This review investigates the connections between chosen supply chain risks (SCR), utilization of services redesign strategies (SRS) and the effect on company execution. An empirical framework is proposed where the presence of SCRs influences the use of SRS to handle peak and lean demand has been explored. The design is to observationally analyse the connections among SCR, including request inconstancy, compelled limit, nature of conveyance, utilization of different SRS and effect of something similar on organization execution. This review depends on an overview of 439 organizations in India addressing 10 gatherings of administration businesses utilizing organized condition demonstrating (structural equation modelling (SEM)) techniques. The initial survey construct was piloted with 20 expert respondents by personal interviews, based on the inputs the final questionnaire was developed and launched to 2000 services organizations of large, medium, and small sizes in respective service industry groups in India. The outcomes show that the presence of demand variability SCR impacts utilization of SRS. The presence of the hazard of capacity constrained and nature of conveyance do not impact the utilization of SRS. There is an unexpected connection between SCR and company execution which is interceded utilizing SRS. SRS assume fluctuated part in improving various sorts of execution (SC competition, customer satisfaction and financial performance).
AbrahamssonM., & BregeS. (1997). Structural changes in the supply chain. International Journal of Logistics Management, 8(1), 35–44. 10.1108/09574099710805574
2.
AgariyaA. K., & TikoriaJ. (2017). CRM scale development for enhancing business excellence in the Indian airline sector. International Journal of Business Excellence, 12(4), 537–557. 10.1504/IJBEX.2017.085017
3.
AgarwalA., ShankarR., & TiwariM. K. (2006). Production, manufacturing and logistics modelling the metrics of lean, agile and leagile supply chain: An ANP-based approach. European Journal of Operational Research, 173(1), 211–225. 10.1016/j.ejor.2004.12.005
4.
AhmedS., & AmagohF. (2014). Process analysis and capacity utilization in a dental clinic in Kazakhstan. Competitiveness Review, 24(4), 347–356. 10.1108/CR-04-2013-0037
5.
AlhawariO. I., SüerG. A., KhurrumM., & BhuttaS. (2021). Operations performance considering demand coverage scenarios for individual products and products families in supply chains. International Journal of Production Economics, 233. 10.1016/j.ijpe.2020.108012
6.
AliR., & DeifA. (2016). Assessing leanness level with demand dynamics in a multi-stage production system. Journal of Manufacturing Technology Management, 27(5), 614–639. 10.1108/JMTM-08-2015-0064
7.
AmbulkarS., BlackhurstJ., & GraweS. (2015). Firm’s resilience to supply chain disruptions: Scale development and empirical examination. Journal of Operations Management, 33–34(1), 111–122. 10.1016/j.jom.2014.11.002
8.
AndersonE. G., & MorriceD. J. (1999, December5–8). A simulation model to study the dynamics in a service-oriented supply chain [Conference session]. Proceedings of the Winter Simulation Conference, Phoenix, AZ, USA.
9.
Anning-DorsonT. A. (2017). Moderation-mediation effect of market demand and organization culture on innovation and performance relationship. Marketing Intelligence and Planning, 35(2), 222–242. 10.1108/MIP-04-2016-0066
10.
AntierensA., BeeckmanD., VerhaegheS., MynyD., & HeckeA. V. (2018). How much of Toyota’s philosophy is embedded in health care at the organisational level? A review. Journal of Nursing Management, 26(4), No., 348–357. 10.1111/jonm.12555
11.
Arias ArandaD. A. (2003). Service operations strategy, flexibility and performance in engineering consulting firms. International Journal of Operations and Production Management, 23(11), 1401–1421. 10.1108/01443570310501907
12.
ArmisteadC., & ClarkG. (1994). The ‘coping’ capacity management strategy in services and the influence on quality performance. International Journal of Service Industry Management, 5(2), 5–22. 10.1108/09564239410057654
13.
AronssonH., AbrahamssonM., & SpensK. (2011). Developing lean and agile health care supply chains. Supply Chain Management, 16(3), 176–183. 10.1108/13598541111127164
14.
AsianS., PoolJ. K., NazarpourA., & TabaeeianR. A. (2019). On the importance of service performance and customer satisfaction in third-party logistics selection: An application of Kano model. Benchmarking, 26(5), 1550–1564. 10.1108/BIJ-05-2018-0121
15.
AuramoJ., & Ala-RiskuT. (2005). Challenges for going downstream. International Journal of Logistics Research and Applications, 8(4), 333–345. 10.1080/13675560500407358
16.
AveryS. L., & SwaffordP. M. (2009). Social capital impact on service supply chains. Journal of Service Science, 2(2), 9–16. 10.19030/jss.v2i2.4282
17.
BagS., WoodL. C., XuL., DhamijaP., & KayikciY. (2020). Big Data analytics as an operational excellence approach to enhance sustainable supply chain performance. Resources, Conservation and Recycling, 153. 10.1016/j.resconrec.2019.104559
18.
BagozziR. P., & YiY. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. 10.1007/BF02723327
19.
BalaP. K. (2012). Improving inventory performance with clustering-based demand forecasts. Journal of Modelling in Management, 7(1), 23–37. 10.1108/17465661211208794
20.
BandyopadhyayN. (2018). Whether service quality determinants and customer satisfaction influence loyalty: A study of fitness services. International Journal of Business Excellence, 15(4), 520–535.
21.
BarveA. (2011). Impact of supply chains agility on customer satisfaction. In 2010 international conference on e-business, management and economics (Vol 3, pp. 325–329). IACSIT Press.
22.
BaskA. H., TinniläM., & RajahonkaM. (2010). Matching service strategies, business models and modular business processes. Business Process Management Journal, 16(1), 153–180. 10.1108/14637151011017994
23.
BasuP., & K. DanP. K. (2014). Capacity augmentation with VSM methodology for lean manufacturing. International Journal of Lean Six Sigma, 5(3), 279–292. 10.1108/IJLSS-07-2013-0036
24.
BerryD., & NaimM. M. (1996). Quantifying the relative improvements of redesign strategies in a PC supply chain. International Journal of Production Economics, 46, 181–196.
25.
BirkelH. S., & HartmannE. (2020). Internet of Things: The future of managing supply chain risks. Supply Chain Management, 25(5), 535–548. 10.1108/SCM-09-2019-0356
26.
Boon-IttS., & PongpanaratC. (2011). Measuring service supply chain management processes: The application of the Q-sort technique. International Journal of Innovation, Management Technology, 2(3), 217.
27.
BoseD., ChatterjeeA. K., & BarmanS. (2018). Capacity decision under preset service level and process flexibility: A combined application of genetic algorithm and sample-based optimization. Journal of Modelling in Management, 13(3), 755–772. 10.1108/JM2-08-2017-0086
28.
BrinkS. V. D., KleijnR., SprecherB., & TukkerA. (2020). Identifying supply risks by mapping the cobalt supply chain. Resources, Conservation and Recycling, 156, 1–11.
29.
BrownA. O., LeeH. L., & PetrakianR. (2000). Xilinx improves its semiconductor supply chain using product and process postponement. Interfaces, 30(4), 65–80. 10.1287/inte.30.4.65.11650
30.
BrozovicD., NordinF., & KindströmD. (2016). Service flexibility: Conceptualizing value creation in service. Journal of Service Theory and Practice, 26(6), 868–888. 10.1108/JSTP-09-2014-0219
31.
BurbidgeJ. L. (1961). The new approach to production. Production Engineering, 40(12), 769–784.
32.
CachonG. P., & FisherM. (2000). Supply chain inventory management and the value of shared information. Management Science, 46(8), 1032–1048. 10.1287/mnsc.46.8.1032.12029
33.
CanelC., RosenD., & AndersonE. A. (2000). Just-in-Time is not just for manufacturing: A services perspective. Industrial Management and Data Systems, 100(2), 51–60. 10.1108/02635570010286104
34.
CarvalhoH., BarrosoA. P., MachadoV. H., AzevedoS., & Cruz-MachadoV. (2012). Supply chain redesign for resilience using simulation. Computers and Industrial Engineering, 62(1), 329–341. 10.1016/j.cie.2011.10.003
35.
ChariF., NgcamuB. S., & NovukelaC. (2020). Supply chain risks in humanitarian relief operations: A case of Cyclone Idai relief efforts in Zimbabwe. Journal of Humanitarian Logistics and Supply Chain Management, 11(1), 29–45. 10.1108/JHLSCM-12-2019-0080
36.
ChaseR. B., & EriksonW. J. (1989). The service factory. Academy of Management Executive (1987–1989), 2(3), 191–196.
37.
ChenJ., SohalA. S., & PrajogoD. I. (2013). Supply chain operational risk mitigation: A collaborative approach. International Journal of Production Research, 51(7), 2186–2199. 10.1080/00207543.2012.727490
38.
ChenM., HuQ., & WeiH. (2017). Interaction of after-sales service provider and contract type in a supply chain. International Journal of Production Economics, 193(November), 514–527. 10.1016/j.ijpe.2017.08.014
39.
ChopraS., & SodhiM. S. (2014). Reducing the risk of supply chain disruptions. MIT Sloan Management Review, 55(3), 72–80.
40.
ChouC. H. (2009). The relationship between customer participation and customer relationship strength. NTU Management Review, 19(2), 233–262.
41.
ChristopherM., & PeckH. (2004). Building the resilient supply chain. International Journal of Logistics Management, 15(2), 1–14. 10.1108/09574090410700275
42.
ChristopherM., & TowillD. (2001). An integrated model for the design of agile supply chains. International Journal of Physical Distribution and Logistics Management, 31(4), 235–246. 10.1108/09600030110394914
43.
ClarksonA. H., StoneM. A., & SteeleM. J. (1990). Competitive strategies in banking services: Implications of segmentation analysis of consumers by age groups. International Journal of Service Industry Management, 1(2), 67–77. 10.1108/EUM0000000002804
CollinJ., ElorantaE., & HolmströmJ. (2009). How to design the right supply chains for your customers. Supply Chain Management, 14(6), 411–417. 10.1108/13598540910995174
46.
CerabonaT., LaurasM., FaugèreL., GittoJ. P., MontreuilB., & BenabenF. (2020). A physics-based approach for managing supply chain risks and opportunities within its performance framework. IFIP Advances in Information and Communication Technology 4.0, Working Conference on Virtual Enterprises, PRO-VE, 418–427. 10.1007/978-3-030-62412-5_34
47.
ChuC. Y., ParkK., & KremerG. E. (2020). A global supply chain risk management framework: An application of text-mining to identify region-specific supply chain risks. Advanced Engineering Informatics, 45. 10.1016/j.aei.2020.101053
48.
CovaB., & SalleR. (2008). Marketing solutions in accordance with the S-D logic: Co-creating value with customer network actors. Industrial Marketing Management, 37(3), 270–277. 10.1016/j.indmarman.2007.07.005
49.
CroxtonK. L., LambertD. M., García-DastugueS. J., & RogersD. S. (2002). The demand management process. International Journal of Logistics Management, 13(2), 51–66. 10.1108/09574090210806423
50.
DavilaT., & WoutersM. (2007). An empirical test of inventory, service and cost benefits from a postponement strategy. International Journal of Production Research, 45(10), 2245–2267. 10.1080/00207540600725002
51.
DelerisL. A., & ErhunF. (2007). Risk management in a supply network: A case study based on engineering risk analysis concepts. In KempfK., KeskinocakP. & UzsoyR. (Eds.), Kluwer International Series in Operations Research and Management Science: Handbook of production planning. Kluwer Academic Publishers.
52.
DobrzykowskyD., SabooriV. D., HongP., & KimS. C. (2014). A structured analysis of operations and SCM research in healthcare (1982–2011). International Journal of Production Economics, 147(B), 514–530.
53.
EsperT. L., EllingerA. E., StankT. P., FlintD. J., & MoonM. (2010). Demand and supply integration: A conceptual framework of value creation through knowledge management. Journal of the Academy of Marketing Science, 38(1), 5–18. 10.1007/s11747-009-0135-3
54.
FarmanovaE., BakerG. R., & CohenD. (2019). Combining integration of care and a population health approach: A scoping review of redesign strategies and interventions, and their impact. International Journal of Integrated Care, 19(2), 5. 10.5334/ijic.4197
55.
FeitzingerE., & LeeH. L. (1997). Mass customization at Hewlett Packard: The power of postponement. Harvard Business Review, 75, 116–124.
56.
FergusonP. R., & FergusonG. J. (1994). The structure–conduct–performance paradigm. In Industrial economics (pp. 13–37). Palgrave.
57.
FieldJ. M., VictorinoL., BuellR. W., DixonM. J., Meyer GoldsteinS. M., MenorL. J., PullmanM. E., RothA. V., SecchiE., & ZhangJ. J. (2018). Service operations: What’s next?Journal of Service Management, 29(1), 55–97. 10.1108/JOSM-08-2017-0191
58.
FisherM. L. (1997). What is the right supply chain for your product: A simple framework will help you figure out the answer. Harvard Business Review, 75, 105–117.
59.
FornellC., & LarckerD. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(1), 382–388
60.
ForresterJ. W. (1968). Industrial dynamics: After the first decade. Management Science, 14(7), 398–415. 10.1287/mnsc.14.7.398
61.
GacengaF., Cater-SteelA., TanW. G., & TolemanM. (2011). It service management: Towards a contingency theory of performance measurement. In Proceedings of the International Conference on Information Systems (ICIS 2011). ICIS.
62.
García-DastugueS. J. G., & LambertD. M. (2003). Internet-enabled coordination in the supply chain. Industrial Marketing Management, 32(3), 251–263. 10.1016/S0019-8501(02)00269-9
63.
GerdoçiB., BortoluzziG., & DibraS. (2018). Business model design and firm performance: Evidence of interactive effects from a developing economy. European Journal of Innovation Management, 21(2), 315–333. 10.1108/EJIM-02-2017-0012
64.
GilmourP., BorgG., DuffyP. A., JohnstonN. D., LimbekB. E., & ShawM. R. (1994). Customer service: Differentiating by market segment. International Journal of Physical Distribution and Logistics Management, 24(4), 18–23. 10.1108/09600039410757603
65.
GodsellJ., DiefenbachT., ClemmowC., TowillD., & ChristopherM. (2011). Enabling supply chain segmentation through demand profiling. International Journal of Physical Distribution and Logistics Management, 41(3), 296–314. 10.1108/09600031111123804
66.
GreenL. V., KolesarP. J., & WhittW. (2007). Coping with time-varying demand when setting staffing requirements for a service system. Production and Operations Management, 16(1), 13–39. 10.1111/j.1937-5956.2007.tb00164.x
67.
HairJ. F.Jr., BlackW. C., BabinB. J., AndersonR. E., & TathamR. L. (2014). Multivariate data analysis (7th ed.). Pearson Education Ltd.
68.
HairJ. F., HultG. T. M., RingleC. M., SarstedtM., & ThieleK. O. (2017). Mirror, mirror on the wall: A comparative evaluation of composite-based SEM methods. Journal of the Academy of Marketing Science, 45(5), 616–632. 10.1007/s11747-017-0517-x
69.
HançerlioğullarıG., ŞenA., & AktunçE. A. (2016). Demand uncertainty and inventory turnover performance: An empirical analysis of the US retail industry. International Journal of Physical Distribution and Logistics Management, 46(6/7), 681–708. 10.1108/IJPDLM-12-2014-0303
70.
HasaniA., & MokhtariH. (2018). Redesign strategies of a comprehensive robust relief network for disaster management. Socio-Economic Planning Sciences, 64, 92–102. 10.1016/j.seps.2018.01.003
71.
HeckmannI., ComesT., & NickelS. (2015, April). A critical review on supply chain risk: Definition, measure and modelling. Omega, 52, 119–132. 10.1016/j.omega.2014.10.004
72.
HeikkiläJ. (2002). From supply to demand chain management: Efficiency and customer satisfaction. Journal of Operations Management, 20(6), 747–767. 10.1016/S0272-6963(02)00038-4
73.
HeiskalaM., PaloheimoK. S., & TiihonenJ. (2005). Mass customisation of services: Benefits and challenges of configurable services. In Frontiers of e-business research. Tampere, (pp. 206–221).
74.
HillA., CuthbertsonR., LakerB., & BrownS. (2017). Service fitness ladders: Improving business performance in low cost and differentiated markets. International Journal of Operations and Production Management, 37(10), 1266–1303. 10.1108/IJOPM-03-2016-0142
75.
Hossan ChowdhuryM. M. H., & QuaddusM. A. (2021). Supply chain sustainability practices and governance for mitigating sustainability risk and improving market performance: A dynamic capability perspective. Journal of Cleaner Production, 278, 2021. 10.1016/j.jclepro.2020.123521
76.
HuangB., & XueX. (2012). An application analysis of cluster supply chain: A case study of JCH. Kybernetes, 41(1), 254–280.
77.
HübnerA. H., KuhnH., & SternbeckM. G. (2013). Demand and supply chain planning in grocery retail: An operations planning framework. International Journal of Retail and Distribution Management, 41(7), 512–530. 10.1108/IJRDM-05-2013-0104
78.
IvanovD., & DolguiA. (2021). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning and Control, 32(9), 775–788. 10.1080/09537287.2020.1768450
79.
JajjaM. S. S., ChathaK. A., & FarooqS. (2018). Impact of supply chain risk on agility performance: Mediating role of supply chain integration. International Journal of Production Economics, 205, 118–138. 10.1016/j.ijpe.2018.08.032
80.
JohanssonP. (2006). Adapting manufacturing strategy to industrial after-sales service operations [PhD Dissertation]. Institutionen för produktionsekonomi. Linköpings Universitet. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-7026
81.
KaipiaR., KorhonenH., & HartialaH. (2006). Planning nervousness in a demand supply network: An empirical study. International Journal of Logistics Management, 17(1), 95–113. 10.1108/09574090610663455
82.
KandampullyJ. (2000). The impact of demand fluctuation on the quality of service: A tourism industry example. Managing Service Quality, 10(1), 10–19. 10.1108/09604520010307012
83.
KayvanfarV., Moattar HusseiniS. M. M., NengShengZ., KarimiB., & SajadiehM. S. (2019). A practical supply–demand hub in industrial clusters: A new perspective. Management Research Review, 42(1), 68–101. 10.1108/MRR-03-2017-0094
84.
KimE. J., & PomirleanuN. (2021). Effective redesign strategies for tourism management in a crisis context: A theory-in-use approach. Tourism Management, 87. 10.1016/j.tourman.2021.104359
85.
KlassenK. J., & RohlederT. R. (2001). Combining operations and marketing to manage capacity and demand in services. Service Industries Journal, 21(2), 1–30. 10.1080/714005019
86.
KumarR. (2017). Supply chain performance of sugar industry using regression analysis. International Journal of Engineering Technology Management and Applied Sciences, 5(7), 744–751.
87.
KumarR. (2020). E-applications for managing trans-logistics activities in sugar supply chain in North India. International Journal of Asian Business and Information Management, 11(1), 92–106. 10.4018/IJABIM.2020010106
88.
KumarR., AgrawalR., & SharmaV. (2013). e-applications in Indian agro-food supply chain: Relationship among enablers. Global Business Review, 14(4), 711–727. 10.1177/0972150913501610
89.
KumarR., AgrawalR., & SharmaV. (2014). Barriers to e-Application in agrifood supply chain. In WangJ. (Ed.), Encyclopedia of business analytics and optimization (pp. 235–248). IGI Global. 10.4018/9781-4666-5202-6.ch022
90.
KumarR., AgrawalR., & SharmaV. (2015). IT Enablement in sugar supply chain: An approach for farmers. International Journal of Business Performance and Supply Chain Modelling, 7(4), 360–381. 10.1504/IJBPSCM.2015.073770
91.
KumarR., & KansaraS. (2018). Information technology barriers in Indian sugar supply chain: An AHP and fuzzy AHP approach. Benchmarking, 25(7), 1978–1991. 10.1108/BIJ-01-2017-0004
92.
KwakK., & KimW. (2016). Effect of service integration strategy on industrial firm performance. Journal of Service Management, 27(3), 391–430. 10.1108/JOSM-03-2014-0088
93.
LancioniR. A., SmithM. F., & OlivaT. A. (2000). The role of the Internet in supply chain management. Industrial Marketing Management, 29(1), 45–56. 10.1016/S0019-8501(99)00111-X
94.
LarssonR., & BowenD. E. (1989). Organization and customer: Managing design and coordination of services. Academy of Management Review, 14(2), 213–233. 10.5465/amr.1989.4282099
95.
LazzariniS. G., ChaddadF. R., & CookM. L. (2008). Integrating supply chain and network analyses: The study of net chains. Journal on Chain and Network Science, 1(1), 7–22.
96.
LeeJ., & MillerD. (1996). Strategy, environment and performance in two technological contexts: Contingency theory in Korea. Organization Studies, 17(5), 729–750. 10.1177/017084069601700502
97.
LeeH. L. (2002). Aligning supply chain strategies with product uncertainties. California Management Review, 44(3), 105–119. 10.2307/41166135
98.
LeeH. L. (2004). The triple: A supply chain. Harvard Business Review, 82(10), 102–113.
99.
LeeH. L., PadmanabhanV., & WhangS. (1997). Information distortion in a supply chain: The bullwhip effect. Management Science, 43(4), 546–558. 10.1287/mnsc.43.4.546
100.
LeeH. L., & WhangS. (2001). E-business and supply chain integration Stanford Global SCM Forum, W2(v). https://www.gsb.stanford.edu/faculty-research/publications/e-business-supply-chain-integration
101.
LiuH., HuangQ., WeiS., & HuangL. (2015). The impacts of IT capability on internet enabled supply and demand process integration, and firm performance in manufacturing and services. International Journal of Logistics Management, 26(1), 172–194. 10.1108/IJLM-11-2013-0132
102.
LiuC. L., ShangK. C., LirnT. C., LaiK. H., & LunY. H. V. (2018). Supply chain resilience, firm performance, and management policies in the liner shipping industry. Transportation Research Part A, 110, 202–219. 10.1016/j.tra.2017.02.004
103.
LiuX., ArthanariT., & ShiY. (2021). Leverage risks for supply chain robustness against corruption. Industrial Management and Data Systems, 121(7), 1496–1521. 10.1108/IMDS-10-2020-0587
104.
LiuX., ZhaoH., & ZhaoX. (2018). Absorptive capacity and business performance: The mediating effects of innovation and mass customization. Industrial Management and Data Systems, 118(9), 1787–1803. 10.1108/IMDS-09-2017-0416
105.
LovellA., SawR., & StimsonJ. (2005). Product value-density: Managing diversity through supply chain segmentation. International Journal of Logistics Management, 16(1), 142–158. 10.1108/09574090510617394
106.
MadhaniP. M. (2018). Building a customer-focused culture in organisations: Developing 7Cs model. International Journal of Business Excellence, 16(2), 199–232
107.
MárcioA. T. T., ScavardaL. F., PiresS. R. I., Paula CerynoA., & KatjaK. N. (2014, December). A multi-tier study on supply chain flexibility in the automotive industry. International Journal of Production Economics, 158, 91–105.
108.
Mason-JonesR., NaylorB., & TowillD. R. (2000a). Engineering the leagile supply chain. International Journal of Agile Management Systems, 2(1), 54–61. 10.1108/14654650010312606
109.
Mason-JonesR., NaylorB., & TowillD. R. (2000b). Lean, agile or leagile? Matching your supply chain to the marketplace. International Journal of Production Research, 38(17), 4061–4070. 10.1080/00207540050204920
110.
McCarthyI. P., PittL. F., & BerthonP. R. (2010). Service customization through dramaturgy. In FogliattoF. S. & MassG. J. C. d. (Eds.), Customization: Engineering and managing global operations (pp. 43–64). Springer.
111.
MentzerJ. T., FlintD. J., & TomasH. G. (2001). Logistics service quality as a segment-customized process. Journal of Marketing, 65(4), 82–104. 10.1509/jmkg.65.4.82.18390
112.
MichaelK., & McCathieL. (2005). The pros and cons of RFID in supply chain management. In Proceedings of the International Conference on Mobile Business (ICMB’05) (pp. 623–629).
113.
MunirM., JajjaM. S. S., ChathaK. A., & FarooqS. (2020). Supply chain risk management and operational performance: The enabling role of supply chain integration. International Journal of Production Economics, 227. 10.1016/j.ijpe.2020.107667
114.
NandiM. L., NandiS., MoyaH., & KaynakH. (2020). Blockchain technology-enabled supply chain systems and supply chain performance: A resource-based view. Supply Chain Management, 25(6), 841–862. 10.1108/SCM-12-2019-0444
115.
NazR. (2009). E-governance for improved public service delivery in Fiji. Journal of Service Science and Management, 2(3), 190–203. 10.4236/jssm.2009.23023
116.
NorthcraftG. B., & ChaseR. B. (1985). Managing service demand at the point of delivery. Academy of Management Review, 10(1), 66–75. 10.5465/amr.1985.4277348
117.
NunnallyJ. C., & BernsteinI. H. (1994). Psychometric theory (3rd ed). McGraw-Hill Inc.
118.
OhmoriS., ArakaneT., Ruiz-TorresA., & YoshimotoK. (2021). The multiple supply chain design problem: Integration of multiple configurations based on product, customer, and supply chain characteristics. International Journal of Information Systems and Supply Chain Management, 14(3), 39–62. 10.4018/IJISSCM.2021070103
119.
ParkJ., LeeB. K., & LimS. (2021). Quality-driven profitability analysis in service operations. Journal of the Operational Research Society, 72(7), 1578–1590. 10.1080/01605682.2020.1740624
120.
PekkarinenS., & UlkuniemiP. (2008). Modularity in developing business services by platform approach. International Journal of Logistics Management, 19(1), 84–103. 10.1108/09574090810872613
121.
PerdikakiO., KostamisD., & SwaminathanJ. M. (2016). Timing of service investments for retailers under competition and demand uncertainty. European Journal of Operational Research, 254(1), 188–201. 10.1016/j.ejor.2016.03.023
122.
PettitT. J., CroxtonK. L., & FikselJ. (2013). Ensuring supply chain resilience: Development and implementation of an assessment tool. Journal of Business Logistics, 34(1), 46–76. 10.1111/jbl.12009
123.
PineB. J.II, & GilmoreJ. H. (2000). Satisfaction, sacrifice, surprise: Three small steps create one giant leap into the experience economy. Strategy and Leadership, 28(1), 18–23. 10.1108/10878570010335958
124.
QaziA., DicksonA., QuigleyJ., & GaudenziB. (2018). Supply chain risk network management: A Bayesian belief network and expected utility-based approach for managing supply chain risks. International Journal of Production Economics, 196, 24–42. 10.1016/j.ijpe.2017.11.008
125.
RainbirdM. (2004). Demand and supply chains: The value catalyst. International Journal of Physical Distribution and Logistics Management, 34(3), 230–250.
126.
RajahonkaM., & BaskA. (2016). The development of outbound logistics services in the automotive industry: A logistics service provider’s view. International Journal of Logistics Management, 27(3), 707–737. 10.1108/IJLM-08-2012-0082
127.
RaoG. G., CrookM., & TillyerM. L. (2003). Pathology tests: Is the time for demand management ripe at last?Journal of Clinical Pathology, 56(4), 243–248. 10.1136/jcp.56.4.243
128.
RayS., & JewkesE. M. (2004). Customer lead time management when both demand and price are lead time sensitive. European Journal of Operational Research, 153(3), 769–781. 10.1016/S0377-2217(02)00655-0
129.
RaykovT. (1997). Estimation of composite reliability for congeneric measures. Applied Psychological Measurement, 21(2), 173–184. 10.1177/01466216970212006
130.
RigdonE. E., SarstedtM., & RingleC. M. (2017). On comparing results from CB-SEM and PLS-SEM: Five perspectives and five recommendations. Marketing ZFP, 39(3), 4–16. 10.15358/0344-1369-2017-3-4
131.
RojaM. P. (2019). Excellence of social media: A quality assessment. InternationalJournal of Business Excellence, 1(1). 10.1504/IJBEX.2019.10016981
132.
RothA. V., & MenorL. J. (2003). Insights into service operations management: A Research agenda. Production and Operations Management, 12(2), 145–164. 10.1111/j.1937-5956.2003.tb00498.x
133.
RuekertR. W., Walker Jr.O. C., & RoeringK. J. (1985). The organization of marketing activities: A contingency theory of structure and performance. Journal of Marketing, 49(1), 13–25. 10.1177/002224298504900102
134.
RustT., SaeedK., Bar-OnI., & PavlovO. (2013). Dynamic analysis of healthcare service delivery: Application of lean and agile, concepts (Vol. 1317). Worcester Polytechnic Institute.
135.
SahuA. K., SahuS. K., DattaS., & MahapatraS. S. (2015). Supply chain flexibility assessment and decision-making: A fuzzy intelligent approach. International Journal of Business Excellence, 8(6), 675–699. 10.1504/IJBEX.2015.072305
136.
SalehzadehR., PoolJ. K., MohseniA. M., & TahaniG. (2017). The role of knowledge sharing and organizational agility. International Journal of Business Excellence, 11(3), 344–356. 10.1504/IJBEX.2017.081930
137.
SasserW. E. (1976, November–December). Match supply and demand in service industries. Harvard Business Review, 54, 133–140.
138.
SawikT. (2014). Optimization of cost and service level in the presence of supply chain disruption risks: Single vs. multiple sourcing. Computers and Operations Research, 51, 11–20. 10.1016/j.cor.2014.04.006
139.
ShaoX.-F., & JiJ.-H. (2008). Evaluation of postponement strategies in mass customization with service guarantees. International Journal of Production Research, 46(1), 153–171. 10.1080/00207540600844027
140.
SharmaR., ShishodiaA., KambleS., GunasekaranA., & BelhadiA. (2020). Agriculture supply chain risks and COVID-19: Mitigation strategies and implications for the practitioners. International Journal of Logistics Research and Applications, 1–27. 10.1080/13675567.2020.1830049
141.
SharmaS. K., BhatA., KumarV., & AgarwalA. (2017). Path analysis model for supply chain risk management. International Journal of Information Systems and Supply Chain Management, 10(2), 21–41. 10.4018/IJISSCM.2017040102
142.
SheeH., MiahS. J., FairfieldL., & PujawanN. (2018). The impact of cloud-enabled process integration on supply chain performance and firm sustainability: The moderating role of top management. Supply Chain Management, 23(6), 500–517. 10.1108/SCM-09-2017-0309
143.
ShemwellD. J., & CroninJ. J. (1994). Services marketing strategies for coping with demand/supply imbalances. Journal of Services Marketing, 8(4), 14–24. 10.1108/08876049410070691
144.
ShrafatF. D., & IsmailM. (2019). Structural equation modeling of lean manufacturing practices in a developing country context. Journal of Manufacturing Technology Management, 30(1), 122–145. 10.1108/JMTM-08-2017-0159
145.
ShuganS. M.2002, Chap. 19. Service marketing and management: Capacity as a strategic marketing variable. In WensleyR. & WeitzB. (Eds.), Handbook of marketing (pp. 484–512). SAGE Publications.
146.
ShuklaR. K., GargD., & AgarwalA. (2018). Modelling supply chain coordination for performance improvement using analytical network process-based approach. International Journal of Business Excellence, 14(1), 18–48. 10.1504/IJBEX.2018.088313
147.
ShuklaV., & NaimM. (2017). Detecting disturbances in supply chains: The case of capacity constraints. International Journal of Logistics Management, 28(2), 398–416. 10.1108/IJLM-12-2015-0223
148.
SilvaC. D., SousaP. S. A., MoreiraM. R. A., & AmaroG. M. (2020, July). Do supply chain management practices influence firm performance? A meta-analytical approach. International Journal of Information Systems and Supply Chain Management, 13(3), 1–22. 10.4018/IJISSCM.2020070101
149.
SinghN. P. (2020). Managing environmental uncertainty for improved firm financial performance: The moderating role of supply chain risk management practices on managerial decision making. International Journal of Logistics Research and Applications, 23(3), 270–290. 10.1080/13675567.2019.1684462
150.
SinghR., SandhuH. S., MetriB. A., & KaurR. (2013). Modeling supply chain performance: A structural equation approach. International Journal of Information Systems and Supply Chain Management, 6(4), 18–41. 10.4018/ijisscm.2013100102
151.
SiragherN. (1999). Enterprise resource planning: The end of the line for the sales automation market and a major step towards sales process definition standards?Supply Chain Management, 4(1), 11–13. 10.1108/13598549910254780
152.
Stentoft ArlbjørnJ., Vagn FreytagP., & de HaasH. (2011). Service supply chain management: A survey of lean application in the municipal sector. International Journal of Physical Distribution and Logistics Management, 41(3), 277–295. 10.1108/09600031111123796
153.
SunilD. T., DevadasanS. R., ThilakV. M. M., & VinodM. (2018). Computer aided design-based assembly sequence planning: A next stage in agile manufacturing research. International Journal of Business Excellence, 16(4), 454–477. 10.1504/IJBEX.2018.096214
154.
TaylorA., & TaylorM. (2014). Factors influencing effective implementation of performance measurement systems in small and medium-sized enterprises and large firms: A perspective from Contingency Theory. International Journal of Production Research, 52(3), 847–866. 10.1080/00207543.2013.842023
155.
ThakkarJ., KandaA., & DeshmukhS. G. (2008). Interpretive structural modeling (ISM) of IT-enablers for Indian manufacturing SMEs. Information Management and Computer Security, 16(2), 113–136. 10.1108/09685220810879609
156.
TowillD. R. (1996). Time compression and supply chain management – A guided tour. Supply Chain Management, 1(1), 15–27. 10.1108/13598549610799040
157.
TowillD. R., & McCullenP. (1999). The impact of agile manufacturing on supply chain dynamics. International Journal of Logistics Management, 10(1), 83–96. 10.1108/09574099910805879
158.
Truong QuangH. T., & HaraY. (2018). Risks and performance in supply chain: The push effect. International Journal of Production Research, 56(4), 1369–1388. 10.1080/00207543.2017.1363429
159.
TsengM. L., WuK. J., ChiuA. S. F., LimM. K., & TanK. (2019). Service innovation in sustainable product service systems: Improving performance under linguistic preferences. International Journal of Production Economics, 203, 414–425. 10.1016/j.ijpe.2019.09.013
160.
TuQ., VonderembseM. A., & Ragu-NathanT. S. (2001). The impact of time-based manufacturing practices on mass customization and value to customer. Journal of Operations Management, 19(2), 201–217. 10.1016/S0272-6963(00)00056-5
161.
Van HoekR. I. V. (1998). Reconfiguring the supply chain to implement postponed manufacturing. International Journal of Logistics Management, 9(1), 95–110. 10.1108/09574099810805771
162.
VananyI., ZailaniS., & PujawanN. (2009). Supply chain risk management: Literature review and future research. International Journal of Information Systems and Supply Chain Management, 2(1), 16–33. 10.4018/jisscm.2009010102
163.
ViswanadhamN., DesaiV., & GaonkarR. (2005). Bullwhip effect in integrated manufacturing and service networks [Conference session]. Proceedings of the 2005 IEEE International Conference on Robotics and Automation Barcelona, Spain.
164.
VossM. D., CalantoneR. J., & KellerS. B. (2005). Internal service quality: Determinants of distribution center performance. International Journal of Physical Distribution and Logistics Management, 35(3), 161–176. 10.1108/09600030510594558
165.
WadongoB., & Abdel-KaderM. (2014). Contingency theory, performance management and organisational effectiveness in the third sector: A theoretical framework. International Journal of Productivity and Performance Management, 63(6), 680–703. 10.1108/IJPPM-09-2013-0161
166.
WaltersD. (2006). Demand chain effectiveness—Supply chain efficiencies: A role for enterprise information management. Journal of Enterprise Information Management, 19(3), 246–261. 10.1108/17410390610658441
167.
WangM., AsianS., WoodL. C., & WangB. (2020). Logistics innovation capability and its impacts on the supply chain risks in the Industry 4.0 era. Modern Supply Chain Research and Applications, 2(2), 83–98. 10.1108/MSCRA-07-2019-0015
168.
WardP. T., McCreeryJ. K., RitzmanL. P., & SharmaD. (1998). Competitive priorities in operations management. Decision Sciences, 29(4), 1035–1046. 10.1111/j.1540-5915.1998.tb00886.x
169.
WernzC., Thakur WernzP. T., & PhusavatK. (2014). Service convergence and service integration in medical tourism. Industrial Management and Data Systems, 114(7), 1094–1106. 10.1108/IMDS-04-2014-0115
170.
WindY. (2001). The challenge of customerization of financial service. Communications of the ACM, 44(6), 39–44. 10.1145/376134.376153
171.
WuD. Y., & KatokE. (2006). Learning, communication, and the bullwhip effect. Journal of Operations Management, 24(6), 839–850. 10.1016/j.jom.2005.08.006
172.
WurzerT., & ReinerG. (2018). Evaluating the impact of modular product design on flexibility performance and cost performance with delivery performance as a moderator. International Journal of Operations and Production Management, 38(10), 1987–2008. 10.1108/IJOPM-03-2017-0152
173.
XuS., ZhangX., FengL., & YangW. (2020). Disruption risks in supply chain management: A literature review based on bibliometric analysis. International Journal of Production Research, 58(11), 3508–3526. 10.1080/00207543.2020.1717011
174.
YaghoubiN. M., & RigiF. (2017). Analysis of e-service quality from the management perspective: An empirical study on e-government services. International Journal of Business Excellence, 11(1), 58–71. 10.1504/IJBEX.2017.080605
175.
YapL. L., & TanC. L. (2012). The effect of service supply chain management practices on the public healthcare organizational performance. International Journal of Business and Social Science, 3(16), 216–224.
176.
YusufY. Y., GunasekaranA., MusaA., DaudaM., El-BerishyN. M. E., & CangS. (2014). A relational study of supply chain agility, competitiveness and business performance in the oil and gas industry. International Journal of Production Economics, 147, 531–543. 10.1016/j.ijpe.2012.10.009
177.
YeY., HuoB., ZhangM., WangB., & ZhaoX. (2018). The impact of modular designs on new product development outcomes: The moderating effect of supply chain involvement. Supply Chain Management, 23(5), 444–458. 10.1108/SCM-01-2018-0021
178.
YerpudeS., SinghalT. K., & RathodH. R. K. (2019). Achieving service process excellence with connected customer: A winning approach. International Journal of Information Systems and Supply Chain Management, 12(1), 66–80. 10.4018/IJISSCM.2019010104
179.
YoungdahlW. E., & LoombaA. P. S. (2000). Service-driven global supply chains. International Journal of Service Industry Management, 11(4), 329–347. 10.1108/09564230010355368
180.
ZhangR., LiM., & LiuB. (2019). Pricing decisions and provider choice on extended warranty service in supply chain. International Journal of Information Systems and Supply Chain Management, 12(4), 55–71. 10.4018/IJISSCM.2019100104
181.
ZhangM., ZhaoX., LylesM. A., & GuoH. (2015). Absorptive capacity and mass customization capability. International Journal of Operations and Production Management, 35(9), 1275–1294. 10.1108/IJOPM-03-2015-0120
