (Constrained Capacity)
5. Ch_Cap_1	0.202	0.188	0.239	0.845	.399
6. Ch_Cap_2	0.533	0.520	0.172	3.094	.002
7. Ch_Cap_3	0.508	0.482	0.179	2.838	.005
Ch_Qual (Quality of Service Delivery
8. Ch_Qual_1	0.563	0.469	0.298	1.891	.059
9. Ch_Qual_2	–0.077	–0.006	0.440	0.174	.862
10. Ch_Qual_3	0.646	0.558	0.271	2.380	.018
Use of SRS (ST_SD)
11. ST_SD1	0.115	0.115	0.009	12.603	.000
12. ST_SD2	0.098	0.099	0.008	13.062	.000
13. ST_SD3	0.116	0.117	0.009	13.576	.000
14. ST_SD4	0.104	0.103	0.010	10.715	.000
15. ST_SD5	0.104	0.104	0.008	13.069	.000
16. ST_SD6	0.061	0.061	0.010	6.131	.000
17. ST_SD7	0.107	0.106	0.009	12.035	.000
18. ST_SD8	0.090	0.090	0.009	9.784	.000
19. ST_SD9	0.097	0.097	0.008	11.851	.000
20. ST_SD10	0.112	0.112	0.009	12.475	.000
21. ST_SD11	0.064	0.064	0.009	7.180	.000
22. ST_SD12	0.080	0.080	0.009	9.103	.000
23. ST_SD13	0.077	0.077	0.008	9.601	.000
24. ST_SD14	0.100	0.100	0.009	11.613	.000
25. ST_SD15	0.110	0.110	0.009	11.807	.000
Outcomes Achieved
R_SP (Supply Chain Competitive Performance)
26. R_SP_1	0.089	0.088	0.012	7.610	.000
27. R_SP_2	0.104	0.105	0.010	10.867	.000
28. R_SP_3	0.120	0.121	0.007	17.462	.000
29. R_SP_4	0.116	0.117	0.007	15.876	.000
30. R_SP_5	0.130	0.130	0.008	15.745	.000
31. R_SP_6	0.134	0.133	0.009	15.403	.000
32. R_SP_7	0.129	0.130	0.008	15.538	.000
33. R_SP_8	0.120	0.121	0.008	14.577	.000
34. R_SP_9	0.137	0.136	0.010	14.287	.000
35. R_SP_10	0.125	0.124	0.008	14.960	.000
36. R_SP_11	0.116	0.116	0.008	14.994	.000
R_CS (Customer Satisfaction)
37. R_CS_1	0.242	0.241	0.015	15.625	.000
38. R_CS_2	0.224	0.225	0.013	17.168	.000
39. R_CS_3	0.237	0.236	0.015	15.537	.000
40. R_CS_4	0.240	0.241	0.016	15.210	.000
41. R_CS_5	0.258	0.260	0.018	14.589	.000
R_FP (Financial Performance)
42. R_FP_1	0.297	0.297	0.015	19.971	.000
43. R_FP_2	0.280	0.281	0.015	19.194	.000
44. R_FP_3	0.296	0.298	0.015	20.186	.000
45. R_FP_4	0.298	0.297	0.014	21.601	.000

Source: Authors’ composition.

Note: * indicates insignificant items at p-value < .05.

To build up the dependability, the reliability was tried according to the determination by (Kumar et al., 2015; Nunnally & Bernstein, 1994; Raykov, 1997). The outcome demonstrates that all the factors were surpassing the cut-forbidden >0.70 (refer to Table 5). To set up any issue of focalized legitimacy, the average variance extracted (AVE) test was led for all the deliberate factors. The AVE for all the factors was over the trimmed off worth of 0.50 with the exception of ST_SD, at 0.480, which was close to the trim off (Bagozzi & Yi, 1988; Hair et al., 2014; Kumar et al., 2013, 2014) (refer to Table 5). The discriminant legitimacy test was controlled to guarantee that the develops were liberated from any multicollinearity issue. The square root of the AVE at the diagonals of the matrix for all the construct was higher than inter construct correlation confirms the discriminant validity test (Fornell & Larcker, 1981). The build dependability was likewise settled through Cronbach’s alpha. The aftereffects of the legitimacy and dependability are exhibited in Table 4; Cronbach’s alpha value and AVE are shown in Table 5 individually.

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Table 5.

Construct Reliability and Validity

		Reliability (Cronbach’s Alpha Value)	Average Variance Extracted (AVE)	Ch_Cap	Ch_Qual	Ch_Dvar	R_CS	R_FP	R_SP	ST_SD
				I	II	III	IV	V	VI	VII
I	Ch_Cap	0.820	0.606	0.778
II	Ch_Qual	0.801	0.583	0.595	0.764
III	Ch_Dvar	0.831	0.555	0.528	0.500	0.745
IV	R_CS	0.919	0.693	0.102	0.095	0.204	0.832
V	R_FP	0.915	0.730	0.099	0.158	0.189	0.723	0.854
VI	R_SP	0.935	0.572	0.147	0.143	0.222	0.697	0.633	0.756
VII	ST_SD	0.932	0.480	0.146	0.121	0.169	0.445	0.488	0.569	0.693

Source: Authors’ composition.

Structured Equation Modelling and Path Analysis

Normal PLS to appraise the way coefficients were utilized and the bootstrap methodology were directed to acquire t-statistics by structural equation modelling (SEM) (Sharma et al., 2017; Singh et al., 2013) utilizing Smart PLS.

Speculations, aftereffects of way model and bootstrap importance in relation to t-statistics are portrayed in Table 6. The model with upheld hypotheses and critical ways is introduced in Figure 2.

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Table 6.

Path Analysis: Structural Model Estimates

Path (Hypothesis)	Original Sample (O)	Estimate	STD Dev	T-statistics	P-values	Hypothesis Support
Supply chain risks → Use of service redesign
H1: Ch_DVAR → ST_SD	0.123	0.127	0.063	1.940	.053	Supported
H2: Ch_Cap → ST_SD	0.070	0.072	0.073	0.962	.336	Not Supported
H3: Ch_Qual → ST_SD	0.018	0.021	0.065	0.276	.782	Not Supported
Use of service redesign → Outcomes
H4: ST_SD → R_SP	0.547	0.545	0.041	13.276	.000	Supported
H5: ST_SD → R_CS	0.426	0.423	0.050	8.505	.000	Supported
H6: ST_SD → R_FP	0.471	0.468	0.042	11.318	.000	Supported

Source: Authors’ composition.

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Figure 2.

Source: Authors’ composition.

Hypotheses Supported

The results of the path analysis and bootstrap procedure demonstrate that

Presence of Risk → Use of SRS

H₁: Presence of demand variability risk influences the use of SRS.

Use of SRS → Outcomes achieved

H₄: Use of SRS enhances the SC competitive performance.

The direct effect suggests that the presence of constrained capacity risk and service delivery quality risk do not influence the use of SRS. This deviates from the theoretical background. We would investigate the indirect and total effect to analyse this further.

Analysis of Mediation: Indirect and Total Effects

The researchers examined the indirect effect (presence of SCR and company performance). At p-value < .05, the results of indirect effect indicate that the presence of risks in items I and II (constrained capacity and quality of delivery) impact the company performance, in items IV, V and VI (customer satisfaction, financial performance and SC competitive performance) and the use of SRS mediate the performance. Table 7 presents an analysis of mediation (indirect effects and total effects). These effects are witnessed only through the analysis of mediation.

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Table 7.

Analysis of Mediation: Indirect and Total Effects

		IV R_CS	V R_FP	VI R_SP	VII ST_SD
Indirect Effects
I	Ch_Cap	0.030	0.033	0.038
II	Ch_Qual	0.008	0.008	0.010
III	Ch_Dvar	0.052*	0.058*	0.067*
Total Effects
I	Ch_Cap	–0.004	–0.051	0.028	0.070*
II	Ch_Qual	–0.008	0.107	0.030	0.018
III	Ch_Dvar	0.210	0.163	0.192	0.123

Source: Authors’ composition.

Note: Significant items at p < .05 are in bold; significant items at p < .10 are marked with *

The total effects are significant (at p ≤ .05) for the following paths:

Presence of constrained capacity risk (Ch_Cap) has positive mediating effect on SC competitive performance (R_SP). Use of SRS (ST_SD) mediates the relationship.

Hypothesis Supported Versus Hypothesis Rejected

The result of hypotheses testing indicates that the presence of interest fluctuation hazard impacts utilization of SRS. The utilization of SRS improves organization execution on all fronts—SC cutthroat execution, consumer loyalty and monetary results.

Mediation Analysis

The total effects are significant for few paths (Ch_Cap→R_SP, Ch_Qual→R_SP, Ch_Qual→ST_SD). These paths were otherwise not found significant in the hypotheses testing.

With the considered indirect and total effects, we find that H₂ and H₃ are now supported with Ch_Cap→ST_SD (significant at p-value = .07) and Ch_Qual→ST_SD (appearing as significant at p-value = 0.018).

As a contingent relationship is a relation between two variables where one is a consequence or contingent of the other, we conclude that there is a contingent relationship between Ch_Cap→R_SP (risk of constrained capacity and SC Competitive performance) and Ch_Qual→R_SP (risk of delivery quality and SC competitive performance) and that the use of SRS mediates the relationship.