Introduction
In the current scenario supply chain (SC) has been recognized as a competitive advantage for any firm. It is a very challenging task to handle SCs (Lee, 2002). Due to continuous advancement, outsourcing, globalization, etc., SC managing has become a very challenging job (Lee, 2004). Fisher (2011) tried matching SC strategies (for instance, responsive vs efficient) to characteristics of goods (for instance, innovative vs. functional). Lee (2002) had proposed strategies (like agile/hedging risk/responsive/efficient) to provide solutions to supply and demand uncertainty. The firms’ SC agility (SCA) can have a direct impact on its capability to deliver and produce ingenious goods for their clients at the correct place, correct cost, and the right time (Khan & Pillania, 2008). In spite of high popularity of SCA little is understood about it (Braunscheidel & Suresh, 2009). As per Liu et al. (2013), firms are becoming reliant on IT advancements due to environmental uncertainties. As per Brusset (2016), there is huge pressure for improving cost of inventory turnover on SC managers. As a result, there is a need to build SC agility for surviving in the market place (Gligor et al., 2016). SCA has appeared as a investigating area that is different from flexibility (Dubey et al., 2018).
As per Choi et al. (2018), there is a significant impact of big data in practices of operations management. Srinivasan and Swink (2018) stated that big data analytics (BDA) has been utilized for understanding client behaviors/intentions. However, for SC operational decision-making is less understood. There is a positive impact of predictive analytics and big data on firm and SC performance (Gunasekaran et al., 2017). A few prior studies (Frisk & Bannister, 2017; Gunasekaran et al., 2017; Ji-fan Ren et al., 2017) stated there is a connection between competitive advantage and BDA and competitive advantage and SCA (Blome et al., 2013; Gligor et al., 2016).
As per Boyd et al. (2012), SC management performance impacts environmental perspectives (Sousa & Voss, 2008). This research contingency theory (CT) has been utilized for examining factors impacting BDA. Organizational flexibility (OF) has been discussed in prior literature. The objective of the current study is to find the impact of the competitive advantage, organizational flexibility, and knowledge management along with mediating variable BDA on SCA, which is the dependent variable.
The next sections of the chapter are as follows: Section 2 discusses the background of the study, Section 3 discusses the theoretical framework and hypothesis development, methodology of research has been discussed in Section 4, which includes sampling techniques and respondents’ demographics; data analysis has been discussed in Section 5, Section 6 contains discussion, managerial implications and practice and research contribution and Section 7 contains the conclusion, scope and future research guidelines.
Background of the Study
Dynamic Capabilities View (DCV)
As per prior studies conducted DCV gave explanation for firms’ competitive advantage in these environment changing circumstances (Eisenhardt & Martin, 2000; Singh et al., 2013; Sirmon et al., 2010). Teece et al. (1997), stated, “firms’ ability to build, integrate and reconfigure external and internal competencies for addressing changing environment.” It adds competence to shape and sense opportunity, and maintaining competency through reconfiguring, protecting, combining, and enhancing resources of a firm. Dynamic capabilities are unstable, simple, experimental processes that enable renewal, transformation, or combinations of competencies and assets that are critical for markets that are uncertain (Eckstein et al., 2015). Based on these debates researchers have decided to adopt BDA, which will be resulting in firms’ ability for reconfiguring firm-level assets.
BDAC
Arguments are going on about BDA essentialness in fulfilling objectives of a firm (Jabbour et al., 2019; Prescott, 2014), but until now there is no general accord on how this BDA should support them (Galbraith, 2014). BD are those data that have variety, velocity, and volume which makes it hard for traditional methods to manage analyses. Analytics means “from data extracting valuable information, building statistical models, visualizing and exploring information … and helps in planning, execution and taking decisions” (Srinivasan & Swink, 2018). BDA helps firms investigate alternatives for demand and supply uncertainties (Wang et al., 2016).
SCA
As per Lee (2004), firms have been investing a lot in developing SC agility to fulfill unexpected and sudden advancements in the market place. As per Swafford et al. (2006), SCA impacts the ability of firms to make and deliver innovative goods to their clients at the right price and time. As per Braunscheidel & Suresh (2009), due to highly competitive pressure and turbulence firms need agility in SCs. As per Dubey et al. (2018), SC visibility increases SCA by combining firm assets. As a result, agility is craving assets of SC that enables it to manage changes in supply and demand instantaneously and handle outside disruptions smoothly.
OF
As per Volberda (1996), OF means a firm should have operational control. Hence, this helps in operating firms in an uncertain environment (Srinivasan & Swink, 2018). As per Sanchez (1993), in a dynamic environment a firm can have competitive advantage by making strategic flexibility. As per Sanhez (1995), flexibility is restrained not only by assets but also by the approaches a firm adopts to utilize assets (Y. Liu et al., 2009).
Competitive Advantage
As per Hinterhuber (2013), associations can obtain a competitive advantage by determining and enforcing different game plans for differentiation of the firm from its peers. As per Mellat-Parast and Spillan (2014), competitive advantage is the potential of a firm to sustain or maintain more than average returns. Chen (2019) stated that competitive advantage will be borrowed from various points. For example, expertise within a firm’s control can be achieved to develop competitive advantage for high accomplishment (Eidizadeh et al., 2017; Nayak et al., 2021)
Theoretical Framework and Development of Hypotheses
CT and DCV had been utilized to propose the research model for the current study. DCV had been utilized for investigating and combining a firm’s competencies and assets in this uncertain environment. Advancement in technology had been argued as a solution to uncertain environments. BDA will help in future decision-making and planning in this environmental uncertainty. Also, OF are proven to be more valuable in this time of uncertainty (Pagell & Krause, 1999; Swamidass & Newell, 1987). Four research hypotheses had been proposed to check the impact through SEM approach (Figure 12.1).
Figure 12.1: Proposed framework.
Competitive Advantage (CA)
CA means having any benefits one firm has over its peer firms (Porter, 1985). As per LaValle et al. (2012), analytics are utilized five times more by a high performing firm than other firms that are below average. Raffoni et al. (2018), stated that if BD is utilized properly then it will help in achieving better outputs. BDA is still in its infant stage, but it will be offering high opportunities. As per Zhang et al. (2017), BD is being exploited to develop CA in a firm. Thus,
H1: CA influences BDA
Organizational Flexibility (OF)
As per Galbraith (1973; 1974), firms require flexibility to apply decisions efficiently and quickly. It had been identified as a critical element for decreasing SC risk (Braunscheidel & Suresh, 2009). As a result, OF has the potential to manage with supplies and demand uncertainties (Swafford et al., 2006) and achieve excellence. Thus,
H2: OF influences BDA
Knowledge Management (KM)
It helps in achieving a firm’s objectives by sharing, creating, and managing information and knowledge of a firm. As per Lugmayr et al. (2017), BD is an incremental or disruptive innovation. Also, a framework for KM and BD was to dig up information from social media and compare it with peers in the marketplace. Thus,
H3: KM influences BDA
BDA and SCA
IT potential had a positive impact on SCA (Swafford et al., 2006). SC visibility is a fundamental need for developing data analytics potential. As per Srinivasan and Swink (2018) firms investing in developing SC visibility are also willing to spend in BDA. Dubey et al. (2018) determined a positive impact of BDA in SCA. As a result, utilizing BDA will help management to understand various changing circumstances for developing strategies for trade. Thus,
H4: BDA influences SCA
Research Methodology
Information was gathered by different sources like primary and secondary. Secondary sources incorporate literature audit and different articles, and essential documents incorporate assortments of information through organized questionnaires. The questionnaire’s reliability test was conducted. The different Indian industries were considered as the target population. Manufacturing companies and listed companies on the stock exchange were set as target population. IT service providers were excluded from the targeted population. The respondents from the selected firms were IT workforce and officers who have IT information of future and present tasks of their individual firms. The sampling method utilized here is simple random sampling (Hair et al., 2010). Finally, 269 responses were utilized for data analysis after cleaning of data in SPSS 20.0. Established scales had been adopted from prior literature here (Malhotra & Grover, 1998).
By keeping away from common methods, the examination group has avoided potential risk amid preinformation assortment level. In the beginning of the questionnaire there was a note that mentioned the information will be utilized only for academic study and privacy of information will be kept up. Case screening was directed in the gained dataset followed by factor screening so explanations can be given for assortment in the data. Data cleaning methods were used to keep data free from bias; for instance, there were very few instances of missing data and so this was not seen as a significant factor. No cases had been eliminated. Notwithstanding, after the information is gathered into the exploration group Harman’s single factor test was conducted. EFA was conducted and outcomes displayed that an initial factor clarifies most extreme covariance (40.184 percent). This value is below 50 percent, which is the suggested value and lies within the acceptance range (Podsakoff et al., 2003).
SEM had been utilized for analysis of data. This was performed in four different phases using firm demographics, validity and reliability test, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM). EFA was done to check the cumulative contrast revealed to recognize and bundle the items utilizing a rotated component matrix table. EFA and the reliability test were conducted using SPSS 20.0 on information gathered. Thereafter, CFA was carried out for testing and supporting the model. CFA was carried out utilizing AMOS 22.0 for assessing model outcomes (Byrne, 2010; Hair et al., 2010). Finally, SEM was conducted for checking model fit and supporting hypotheses (Table 12.1).
Table 12.1:Scales used for the study.
| Construct | Reference | Item | Description |
|---|---|---|---|
| BDA | (Akter et al., 2016; Srinivasan & Swink, 2018) | BDA1 | Advanced tools need to be used for data analysis. |
| BDA2 | Multiple sources have been used for collection of data. | ||
| BDA3 | Data visualization is used for getting complicated information from big data sets. | ||
| BDA4 | It helps in increasing accuracy. | ||
| SCA | (Gligor et al., 2016) | SCA1 | Firm can identify changes in the environment. |
| SCA2 | Firm can recognize opportunities in the environment. | ||
| SCA3 | Firm collects information from clients. | ||
| SCA4 | Firm can make fast decisions to manage changes in the environment. | ||
| OF | Sethi and Sethi (1990), Upton (1994) | OF1 | Our firm can adopt change for changing supply and demand uncertainties. |
| OF2 | Our firm is more flexible than our peers. | ||
| OF3 | We can adopt changes in firm structure. | ||
| OF4 | It helps in achieving excellence. | ||
| KM | KM1 | It helps in managing information. | |
| KM2 | It is a database of information. | ||
| KM3 | It helps in enhancing accuracy in outputs. |
Results
Demographics of Firms Surveyed
A questionnaire-based survey method was used. Table 12.1 shows the distribution of respondents based on different industries. The response was taken from manufacturing and retail firms which involved SC and IT officials. The data is a representation of population. Table 12.2 shows the population representation.
Table 12.2:Respondents’ demographics.
| Item | Percentage |
|---|---|
| Managers | 78 percent |
| IT Managers | 22 percent |
Reliability and Validity
Cronbach’s Alpha (α)
Assessment of reliability helps in examining the level of inner consistency between factor estimation and its freedom of error at any point in time (Kline, 2015). Constructs’ reliability was checked utilizing α-value as its most common method for measurement (Hair, et al. 2013). Constructs’ α-value has an over endorsed mark of 0.70 (Nunnally and Bernstein, 1994). The 7-point Likert scale has been utilized for creating the questionnaire. SPSS 20.0 and AMOS 22.0 had been utilized for analyzing the information gathered. The latent variable CA has four indicators CA1, CA2, CA3, and CA4 and its α-value is 0.860; OF has four indicators OF1, OF2, OF3, and OF4 and its α-value is 0.889; KM has three indicators KM1, KM2, and KM3 and its α-value is 0.850; BDA has four indicators BDA1, BDA2, BDA3, and BDA4 and its α-value is 0.845. Consequently, all values are inside the edge level and the 15 elements are used in further examination.
Exploratory Factor Analysis
Fittingness of sample size is the fundamental step for EFA. SPSS 20.0 had been utilized conducting EFA. Factors’ correlation was investigated by Bartlett’s test of sphericity (Hair et al., 2010). Principal component analysis (PCA) was conducted to recognize significant predisposition and explicit similar characteristics. Varimax rotation was utilized for the explanation of initial results, it is hypothesized (established from the relevant literature) that there is no correlation within the factors (Hair et al., 2010).
Therefore, Kaiser-Meyer-Olkin (KMO) was calculated to evaluate whether those things adequately correspond and to decide if EFA can be conducted. The KMO value for this study is 0.788, which is greater than 0.60, the acceptance level (Hair, et al., 2010). Significance value is 0.000, which is below 0.05, the likelihood value. Table 12.3 displays the KMO and Bartlett’s test output.
Table 12.3:KMO and Bartlett’s Test.
| Kaiser-Meyer-Olkin Measure of Sampling Adequacy. | .788 | |
| Bartlett’s Test of Sphericity | Approx. Chi-Square | 2517.435 |
| Df | 105 | |
| Sig. | 0.000 | |
PCA had been used for method of extraction. The extraction is done for those eigen values that are more than one, which describes maximum variance. For the components, the percentage of total variance is explained by component 1 (20.367 percent), component 2 (19.099 percent), component 3 (18.187 percent), and component 4 (15.671 percent). The total variance explained (cumulatively) is 73.324 percent.
The Rotated Component Matrix is important for deciphering outputs of examination. It organizes the factors, and each gathering encompasses multiple factors at any rate, which helps in structure simplification. Thus, this is the point of the objective of rotation. In this study, we have accomplished this point. This assists with distinguishing the cross-loadings on more than one gathering, and afterward it very well may be adjusted by eliminating those things that are cross-loaded. Here, loadings under |.40| are suppressed because loadings more than |.40| are commonly viewed as high. Eventually, we accomplish a simple structure. There are 11 factors that were gathered under three distinct parts. Varimax was utilized as a rotation technique. OF1, OF2, OF3, and OF4 are assembled under the primary segment with upsides of 0.842, 0.826, 0.877, and 0.828. CA1, CA2, CA3, and CA4 are gathered under the subsequent segment having values 0.823, 0.831, 0.798, and 0.683. BDA1, BDA2, BDA3, and BDA4 are assembled under the third segment with values 0.683, 0.752, 0.810 and 0.748. KM1, KM2, and KM3 are assembled under the fourth part with values 0.849, 0.890, and 0.786. Table 12.4 shows the Rotated Component Matrix output.
Table 12.4:Rotated Component Matrix.
| Component | ||||
|---|---|---|---|---|
| 1 | 2 | 3 | 4 | |
| CA1 | .823 | |||
| CA2 | .831 | |||
| CA3 | .798 | |||
| CA4 | .683 | |||
| OF1 | .842 | |||
| OF2 | .826 | |||
| OF3 | .877 | |||
| OF4 | .828 | |||
| KM1 | .849 | |||
| KM2 | .890 | |||
| KM3 | .786 | |||
| BDA1 | .683 | |||
| BDA2 | .752 | |||
| BDA3 | .810 | |||
| BDA4 | .748 | |||
| Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. | ||||
| a. Rotation converged in 5 iterations. | ||||
CFA was led in the accompanying stage, which creates recognition from composing an outline that can be attempted and determine how well the components address forms. For model validating purpose SEM was used in the research model that is proposed (Byrne, 2010). By then when their instrument shows the ordinary plans inside, this may have been distinct to construct validity (CV) (Moerdyk, 2009) and, especially, factorial validity.
Model Validity Measures
Composite Reliability (CR)
CR was additionally estimated for every one of the parts. It is assessed for internal consistency dependability considering its capability to give improved outputs (Henseler et al., 2009). CR values for CA is 0.864; OF is 0.887; KM is 0.851; BDA is 0.871. CR values of all constructs are >0.7, this shows that values of CR are reliable (Hair et al. 2010). Table 12.5 displays the CR values.
Convergent Validity
This is assessed with the average variance extracted (AVE). As per Fornell and Larcker (1981), AVE > 0.5 for satisfying this validity concern. Table 12.4 displays the AVE values. All AVE values are more than 0.5 which fulfils this effectiveness conditions for every construct (Hair, et al. 2013).
Divergent Validity
To ascertain this validity, Fornell and Larcker (1981) recommended that constructs’ AVE should be more than a square of the correlation between constructs (Hair, et al. 2013). Table 12.5 represents values for divergent validity output (MSV < AVE) and it was obtained using the master validity plugin in AMOS 22.0.
Table 12.5:Model validity.
| CR | AVE | MSV | MaxR(H) | CA | OF | KM | BDA | |
|---|---|---|---|---|---|---|---|---|
| CA | 0.864 | 0.616 | 0.455 | 0.88 | 0.785 | |||
| OF | 0.887 | 0.664 | 0.249 | 0.903 | 0.343✶✶✶ | 0.815 | ||
| KM | 0.851 | 0.656 | 0.24 | 0.855 | 0.428✶✶✶ | 0.261✶✶✶ | 0.81 | |
| BDA | 0.848 | 0.584 | 0.455 | 0.854 | 0.675✶✶✶ | 0.499✶✶✶ | 0.490✶✶✶ | 0.764 |
| Significance of Correlations: † p < 0.100 ✶ p < 0.050 ✶✶ p < 0.010 ✶✶✶ p < 0.001 | ||||||||
Structural Model and Testing of Hypothesis
The model fit measures for model 1 (CFA) which has the latent variables is shown in Table 12.5. Latent variables along with its indicators are CA: competitive advantage along with four indicators: CA1, CA2, CA3, and CA4; OF: organizational flexibility has four indicators OF1, OF2, OF3, and OF4; KM: knowledge management has three indicators KM1, KM2, and KM3; and BDA: big data analytics has four indicators BDA1, BDA2, BDA3, and BDA4. The CMIN/Df is 5.354; goodness of fit indices (GFI) is 0.845 (Forza & Filippini, 1998; Greenspoon & Saklofske, 1998); incremental fit index (IFI) is 0.894, Tucker-Lewis’s coefficient (TLI) is 0.883; comparative fit index (CFI) is 0.889; parsimony comparative fit index (PCFI) is 0.681; parsimony normed fit index (PNFI) is 0.660. All the items’ loadings were greater than 0.5 and SE <± 2.5, which is acceptable.
SEM was used to check the hypotheses (Byrne, 2010). AMOS 22.0 was used for this assessment due to its astounding practical portrayals and easy to use coalition. Figure 12.2 displays last model and dependent variable, their latent variable, and their indicators. The indicators along with the latent variables are KS: knowledge sharing along with three indicators: KS1, KS2, KS3, and KS4; BPP: business partners’ pressure has four indicators BPP1, BPP2, BPP3, and BPP4; HAS: higher authority support has three indicators HAS1, HAS2, and HAS3; BTA: blockchain technology analytics has four indicators BTA1, BTA2, BTA3, and BTA4. The dependent variable is SCP: supply chain performance with four indicators SCP1, SCP2, SCP3, and SCP4.
Table 12.6 shows the fit indices. For the final model, the value of CMIN is 588.592, and df is 145. Estimations of absolute fit indices are: CMIN/Df 4.059, which is lower than 5, it is an accepted value (McIver& Carmines, 1981). The GFI value is 0.834 (Forza & Filippini, 1998; Greenspoon & Saklofske, 1998) and RMSEA value is 0.08 that is within acceptable value of 0.08. TLI is 0.901, IFI is 0.914, and CFI is 0.90; PCFI is 0.734; PNFI is 0.704, which are acceptable and within accepted level (Byrne 2010). Figure 12.2 displays final structural model achieved in AMOS 22.0.
Table 12.6:Final goodness of fit indices for the CFA and structural model.
| Goodness-of-fit Indices | Model 1 | Final model | Benchmark |
|---|---|---|---|
| CMIN/Df | 5.354 | 4.059 | Lower Limit:1.0; Upper Limit 2.0/3.0 or 5.0 |
| GFI | 0.845 | 0.834 | >0.80 |
| Absolute badness of fit measure | |||
| RMSEA | 0.089 | 0.08 | ⩽0.08 |
| Incremental fit measure | |||
| CFI | 0.889 | 0.901 | ⩾0.90 |
| IFI | 0.894 | 0.914 | ⩾0.90 |
| TLI | 0.883 | 0.90 | ⩾0.90 |
| Parsimony fit measure | |||
| PCFI | 0.681 | 0.734 | ⩾0.50 |
| PNFI | 0.660 | 0.704 | ⩾0.50 |
Figure 12.2: Final structural model.
Hence, we can see that the model fit values of the final model are better than model 1. The mediating variable (BTA) and dependent variable plays a significant contribution along with the three latent variables in establishing the model fit.
The path estimates analysis results had been displayed in Table 12.6. Output displays four hypotheses which are accepted with by P-value (Hair, et al. 2010). The three factors, KM, OF, and CA along with mediating variable BDA have positive impacts on SCA. The square multiple correlation (R2) assists with estimating how well a relapse line gauges the genuine information focuses somewhere in the range of 0 and 1, which adds how adequately a variable is anticipating another (Hair, et al., 2010). The more the worth is more like 1, the better is the model’s capacity to foresee that innovation (Kline, 2015). Model proposed defined variance of 61 percent in BDA and variance of 54 percent in SCA (Table 12.7).
Table 12.7:Path estimate results.
| Estimate | S.E. | C.R. | P | |
|---|---|---|---|---|
| BDA<—KM | 0.245 | 0.049 | 5.000 | ✶✶✶ |
| BDA<—OF | 0.280 | 0.048 | 5.833 | ✶✶✶ |
| BDA<—CA | 0.487 | 0.083 | 5.867 | ✶✶✶ |
| SCA<—BDA | 0.737 | 0.127 | 5.803 | ✶✶✶ |
Discussion
The objective of the research is to find out the utilization of big data in the achievement of supply chain agility for the firms. Earlier research by various researchers also supports the BDA in SCA (Chen, 2019; Dubey et al., 2018; Gligor et al., 2016; H. Liu et al., 2013; Y. Liu et al., 2009; Nayak et al., 2021; Zhang et al., 2017). This research is further extended and added KM as one of the independent variables to achieve SCA in the adoption of BDA. The α-worth and CR values for all four variables were more than 0.7, that is, acceptance level (Nunnally 1978; Hair et al. 2010). The KMO is more than 0.6 which allows for performing EFA (Hair et al., 2010). Total variance explained was 73.324 percent, and rotated component matrix, factors were assembled under three gatherings. Just loadings with values above |.40| are kept in this exploration because those are viewed as normally high and subsequently are more critical (Hair et al., 2010).
Latent variables along with their indicators are KS: knowledge sharing along with three indicators: KS1, KS2, KS3, and KS4; BPP: business partners’ pressure has four indicators BPP1, BPP2, BPP3, and BPP4; HAS: higher authority support has three indicators HAS1, HAS2, and HAS3; BTA: blockchain technology analytics has four indicators BTA1, BTA2, BTA3, and BTA4. The dependent variable is SCP: supply chain performance with four indicators SCP1, SCP2, SCP3, and SCP4. There are 11 total indicators that were categorized under three different components. Varimax was utilized as a rotation technique. OF1, OF2, OF3, and OF4 are assembled under the primary segment with upsides of 0.842, 0.826, 0.877, and 0.828. CA1, CA2, CA3, and CA4 are gathered under the subsequent segment having values 0.823, 0.831, 0.798, and 0.683. BDA1, BDA2, BDA3, and BDA4 are assembled under the third segment with values 0.683, 0.752, 0.810 and 0.748. KM1, KM2, and KM3 are assembled under the fourth part with values 0.849, 0.890, and 0.786. This shows that it has very high loadings (>|.40|).
For final model, the value of CMIN is 588.592, and df is 145. Absolute fit indices estimations are CMIN/Df 4.059, which is lower than 5, its accepted value (McIver& Carmines, 1981). The GFI value is 0.834 (Forza & Filippini, 1998; Greenspoon & Saklofske, 1998) and RMSEA value is 0.08, which is within the acceptable value of 0.08. TLI is 0.901, IFI is 0.914, and CFI is 0.90; PCFI is 0.734; PNFI is 0.704, which are acceptable and within accepted levels (Byrne, 2010). Here, the elements are described and substantial with assistance of SEM approach, its most proper strategy to state evidence. This method has not been used in earlier studies, which is why it is unique.
Conclusion
The aim of this chapter is to investigate how various organizations use big data in the system to achieve supply chain agility. Three independent variables were identified from the literature—competitive advantages, organizational flexibility, and knowledge management. Two more variables were found, one mediating variable, big data analytics, and one dependent variable, supply chain agility. A questionnaire was prepared for survey-based research. The target population were mainly IT employees working in various manufacturing companies in India. After the collection of information was inspected, it was determined that the information was not biased. For further analysis EFA and SEM approaches were being used. The software being used was SPSS 20.0 and AMOS 22.0. A model being developed that showed a good fit and proposed hypothesis was accepted. Further study can be extended to various other industries like healthcare, retail, and many others.
References
Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to Improve Firm Performance Using Big Data Analytics Capability and Business Strategy Alignment? International Journal of Production Economics, 182: 113–131. https://doi.org/10.1016/j.ijpe.2016.08.018 →
Blome, C., Schoenherr, T., & Rexhausen, D. (2013). Antecedents and Enablers of Supply Chain Agility and its Effect on Performance: A Dynamic Capabilities Perspective. International Journal of Production Research, 51(4): 1295–1318. https://doi.org/10.1080/00207543.2012.728011 →
Boyd, B. K., Takacs Haynes, K., Hitt, M. A., Bergh, D. D., & Ketchen, D. J. (2012). Contingency Hypotheses in Strategic Management Research. Journal of Management, 38(1): 278–313. https://doi.org/10.1177/0149206311418662 →
Braunscheidel, M. J., & Suresh, N. C. (2009). The Organizational Antecedents of a Firm’s Supply Chain Agility for Risk Mitigation and Response. Journal of Operations Management, 27(2): 119–140. https://doi.org/10.1016/j.jom.2008.09.006 a, b, c
Brusset, X. (2016). Does Supply Chain Visibility Enhance Agility? International Journal of Production Economics, 171: 46–59. https://doi.org/10.1016/j.ijpe.2015.10.005 →
Byrne, B. M. (2010). Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming, 2nd edition. New York: Routledge Taylor & Francis Group. a, b, c, d, e
Chen, C. J. (2019). Developing a Model for Supply Chain Agility and Innovativeness to Enhance Firms’ Competitive Advantage. Management Decision, 57(7): 1511–1534. https://doi.org/10.1108/MD-12-2017-1236 a, b
Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big Data Analytics in Operations Management. Production and Operations Management, 27(10): 1868–1883. https://doi.org/10.1111/poms.12838 →
DeVellis, R. F., Lewis, M. A., & Sterba, K. R. (2003). Interpersonal Emotional Processes in Adjustment to Chronic Illness. Social Psychological Foundations of Health and Illness, 3: 256–287.
Dubey, R., Gunasekaran, A., Childe, S. J., & Papadopoulos, T. (2018). Skills Needed in Supply Chain-Human Aand Social Capital Analysis in Third Party Logistics. Management Decision, 56(1): 143–159. https://doi.org/10.1108/MD-04-2017-0428 a, b, c, d
Eckstein, D., Goellner, M., Blome, C., & Henke, M. (2015). The Performance Impact of Supply Chain Agility and Supply Chain Adaptability: The Moderating Effect of Product Complexity. International Journal of Production Research, 53(10): 3028–3046. https://doi.org/10.1080/00207543.2014.970707 →
Eidizadeh, R., Salehzadeh, R., & Chitsaz Esfahani, A. (2017). Analysing the Role of Business Intelligence, Knowledge Sharing and Organisational Innovation on Gaining Competitive Advantage. Journal of Workplace Learning, 29(4): 250–267. https://doi.org/10.1108/jwl-07-2016-0070 →
Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic Capabilities: What Are They? Strategic Management Journal, 21(10–11): 1105–1121. https://doi.org/10.1002/1097-0266(200010/11)21:10/11<1105::AID-SMJ133>3.0.CO;2-E →
Fisher, M. L. (2011). What Is the Right Supply Chain for Your Product? New York: Harvard Business Publishing. →
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39–50. a, b
Forza, C., & Filippini, R. (1998). TQM Impact on Quality Conformance and Customer Satisfaction: A Causal Model. International Journal of Production Economics, 55(1): 1–20. a, b, c
Frisk, J. E., & Bannister, F. (2017). Improving the Use of Analytics and Big Data by Changing the Decision-Making Culture: A Design Approach. Management Decision, 55(10): 2074–2088. https://doi.org/10.1108/MD-07-2016-0460 →
Galbraith, J. R. (1973). Designing Complex Organizations. New York: Addison-Wesley Longman Publishing Co., Inc. →
Galbraith, J. R. (1974). Organization Design: An Information Processing View. Interfaces, 4(3): 28–36. →
Galbraith, J. R. (2014). Organizational Design Challenges Resulting from Big Data. https://papers.ssrn.com/abstract=2458899 →
Gligor, D. M., Holcomb, M. C., & Feizabadi, J. (2016). An Exploration of the Strategic Antecedents of Firm Supply Chain Agility: The Role of a Firm’s Orientations. International Journal of Production Economics, 179: 24–34. https://doi.org/10.1016/j.ijpe.2016.05.008 a, b, c, d
Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S. F., Childe, S. J., Hazen, B., & Akter, S. (2017). Big Data and Predictive Analytics for Supply Chain and Organizational Performance. Journal of Business Research, 70: 308–317. https://doi.org/10.1016/j.jbusres.2016.08.004 a, b
Greenspoon, P. J., & Saklofske, D. H. (1998). Confirmatory Factor Analysis of the Multidimensional Students’ Life Satisfaction Scale. Personality and Individual Differences, 25(5): 965–971. a, b, c
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010). Cluster Analysis. Multivariate Data Analysis, 7th ed. Upper Saddle River, NJ: Pearson University Press. a, b, c, d, e, f, g, h, i, j, k
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance. Long Range Planning, 46(1–2): 1–12. a, b, c
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The Use of Partial Least Squares Path Modeling in International Marketing. New Challenges to International Marketing, 20: 277–319. →
Hinterhuber, A. (2013). Can Competitive Advantage be Predicted?: Towards a Predictive Definition of Competitive Advantage in the Resource-Based View of the Firm. Management Decision, 51(4): 795–812. https://doi.org/10.1108/00251741311326572 →
Jabbour, C. J. C., Jabbour, A. B. L. de S., Sarkis, J., & Filho, M. G. (2019). Unlocking the Circular Economy Through New Business Models Based on Large-Scale Data: An Integrative Framework and Research Agenda. Technological Forecasting and Social Change, 144: 546–552. https://doi.org/10.1016/j.techfore.2017.09.010 →
Ji-fan Ren, S., Fosso Wamba, S., Akter, S., Dubey, R., & Childe, S. J. (2017). Modelling Quality Dynamics, Business Value and Firm Performance in a Big Data Analytics Environment. International Journal of Production Research, 55(17): 5011–5026. https://doi.org/10.1080/00207543.2016.1154209 →
Kline, R. B. (2015). Principles and Practice of Structural Equation Modelling. New York: Guilford publications. a, b
Kline, R. B. (2011). Principles and Practice of Structural Equation Modelling, 3rd ed. New York: Guilford Press.
Khan, A. K., & Pillania, R. K. (2008). Strategic Sourcing for Supply Chain Agility and Firms’ Performance: A Study of Indian Manufacturing Sector. Management Decision, 46(10): 1508–1530. https://doi.org/10.1108/00251740810920010 →
LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big Data, Analytics and the Path from Insights to Value. MIT Sloan Management Review, 52(2): 21–32. →
Lee, H. L. (2002). Aligning Supply Chain Strategies with Product Uncertainties. California Management Review, 44(3): 105–119. https://doi.org/10.2307/41166135 a, b
Lee, H. L. (2004). The Triple-A Supply Chain. www.hbr.org a, b
Liu, H., Ke, W., Wei, K. K., & Hua, Z. (2013). The Impact of IT Capabilities on Firm Performance: The Mediating Roles of Absorptive Capacity and Supply Chain Agility. Decision Support Systems, 54(3): 1452–1462. https://doi.org/10.1016/j.dss.2012.12.016 a, b
Liu, Y., Li, Y., & Wei, Z. (2009). How Organizational Flexibility Affects New Product Development in an Uncertain Environment: Evidence from China. International Journal of Production Economics, 120(1): 18–29. https://doi.org/10.1016/j.ijpe.2008.07.026 a, b
Lugmayr, A., Stockleben, B., Scheib, C., & Mailaparampil, M. A. (2017). Cognitive big data: Survey and review on big data research and its implications. What is really “new” in big data? Journal of Knowledge Management, 21(1): 197–212. https://doi.org/10.1108/JKM-07-2016-0307 →
Mellat-Parast, M., & Spillan, J. E. (2014). Logistics and supply chain process integration as a source of competitive advantage: An empirical analysis. International Journal of Logistics Management, 25(2): 289–314. https://doi.org/10.1108/IJLM-07-2012-0066 →
Malhotra, M.K. and Grover, V. (1998). An assessment of survey research in POM: From constructs to theory. Journal of Operations Management, 16(4): 407–425. →
McIver, J., & Carmines, E. G. (1981). Unidimensional Scaling, Volume 24. Beverly Hills: SAGE. a, b
Moerdyk, A., (2009). The Principles and Practice of Psychological Assessment. Pretoria, South Africa: Van Schaik. →
Nayak, B., Bhattacharyya, S. S., & Krishnamoorthy, B. (2021). Strategic Advantage Through Social Inclusivity: An Empirical Study on Resource Based View in Health Insurance Firms in India. Journal of Cleaner Production, 298: 126805. https://doi.org/10.1016/j.jclepro.2021.126805 a, b
Nunnally, J. C. (1978). An Overview of Psychological Measurement. Clinical Diagnosis of Mental Disorders, 34: 97–146. →
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory, 3rd ed. Chicago: McGraw-Hill. →
Pagell, M., & Krause, D. R. (1999). A Multiple-Method Study of Environmental Uncertainty and Manufacturing Flexibility. Journal of Operations Management, 17(3): 307–325. →
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common Method Biases in Behavioural Research: A Critical Review of the Literature and Recommended Remedies. Journal of Applied Psychology, 88(5): 879. →
Porter, M.E. (1985). Competitive Advantage: Creating and Sustaining Superior Performance. New York: Free Press. →
Prescott, M. E. (2014). Big Data and Competitive Advantage at Nielsen. Management Decision, 52(3): 573–601. https://doi.org/10.1108/MD-09-2013-0437 →
Raffoni, A., Visani, F., Bartolini, M., & Silvi, R. (2018). Business Performance Analytics: Exploring the Potential for Performance Management Systems. Production Planning & Control, 29(1): 51–67. https://doi.org/10.1080/09537287.2017.1381887 →
Sanchez, R. (1993). Strategic Flexibility, Firm Organization, and Managerial Work in Dynamic Markets: A Strategic Options Perspective. Advances in Strategic Management, 9(1): 251–291. →
Sanchez, R. (1995). Strategic Flexibility in Product Competition. Strategic Management Journal, 16(S1): 135–159. →
Sethi, A. K., & Sethi, S. P. (1990). Flexibility in Manufacturing: A Survey. International Journal of Flexible Manufacturing Systems, 2(4): 289–328. →
Singh, D., Oberoi, J. S., & Ahuja, I. S. (2013). An Empirical Investigation of Dynamic Capabilities in Managing Strategic Flexibility in Manufacturing Organizations. Management Decision, 51(7): 1442–1461. https://doi.org/10.1108/MD-05-2012-0332 →
Sirmon, D. G., Hitt, M. A., Arregle, J.-L., & Campbell, J. T. (2010). The Dynamic Interplay of Capability Strengths and Weaknesses: Investigating the Bases of Temporary Competitive Advantage. Strategic Management Journal, 31(13): 1386–1409. https://doi.org/10.1002/smj.893 →
Sousa, R., & Voss, C. A. (2008). Contingency Research in Operations Management Practices. Journal of Operations Management, 26(6): 697–713. https://doi.org/10.1016/j.jom.2008.06.001 →
Srinivasan, R., & Swink, M. (2018). An Investigation of Visibility and Flexibility as Complements to Supply Chain Analytics: An Organizational Information Processing Theory Perspective. Production and Operations Management, 27(10): 1849–1867. https://doi.org/10.1111/poms.12746 a, b, c, d, e
Swafford, P. M., Ghosh, S., & Murthy, N. (2006). The Antecedents of Supply Chain Agility of a Firm: Scale Development and Model Testing. Journal of Operations Management, 24(2): 170–188. https://doi.org/10.1016/j.jom.2005.05.002 a, b, c
Swamidass, P. M., &Newell, W. T. (1987). Manufacturing Strategy, Environmental Uncertainty and Performance: A Path Analytic mModel. Management Science, 33(4): 509–524. →
Tabachnick, B. G., & Fidell, L. S., (2007). Using Multivariate Statistics, 5th ed. Boston: Pearson.
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic Capabilities and Strategic Management. Strategic Management Journal, 18(7): 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z →
Upton, D. M. (1994). The Management of Manufacturing Flexibility. California Management Review, 36(2): 72–89. →
Volberda, H. W. (1996). Toward the Flexible Form: How to Remain Vital in Hypercompetitive Environments. Organization Science, 7(4): 359–374. https://doi.org/10.1287/orsc.7.4.359 →
Wang, G., Gunasekaran, A., Ngai, E. W. T., & Papadopoulos, T. (2016). Big Data Analytics in Logistics and Supply Chain Management: Certain Investigations for Research and Applications. International Journal of Production Economics, 176: 98–110. https://doi.org/10.1016/j.ijpe.2016.03.014 →
Zhang, Y., Ren, S., Liu, Y., & Si, S. (2017). A Big Data Analytics Architecture for Cleaner Manufacturing and Maintenance Processes of Complex Products. Journal of Cleaner Production, 142: 626–641. https://doi.org/10.1016/j.jclepro.2016.07.123 a, b