The influence of big data analytics management capabilities on supply chain preparedness, alertness and agility: An empirical investigation

Santanu Mandal (Department of Operations and Information Technology, IBS Hyderabad, Hyderabad, India)

Information Technology & People

ISSN: 0959-3845

Publication date: 1 April 2019

Abstract

Purpose

The importance of big data analytics (BDA) on the development of supply chain (SC) resilience is not clearly understood. To address this, the purpose of this paper is to explore the impact of BDA management capabilities, namely, BDA planning, BDA investment decision making, BDA coordination and BDA control on SC resilience dimensions, namely, SC preparedness, SC alertness and SC agility.

Design/methodology/approach

The study relied on perceptual measures to test the proposed associations. Using extant measures, the scales for all the constructs were contextualized based on expert feedback. Using online survey, 249 complete responses were collected and were analyzed using partial least squares in SmartPLS 2.0.M3. The study targeted professionals with sufficient experience in analytics in different industry sectors for survey participation.

Findings

Results indicate BDA planning, BDA coordination and BDA control are critical enablers of SC preparedness, SC alertness and SC agility. BDA investment decision making did not have any prominent influence on any of the SC resilience dimensions.

Originality/value

The study is important as it addresses the contribution of BDA capabilities on the development of SC resilience, an important gap in the extant literature.

Keywords

Citation

Mandal, S. (2019), "The influence of big data analytics management capabilities on supply chain preparedness, alertness and agility: An empirical investigation", Information Technology & People, Vol. 32 No. 2, pp. 297-318. https://doi.org/10.1108/ITP-11-2017-0386

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Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited


Introduction

The recent business environment is heavily tormented by ever-increasing uncertainties and associated vulnerabilities (Ambulkar et al., 2015). Such unstable environments have increased the importance of supply chain (SC) risk mitigation strategies and associated SC capabilities, i.e. SC resilience. Resilience in SCs have received well recognition as an important dynamic capability (Ponomarov and Holcomb, 2009; Pettitt et al., 2010; Jüttner and Maklan, 2011; Ponomarov, 2012; Hohenstein et al., 2015; Ambulkar et al., 2015; Bellow, 2016; Mandal et al., 2016; Lam and Bai, 2016; Li et al., 2017). The essence of SC resilience is to restore operations to an optimal state or an improved state post-disruption (Jüttner and Maklan, 2011; Wieland and Wallenburg, 2013; Hohenstein et al., 2015; Bellow, 2016).

While studies have explored several dimensions of SC resilience (Li et al., 2017; Gu and Huo, 2017; Liu et al., 2018; Chowdhury and Quaddus, 2017; Cheng and Lu, 2017), the importance of big data analytics (BDA) in the development of principal dimensions of SC resilience is still lacking. BDA combines two major concepts: big data that refers to the capacity to process data with following characteristics: volume, variety and velocity (Wang, Gunasekaran, Ngai and Papadopoulos, 2016). The ability to draw meaningful conclusions from data through application of statistics, mathematics, econometrics, simulations, optimizations or other techniques to aid firms for better decision making (Accenture Global Operations Megatrends Study, 2014). BDA has been extensively attracting research attention for verification of existing theories and also for effective decision making for organizations (Muhtaroglu et al., 2013), especially in SC management (Wamba et al., 2015; Wang, Gunasekaran and Ngai, 2016; Wang, Gunasekaran, Ngai and Papadopoulos, 2016). BDA therefore deploys advanced analytics tools to derive valuable knowledge from enormous data, aiding in effective decision making (Tan et al., 2015). Most of the existing studies regarding the application of BDA within SC management have concentrated mainly on offering definitions through comprehensive literature reviews (Wamba et al., 2015; Wang, Gunasekaran and Ngai, 2016; Wang, Gunasekaran, Ngai and Papadopoulos, 2016; Waller and Fawcett, 2013). Consequently, the existing literature on BDA is still under development and therefore calls for empirical testing and theory development (Arunachalam et al., 2018; Wamba et al., 2017). Extant studies has stressed that BDA can aid in different areas of effective SC management, for example, in supply network design through proper analysis of service-level and penalty cost data (Wang, Gunasekaran and Ngai, 2016; Wang, Gunasekaran, Ngai and Papadopoulos, 2016), in product design and development through analysis of customer purchase record and online behavior (Afshari and Peng, 2015), in demand planning (Chase, 2013; Hassani and Silva, 2015) and procurement (Wang, Gunasekaran and Ngai, 2016; Wang, Gunasekaran, Ngai and Papadopoulos, 2016; Fan et al., 2015).

However, extant literature conceptualized BDA capabilities (Wamba et al., 2017) as a third-order formative construct of BDA management capability, BDA personnel expertise capability and BDA infrastructure flexibility capability. Each of these was further conceptualized as a second-order formative constructs of several first-order latent variables. In this regard, we argue that BDA management capability is of prime importance for successful SC management as they are directed toward developing coordination and control through appropriate planning and investments (Wamba et al., 2017). BDA management capabilities comprise of essential first-order capabilities of planning, investment decision making, coordination and control. BDA planning ensures that firms can effectively use data and information appropriate for planning future operation strategies. BDA investment decision-making capability aids firms to analyze and prioritize areas requiring investment for development. BDA coordination helps SC firms to effectively synchronize their operations through appropriate analysis and sharing of information. BDA control aids SC firms to maintain control over execution of key SC processes through appropriate analysis and information sharing. Hence, in line with extant literature (Wamba et al., 2017), BDA management capabilities of planning, investment decision making, coordination and control are prominent for effective planning and deployment of strategies in SC management (Wamba et al., 2017; Hazen et al., 2014; Waller and Fawcett, 2013).

Extant literature has suggested two prominent dimensions of SC resilience: proactive and reactive (Ponomarov and Holcomb, 2009; Välikangas, 2010; Wieland and Wallenburg 2013; Durach et al., 2015; Cheng and Lu, 2017). While the proactive strategy ensures the stability of the system to maintain its normal executions despite internal disturbances (Stonebraker et al., 2009; Cheng and Lu, 2017); the reactive strategy focuses on rapidly responding to market changes in an uncertain business environment (Wieland and Wallenburg 2013; Durach et al., 2015; Cheng and Lu, 2017). While studies emphasized either a proactive or reactive strategy sufficient for building SC resilience (Wieland and Wallenburg, 2013; Durach et al., 2015), the combined effect of these strategies would lead more effective resilience (Li et al., 2017). In this regard, Li et al. (2017) further added SC preparedness as the key to proactive dimension, while SC alertness and SC agility are key elements of the reactive dimension of SC resilience. Through SC preparedness, SCs can withstand disruptions, while maintaining connectedness and stability across processes, instead of adapting to changes through disruptions (Wallace and Choi, 2011). Furthermore, SC alertness aids in recognizing changes in a timely manner (Li et al., 2008), while SC agility refers to the capability to respond in a fast manner to changes through required process re-configurations (Swafford et al., 2006). Hence, SC preparedness, alertness and agility together constitute the mechanism through which SC resilience operates. As a result, the development of these key elements of SC resilience requires empirical attention. As BDA management capabilities ensures in effective SC management through appropriate planning, investments, coordination and control, their role in the development of key elements of SC resilience requires investigation. Accordingly, the current investigation addresses the following research questions:

RQ1.

Do BDA management capabilities of planning, investment decision making, coordination and control influence SC preparedness?

RQ2.

Do BDA management capabilities of planning, investment decision making, coordination and control influence SC alertness?

RQ3.

Do BDA management capabilities of planning, investment decision making, coordination and control influence SC agility?

Theoretical background

BDA management capabilities

IT capabilities have been classified in several ways. While IT capabilities may be manifested through value, heterogeneity and imperfect mobility directed for competitive advantage (Bhatt and Gorver, 2005); IT infrastructure quality, IT business expertise and learning intensity of firms also influence the degree of competitive advantage (Bhatt and Gorver, 2005). An important classification of IT capabilities undersigns three important IT capabilities through socio-materialistic lens, namely, IT management capability, IT personnel capability and IT infrastructure capability.

In similar lines, Wamba et al. (2017) conceptualized BDA capabilities as a third-order formative factor of three second-order formative factors: BDA management capability, BDA infrastructure flexibility capability and BDA personnel expertise capability. BDA management capabilities comprise of first-order capabilities of planning, investment decision making, coordination and control. BDA planning aids SC professionals in the development of procurement schedules, production schedules and risk mitigation strategy development through appropriate information collection, analysis and interpretation (Wamba et al., 2017). BDA investment decision making helps SC professionals to develop their IT infrastructure to aid real-time information sharing and decision making. BDA coordination emphasizes operation synchronization among SC entities through real-time information sharing. BDA control ensures appropriate authority and control over SC operations among different SC entities through appropriate software and hardware technologies (Wamba et al., 2017). The role of BDA management capabilities must be explored in their influence on the development of key SC resilience components, to understand their contribution to overall SC resilience development.

While the above conceptualization and classification of BDA capabilities followed that of IT capabilities; there is a difference between IT capabilities and BDA capabilities. While IT capabilities focus on the efficient implementation and usage of information technology resources and infrastructure, BDA capabilities focus more on data analysis, drawing meaningful insights and data-driven decision making. Hence, IT capabilities are more about the technical IT infrastructure (Bhatt and Gorver, 2005), while BDA capabilities are more concerned about the application of the IT infrastructure to share and analyze information to aid strategy formulation for routine and strategic operations (Wamba et al., 2017).

Studies have explored several facets of BDA and their interfaces in SC management. Hazen et al. (2014) suggested that appropriate analysis of big data can lead to meaningful decisions for effective SC management. However, data quality must be monitored to ensure success of such managerial decisions for effective SC management. In this regard, Schoenherr and Speier‐Pero (2015) further added that predictive analytics in SC has tremendous potential to transform SC management completely and, hence, universities should incorporate predictive analytics courses and applications in SC management. Furthermore, SC analytics “helps organizations measure the performance of various areas in logistics and supply chain management and provide them with the ability to establish a benchmark to determine value-added operations” (Wang, Gunasekaran, Ngai and Papadopoulos, 2016, p. 107). Such SC analytics found application across firms through four capability levels: functional, process-based, collaborative and agile SCA (Wang, Gunasekaran and Ngai, 2016; Wang, Gunasekaran, Ngai and Papadopoulos, 2016). Hence, SC analytics can act as a strategic resource for SC firms to overhaul SC operations for achieving optimal performance. In similar lines, Gunasekaran et al. (2017) found connectivity and information sharing to be positively related with big data and predictive analytics (BDPA) acceptance under mediating effect of top management commitment. Furthermore, such BDPA acceptance enhances BDPA assimilation and positively impacts SC performance and organizational performance. Hence, BDA can improve SC management, if can be strategically utilized. Zhong et al. (2016), in their extensive review of the state of BDA in SC management found that companies like Fedex Corporation, UPS, Royal Mail Group Limited, DHL express and several others are devising on several strategies to improve logistics and SC management through effective deployment of BDA. For example, Royal Mail Group planned to develop a multi-function capacity to use big data methods to encourage innovation in operations (LMG, 2014); DHL express started to publish reports to show the implementation of BDA to develop solutions for logistics problems (Martin et al., 2013); and Fedex has successfully developed a solution for its customers to track their products, based on big data (Capron, 2013).

Addo-Tenkorang and Helo (2016), in their comprehensive literature review of big data in SC management between 2010 and 2015, found that BDA can add “value” – a fifth dimension, based on their applications across diverse industry segments. Traditionally, four dimensions, namely, variety, velocity, volume and veracity of BDA were identified. Consequently, the study proposed “Big data II” (IoT – value-adding) framework to enable SC managers to utilize BDA to complement their routine decision making and strategy development process. In this way, BDA can aid SC firms to enhance “value-addition” to effective SC management. While there is a persistent growth of big data, a scarcity of adequate data analytics techniques often restricts firms to realize the full benefits in terms of innovation offered by big data. In this regard, Tan et al. (2015) proposed an analytic infrastructure using deduction graph technique. The proposed analytic infrastructure would allow firms to successfully take advantage of big data to enhance their SC innovation capabilities. More recently, Tiwari et al. (2018) highlighted that BDA can aid in supplier management decision through providing insight on firm spending pattern (Panchmatia, 2015); can aid in supply network design through proper analysis of service level and penalty cost data (Wang, Gunasekaran and Ngai, 2016; Wang, Gunasekaran, Ngai and Papadopoulos, 2016); can aid in product design and development through analysis of customer purchase record and online behavior (Afshari and Peng, 2015); and can further aid in demand planning (Chase, 2013; Hassani and Silva, 2015), procurement (Wang, Gunasekaran and Ngai, 2016; Wang, Gunasekaran, Ngai and Papadopoulos, 2016; Fan et al., 2015), production (Stich et al., 2015; Katchasuwanmanee et al., 2016), inventory logistics and distribution (Mehmood and Graham, 2015; Brouer et al., 2016), SC agility (Giannakis and Louis, 2016) and sustainability (Wu et al., 2017; Zhao et al., 2017). Furthermore, Hazen, Skipper, Ezell and Boone (2016) suggested eight theories, namely, actor-network theory, social capital theory, institutional theory, resource-dependence theory, transaction cost economics, agency theory, resource-based view (RBV) and ecological modernization theory, to explore BDPA’s influence on SC sustainability. Roßmann et al. (2017) undertook a Delphi survey to understand the future of BDA in SC management. Their study found that BDA likely to aid in accurate demand forecasting, improve supplier performance and decrease safety stocks. Arunachalam et al. (2018), through a comprehensive review of 82 papers, suggested that SC firms must deploy strategically to reap the vast potential in transforming SC operations. Furthermore, the study suggested several problems like privacy and security concerns, insufficient resources, behavioral issues, lack of skills, etc., to restrict the adoption of BDA. However, the success of BDA is transforming organization and SC operations depends on SC firms to develop their BDA capabilities. Hence, the current study further adds to the growing literature on BDA in SC management through exploring the influence of BDA management capabilities in the development of SC preparedness, alertness and agility.

The next section discussed the RBV and associated dynamic capability conceptualization of SC resilience. The subsequent section discusses the hypotheses development and research methodology. The study concludes with a discussion of the findings and managerial implications.

A dynamic capability view on SC preparedness, SC alertness and SC agility

The RBV of the firm (Barney et al., 2001) has been instrumental in explaining the differences in firm performances. Hence, studies have found RBV extremely useful in exploring the development of SC management capabilities (Yu, Chavez, Jacobs and Feng, 2017; Chavez et al., 2017; Yu, Jacobs, Chavez and Feng, 2017; Cheng and Lu, 2017; Huo et al., 2016; Prajogo et al., 2016). For example, Cheng and Lu (2017) investigated the impact of operating frontier, trajectory and absorptive capacity on the proactive and reactive dimension of SC resilience using RBV; Prajogo et al. (2016) utilized relational lens of RBV to understand several value chain processes between supply logistics integration and operational performance of firms. However, we content that SC resilience is an important dynamic capability (Teece et al., 1997) that can be developed due to appropriate deployment of firm-level resources and capabilities with suitable integration at the SC level (Ponomarov, 2012; Ponomarov and Holcomb, 2009; Jüttner and Maklan, 2011; Wieland and Wallenburg, 2013).

RBV explained the difference in firm performance to different resource ownership of firms (Barney et al., 2001). Employee skill and talent are usually referred to as intangible resources while firm infrastructure may be undersigned as tangible ones (Amit and Schoemaker, 1993). This RBV was further extended by Teece et al. (1997) suggesting that firms need to adapt to the changes and uncertainties in their business environment through suitable modifications in their resources, capabilities and routines. Firms’ capacity to adapt to such dynamic changes to business conditions are referred to as dynamic capabilities (Teece et al., 1997; Teece, 2016; Makadok, 2001; Eisenhardt and Martin, 2000). SC resilience is rightly attributed as a dynamic capability of SCs as they enable SC firms to restore operations in the event of a disruption (Ponomarov, 2012; Ponomarov and Holcomb, 2009; Chowdhury and Quaddus, 2017).

However, studies have undersigned several dimensions of SC resilience (Liu et al., 2018; Chowdhury and Quaddus, 2017; Cheng and Lu, 2017). Hence, to understand the development of SC resilience as a dynamic capability, it is required to understand the development of its component capabilities (Liu et al., 2018). The study contends that the component capabilities of SC preparedness, SC alertness and SC agility are also higher order dynamic capabilities that can be developed to respond to environmental changes in a positive manner (Li et al., 2017). SC firms need to prepare for uncertainties through appropriate contingency planning and development. SC alertness helps to aware SC firms of any upcoming emergency through real-time communication and information sharing (Li et al., 2017). SC agility aids in responding to environmental changes at a fast pace. Hence, the study contends SC preparedness, SC alertness and SC agility as dynamic capabilities as they aid SC firms to adapt to environmental changes in a positive manner (Mandal et al., 2016; Denrell and Powell, 2016).

In this regard, we argue in line with RBV and its dynamic capability extension (Teece, 2017) that such dynamic capabilities are higher order SC-level capabilities developed due to the suitable culmination of firm-level BDA-based management capabilities (Wilden et al., 2016). BDA management capabilities of planning, investment decision making, coordination and control, when deployed appropriately, positively influence the development of SC preparedness, SC alertness and SC agility. These SC capabilities enable SCs to accumulate, convert and reorganize resources to respond and adapt to changing business scenarios.

Hypotheses development

BDA management capabilities and SC preparedness

BDA management capabilities are analytics-based capabilities that aid SC firms to collect, exchange and analyze information on a real-time basis and make meaningful decisions required for supporting routine and strategic operations of the SC (Wamba et al., 2017). BDA planning ensures SC firms can develop their resource and production planning schedules through the appropriate decision making regarding procurement schedules and contingency planning. Further, BDA investment decision making aids SC firms to analyze resource investments and prioritize them based on available funds, and thereby aids in IT infrastructure development and implementation of emerging software technologies to support SC operations. BDA coordination aims to develop appropriate synchronization of operations of SC firms through real-time information sharing. BDA control aids SC firms to gain control over SC operations and synchronization through the collection, analysis and routine decision making through analytics.

SC preparedness is the capacity of an SC to tolerate the impact of probable changes (Tang, 2006). SC firms develop business contingency plans that make an SC ready to sustain well for different business scenarios (Christopher and Peck, 2004). Every SC firm has a certain set of routines and protocols that must be synchronized with one another based on mutual collaboration for responding to changes (Lee, 2004). Several established procedures are practiced for operation synchronization within SCs. In the first place, SC partner selection practice influences the stability of an SC network in uncertain business conditions (Krause et al., 2007) because of partner’s reliability (MacCarter and Northcraft, 2007) and motivation for collaboration (Cao and Zhang, 2011; Miles and Snow, 2007). Second, there should be real-time information sharing for transparency of forecasts, sales data and plans with SC partners (Hult et al., 2004; Ketchen and Hult, 2007). Third, incentive schemes should be made transparent with clear policies and guidelines (Morgan et al., 2007).

In this context, BDA planning and BDA investment decision making aid in appropriate SC partner selection through suitable performance monitoring and feedback in real time. BDA investment decision making suggests firms continue their existing partner relationship or to make new ones based on performance feedback and investment decisions. BDA coordination and control aids in enhanced SC preparedness through increased operation synchronization through sharing forecasts, sales data and plans. Hence, BDA management capabilities positively influence SC preparedness for unforeseen events. Accordingly, we hypothesize that:

H1a.

BDA planning positively influences SC preparedness.

H1b.

BDA investment decision making positively influences SC preparedness.

H1c.

BDA coordination positively influences SC preparedness.

H1d.

BDA control positively influences SC preparedness.

BDA management capabilities and SC alertness

SC alertness refers to a capability for identifying threats or changes in the business environment in a timely manner (Li et al., 2009). Such challenges may arise from supplier network or operating business environment due to competitor’s movement and strategies (Christopher and Holweg, 2011). It is required for SC focal firms to recognize problems and changes due to dynamicity in internal SC network for enhanced visibility in SCs (Christopher and Peck, 2004). Further, such higher visibility results in acknowledging complex interactions of firm’s capabilities with changes in SC processes (Sambamurthy et al., 2003).

BDA planning aids SC firms to develop business continuity plans for effectively addressing changes in their internal supplier network or the external business environment. BDA investment decision making aids SC focal firms to develop their resources and capabilities required for supporting their contingency plans. BDA coordination and control aid SC firms to develop a synergistic effort to respond in a positive manner to environmental changes. Hence, BDA management capabilities help SC firms to identify changes and respond to the same in a positive manner. Hence, we hypothesize that:

H2a.

BDA planning positively influences SC alertness.

H2b.

BDA investment decision making positively influences SC alertness.

H2c.

BDA coordination positively influences SC alertness.

H2d.

BDA control positively influences SC alertness.

BDA management capabilities and SC agility

SC agility implies an SC capability to respond to changes in a fast manner through suitable adaptation to environmental situations (Lee, 2004). SC agility also refers to an adaptation orientation in practicing different SC processes and SC facilities in the accomplishment of SC goals (Swafford et al., 2006; Dubey et al., 2017). Manuj and Mentzer (2008) suggested speed as the intrinsic quality of SC agility. Studies have highlighted SC agility goals in different ways. For example, SC agility aims to alter critical SC processes quickly to address dynamicity in supply/demand (Gligor and Holcomb, 2012), reduction of lead time and product development cycle times, etc. (Blome et al., 2013). Hence, SC agility aims to respond in a positive manner to business environment changes.

BDA planning aids SC focal firms to alter their resource planning in a fast manner to meet changes in the marketplace on time. BDA investment decision making helps SC firms to carefully select resources for upgradation and development to obtain complimentary benefits for addressing market changes in a rapid manner. BDA coordination and control aids SC firms to re-align their individual resources and capabilities in a synergistic manner in a fast manner. Consequently, SC firms can respond quickly to environmental changes. Hence, we hypothesize that:

H3a.

BDA planning positively influences SC agility.

H3b.

BDA investment decision making influences SC agility.

H3c.

BDA coordination positively influences SC agility.

H3d.

BDA control positively influences SC agility.

Figure 1 shows the proposed hypotheses in a theoretical model.

Research methodology

Sample and data collection

The study employed several constructs with established scales of measurement. The constructs of BDA planning, investment decision making, coordination and control have developed scales based on recent literature (Wamba et al., 2017). For SC preparedness, alertness and agility, extant scales of measurement were referred as benchmarks for further improvement (Li et al., 2017). The study utilized Churchill’s (1979) guidelines to develop the measures. An expert team consisting of IT professionals engaged in analytics (with a minimum experience of five years) were chosen to review and revise the items (as necessary). Further, the measures were pre-tested with 32 IT professionals engaged in different industry sector involving SC management, increasing the face and content validity of the survey items. The final survey questionnaire was developed in Google Docs, and the survey link was mailed to 726 IT professionals with a minimum experience of two years in the field of analytics related to SC management. A list of such professionals was procured from a marketing research firm. The respondents were mailed a gentle reminder after a gap of two weeks. Repeated polite reminders were also made through phone (wherever possible). The survey participation also carried a token of appreciation of $3. These resulted in 249 final responses yielding a response rate of 34.29 percent. Table I shows the profile of final respondents.

As shown in Table I, 48.59 percent of the respondents have experience between 5 and 10 years, and 22.89 percent have to experience between 10 and 15 years. Further, 22.89 percent were engaged in manufacturing analytics, 15.26 percent involved in construction analytics, 29.32 percent engaged in retail analytics and 10.44 percent were engaged in textile analytics. Overall, the diversified sample ensured greater generalizability and validity of the findings.

Measures

Our study involved four first-order factors from BDA management capabilities and three first-order factors, namely, SC preparedness, SC alertness and SC agility. The extant scales of these seven factors were used as an initial platform for further refinement by the guidelines of Churchill (1979). The study executed an extensive literature review for the seven constructs for identification of initial survey items. Further, an expert group consisting of six practitioners and four researchers (with extensive experience in analytics and SC management) were requested to criticize the scale items for their expression and clarity and suggest suitable modifications.

The final measures went pre-testing with 59 contacts chosen randomly from the contacts developed earlier. Of these 59 respondents, 17 were in manufacturing analytics with average experience>6.5 years, 24 were in retail analytics with average experience >7.25 years, 8 were in construction analytics and remaining 10 were in textile analytics with average experience>8.25 years. Such a diversified and experienced sample suggested relevant face and content validity for the measures.

After suitable modifications and subsequent adaptations, every latent first-order factor had around four measurement items that were measured using a seven-point Likert scale (1=strongly disagree, 7=strongly agree).

Non-response bias

The study evaluated the presence/absence of non-response bias through early and late response comparison (Armstrong and Overton, 1977). The study did not find any significant differences between the means. Furthermore, the study employed Mann–Whitney U-tests for the two categories of responses that did not reveal any meaningful differences (p>0.05) across firm size and industry. The findings suggested the absence of any significant non-response bias.

Common method bias

An assessment of common method bias was deemed necessary since a single participant per firm was approached. Analysis of Harman’s single-factor test of common method bias (Podsakoff, 2003) showed seven factors with eigenvalues>1, explaining 81.6 percent variance. The first factor explained 31.5 percent variance. Next, a confirmatory factor analysis to Harman’s single-factor model was applied (Flynn et al., 2010). The model’s fit indices of χ2/df=15.3, NFI=0.55, CFI=0.61 and RMSEA=0.15 suggested rejection of a single-factor model. Hence, common method bias is not of significant concern in this study.

Path modeling and hypotheses testing

The study executed an exploratory factor analysis to check if the items are loading appropriately. Barlett’s test of sphericity evaluated to 10,484.75, and was significant. Further, KMO was 0.828 (threshold is 0.5), implying satisfactory results for exploratory factor analysis.

The factor analysis showed few items that did not load appropriately and therefore was eliminated (IDMC2, CON4, PREP1, ALERT3, AG2). Partial least squares was widely used for hypotheses testing (Peng and Lai, 2012). Apart from several major reasons like presence of formative construct, minimal sample size, non-normal data, etc., we argue that the current study employed PLS for hypotheses testing since path modeling is of the primary concerns here. Since the whole objective of this study is to check the validity of the proposed paths, we utilized PLS instead of structural equation modeling (SEM). SEM is more stringent in that several assumptions like multivariate normality, etc., must be satisfied. PLS is more relaxed in such sense and focuses on the validity of the path coefficients. In adherence to prescribed guidelines of Peng and Lai (2012), the study utilized PLS for checking the validity of the proposed paths. Further, a component-based approach is more practical so far as hypotheses testing are concerned. Also, PLS is deemed suitable for predictive purposes (Peng and Lai, 2012).

The sample size while using PLS should be >10 times the number of items of the largest latent variable. Since significance tests are absent in PLS, a bootstrapping analysis was executed with 1,000 sub-samples (Chin, 1998; Peng and Lai, 2012) for calculating the path coefficient, statistical significance and allied parameters. The study assessed first reliability, followed by an assessment of convergent and divergent validity.

Table II shows the latent variables with item loadings and corresponding t-stats. Further, Table III shows the average variance extracted (AVE), Cronbach’s α and composite reliability for each latent variable.

The study first assessed reliability using the criterion, Cronbach’s α>0.7 (Chin, 1998). Since all the latent variables reported to have Cronbach’s α>0.7, the measures are deemed reliable. Convergent validity was next assessed using multiple criteria: item loading>0.70 and statistical significance; composite construct reliability>0.80; and AVE>0.50 (Fornell and Larcker, 1981). Further, discriminant validity was assessed using the criterion: the square root of AVE for each latent variable>its correlations with other latent variables (Fornell and Larcker, 1981).

As indicated in Tables II and III, standardized item loadings range from 0.867 to 0.973, composite reliabilities range from 0.779 to 0.972 and AVE values range from 0.776 to 0.911. In Table IV, the square root of AVE for each construct is larger than its correlations with all other constructs. Hence, these results show a highly acceptable level of reliability, convergent and discriminant validity. In addition, we have included Table V showing cross-loadings as other quality criteria.

Structural model assessment

The study utilized partial least squares in SmartPLS 2.0.M3 to estimate the value of path coefficients. However, in line with Peng and Lai (2012), the study assessed the validity of the structural model. The study assessed the predictive relevance of the proposed model through suitably deploying the blindfolding procedure of SmartPLS 2.0.M3. Construct cross-validated redundancy of SmartPLS gives Stone–Geisser’s Q2. This is obtained by executing the blindfolding procedure.

Stone–Geisser’s Q2 of dependent variables in our model, i.e. SC preparedness, SC alertness and SC agility, are, respectively, 0.469, 0.148 and 0.245. Thereby, the model in the study possess adequate, predictable relevance (Q2>0 indicates acceptable predictable relevance).

Next, the study evaluated the effect of the latent predictor variables, i.e. BDA planning, BDA investment decision making, BDA coordination and BDA control on SC preparedness, alertness and agility using Cohen f2. Table VI shows the relative effect sizes using Cohen f2. According to Cohen (1988), f2 values of 0.35, 0.15 and 0.02 are considered large, medium and small, respectively. Hence, we conclude that BDA planning, investment decision making, coordination and control are influential in their effect on SC preparedness, alertness and agility (Table VII).

Peng and Lai (2012) recommended the evaluation of a global goodness-of-fit index (Tenenhaus et al., 2005) for assessing the holistic quality of the proposed model; however, later studies questioned its validity (Henseler and Sarstedt, 2013). Still, as a best practice, we calculated GOF : average communality × average R 2 = 0.871 × 0.332 = 0.537.

This GOF evaluates the measurement model quality through average communality and the quality of the complete structural model in terms of average R2 (Peng and Lai, 2012). Further, the sample size of 249 is well above the minimum sample size requirement of 140 as determined by the “5 times” rule of thumb (Hair et al., 2006).

Using PLS, the study evaluated the path values and associated significance (t-statistics). Table VIII summarizes the results of hypotheses testing. Most of the hypotheses were supported except H1b, H2b and H3b. The proposed model explained 53 percent variance in SC preparedness, 18.1 percent in SC alertness and 28.6 percent in SC agility. Following Chin (1998), R2 values of 0.67, 0.33 and 0.19 suggest substantial, moderate and weak predictive power, respectively, of the model under consideration. Hence, the proposed model exhibited sufficiently strong predictive ability.

Figure 2 summarizes the hypotheses testing results in a structural model.

Discussion

The study hypothesized positive effects of BDA planning, BDA investment decision making, BDA coordination and BDA control on SC resilience dimensions, namely, SC preparedness, SC alertness and SC agility. Results suggested that the model explained 53 percent variance in SC preparedness, 18.1 percent in SC alertness and 28.6 percent variance in SC agility. Further, the study found support for nine of the proposed hypotheses.

H1aH1d posited positive effects of BDA planning, investment decision making, coordination and control of SC preparedness. The path coefficients are positive and significant for H1a, H1c and H1d (0.348, 0.623 and 0.345, respectively). Hence, H1a, H1c and H1d are supported. Again, H2aH2d posited positive effects of BDA management capabilities on SC alertness. The path coefficients are positive and significant for H2a, H2c and H2d (0.288, 0.258 and 0.186, respectively). Hence, H2a, H2c and H2d are supported.

Furthermore, H3aH3d posited positive effects of BDA management capabilities on SC agility. The path coefficients are positive and significant for H3a, H3c and H3d (0.281, 0.310 and 0.210, respectively). Hence, H3a, H3c and H3d are supported. The study, therefore, did not find support for H1c, H2c and H3c, thereby indicating that BDA investment decision making may not be an important enabler for SC preparedness, SC alertness and SC agility.

The findings, therefore, established BDA planning, BDA coordination and BDA control as significant enablers of SC preparedness, SC alertness and SC agility. The study identified that the extant literature is yet to explore the importance of BDA in the development of SC capabilities, especially SC risk management capabilities. SC resilience is an effective risk mitigation capability as it enables SCs to restore operations when disrupted. In this arena, the study identified three dimensions of SC resilience based on extant literature (Li et al., 2017).

Hence, the study contributed prominently to the identified gaps in the literature of BDA and SC management interfaces, by recognizing the importance of big data management capabilities in the development of SC resilience dimensions. The study posited each of the SC resilience dimensions as an important dynamic capability that could be developed due to the proper deployment of BDA management capabilities. Hence, the study also contributed in utilizing the dynamic capability extension (Teece, 2016) of RBV in comprehending the importance of BDA capabilities on SC resilience.

Implications

With the use of advanced analytical tools, BDA can derive meaningful insights from vast amounts of data to aid timely decision making (Tan et al., 2015). While sensors, barcodes and RFID were widely being used to enhance integration and coordination in SCs, BDA had the potential of changing the game in traditional SC management (Fawcett and Waller, 2014; Dubey et al., 2016) through decreased operational costs and enhanced customer satisfaction (Sheffi, 2015; Ramanathan et al., 2017). However, extant studies were mostly limited to conceptual investigations (Arunachalam et al., 2018; Addo-Tenkorang and Helo, 2016) and, therefore, empirical examinations regarding BDA in SC management was encouraged (Wamba et al., 2017; Arunachalam et al., 2018). While the potential for enhancing SC performance was suggested (Nguyen et al., 2017), only 17 percent of firms have implemented BDA in one or more SC function (Wang, Gunasekaran and Ngai, 2016; Wang, Gunasekaran, Ngai and Papadopoulos, 2016). This can be attributed to the lesser recognition of benefits associated with BDA for many organizations, due to which they are hesitant to invest for BDA (Arunachalam et al., 2018). Hence, our study contributed through an empirical study to identify the potential benefits of BDA capabilities in the development of SC preparedness, SC alertness and SC agility.

Our study has several implications for theory development and SC managers. First, the study utilized the dynamic capability view (Teece et al., 1997; Teece, 2016) to explore the importance of BDA management capabilities (BDA planning, BDA investment decision making, BDA coordination and BDA control) on SC resilience dimensions. The study therefore advanced dynamic capability theory to explore the development of SC preparedness, SC alertness and SC agility through BDA management capabilities.

First, the study underscored that BDA planning, BDA coordination and BDA control are prominent enablers of SC preparedness. SC preparedness aims to select partners that are reliable and willing to collaborate for enhancing sustainability of SC operations (Li et al., 2017). BDA planning, therefore, can aid focal firms to analyze past performance data of potential suppliers and make appropriate decisions. Furthermore, this analysis of past performance also aids in effective supplier performance management (Talluri and Narasimhan, 2004). Furthermore, BDA planning also helps in supplier management decision through appropriate analysis of spending pattern (Panchmatia, 2015). BDA coordination allows focal SC firms to share their production forecasts and sales data in real-time mode with their suppliers, thereby leading to enhanced transparency. Such a real-time information sharing aids in building sustained SC partnerships, required for building effective resilience. Such partnerships help in enhanced preparedness for addressing disruptions. BDA control aids focal firms to gain better control through real-time analysis of data and adequate monitoring. Hence, the firms in an SC can plan well their contingency plans efficiently for addressing uncertainties (Wu et al., 2017). Hence, the study suggests that managers should train their employees for effectively using in their routine and strategic operations.

Second, the study proved that BDA planning, BDA coordination and BDA control are critical for developing SC alertness. SC alertness aims to identify changes in internal network and external business environment that can be potential threats for SC survival (Li et al., 2017). BDA planning aids focal firms to detect such changes through appropriately analyzing and monitoring business changes. Furthermore, focal SC firms can detect changes in internal and external environment more readily due to higher coordination lead by big data (Gunasekaran et al., 2017). SC firms can have therefore had better control over their operations and, hence, can plan for addressing disruptions for maintaining sustainability (Zhao et al., 2017). Hence, managers should emphasize their peers and employees to use BDA to plan and coordinate SC operations. BDA control would aid SC firms to alert their partners of any potential changes in the business conditions, while maintaining stability over operations (Wamba et al., 2017).

Third, the study also highlighted BDA planning, BDA coordination and BDA control as prominent enablers of SC agility. SC agility aims to respond to market changes in a fast manner (Li et al., 2017). BDA planning therefore aids SC firms to detect and respond to market changes in a fast manner through timely information sharing, monitoring, decision making and strategy implementation (Giannakis and Louis, 2016). BDA coordination helps SC firms to develop synchronization of operations with one another. Such synchronized effort aids SC firms to respond in a unified and speedy manner. BDA control help SC firms to maintain stability over SC operations for enhanced sustainability (Wu et al., 2017), while allowing them to address changes in a fast pace (Giannakis and Louis, 2016). Hence, managers should develop plans for upgrading their IT infrastructure required for effective functioning of BDA.

Fourth, our study showed that BDA investment decision making does not have any prominent influence on SC preparedness, SC alertness and SC agility. This may be attributed to the fact that SC capabilities require effective development of contingency plans and synchronized effort for enhanced risk mitigation and disaster preparedness. The decisions for developing BDA infrastructure and associated technologies are not directly related to the development of SC preparedness, SC alertness and SC agility.

Limitations and future research

Our study explored the importance of BDA management capabilities on the development of important SC resilience dimensions. Using dynamic capability view, the study posited BDA management capabilities of planning, investment decision making, coordination and control are important enablers of SC preparedness, SC alertness and SC agility. However, like other survey-based studies, the current investigation also has its limitations. Based on extant literature (Wamba et al., 2017), the study concentrated on established dimensions of BDA management capabilities. However, the study did not explore the influence of BDA infrastructure flexibility and BDA personnel expertise capability on SC resilience dimensions. Second, the study relied on single informant approach per organization. Although sufficient care has been taken to ensure reliability and validity of our findings, future studies should resort to multiple informant approach for increased generalizability and validation. Future studies should explore the role of other BDA-based capabilities in the development of SC resilience dimensions. Furthermore, many studies have connoted several other dimensions of SC resilience (Gu and Huo, 2017; Liu et al., 2018; Chowdhury and Quaddus, 2017; Cheng and Lu, 2017). Future studies can explore the importance of BDA-based capabilities in the development of other SC resilience dimensions.

Figures

Theoretical model

Figure 1

Theoretical model

Structural model results

Figure 2

Structural model results

Respondents profile

No. Percentage
Sector
Manufacturing 57 22.89
Construction 38 15.26
Retail 73 29.32
Textile 26 10.44
Others 55 22.09
Age (years)
less than 30 118 47.39
30–35 67 26.91
35–40 38 15.26
40 and above 26 10.44
Experience (years)
less than 5 71 28.51
5–10 121 48.59
10–15 57 22.89

Final measurement items

Loadings t-stats
BDA planning capability (PLAN) (adapted from Wamba et al., 2017)
As a key SC member, your firm continuously examines innovative opportunities for the strategic use of business analytics 0.933 47.62
As a key SC member, your firm implement adequate plans for the utilization of business analytics 0.972 151.82
As a key SC member, your firm perform business analytics planning processes in prescribed ways 0.949 44.72
As a key SC member, your firm frequently modify business analytics plans to better adapt to changing conditions 0.964 99.18
BDA investment decision-making capability (IDMC) (adapted from Wamba et al., 2017)
Regarding investments in business analytics, your firm evaluates the effect they will have on the productivity of the employees’ work 0.933 43.34
Regarding investments in business analytics, your firm projects how much these options will help end users make quicker decisions 0.969 120.95
Regarding investments for business analytics, your firm estimates the cost of training that end users will need 0.949 40.37
Regarding investments for business analytics, your firm estimates the time managers will need to spend overseeing the change 0.956 92.82
BDA coordination capability (COORD) (adapted from Wamba et al., 2017)
In your firm, business analysts and line people meet regularly to discuss important issues 0.779 12.77
In your firm, business analysts and line people from various departments regularly attend cross-functional meetings 0.925 42.50
In your firm, business analysts and line people coordinate their efforts harmoniously 0.916 37.76
In your firm, information is widely shared among business analysts and line people so that those who make decisions or perform jobs have access to all available know-how 0.898 37.67
BDA control capability (adapted from Wamba et al., 2017)
In your organization, the responsibility for analytics development is equally distributed 0.904 33.51
Your firm is confident that analytics project proposals are properly appraised 0.955 66.03
Your firm constantly monitors the performance of the analytics function 0.951 71.02
Your firm is better than competitors in connecting (e.g. communication and information sharing) parties within a business process 0.935 86.24
SC preparedness (adapted from Li et al., 2017)
Your business unit select firms that are easy to work with as SC partners 0.957 95.84
Your business unit select trustworthy firms for developing SC partnerships 0.951 74.88
Your business unit frequently shares forecasts, sales data and plans with your SC partners 0.932 44.97
Your business unit develops contingency plan jointly with SC partners for increasing SC stability 0.947 81.15
SC alertness (adapted from Li et al., 2017)
Your business can detect sudden changes in demand 0.894 34.72
Your business unit can detect threats to SC network 0.951 79.12
Your business unit can identify new technologies for increasing SC visibility 0.928 37.65
Your business unit can detect unexpected changes in physical flow throughout the SC 0.926 56.55
SC agility (adapted from Li et al., 2017)
Your business unit can rearrange SC resources to address sudden demand fluctuations 0.893 36.68
Your business unit can adjust SC processes to reduce lead time 0.966 28.28
Your business unit can accommodate changes in SC processes to increase on-time delivery 0.940 44.77
Your business unit can synchronize SC processes to reduce non-value-added activities 0.960 79.13

Notes: SC, supply chain; BDA, big data analytics. All constructs were measured on a seven-point Likert scale with 1= strongly disagree and 7=strongly agree

AVE, composite reliability and Cronbach’s α of constructs

Construct Items Item loadings Composite reliability AVE Cronbach’s α
BDA planning 4 0.933–0.972 0.976 0.911 0.967
BDA investment DM 4 0.933–0.969 0.974 0.906 0.965
BDA coordination 4 0.779–0.925 0.932 0.776 0.903
BDA control 4 0.904–0.955 0.965 0.876 0.952
SC preparedness 4 0.932–0.957 0.971 0.896 0.961
SC alertness 4 0.894–0.951 0.959 0.855 0.943
SC agility 4 0.893–0.966 0.966 0.879 0.953

Discriminant validity

X1 X2 X3 X4 X5 X6 X7
BDA control (X1) 0.935
BDA coordination (X2) 0.163 0.881
BDA investment decision making (X3) 0.337 0.483 0.951
BDA planning (X4) 0.251 0.501 0.549 0.954
SC agility (X5) 0.070 0.445 0.290 0.415 0.937
SC alertness (X6) 0.107 0.321 0.115 0.313 0.826 0.924
SC preparedness (X7) 0.418 0.588 0.400 0.146 0.165 0.122 0.946

Note: Diagonal: Sqrt of AVE

Cross-loadings of the items

BDA control BDA coordination BDA investment decision making BDA planning SC agility SC alertness SC preparedness
AG1 0.004 0.430 0.299 0.409 0.893 0.437 0.144
AG3 0.059 0.418 0.286 0.391 0.966 0.268 0.171
AG4 0.109 0.406 0.264 0.381 0.940 0.394 0.143
AG5 0.089 0.414 0.239 0.375 0.950 0.196 0.161
ALERT1 0.027 0.291 0.098 0.293 0.203 0.894 0.114
ALERT2 0.090 0.294 0.101 0.261 0.354 0.951 0.123
ALERT4 0.139 0.301 0.136 0.313 0.597 0.928 0.107
ALERT5 0.129 0.302 0.089 0.289 0.492 0.926 0.108
CON1 0.904 0.129 0.267 0.171 0.068 0.077 0.410
CON2 0.955 0.189 0.329 0.252 0.043 0.060 0.415
CON3 0.951 0.142 0.334 0.244 0.084 0.149 0.377
CON5 0.935 0.149 0.336 0.277 0.066 0.118 0.359
COORD1 0.122 0.779 0.452 0.472 0.353 0.279 0.340
COORD2 0.151 0.925 0.405 0.471 0.422 0.332 0.524
COORD3 0.184 0.916 0.433 0.417 0.391 0.254 0.586
COORD4 0.113 0.898 0.428 0.425 0.399 0.274 0.589
IDMC1 0.312 0.448 0.933 0.513 0.298 0.137 0.364
IDMC3 0.330 0.468 0.969 0.521 0.274 0.088 0.384
IDMC4 0.299 0.442 0.949 0.508 0.248 0.079 0.362
IDMC5 0.341 0.478 0.956 0.545 0.282 0.130 0.411
PLAN1 0.231 0.478 0.519 0.933 0.359 0.273 0.133
PLAN2 0.262 0.496 0.545 0.972 0.412 0.301 0.166
PLAN3 0.224 0.465 0.527 0.949 0.390 0.298 0.131
PLAN4 0.239 0.476 0.504 0.964 0.421 0.321 0.128
PREP2 0.445 0.561 0.380 0.131 0.138 0.102 0.957
PREP3 0.462 0.544 0.386 0.108 0.137 0.085 0.951
PREP4 0.303 0.598 0.423 0.181 0.194 0.144 0.932
PREP5 0.362 0.525 0.324 0.137 0.160 0.136 0.947

Cohen f2 values

Construct Dependent variable f2
BDA planning SC preparedness 0.38
BDA investment decision making SC preparedness 0.04
BDA coordination SC preparedness 0.56
BDA control SC preparedness 0.104
BDA planning SC alertness 0.163
BDA investment decision making SC alertness 0.07
BDA coordination SC alertness 0.255
BDA control SC alertness 0.136
BDA planning SC agility 0.168
BDA investment decision making SC agility 0.12
BDA coordination SC agility 0.192
BDA control SC agility 0.253

Average R2 and AVEs

Construct R2 Communality (AVE)
BDA planning 0.911
BDA investment decision making 0.906
BDA coordination 0.776
BDA control 0.876
SC preparedness 0.53 0.896
SC alertness 0.181 0.855
SC agility 0.286 0.878
Average 0.332 0.871

Results of hypotheses testing

Summary of hypotheses testing
No. Relationship Path coefficient t-values Supported?
H1a BDA planning (+)→SC preparedness 0.348 3.030 Yes
H1b BDA investment decision making (+)→SC preparedness 0.174 1.325 No
H1c BDA coordination (+)→SC preparedness 0.623 6.116 Yes
H1d BDA control (+)→SC preparedness 0.345 3.549 Yes
H2a BDA planning (+)→SC alertness 0.288 2.784 Yes
H2b BDA investment decision making (+)→SC alertness 0.105 1.164 No
H2c BDA coordination (+)→SC alertness 0.258 2.279 Yes
H2d BDA control (+)→SC alertness 0.186 2.197 Yes
H3a BDA planning (+)→SC agility 0.281 2.879 Yes
H3b BDA investment decision making (+)→SC agility 0.057 0.079 No
H3c BDA coordination (+)→SC agility 0.310 3.116 Yes
H3d BDA control (+)→SC agility 0.210 2.457 Yes

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Further reading

Hazen, B.T., Skipper, J.B., Boone, C.A. and Hill, R.R. (2016), “Back in business: operations research in support of big data analytics for operations and supply chain management”, Annals of Operations Research, S.I.: Big Data Analytics in Operations and Supply Chain Management, available at: http://dx.doi.org/10.1007/s10479-016-2226-0

Pettit, T.J., Croxton, K.L. and Fiksel, J. (2013), “Ensuring supply chain resilience: development and implementation of an assessment tool”, Journal of Business Logistics, Vol. 34 No. 1, pp. 46-76.

Corresponding author

Santanu Mandal can be contacted at: shaan.nitw@gmail.com

About the author

Dr Santanu Mandal is Associate Professor in the Department of Operations and IT at Amrita School of Business, Amrita Vishwa Vidyapeetham, Coimbatore. Prior to joining Amrita, he worked with IBS Hyderabad, IFHE University for several years. He was Visiting Scholar in the Department of Management Science, Spears School of Business, Oklahoma State University, USA. He recently won the Best Paper Award at International Conference on Management of Ergonomic Design, Industrial Safety and Healthcare (MESH) 2016 held at IIT Kharagpur in Dec 2016. He has also organized a workshop on “Analytics Using R” at IBS Hyderabad in June, 2016. He has presented papers in several national and international conferences in India and abroad. Furthermore, he has published in several international journals. His research interests are in technology management, supply chain management, operations management, services supply chain, healthcare management, etc.