Supply chain viability: conceptualization, measurement, and nomological validation

Ruel, Salomée; El Baz, Jamal; Ivanov, Dmitry; Das, Ajay Article — Published Version Supply chain viability: conceptualization, measurement, and nomological validation Annals of Operations Research Provided in Cooperation with: Springer Nature Suggested Citation: Ruel, Salomée; El Baz, Jamal; Ivanov, Dmitry; Das, Ajay (2021) : Supply chain viability: conceptualization, measurement, and nomological validation, Annals of Operations Research, ISSN 1572-9338, Springer US, New York, NY, Vol. 335, Iss. 3, pp. 1107-1136, https://doi.org/10.1007/s10479-021-03974-9 This Version is available at: https://hdl.handle.net/10419/317742 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Annals of Operations Research (2024) 335:1107–1136 https://doi.org/10.1007/s10479-021-03974-9 1 3 ORIGINAL RESEARCH Supply chain viability: conceptualization, measurement, andnomological validation SaloméeRuel1 · JamalElBaz2· DmitryIvanov3 · AjayDas4 Accepted: 28 January 2021 / Published online: 8 March 2021 © The Author(s) 2021 Abstract Supply chain viability (SCV) is an emerging concept of growing importance in operations management. This paper aims to conceptualize, develop, and validate a measurement scale for SCV. SCV is first defined and operationalized as a construct, followed by content validation and item measure development. Data have been collected through three independent samplings comprising a total of 558 respondents. Both exploratory and confirmatory factor analyses are used in a step-wise manner for scale development. Reliability and validity are evaluated. A nomological model is theorized and tested to evaluate nomological validity. For the first time, our study frames SCV as a novel and distinct construct. The findings show that SCV is a hierarchical and multidimensional construct, reflected in organizational structures, organizational resources, dynamic design capabilities, and operational aspects. The findings reveal that a central characteristic of SCV is the dynamic reconfiguration of SC structures in an adaptive manner to ensure survival in the long-term perspective. This research conceptualizes and provides specific, validated dimensions and item measures for SCV. Practitioner directed guidance and suggestions are offered for improving SCV during the COVID-19 pandemic and future severe disruptions. Keywords Supply chain viability· COVID-19 pandemic· Measurement· Scale development· Second-order construct * Dmitry Ivanov [email protected] Salomée Ruel salomee.r[email protected] Ajay Das [email protected] 1 MOSI department - CSR excellence center, KEDGE Business School, Domaine de Luminy, Rue Antoine Bourdelle, 13009Marseille, France 2 Ibn Zohr University Agadir, ERETTLOG, Agadir, Morocco 3 Department ofBusiness andEconomics, Berlin School ofEconomics andLaw, 10825Berlin, Germany 4 Operations Management Group, N.P. Loomba Dept. ofManagement, Zicklin School ofBusiness, Baruch College, CUNY, One Bernard Baruch Way, NewYork, NY10010, USA 1108 Annals of Operations Research (2024) 335:1107–1136 1 3 1 Introduction Paraphrasing Charles Darwin’s central thesis, “It is not the most intellectual of the species that survives; it is not the strongest that survives; but the species that survives is the one that is able best to adapt and adjust to the changing environment in which it finds itself” (Megginson 1963). Yet Darwin envisaged change and adaptation over generations and eons—how would sudden catastrophic changes affect organisms? How would they adapt to ensure the immediate need for survival? In the context of supply chains (SC), the COVID-19 crisis has rendered sudden and catastrophic change in the business environment and beyond, impacting and disrupting operations and SCs in terms of scale, complexity, severity, and duration of impact. With the COVID-19 pandemic, some novel context has been unveiled which goes beyond an instantaneous event-driven understanding of disruptions and can be described as an SC crisis characterized by long and severe uncertainty of current and future conditions and entailing extensions toward SC viability. To survive or maintain a viable SC can be a challenge when faced with such ‘super disruptions’ that can radically change operational conditions over long durations (Ivanov 2020a; Singh etal. 2020; ElBaz and Ruel 2021; Jang etal. 2021). Despite remarkable progress in our understanding of the disruptions, the COVID-19 pandemic has revealed research gaps with regard to such superdisruptions. We posit that survival and adaptation in confronting such super-disruptive changes require a special property—the capability to survive, to remain viable—that is, SC Viability (SCV) (Ivanov 2021c). Supply chain viability (SCV) is an emerging concept of growing importance in operations management in times of COVID-19 pandemic and well recognized by practitioners (Ivanov 2020b). In recent decades, an increasing number of uncertainties and disruptions has stimulated researcher interest in the theme of SC resilience, a firm’s capability to recover from disruptions to meet customer demand, ensure target performance, and maintain operations in vulnerable environments (Dubey et al. 2019; Hosseini et al. 2019; Sawik 2020; Choi 2020; Azadegan and Dooley 2021). SC resilience has been viewed as the ability to bounce-back and recover towards an “old normal” (Fiksel 2006; Pettit etal. 2010) once disrupted. The COVID-19 pandemic is a very special kind of disruption (Gunessee and Subramanian 2020; Ivanov 2020a, b; Paul and Chowdhury 2020; Queiroz etal. 2020; Ivanov 2021b; Tang etal. 2021), and it has raised novel questions within a decision-making context which frequently go beyond the scope of SC resilience (Hosseini etal. 2020; Dolgui and Ivanov 2021; Ivanov 2021a). The COVID-19 pandemic has posed a new disruption context for firms, stimulating attempts to define new theoretical lens which overarch existing resilience capabilities (Chowdhury and Quaddus 2017). Craighead etal. (2020) use ten different theories— resource dependence theory, institutional theory, game theory, and others—to draw out research questions, offering ways for simultaneous transformation and resilience, i.e. transiliency (i.e., the ability to simultaneously restore some processes and change— often radically—others). Hosseini etal. (2020) elaborate on the open-system view and propose novel metrics to measure resilience in the open-system context. Wieland (2020) proposed a panarchy framework for SCs based on adaptive cycles linked across SC, political-economical, and planetary levels on scales of time, space, and meaning. Considering SC structures and processes to be reconfigurable (i.e., “fluid”), Wieland reinterprets the SC as a social–ecological system replacing a static view of SC management with a vision of “dancing the SC” which is in line with the 1109 Annals of Operations Research (2024) 335:1107–1136 1 3 structural dynamics control approach by Ivanov etal. (2010), reconfigurable SC framework by Dolgui etal. (2020), and the viable SC framework by Ivanov (2020b). In the emerging spirit of re-thinking and re-inventing SC management driven by the pandemic context, we posit that certain aspects of this pandemic-related context can be approached using the notion of SCV. Insituations where SCs were literally crumbling, the question no longer concerned bouncing back and recovering to some “normal” state, but rather how to adapt and survive in radically changed internal and external conditions. To address this and related questions, we build on a novel theoretical underpinning of SCV (Ivanov 2020b). “Viable Supply Chain (VSC) is a dynamically adaptable and structurally changeable value-adding network able to (i) react agilely to positive changes, (ii) be resilient to absorb negative events and recover after the disruptions, and (iii) survive at the times of long-term, global disruptions by adjusting capacities utilizations and their allocations to demands in response to internal and external changes in line with the sustainable developments to secure the provision of society and markets with goods and services in long-term perspective (Ivanov 2020b).” SCV can be viewed from an overarching adaptation perspective that extends the SC resilience notion of a closed-system, “bounce-back” view, with a viable, open SC system perspective incorporating “bounce-forward-and-adapt” options (see Fig.1).1 SCV is an emerging but increasingly recognized capability in industry, with practitioners valuing and using it as a critical resource in COVID-19 stricken business environments (Hofmann and Langner 2020). Examples abound in industry. With demand plummeting, Fig. 1 SC viability as an extended resilience perspective 1 Commonalities and differences between SC resilience and SCV can be illustrated in the following simple format. In an operational view, we are driving cars to get faster at some destinations. Strategically, we are using cars to stay mobile. If a car breaks down then it should be repaired. For the time of service, one can receive another car as a substitute. Then we receive the car back in an old, normal (or equivalent) state. This is a classical resilience profile “disruption—backup—recovery—old normal”. However, if the car cannot be repaired any more, we should adapt structurally. One can start using public transportation to stay mobile. Or one can purchase a new car. In both cases, the decisions to adapt are driven by the strategic objective to stay mobile rather than by the operational objective to get faster at some destination. And this is the viability profile: “disruption–performance and structure degradation—adaptation-to-survive (i.e. to ensure/secure some strategically important service; the mobility)—search for “new normal”—stabilization in the “new normal”—performance recovery”. 1110 Annals of Operations Research (2024) 335:1107–1136 1 3 companies such as LVMH (perfumes) and Skyrora (rockets) adapted to manufacturing hand sanitizer. The SC for sanitizers is very different from that for perfumes or rockets. Yet, these companies made the adaptation in record time, making rapid structural changes to their existing SCs and other areas. Similarly, faced with a sales decline of 90% in the China market, automotive manufacturer BYD Co. switched to making surgical masks, while Ford and GM quickly adapted to making ventilators, all at extremely short notice (Wade and Bjerkan 2020). However, an empirical examination of SCV is still missing in the literature. One reason for the lag between practice and theory could potentially be the lack of a validated scale for SCV. The purpose of the study is to theorize, operationalize, and develop an empirical measurement scale for SCV. A review of the extant literature reveals that a theoretically grounded, comprehensive conceptualization and measurement of SCV is lacking. Our study is the first to address this deficiency by undertaking an empirically driven study to develop and validate a hierarchical and multidimensional measurement scale for SCV. Churchill’s (1979, 1995) protocol is undertaken to develop a hierarchical and multidimensional measurement scale for SCV. We define and operationalize SCV, with subsequent content validation and reliability assessment. Scale psychometric properties are established, with convergent, discriminant, and nomological validity examinations using multiple samples. Our conceptualization, development, and nomological validation of a measurement scale for SCV make several, substantive contributions. First, our study enables the identification of methodical commonalities and differentiations of viability and resilience in order to frame SCV as a specific construct. Considering the nascent and rarely defined nature of the SCV concept, there appears an urgent need to clearly identify the dimensions of this complex construct. Second, our study represents a pioneering research initiative, providing the research community with the first empirically derived and validated scale for examining SCV as a distinct construct. Third, our findings reveal adaptability as the central perspective of SCV contributing to building the theory of SC management during a pandemic. The major concept of the viable SC (Ivanov 2020b) —adaptability as an ability to redesign the SC in the face of severe changes in its environment by relying on feedback mechanisms—is confirmed and extended. Our findings confirm that SCV is a hierarchical and multidimensional construct, which is reflected by organizational structures and resources and dynamic design capabilities. Finally, our research provides guidance to practitioners on SCV at a granular practice level, and confers on ways to improve SCV in COVID-19 pandemic times and future severe disruptions. The rest of the paper is structured as follows. Section 2 presents a review of closely related literature. Section 3 describes the scale development protocol and methodology, including scale psychometric properties. Section 4 discusses the results and associated implications. Section5 concludes the research with a discussion of study contributions and limitations, and a brief deliberation on opportunities for future research. 2 Literature review 2.1 Defining anddistinguishing supply chain viability The scale development process begins with construct definition and boundary delineation. We briefly trace the origins of systems viability and then transit to a discussion 1111 Annals of Operations Research (2024) 335:1107–1136 1 3 on SCV. The concept of system viability was first developed in ecology and biological systems (Aubin 1991) and cybernetics. The Viable System Model by Beer (1981) and ecology modelling perspectives from Aubin (1991) are inspirations for the emerging concept of SCV: they highlight the ability of a system to survive in a turbulent environment. By analogy, Ivanov and Dolgui (2020b) point out that SCs can be compared to the complex nature systems. Ivanov (2020b) sees SCV as “the ability to maintain itself and survive in a changing environment over a long period of time through a redesign of the structures and replanning of economic performance with long-term impacts.” He defines three major pillars of SCV, i.e., the viable SC model, a multi-structural view of SC viability, and an ecosystem of a viable SC. The viable SC model is based on the development of multiple, alternative structural network designs for supply–demand allocations during normal, disruptive and super disruptive times, and importantly, the establishment and control of adaptive mechanisms for transitions between these structural designs. The multi-struc- tural view decomposes viable SC into organizational, informational, process-functional, technological, and financial structures and resources, spanning various management and organizational perspectives. The ecosystem view of the viable SC entails major feedback cycles in SC network interactions with environment. A SCV based SC design would have the potential to rapidly serve new markets, and/or pivot to new SCs for new products for business survival imperatives. The Panera Bread chain, having lost about 50% of its largely indoors business to COVID-19, adapted to a new SC in order to offer staple groceries along the traditional soups and bread. Burger chain Fuddruckers sold toilet paper, gloves, and bleach at specific locations—products far removed from its regular fast food product line, requiring entirely different SC infrastructure (Taylor 2020). A firm with SCV capability could also tap non-traditional supply markets for its existing products, in order to meet disruption induced surges in demand, as well as compensate for sudden deficiencies in its regular SCs. Amazon turned to demand decline hit Lyft for warehouse and logistical staffing needs, with the latter directing its employees to Amazon positions (Statt 2020). Gathering the above discussion, SCV offers a means for the long-term maintenance of survivability under different and ever-changing environmental conditions. We operationalize SCV through its four primary dimensions of “Organizational structures and resources”, “Dynamic design capabilities”, “Time window”, and “Operational performance” following the SCV framework proposed in (Ivanov 2020b). 2.2 Supply chain viability dimensions In Fig.2, we summarize major SCV dimensions as posed in Ivanov (2020b). We use a triangulation of the SCV notion from (Ivanov 2020b) which is comprised of a multistructural viability view, Viable SC Model (Tables1 and 2), and SC ecosystem view (Fig.2). In their totality, these complementary parts of the SCV concept are comprehensively addressed in our study when developing and validating the SCV measurement scale. 1112 Annals of Operations Research (2024) 335:1107–1136 1 3 (a) (b) Fig. 2 Supply chain viability dimensions 1113 Annals of Operations Research (2024) 335:1107–1136 1 3 2.2.1 Organizational structures andresources forSCV Building on Ivanov’s (2020b) multi-structural view of SCV, Fig.2 offers a systemic view of organizational structure and resources for SCV based on the literature around the stability, robustness, resilience, and viability concepts. Ivanov and Dolgui (2020b) propose that the SCV concept is embedded in multiple structures and associated resources. 2.2.2 Dynamic design capabilities forSCV Another important aspect of SCV are dynamic design capabilities that facilitate fast transitions among (and testing of) alternative multiple structural SC designs, to meet volatile supply and demand conditions (Teece et al. 1997; Eisenhardt and Martin 2000; Winter 2003; Caniato etal. 2013). Ivanov (2020b) underlines the need for established and manageable adaptive mechanisms owing to the fact that it is nearly impossible to predict all possible future disruptions, and a-priori match respective SC designs to emergent scenarios. (c) (d) Fig. 2 (continued) 1114 Annals of Operations Research (2024) 335:1107–1136 1 3 Quick plug and play adaptive mechanisms such as dynamic design capabilities are useful. Figure 2 shows the factors and items included in dynamic design capabilities. 2.2.3 Time window aspect ofSCV As viability has the objective of both helping SCs to meet sustainability objectives (Queiroz etal. 2020) and survive in time over disruptions, the time window is long-term. Several ideas around “time” can be found in the literature, as presented in Fig.2. In the context of SCV, Ivanov and Dolgui (2020b) specified that “long-term” means “no fixed time window” when analyzing the SC. 2.2.4 Operational performance aspect ofSCV The literature also discusses operational tasks that, when undertaken, could improve SCV. Figure2 synthesizes these aspects using the Ivanov’s (2020b) framework of SC ecosystem. 3 Methodology We develop the SCV scale in four phases. Phase 1 operationalizes SCV using previous conceptualization and cited literature. Phase 2 engages with the instrument development process. We use in-depth interviews and discussions with functional experts to sort item measures, possibly find new dimensions/variables, strengthen literature based content Table 1 Demographic profile of field study respondents N Length Job position Industry Experience in SCM (years) I1 1h 07min Supply Chain Manager (freelance) Consulting 28 I2 46min Global distribution and Supply Chain project manager Pharmaceuticals 16 I3 59min Supply Chain Manager Telecom 23 I4 51min Supply Chain Manager Aeronautics 14 I5 49min Supply Chain Consultant Consulting (in agribusiness) 24 I6 1h 08 Supply Chain Director Agrofood 32 I7 52min VP supply chain Beauty & care 25 I8 58min Supply Chain Director freelance Consulting (trading) 28 I9 1h13min Supply Chain Manager (freelance) Consulting (all) 32 I10 55min Supply Chain Manager Automobile 14 I11 50min Supply Chain Consultant Consulting (all) 14 Summary 57min on average All are at management or top management positions Several types of industries 23.7years on average 1121 Annals of Operations Research (2024) 335:1107–1136 1 3 3.3.3 Reliability assessment Reliability is the relative percent of variance in an observed variable that is accounted for by the true scores. However, the true score cannot actually be obtained, so a more accurate definition refers to the stability of the scores for a particular scale (Hatcher 1994). The most common reliability aspect is the assessment of internal consistency, where an internally consistent measure is the one with highly correlated items with both each other and the total scale (Hatcher 1994; Hair etal. 2010). The most common metrics used to assess internal consistency are Cronbach’s coefficient alpha and composite reliability (Hatcher 1994). In both cases, the expectation is that the individual construct score will exceed a Table 4 Exploratory factor analysis *In the case of scale development, Alpha’s values exceeding 0.6 are deemed acceptable (Dunn etal. 1994; Nunnally and Bernstein 1994) Initial Sample N = 163 Final Sample N = 265 Items 1 2 3 4 5 1 2 3 4 5 Structure_1 .565 .008 .006 .011 .211 .752 .118 .003 .007 .011 Structure_2 .779 .003 .010 .008 .111 .854 .002 .008 .021 .008 Structure_3 .941 .001 .009 .060 .010 .898 .011 .060 .010 .060 Structure_4 .858 .070 .003 .010 .004 .849 .002 .008 .004 .107 System_1 .019 .612 .005 .182 .201 .322 .605 .003 .020 .005 System_2 .118 .766 .010 .098 .039 .112 .934 .220 .018 .010 System_3 .009 .872 .009 .063 .069 .008 .899 .103 .015 .009 System_4 .010 .710 .061 .082 .073 .009 .773 .003 .030 .007 Redesign_1 .003 .010 .586 .060 .010 .028 .010 .691 .003 .009 Redesign_2 .020 .009 .752 .010 .004 .055 .009 .890 .220 .010 Redesign_3 .122 .160 .751 .042 .002 .111 .001 .745 .028 .004 Feedback_1 .004 .076 .088 .710 .221 .022 .122 .003 .813 .009 Feedback_2 .007 .052 .082 .893 .006 .102 .112 .220 .917 .019 Feedback_3 .039 .008 .004 .856 .087 .018 .068 .021 .903 .029 Process_1 .010 .002 .068 .001 .701 .005 .0333 .028 .118 .699 Process_2 .009 .003 .072 .006 .598 .009 .006 .059 .009 .783 Process_3 .010 .008 .068 .044 .601 .109 .022 .073 .018 .677 Process_4 .027 .220 .044 .039 .658 .087 .201 .009 .007 .802 Cronbach’s Alpha by factor* .716 .799 .756 .896 .602 .853 .844 .772 .856 .690 Cronbach’s Alpha of the scale .906 .899 KMO sampling adequacy = .868 Bartlett’s test of Sphericity χ2 = 2.813 df = 253 Sig = .000 Total variance extracted = 69.9% There are less than 3% non-redundant residuals in factor matrix with values greater than .05 KMO sampling adequacy = .875 Bartlett’s test of Sphericity χ2 = 2.952 df = 325 Sig = .000 Total variance extracted = 71.9% There are less than 3% non-redundant residuals in factor matrix with values greater than .05 1122 Annals of Operations Research (2024) 335:1107–1136 1 3 value of 0.70 (Nunnally 1978). Average variance extracted (AVE) is one of the metrics used to assess the validity of constructs, but Malhotra and Dash (2011) argue that AVE is often too strict, and reliability can be established through CR alone. We assessed each of the SCV constructs for both Cronbach’s alpha and composite reliability (Table 5). As is evident, all constructs exhibit acceptable levels in both metrics, which indicates that the newly created scales are reliable. 3.3.4 Convergent validity assessment Convergent validity is the extent to which varying approaches to construct measurement yield the same results (Campbell and Fiske 1959). In addition, it can refer to whether items comprising a scale behave as if they are measuring one common construct (Davis 1989). Convergent validity can be assessed using several different methods, with the most common ones including evaluating the multi-trait matrix or evaluating the measurement model for the constructs under consideration. As multiple methods were not used in this research, we used the CFA approach to assess convergent validity. Specifically, convergent validity is demonstrated when individual items load significantly on a single construct and the measurement model has acceptable fit statistics (Hatcher 1994). As demonstrated by the acceptable fit statistics of the measurement model (χ2 = 236.22, df = 109, CFI = 0.942, TLI = 0.927, IFI = 0.942) and the significant path loadings for all measured constructs, the measures demonstrate convergent validity (“Appendix1”). Beyond the model fit tests, convergence was also tested through the utilization of the AVE for each individual construct. In this case, the AVE should exceed a recommended threshold value of 0.50 to determine if the variance shared between the measurement items and the construct exceed the variance that would be explained by the individual measurement errors associated with each item. As is evident from Table 5, the scales meet the threshold, with the AVE values exceeding 0.50. Taken collectively, the tests undertaken provide assurance that convergent validity is supported. 3.3.5 Discriminant validity assessment Scales demonstrate discriminant validity if the items of each construct only reflect that single construct (Bagozzi etal. 1991). Discriminant validity is the ability of a set of measurement items to differentiate between two related but conceptually different constructs. Table 5 Construct reliability AVE correlations and shared variance Items in bold on the diagonal are the square root of AVE figures, the lower left triangle contains the construct correlations, and the upper right triangle contains the shared variance between constructs Construct (1) (2) (3) (4) (5) Composite Reliability (CR) Cronbach α AVE Structure and mechanisms (1) .782 .284 .271 .214 .164 .858 .854 .606 System development (2) .526 .766 .523 .314 .298 .850 .846 .579 SC redesign (3) .442 .636 .734 .350 .402 .777 .775 .537 SC feedback (4) .393 .428 .420 .829 .316 .868 .866 .682 SC Process (5) .395 .532 .634 .559 .770 .762 .762 .591 1123 Annals of Operations Research (2024) 335:1107–1136 1 3 Discriminant validity is ensured when a scale does not measure the construct it was not intended to measure. Traditionally, a multi-trait matrix is used to assess discriminant validity (Campbell and Fiske 1959). However, more recent research has evolved to utilize factor analysis (Netemeyer etal. 2003) as well as the AVE test (Fornell and Larcker 1981). We employed both EFA and CFA to provide initial support for the existence of discriminant validity. In this case, the EFA results establish the initial evidence for discriminant (and convergent) validity. The EFA is followed by the CFA, whereby the factor structure is verified via the utilization of a structural equation model (SEM) methodology. As noted above, the model fits the data well, which provides evidence that the scales are, in fact, discriminant. In addition, the AVE was calculated for each construct, and this value was compared with the shared variance between all the associated construct pairings. To demonstrate an appropriate level of validity, each individual AVE should exceed the squared correlation (shared variance) between constructs. The results (see Table5) provide support for discriminant validity, as each AVE exceeds the squared correlation between construct pairs. We also tested discriminant validity employing the more current Heterotrait–monotrait (HTMT) discriminant validity analysis approach (Henseler etal. 2015). HTMT produces a ratio of the average correlations of indicators across two constructs relative to the geometric mean of the averages of the correlations of indicators within each construct. The analysis suggests that discriminant validity is achieved when the average item correlations within a construct are substantially greater than the average item correlations across constructs. An HTMT ratio < 0.85 suggests discriminant validity. HTMT has been shown to be a more reliable and powerful test of discriminant validity than traditional methods, such as the Fornell–Larcker criterion and the examination of cross-loadings (Henseler etal. 2015). The results presented in Fig.3 confirm the discriminant validity of SCV constructs. 3.3.6 Second‑order construct analysis When theory suggests that the correlations among first-order constructs can potentially be more effectively explained by a higher-order factor, additional analyses can be conducted to test for the existence of a second-order construct. In the case of the SCV concept, there is no explicit guidance in the literature, as this is an emergent theoretical domain. Our conceptualization of SCV based on literature and earlier cited rationales, and subsequent operationalization, argues for SCV being a second order latent construct that is reflected in subordinate first-order constructs. An important note is that the higher-order factor is the theoretical explanation for the covariation of the first-order constructs (Segars and Grover 1999). Therefore, the second order model cannot exhibit an improved fit when compared to the correlated, first-order model. However, the lowlevel model can be used as the target fit for the high-level model, with the aim of providing a comparable fit via a more parsimonious, theoretically relevant model. The efficacy of this comparison can be examined through the utilization of a target coefficient (T), which is calculated as the chi-square of the first-order model divided by the chisquare of the second-order model [χ2 FirstOrder ÷ χ2 SecondOrder] (Marsh and Hocevar 1985). As the coefficient is a comparison of an “ideal” model to a competing model, it has an upper bound of 1.0, with higher numbers indicating that the relationship among the first-order factors is effectively being captured by the second-order model. Following the procedure of Segars and Grover (1999), each model’s chi-square value is adjusted for the degrees of freedom for the individual model (i.e., χ2/df). The adjusted 1124 Annals of Operations Research (2024) 335:1107–1136 1 3 χ2 for the first-order model is 2.17 (236.22/109), and the second-order model value is 2.27(259.097/114). The target coefficient is then calculated to be 0.96, which lends support for the second-order model being a valid, parsimonious representation of the relationships between the first-order constructs. In addition, support for the second-order model is demonstrated by the paths between the first- and second-order constructs all being significant (see Fig.3). According to the data of the final sample, SCV as a second order construct has a composite reliability of 0.831, an AVE of 0.50, and a maxR(h) of 0.834. Fit measures are also adequate: CMIN/DF 2.273 Between 1 and 3 Excellent CFI 0.933 > 0.95 Acceptable RMSEA 0.069 < 0.1 Acceptable 3.4 Phase 4: Nomological validity assessment The nomological validity (Churchill 1995) of the proposed scale of SCV was examined by testing the relationship with a related outcome construct: SC performance. Our nomological rationales are underpinned by the resource based view (RBV) and the contingent resource based view in particular (CRBV—Brush and Artz 1999). RBV is a theoretical approach that emerged as a response to the turbulence in the business environment, accentuated by crises (Wernerfelt 1984; Barney 1991, 2012; Peteraf 1993). RBV attributes firm specific competitive advantage to the possession and deployment of scarce, valuable and inimitable resources. Despite its popularity, RBV has been criticized by some scholars for the ambiguity of the resources’ concept, its static approach (Priem and Butler 2001a, b), Construct HTMT ratio* (1)(2)(3)(4)(5) Structure and mechanisms System development.565 SC redesign .504 .647 SC feedback .411 .449 .436 SC processes.446.570.638.535 *Thresholds are 0.850 for strict and 0.900 for liberal discriminant validity (a) Discriminant validity tests Construct Initial Sample N=163 Final Sample N=265 Standardized factor loading t-valueStandardized factor loading t-value Structure and mechanisms .647 3.170**.6083.24** System development.742 5.206***.7535.106*** SC redesign .835 5.463***.7844.996*** SC feedback .625 5.131***.6265.032*** SC Process.751 5.208***.7395.091*** *** p < 0.001; ** p < 0.010 (b) Second-order construct standardized path loadings Fig. 3 Discriminant validity tests and second-order construct standardized path loadings 1125 Annals of Operations Research (2024) 335:1107–1136 1 3 difficulty to be operationalized (Bromiley and Rau 2016), and context insensitivity (Ling- Yee 2007; Brandon-Jones etal. 2014). On the other hand, the CRBV integrates the idea of a dynamic environment and suggests that a competitive advantage may be contingent: some specific conditions have a significant effect on the impact of resource bundling and capability building (Brandon-Jones etal. 2014). In the SCM research field, the contingent perspectives of RBV enables to consider the necessity to adapt firm’s resources and capabilities to the environment in order to achieve a better SC performance (Brandon-Jones etal. 2014; Eckstein etal. 2015; Dubey etal. 2020). Additionally, the way SC resources and capabilities could be bundled in order to align with external contingency such as a dynamic environment punctuated by severe disruptions and uncertainties (Aragon-Correa and Sharma 2003) is still an understudied research area (Brandon-Jones etal. 2014). Our psychometric treatment of SCV identifies five specific resources and capabilities, viz. Structure and Mechanisms, System Development, SC Redesign, SC Feedback, and SC Process that firms could develop and dynamically orchestrate to improve survivability over time during acute disruptions (Eddleston etal. 2008; Blackhurst etal. 2011; Queiroz etal. 2020). To wit, from the RBV perspective, such resources and capabilities can be considered as valuable, rare, inimitable (these are in the main high-level organizational capabilities and difficult for competitors to mimic or replicate at least in the short to medium run—Barney 1991), and dynamic (Dubey etal. 2021). Moreover, by mobilizing the CRBV (Brush and Artz 1999), these five specific resources and capabilities can be combined in such a way as to be able to respond to external contingencies, i.e., a particularly uncertain environment. RBV posits that firms can achieve sustained competitive advantage with the possession and use of valuable, inimitable, and non-substitutable resources (Hart 1995). Additionally, based on the literature, we argue that the SCV resources and capabilities possessed by the firm may have impact on SC performance under the contingency of severe disruptions. Thus, we theorize a positive relationship between SCV and SC performance (Ivanov 2020b; Ivanov and Dolgui 2020b). SC performance is measured using the following items: order fulfilment, delivery as promised, delivery flexibility, flexibility to change output volume (Chae etal. 2014), all reported relative to competition. The selection of this scale is based on two factors. First, these performance indicators reflect the adaptation abilities of an SC as manifested in SCV. Second, this scale has been cited extensively in past research. We collected data using a third and independent sample of 119 respondents to conduct the nomological validity analysis of the proposed SCV measurement scale. Figure4 exhibits the model and the path coefficients. The fit indexes of the proposed model are acceptable with χ2 = 441.61, Degrees of freedom (df) = 309, p < 0.001, CFI = 0.901, TLI = 0.854, IFI = 0.873, RMSEA = 0.06 (see Table6). As theorized, SCV is significantly and positively related to SC performance (0.503 path coeff—see Fig. 4), providing support for the nomological validity of our SCV scale. Collecting the preceding discussions, we describe a defined, systemic phase driven scale development process, commencing with construct operationalization and instrument generation, and concluding with an examination and verification of scale psychometric properties. We emerge with a scale that measures the concept and construct of SCV with substantive content validity, scale reliability, and convergent, discriminant and nomological validity. Our sampling plan imparts diversity and rigor to 1126 Annals of Operations Research (2024) 335:1107–1136 1 3 the scale. We triangulated data collection to include interviews, field visits, and separate, repeated surveys. Our data is cross-sectional in industry scope, and sourced from business respondents with considerable experience and expertise in the SC and operations functions, lending added authenticity as well as external validity to our scale. 4 Discussion ofresults andimplications 4.1 Implications fortheory Our findings make several contributions to the SC literature. First, we add empirical tangibility to the conceptual notion of SCV, building on, and extending theory. We introduce a rigorously developed and diligently validated scale for measuring SCV. Future studies that extend research on this nascent construct should find our scale to be of use. Second, we develop a hierarchical model of SCV. The validated SCV ***regression weights significant at 0.001 Feedback Redesign .574*** .843*** SC Process System .733*** .622*** .880*** SC performance R²=0.25 .503*** Supply Chain Viability Structure Fig. 4 Research model for nomological validity Table 6 Fit indexes for nomological validity *References: Azadegan etal. (2020), Hair etal. (2010) Fit statistics Statistics Recommended range* χ2/df 1.429 < 3.0 RMSEA .06 < 0.1 PGFI .705 > 0.5 PNFI .772 > 0.5 TLI .854 > 0.9 CFI .901 > 0.9 1127 Annals of Operations Research (2024) 335:1107–1136 1 3 measurement scale is a second order construct which contains five unidimensional constructs, namely “structures and mechanisms,” “systems development,” “SC redesign,” “SC feedback,” and “SC processes.” The scale adds to the body of knowledge by identifying key resources and dynamic processes required for fostering SCV. Further, the SC orientation of the scale is consistent with, and reinforces the general belief that RBV should not be limited within internal organizational boundaries (Paulraj 2011; Hitt etal. 2016). Finally, this scale is a way of bundling the SC resources and capabilities in the face of external contingencies, in this case "super disruptions" such as those generated by the COVID-19 pandemic. Thus, this study contributes to the stream of SCM research that seeks to demonstrate the usefulness of CRBV as a theoretical perspective (Grötsch etal. 2013; Brandon-Jones etal. 2014; Dubey etal. 2020). We dropped several items during scale content validation and psychometric evaluation. This has implications for theory. SCV research is emergent (Ivanov 2018b, 2020b; Ivanov and Dolgui 2020b) and thus, just as with any exploratory topic, there are, and will be, conflicting concepts which may in turn bring some redundancies in pre-existing concepts. Building a measurement scale enables us to consider, specify, and examine key elements of a theoretical concept. However, this is a dynamic process and we fully anticipate that our scale will see changes as contingencies are applied in the future. Some of the items dropped were of interest to the interviewees, but did not survive statistical analysis. It is conceivable that such items may re-emerge in other contexts or may be linked to SCV in some other, still unexamined, way. Future research may look into such possibilities. A consequential finding is the positive and significant nomological link between SCV and SC performance. Admittedly not the central focus of this research, this relationship harbors important meaning for theory and practice—and offer interesting opportunities for future research. Another distinguishing feature of our study is that data collection was confined to business respondents with maturity and experience in the SC and operations functions. We did not seek or collect data from students or other proxies. The scale is thus built on real world business data foundations, and will therefore reflect reality and relevance to future respondents and researchers. 4.2 Implications forpractice An important finding from a practitioner perspective is the positive and significant link between SCV and SC performance. SCV builds a set of capabilities that enable quick pivots and adaptation in response to super-disruptions.These capabilities, by virtue of being at the organizational level, are rare, valuable and difficult to imitate—and can thus create firm specific competitive advantage, if exploited with appropriate strategies. Global super disruptions such as the COVID-19 outbreak demand that SC professionals look beyond SC stability, robustness, resilience, agility, flexibility, or even efficiency. Resources should be developed and/or acquired and processes should be setup to build SCV. SC professionals may find our validated SCV measurement scale useful in this regard—initially perhaps as a diagnostic tool, to identify areas that require specific improvements. More precisely, the SCV measurement scale shows the considerable need to: (1) focus on key SC resources and processes (2) to adapt the SC design by using feedback cycles in order to face environmental changes and uncertainties. Besides, undergoing the steps involved in the process of evaluating or developing SCV would in itself provide a useful mechanism 1128 Annals of Operations Research (2024) 335:1107–1136 1 3 for preparedness, collaboration and awareness of risks and opportunities in the supply base, internal processes and market and ecological systems. The next SC trauma could be just around the corner. 5 Conclusion The concept of SCV with its emphasis on adaptation and survival has attracted attention from COVID shocked industry. However, the relative novelty of the concept makes defining the pathway to reach SCV a difficult task. Our study provides definition to this endeavor. We define SCV and associated dimensions, and develop new multi-item measurement scales for measuring these constructs. Unlike prior, our study is purely empirical, obtaining and analyzing SC professionals’ perspectives on SCV. A secondary contribution of this work is the demonstration of a rigorous empirical scale and item development process. Our validation of a measurement scale for SCV also provides a degree of clarity on the differences between the SCV and overlapping concepts like SC resilience. As in most research, this study has some limitations. The use of a convenience sample for interviews during the qualitative content and face validation phase may have limited our insights early in the research process. The use of a convenience sample at this first step is justified by the well-known disadvantage of conducting scale-development research, namely the time commitment required (Hensley 1999). However, we used random sampling for the three subsequent quantitative data collections, alleviating concerns regarding this issue. Our scale is certainly not final in any way—future studies will, we hope, test, refine, and improve the scale in different conditions and contexts. Nonetheless, we believe that the rigor of our scale development process makes the SCV foundational dimensions contingent-agnostic to an extent. Despite these limitations, we believe our SCV scale will provide researchers with a robust construct measurement mechanism scales when investigating this emerging concept (Ivanov 2018b, 2020). Industry would also benefit from considering these measurements scales as diagnostic tools and pathways in designing viability into SCs. In addition, we believe that both industry and academia are likely to benefit from new research on how SCV is related to other concepts such as SC digitalization (Blackhurst etal. 2021; Ivanov etal. 2020a, b; Zouari etal. 2021). To do so, the new clearly defined constructs and robust measurement scales of SCV will assist these future investigations. Finally, some “classic” SCM fundamentals feature in the SCV measurement scale. More research is required to fully understand how and why such SCM fundamentals integrate into the concept of SCV. For example, in the area of SC design, the cost-efficiency oriented models can be extended by multi-objective functions and multi-level viable SC designs. We consider these issues to be future research opportunities. We expect that our study and operationalization of SCV will stimulate further theory development as researchers begin developing SCV centered nomological frameworks, and use the SCV scale to test such conceptualizations. Research on possible negative aspects of SCV, SCV development and maintenance expense, SCV sustainability, and the SCV development process (particularly when visibility and control is often limited to a Tier 1 or Tier 2 supply level) would be of interest and utility. 1129 Annals of Operations Research (2024) 335:1107–1136 1 3 Appendix Appendix1: Sample analysis Construct Sample 1N = 163 Sample 2N = 265 Mean S.D Mean S.D EFA retained items means and standard deviations Structure and mechanisms Structure_1 5.48 1.157 5.25 1.2020 Structure_2 5.07 1.311 5.68 1.147 Structure_3 5.74 1.098 5.26 1.208 Structure_4 5.66 1.073 5.02 1.114 System development System_1 5.71 1.286 5.88 1.278 System_2 5.61 1.167 5.92 1.174 System_3 5.54 1.228 5.81 1.229 System_4 5.79 0.954 5.98 1.108 SC redesign Redesign_1 5.66 1.182 5.59 1.385 Redesign_2 5.34 1.292 5.50 1.303 Redesign_3 5.63 1.160 5.67 1.229 SC Feed back Feedback_1 5.75 1.096 5.86 1.088 Feedback_2 5.66 1.073 5.70 1.233 Feedback_3 5.66 1.073 5.72 1.275 SC Process Process_1 5.58 1.257 6.36 0.920 Process_2 6.06 0.914 6.18 1.126 Process_3 6.12 1.108 6.55 0.900 Process_4 6.11 1.107 6.50 0.887 Construct Sample 1 Sample 2 Standardized path loading Standardized path loading Nomological analysis: Item loadings Structure and mechanisms Structure_1 0.549 0.527 Structure_2 0.775 0.736 Structure_3 0.927 0.916 Structure_4 0.889 0.887 1130 Annals of Operations Research (2024) 335:1107–1136 1 3 Construct Sample 1 Sample 2 Standardized path loading Standardized path loading System development System_1 0.668 0.673 System_2 0.821 0.797 System_3 0.814 0.793 System_4 0.806 0.795 SC redesign Redesign_1 0.727 0.702 Redesign_2 0.692 0.681 Redesign_3 0.769 0.813 SC feedback Feedback_1 0.798 0.788 Feedback_2 0.877 0.877 Feedback_3 0.847 0.821 SC processes Process_1 0.705 0.584 Process_2 0.789 0.633 Process_3 0.675 0.753 Process_4 0.609 0.687