Advancing supply chain management from agility to hyperagility: a dynamic capability view

Abstract

Research and practice emphasize the criticality of supply chain agility in responding to external disruptions. However, many organizations struggled to respond at enhanced speed to the global supply chain shocks caused by the COVID-19 pandemic. Organizations need hyperagile supply chains to survive and remain competitive in an environment characterized by unexpected and sudden disruptions such as the COVID-19 pandemic. We propose that supply chain hyperagility (SCH) is a distinctive organization-specific capability. It enables organizations to effectively manage demand shocks at extreme speeds and under extreme time pressures. We advance the concept of supply chain hyperagility and establish its antecedents, taking the dynamic capability perspective. This study operationalizes the SCH construct for the first time and investigates its antecedents using structural equation modeling. The results highlight the significance of data analytical capabilities, market orientation, entrepreneurial orientation, and supply chain integration in shaping supply chain hyperagility. The study offers practical insights for managers regarding designing supply chains that can navigate hyperagile environments and benefit from the opportunities presented by such environments.

Introductions

The recent outbreak of the COVID-19 pandemic has caused substantial disruptions to supply chains. While supply chain disruptions are not uncommon, businesses were caught off guard by the pandemic's speed, scale, and severity, which exposed the latent limitations of their supply chains (Cohen & Kouvelis, 2021; Müller et al., 2022). The pandemic engendered formidable challenges for business operations and supply chains, such as increasing pressure from consumers and retailers to reduce the delivery lead time, increasing bankruptcy of supply chain agents, change in distribution networks, and high employee layoffs (Paul et al., 2021). Several studies suggest that supply chain agility (SCA) could help organizations to respond quickly to such challenges (Do et al., 2021; Dubey et al., 2021). In literature, SCA is understood to provide superior value to the organization due to its ability to handle uncertainty quickly, thus providing a competitive advantage (Dubey et al., 2021). However, many organizations with supply chain agility could not respond quickly enough to the disruptions caused by the COVID-19 pandemic. Thus, it raises the question of what additional capabilities other than SCA need to be developed to cope with such disruptions in the future.

The COVID-19 pandemic presented organizations with unprecedented challenges that were beyond the scope of SCA, which have almost ignored the temporality of response (e.g., immediate need and short-term perspective). One of the major challenges was the immense time pressure the organizations faced when trying to respond to very high demands for certain categories of products and services caused by the pandemic. For instance, organizations faced immense time pressure to produce medical equipment such as ventilators and PPE kits (Kamar et al., 2021). Usually, in normal situations, organizations produce medical equipment and supply oxygen to hospitals at regular intervals based on required demand. However, during COVID-19, there was a spike in demand only for short intervals. In these situations, organizations are required to accelerate production at a much more extreme speed compared to the time frames of regular business operations, and it needs to be delivered within days/weeks (Sharma et al., 2020). US acute care hospitals owned approximately 62,000 full-featured mechanical ventilators before the pandemic started (Kobokovich, 2020). However, there were requirements to produce 880,000 ventilators in the United States to cope with the COVID-19 outbreak, which is very high compared to normal demand (Ranney et al., 2020). In the case of vaccines, normally, it takes ten years from discovery to commercialization; however, in the case of the COVID-19 vaccine, there was immense pressure on vaccine producers to produce vaccines as soon as possible (Broom, 2020).

There are some other examples of organizations, such as 3M, Amazon, and Shopify, that responded to the pandemic by exhibiting an extraordinary degree of SCA. During the COVID-19 pandemic, the extraordinary agile supply chain structure of 3M allowed them to rapidly increase the production of the N95 masks from 630 million units in 2019 to 2 billion units in 2020 (Kapadia, 2021). Thus, on the one hand, due to this immense pressure during the pandemic, several organizations' supply chains were completely disrupted, leading to the failure of operations. On the other hand, several organizations could successfully reconfigure themselves to respond to the pandemic based on an extraordinary version of SCA. We refer to this extension of SCA as supply chain hyperagility (SCH).

A hyperagile supply chain has the potential to accelerate design, production, and delivery processes in very short duration to survive the time pressure. SCH refers to the capability of a supply chain that fulfills immediate, time-limited, and extremely high demands (Müller et al., 2022). Although substantial research has been carried out in the past on SCA, organizations' ability to manage the supply chain under time pressure and respond to external shocks with a short-term perspective has not drawn much attention so far (Dubey et al., 2018). SCH has emerged as a distinct concept that specifically focuses on extreme time pressure and short-term goals (Müller et al., 2022). It can be an important capability that organizations can develop to cope with future disruptions similar to COVID-19. Recently, some studies indicated a need to extend the concept of SCA to respond quickly under extreme pressure and suggested ways of achieving SCH (Cohen & Kouvelis, 2021; Do et al., 2021; Ketchen & Craighead, 2020; Müller et al., 2022). However, the literature fails to reveal possible capabilities that can foster the adoption of SCH. The dynamic capability view can be used to address this gap because of its ability to explain the capabilities required for organizations' response to uncertainty (Teece et al., 1997). Thus, we posit our first research objective (RO1) as: To identify the dynamic capabilities that facilitate supply chains to fulfill immediate, time-limited, and extremely high demands. Additionally, since the capabilities are going to be purely theoretical, it is important to establish the empirical validity of their influence on SCH. Thus, we posit our second research objective (RO2) as: To empirically investigate the influence of identified dynamic capabilities on supply chain hyperagility.

To realize these research objectives, we have identified four organizational capabilities based on their relevance with SCH in the literature and their alignment with dynamic capabilities view's (DCV's) sensing, seizing, and reconfiguration capabilities. According to the literature, data analytical capability (DAC) successfully enables organizations to accelerate their responses to disruptions, which is also a critical requirement of SCH (Cadden et al., 2022; Srinivasan & Swink, 2018). A further element of SCH is coping with turbulent surroundings and constant disruptions. Literature suggests that such elements could be captured by market orientation (MO) and entrepreneurial orientation (EO) (Morgan & Anokhin, 2020). Additionally, in order to react smoothly and quickly to such disruptions, organizations need to collaborate with and align with their internal and external partners. In light of this, the literature suggests adopting supply chain integration (SCI) as a means of collaborating and aligning the efforts with their stakeholders (Dubey et al., 2021; Irfan et al., 2019a). These identified higher-order capabilities also contribute to sensing, seizing, and reconfiguration capabilities under the dynamic capability view (Teece et al., 1997). Market orientation and Data analytical capabilities are considered to enhance sensing capabilities of an organization (Teece, 2007). Whereas entrepreneurial orientation and supply chain integration are proposed to strengthen seizing and reconfiguration capabilities respectively (Teece, 2007).

Therefore, we developed a conceptual model grounded in dynamic capability theory and empirically examined it based on 120 valid responses from supply chain managers who had faced time pressure for a short interval in their supply chains. The main contribution of this study is to establish SCH as a higher order dynamic capability that specifically focuses on addressing the immediate demands for a limited period of time. It also highlights the antecedents as higher-order dynamic capabilities and their role in developing SCH. The study offers direction to the managers to adopt SCH to deal with future disruptions like COVID-19.

We have organized our paper as follows. In Sect. 2, we introduce the concept of SCH, followed by the grounding theory of dynamic capability and potential antecedents of hyperagility. In Sect. 3, we propose our hypotheses along with the theoretical model. Section 4 presents the research design used in the study. In Sect. 5, we analyze the data. Under Sect. 6, we discuss the theoretical and managerial implications of the study. Finally, Sect. 7 concludes our study and lists its limitations.

Underpinning theories and concepts

Supply chain hyperagility: an extension to supply chain agility

The concept of supply chain agility (SCA) attracted a lot of attention from supply chain scholars in the early 2000s (Lee, 2004; Swafford et al., 2008). It has been considered an important capability to achieve competitive advantage as a part of the triple-A strategy (Lee, 2004). The present literature extensively explores SCA's role in managing the changes in demand and risks of supply chain disruptions (Fayezi et al., 2017). However, SCA's scope doesn't acknowledge the need to fulfill extremely high demands during a short period. It fails to do so due to the consideration of long-term goals. During 2020, several businesses such as pharmaceuticals & healthcare, e-commerce, and the gig economy have faced exceptionally high demands due to the COVID-19 pandemic (Remko, 2020). The businesses had to swiftly reconfigure their supply chain structure using SCA to address the immediate surge in demand for a short time (Do et al., 2021). However, the SCA's strategic perspective does not endorse this perspective of short-term response to events like the COVID-19 pandemic (Do et al., 2021). As a result, the present conceptualization of SCA doesn't immediately address the short-term extremely high demands. Thus, supply chain scholars and practitioners must rethink the concept of agility to operate under time pressures resulting from sudden and unforeseen disruptions like COVID-19 pandemic (Cohen & Kouvelis, 2021; Müller et al., 2022).

In the literature, several recent studies acknowledge the inability of SCA to fulfill immediate demands that last for a short period (Cohen & Kouvelis, 2021; Do et al., 2021; Ketchen & Craighead, 2020; Müller et al., 2022). Cohen and Kouvelis (2021) highlight how SCA's function to handle variations in demand and supply is not enough to handle significant and sudden shocks. As a result, they suggest formulating enhanced agility that could respond in real-time at high speeds to short-term risk scenarios. Do et al. (2021) highlight the need to evolve SCA from the long-term to a new version incorporating immediate, sudden, uncertain, temporary, abrupt, and unexpected events. Ketchen and Craighead (2020) discuss the inability of SCA during the COVID-19 pandemic and poses questions to identify ways to achieve enhanced SCA. Realizing the need for advanced SCA, Müller et al. (2022) propose to build ad hoc supply chains by extending SCA to address the extreme demands during the COVID-19 pandemic. The extension of SCA is done by incorporating the importance of the immediate and specific need for a limited period of time (Müller et al., 2022).

This study proposes the concept of supply chain hyperagility (SCH) to respond to the calls for the extension of SCA. The SCH concept is inspired by the SCA extensions in Cohen and Kouvelis (2021) and Müller et al. (2022). SCH is considered a distinct concept from SCA that enables organizations to respond successfully to the disruptions that pose immediate and time-limited needs. When disruptions like the COVID-19 pandemic happen, business organizations are required to quickly grab emerging opportunities to ensure their sustenance. In such cases, the organization cannot afford to plan the operations for a long period which is a usual norm in SCA (Do et al., 2021). The ability to launch new products and services under SCH should be within a few days. The same process could take months under a regular supply chain. Thus, organizations with SCH must be able to respond under extreme time pressure (Müller et al., 2022).

Additionally, it is possible that the opportunities created during the disruptions are only relevant for a brief period and will not have long-term returns for the organizations (Müller et al., 2022). However, this should not stop organizations from grabbing those opportunities as they could be critical in the short term for the survival of organizations during the disruption (Müller et al., 2022). This short-term perspective is also not incorporated in SCA. Therefore, organizations with SCH must be able to consider the opportunities from a short-term perspective (Müller et al., 2022). This perspective allows organizations to prioritize short-term goals and provide high-speed responses to address the sudden extreme demands (Cohen & Kouvelis, 2021). Based on the above discussion, in the case of SCH, the organizations are supposed to prioritize speed for the short term above other long-term requirements such as efficiency and sustainability. Thus, SCH could be a critical characteristic for the supply chains that can deal with situations where responding to temporary extreme demands is of utmost importance in a short time.

Although the literature in the context of SCH is still nascent, several pieces of evidence in the practitioner literature support its conceptualizations (Cavarec & Fargis, 2016; Leung, 2021; Satiya et al., 2021). SCH is specifically related to the contexts where there is pressure on the supply chains to address the huge demand for their products for a short duration (Müller et al., 2022). For instance, some of the organizations, such as 3M, ramped up their production in a short period. Some organizations even shifted to different product lines to meet demand. For example, automobile (Mahindra and Mahindra) and aerospace organizations (Hindustan Aeronautics Limited) started manufacturing ventilators. During the pandemic, there was a global shortage of medical supplies such as hydroxychloroquine, PPE kits, surgical masks, and hand sanitizers (Kamar et al., 2021). All major organizations involved in producing the above products significantly ramped up their production and supply operations to address the extraordinary market demands (Sharma et al., 2020).

However, the global shortage problem remained unsolved, and governments of several nations needed to interfere. Governments worldwide were involved in modifying and simplifying supply chains (Iyengar et al., 2020). They allowed non-medical organizations to manufacture medical equipment to fulfill the market demands (Kubota, 2020). Hence, it is clear that although traditional SCA could handle day-to-day supply disruptions, it is still not enough to meet extreme demands in a very short interval of time. Thus, it becomes essential to equip our supply chains with SCH to manage severe demand shocks under a short time frame, such as weeks/days.

In addition to managing demand shocks, SCH allows organizations to re-align their supply chains to cater to new business opportunities arising from disruptions. In the initial phase of the pandemic, the supply chain of Amazon was severely affected, resulting in month-long delivery delays and out-of-stock products. Amazon’s supply chain is agile enough to handle demand fluctuations on festivals and seasonal sales. However, they could not handle the extreme surge in demand due to the closure of brick-and-mortar stores and consumers panic-shopped for essential supplies. Its supply chain was surprised and exposed to disruption without much preparation (Palmer, 2020). To respond quickly to the demand surge, the organization let go of its long-term projects and mobilized all its resources to meet the enormous surge in demand. They responded by recruiting more workers, leveraging their online presence, and utilizing automation and analytics to streamline operations (Harris, 2020). As a result, Amazon successfully catered to the demand surge and doubled its quarterly profits in the second quarter of 2020 (Harris, 2020).

Another example of hyperagility is the case of Terraboost Media—an advertising agency. During the pandemic, it utilized the existing distribution system and quickly reacted to the high demand for sanitizing products in the market. It remodeled its advertising kiosks into a portfolio of hand sanitizer stations installed in commercial and high-traffic locations (Arora, 2020). An organization whose revenues were primarily derived from advertising could completely re-engineer its operations to exploit the demand surge for hand sanitizing stations. This hyperagile capability enabled Terraboost to realize 420% sales growth in Q4 2020 (Terraboost Media 2020). Based on the above discussion, SCH is an organizational capability that has helped organizations manage demand disruption under limited time frames. This behavior of SCH can be best explained under the dynamic capabilities theory, which is discussed in the following section.

Dynamic capabilities view (DCV)

A dynamic capability is defined as "the organization's ability to integrate, build and reconfigure internal and external competencies to address rapidly changing environments" (Teece et al., 1997). The dynamic capability view has emerged as an important theoretical perspective that helps organizations to develop capabilities to deal with uncertain environment (Gupta et al., 2021). The extant literature demonstrates the role of DCV in understanding the organizations' response to uncertainties in their surroundings (Dovbischuk, 2022; Kalubanga & Gudergan, 2022).

Dovbischuk (2022) uses DCV to understand how organizations can adapt and recover in highly dynamic and vulnerable environments during the COVID-19 Pandemic. Ali et al. (2022) emphasize the role of DCV in understanding an organization's development of dynamic capabilities while facing pandemic-related shocks. Kalubanga and Gudergan (2022) utilize the DCV framework to understand the role of dynamic capabilities while operating under turbulence and interdependencies. Further, specific to supply chain agility, Irfan et al. (2019a, b) draw on DCV to reveal capabilities that enhance the SCA. Based on the DCV's relevance with COVID-19 related disruptions, environmental turbulence, and SCA, we suggest that DCV is an appropriate perspective to understand organizational abilities to respond to rapid changes in the business environment. It has the potential to identify the capabilities that can influence hyperagile behavior during an uncertain and disruptive environment.

Additionally, SCH's objective to address the surge in demand due to uncertain events aligns with the DCV's focus on reconfiguring organizational competencies to manage changes in the surroundings (Teece et al., 1997). It has the potential to explain how organizations shape and reconfigure their activities, which would lead to SCH. (Teece et al., 1997). Also, DCV's focus on sensing the business surroundings and developing capabilities to align the organizations’ interest with the market requirement could explain the management of extreme market demand under time pressure (Dovbischuk, 2022). Thus, this study uses DCV to understand the pathways to achieve hyperagility in supply chains. According to DCV, organizations require higher order dynamic capabilities that can channel into other capabilities that can further lead to SCH (Teece, 2018). These capabilities are categorized under sensing, seizing, and reconfiguration activities (Teece, 2018). This study utilizes this categorization framework to identify second-order dynamic capabilities that lead to SCH (Teece, 2007). (Refer to Table 1).

Table 1.

Literature summary on higher-order dynamic capabilities

Lower-order dynamic capability	Higher order dynamic capability	Supporting statements	References
Sensing	Market orientation	Market orientation as a higher-order construct of customer orientation, competitor orientation, and inter-functional coordination	Menguc and Auh (2006)
		Market orientation influences the organization's behavior based on accumulating, sharing, and replying to correlative information from customers and contenders	Zhou et al. (2021)
		Gathering and analyzing data through market orientation creates information and knowledge that is needed to create market intelligence	Foerstl et al. (2020)
	Data analytical capability	Analytics capability enables organizations to increase their information processing capacity, whereby organizations gather data from various sources and analyze it to gain insights for managers	Srinivasan and Swink (2018)
		Marketing analytics is seen to create difficult-to-trade knowledge assets that mainly relate to customers' and competitors' domains and are part of the micro-foundations of the organization's sensing capability, allowing the organization to gain valuable data-driven insights to sense threats and create opportunities	Cao et al. (2019)
		Supply chain analytics can be conceptualized as a unique capability that improves SCA to allow managers to develop a better understanding of the market situation and thus capitalize on environmental changes	Cadden et al. (2022)
		Data analytical capability enables an organization to quickly combine and process multiple data formats that provide the organization with the required information processing capabilities for handling variability and uncertainty	Yu et al. (2021)
Seizing	Entrepreneurial orientation	An organization with entrepreneurial orientation bets on the innovation of products and/or markets with some risk and acts proactively before its competition	Miller (1983)
		Entrepreneurs have a unique cognitive ability to recognize venture opportunities and organize resources	Alvarez and Busenitz (2001)
		It promotes values such as being highly proactive toward market opportunities, tolerant of risk, and receptive to innovations	Matsuno et al. (2002)
		Entrepreneurial Orientation is an entrepreneurial process by which creative ideas are implemented within an organization, thus leading to organization performance	Ahlin et al. (2014)
Reconfiguration	Supply chain integration	Supplier integration leads to better knowledge transfer across boundaries, reduces the cost of managing tacit knowledge, and exposes the organization to new knowledge	Parente et al. (2011)
		Integration of supply chain management processes allows organizations to leverage underlying capabilities and more efficiently manage their resources	Bechtel and Jayaram (1997)
		Organizations seeking closer ties with their suppliers, resulting in closer inter-organizational relationships, have improved resource integration. This facilitates the transfer of competences and results in increased competitiveness	Scannell et al. (2000)
		Supplier integration allows organizations to learn and adapt quickly. Organizations outsourcing supply chain components can leverage their capabilities and maximize flexibility in responding to changing market conditions	Gupta and Zhender (1994)
		SCI also engenders the abilities of its tightly networked organizations to identify synergies among their varied tasks, activities, and resources, leading to better orchestration and deployment in creating competitive capabilities	Vickery et al. (2013)

Sensing as a dynamic capability

According to DCV, sensing capabilities refer to the combination of activities that allows an organization to regularly scan its surroundings and provide sufficient information for effective decision-making (Teece, 2018). This information allows organizations to evaluate their future actions based on the changing market conditions. Organizations may use the information to understand latent demand, the evolution of surroundings, competitor strategy, and supplier preparedness (Teece, 2007). Since sensing requires gathering a large amount of information from the market regarding the competitors and customers, market orientation (MO) could be a higher-order dynamic capability that further enhance the sensing capabilities of the organization (Kohli & Jaworski, 1990). MO enables organizations to generate market intelligence to understand the changes in their business environment (Foerstl et al., 2020). It refers to the activities in the marketing process that are characterized based on three important pillars: intelligence generation, intelligence dissemination, and responsiveness (Kohli et al., 1993). It allows organizations to generate market intelligence to understand the changes in their business environment that enable the deployment of internal resources as a response (Foerstl et al., 2020). Thus, organizations with market orientation could access a large amount of data and information regarding market dynamics.

However, organizations need further capabilities to make sense of the collected information that ensures effective decision-making. In literature, data analytical capability (DAC) has demonstrated significant potential to convert market information into actionable intelligence (Akter et al., 2021). Therefore, DAC could be another higher-order dynamic capability that strengthens an organization's sensing capabilities. It refers to "tools, techniques, and processes that enable an organization to process, organize, visualize, and analyze data, thereby producing insights that enable data-driven operational planning, decision-making, and execution (Srinivasan & Swink, 2018)". Organizations' data analytics capability helps them to predict accurate demand, real-time monitoring, and proper allocation of resources at different production and consumption points (Bahrami & Shokouhyar, 2021).

Seizing as a dynamic capability

As per DCV, seizing capabilities are based on the ability of an organization to react swiftly toward opportunities and threats (Teece, 2018). Organizations with seizing capabilities are expected to quickly grab the newly formed opportunities. It gives them significantly less time to prepare for the opportunity and requires a significant level of preparedness by the organizations (Teece et al., 2016). Additionally, organizations cannot evaluate their options and tradeoffs, leading them to take significant risks while investing in new opportunities (Teece, 2007). The extant literature advocates the potential of entrepreneurial orientation (EO) to demonstrate proactiveness, risk-taking abilities, and innovativeness (Covin & Wales, 2019; Morgan & Anokhin, 2020). EO is an organizational attribute that explains how entrepreneurial characteristics manifest in a business model. The operationalization of the EO construct is based on the organization's ability to take risks and innovate (Lumpkin & Dess, 1996). Thus, it enables organizations to pursue innovation, enter new markets aggressively, and accept strategic and financial risks to explore emerging business opportunities (Covin & Wales, 2019; Lumpkin & Dess, 1996).

Reconfiguration as a dynamic capability

The DCV suggests that organizations must reconfigure their resources and capabilities to extract maximum value and sustain the benefits of seizing the new opportunities (Teece, 2018). This re-configuration is considered a critical capability that ensures evolutionary fitness and reduces dependencies on unfavorable strategies (Teece, 2007). Re-configuration could lead to significant changes in organizational processes both internally and externally. Due to the complexity of organizational systems, the implementation of these changes can be difficult and can disturb the present operations (Teece, 2018). However, organizations that have developed integration and coordinating skills should be able to reconfigure their resources seamlessly without compromising on the operational goals (Teece, 2007). Supply chain integration (SCI) enables organizations to transfer knowledge and competencies with their suppliers and internal functional units, which further helps in smoothening the reconfiguration process (Teece, 2007; Vickery et al., 2013). Also, organizations' close integration with their customers allows them to reduce the potential impact on customers due to reconfiguration. Thus, supply chain integration is an important organizational aspect that facilitates the reconfiguration of organizational systems (internal and external) under DCV.

Integration in a supply chain refers to coordinating and collaborating across internal and external entities. Primarily, there are two mechanisms of achieving integration: external integration and internal integration (Demeter et al., 2016; Zhao et al., 2011). External integration (EI) focuses on forming collaborations with supply chain partners to leverage their core competencies and fulfill customer requirements (Zhao et al., 2011). Internal integration (II) refers to integrating all the subsystems of an organization to attain high-performance levels (Zhao et al., 2011). The internal and external integration of the organization facilitates capability building to manage external disruptions such as COVID-19 (Swafford et al., 2008).

Theoretical framework and hypothesis development

The proposed research model for this study is shown in Fig. 1. We propose six hypotheses for the analysis.

Fig. 1.

Proposed research model

Data analytics enhances market sensing and customer-linking capabilities, influencing customer loyalty and satisfaction (Dubey et al., 2020). Under DCV, it enables organizations to develop sensing capabilities (Teece, 2007). It increases the ability to create new ideas, products, and processes to enhance the organization's innovativeness. The processing power enabled by data analutical capability allows managers to efficiently and quickly convert the raw data into actionable insights efficiently and quickly while addressing opportunities and threats in an uncertain environment. For instance, Southwest Airlines uses their data analytical capability to understand customer needs using speech analytics methods (Mikalef & Gupta, 2021). The insights obtained from speech analytics help Southwest Airlines to understand customer food and beverage preferences and make personalized offers to its customers.

Several organizations track customer sentiment on social media, which helps them analyze customer needs regarding products or services in the future. For instance, Nedbank has developed a data analytics tool that allows them to design new marketable offerings for its clients, better capture market needs, and seize the opportunity through highly detailed data (Mikalef et al., 2020). Thus, data analytical capability fosters intelligence generation, intelligence dissemination, and response to the market with higher speed; consequently, we can hypothesize that:

H1

Data analytical capability is associated with Market orientation.

Data analytical capability contributes to the sensing capabilities by anticipating future events and helps forecast products more precisely (Dubey et al., 2020; Srinivasan & Swink, 2018). It offers a solution to process a large amount of data using statistical knowledge and tools, enabling improved decision-making. During the COVID-19 outbreaks, organizations extensively relied on epidemiological models and modeling expertise to succeed in this new environment (Spagnoletti et al., 2021). Data capability and analytical techniques enable managers to better sense environments and help managers cope with extreme uncertainties and unpredictable events (Dubey et al., 2020).

As DCV suggests, during external disruption, such as the COVID-19 pandemic, organizations need an efficient understanding of their surroundings to optimize resource deployment (Bahrami & Shokouhyar, 2021). Data analytical capabilities allow organizations to use data and derive meaningful and actionable intelligence. For instance, organizations can leverage data analytical capability to enter particular markets, identify potential alliance partners, launch new products and services, anticipate potential risks and adjust their operational strategy to mitigate threats. Thus, sensing through the analytical capability of an organization may provide the ability to launch new products and services within a few days, which is required to achieve hyperagility. While past literature suggests that Information technology (IT) capability has a positive effect on SCA, to our knowledge, its effect on SCH remains unexplored (Akter et al., 2021). One could reasonably argue that using analytical techniques may help organizations sense the rapid changes in their environment and allow managers to respond quickly to these changes with optimal decisions. This also aligns with the influence of sensing capabilities towards seizing and reconfiguration capabilities of an organization under DCV. Thus, data analytical capabilities would enable organizations' supply chains to respond faster and become hyperagile, which leads us to our hypothesis.

H2

Data analytical capability is associated with supply chain hyperagility.

The concept of EO is primarily linked with entrepreneurial activities by organizations, innovation of products and services, exploring new opportunities, and utilizing available resources effectively (Miller, 1983). In contrast, non-EO is related to a more conservative business style characterized by high risk-aversion and low innovativeness. We hypothesize that DAC has a positive association with EO. DAC acts as a higher-order dynamic capability enabling the sensing capabilities of an organization. These sensing capabilities further inform organizations about the opportunities, risks, and appropriate strategies that enable seizing capabilities of the organizations (Teece et al., 2016). Thus, DAC supports organizations in deciding on creating new products and services while reducing the market risk associated with it.

Additionally, organizations require innovation capabilities to seize new opportunities in the market, which could be risky and costly. DAC can minimize the risk by analyzing the data from millions of consumers to better understand their preferences, allowing organizations to develop new offerings closely aligned with customer requirements. Using DAC, an organization can involve consumers from the design phase to mitigate the failure risk. As prior studies suggest, DAC enables organizations to foster innovation, help the business navigate new markets, and reduce failure risk (Ciampi et al., 2021; Donbesuur et al., 2020). Hence, we hypothesize:

H3

Data analytical capability is associated with entrepreneurial orientation.

Our next two hypotheses are related to the relationship between MO and supply chain integration (SCI). SCI is a combination of external integration and internal integration. It enables the strategic alignment of functions and processes within an organization and between supply chain stakeholders (Jajja et al., 2018). Through SCI, organizations can achieve close interactions, collaborations, and transparent information exchange between organizations, suppliers, and customers (Jajja et al., 2018). As a result, SCI allows organizations to an effective transfer of competencies and transform their capabilities quickly both internally and externally (Gupta & Zhender, 1994; Scannell et al., 2000). As per DCV, market orientation can enhance sensing capabilities which can lead to the identification of new opportunities and reconfiguration of existing activities to exploit these opportunities. Market-oriented organizations would leverage the obtained market information to reconfigure their internal and external capabilities (Foerstl et al., 2020). As SCI involves coordinating and collaborative competence, it can enable firms to reconfigure their internal and external capabilities smoothly to respond to emergent opportunities. Thus, organizations would want to adopt SCI to ensure that their market intelligence is effectively utilized in reconfiguring their capabilities (Jajja et al., 2018). Hence, we hypothesize:

H4a

Market orientation is associated with internal integration.

H4b

Market orientation is associated with external integration.

The next two hypotheses explain the association between EO and supply chain integration (SCI). According to DCV, EO's seizing capabilities enable an organization to innovate, enter new markets aggressively and accept risks to explore business opportunities (Covin & Wales, 2019). To ensure competitive advantage, DCV also suggests that the seized new opportunities must lead to a reconfiguration of internal and external capabilities (Teece, 2018). This reconfiguration would help organizations effectively align themselves with the new market conditions (Teece, 2007). Since SCI involves coordinating and collaborating the organization's activities with external and internal stakeholders, it enables effective reconfiguration of internal and external capabilities.

Additionally, it allows organizations to leverage close networks between stakeholders to form synergies between various activities and resources, leading to better orchestration and deployment of competencies (Vickery et al., 2013). For instance, in a recent study, EO-inclined organizations are observed to explore ways to foster connections and collaborations with external and internal stakeholders (Dubey et al., 2020). Thus, organizations with EO tend to incorporate SCI to convert the seized opportunities into a competitive advantage through SCI-enabled reconfiguration of competencies. Hence, we hypothesize:

H5a

Entrepreneurial orientation is associated with internal integration.

H5b

Entrepreneurial orientation is associated with external integration.

Supply chain integration allows organizations to understand each other requirements and respond quickly (Fayezi et al., 2017). Organizations can absorb and create new knowledge quickly and effectively through supply chain integration (Jajja et al., 2018). It can help organizations to update new knowledge and reconfigure it in rapidly changing environments (Müller et al., 2022). Internal integration enables organizations to communicate effectively among functional departments and shortens product development time. As a result, the integrated efforts accentuate the organization's response to achieve customer requirements. Thus, internal integration improves employee engagement, which is essential when they are expected to execute new tasks quickly. This capability of internal integration aligns with the DCV's reconfiguration capability and leads to the fulfilment of the organization's short-term and immediate needs, resulting in a competitive advantage.

External integration, such as integration with suppliers, improves supply chain’s response to demand fluctuations (Swafford et al., 2008). This is because a better supplier–buyer relationship facilitates the effective utilization of resources and a superior understanding of each other's needs. The joint decision-making between buyers and suppliers leads to more confidence in suppliers' decision-making process (Demeter et al., 2016). Thus, it enhances the organizations' reconfiguration capability through which they can shift their capacity and deploy new emerging products that are required immediately. For instance, many organizations produced PPE during COVID-19 outbreaks with the help of their suppliers. However, PPE manufacturing "would never have worked out" for some organizations without partner support and collaboration derived from integration capabilities (Müller et al., 2022). Organizations lacking those capabilities need more time to respond to changing environments. Internal and external integration increases agility by allowing organizations to collect the supply and demand data precisely, which helps them optimize production plans. Thus, we propose the following hypotheses:

H6a

Internal integration is associated with supply chain hyperagility.

H6b

External integration is associated with supply chain hyperagility.

Research design

Sample and data collection

The data collection for the study was carried out in August 2021 through a web-based survey constructed on the Qualtrics platform. This study analyzes this cross-sectional dataset to test the proposed theoretical model. The unit of analysis for the study is an organization, and the survey is designed for a single respondent. The target audience for the survey included managers working in the supply chain, operations, and procurement domain in India. These managers are likely to have information about their organization's supply chain strategy, operations, performance, and technological infrastructure. We approached our respondents through a snowball sampling technique in which we targeted a training program for supply chain professionals at one of the top business institutes in India and received participation from across India. We identified potential respondents from the training program and used their referrals to connect with other professionals. A total of 496 invitations were sent out. Some of the responses were dropped due to the following criteria. The respondents were not directly related to supply chain management or operations management, and those whose organization's business profile does not require explicit supply chain operations. Next, we used a filter question to ensure that respondent's organization had faced time pressure to fulfill short-term market demands. The question specifically asked the respondents whether they had faced any pressure to fulfill market demands quickly during the COVID-19 pandemic. The final data set comprises 120 responses corresponding to a response rate of 24.19%. The profile of the respondents is shown in Table 2.

Table 3.

Assessment of first-order model

Latent Constructs	First Order Constructs	Items	Loadings	CA	CR	AVE	t-statistics
Market Orientation (MO)	Market Intelligence Generation (IG)	IG1	0.835	0.893	0.921	0.700	45.745
		IG2	0.838
		IG3	0.861
		IG4	0.800
		IG5	0.847
	Intelligence Dissemination (ID)	ID1 ID2	0.877 0.921	0.766	0.894	0.809	12.374
	Response to Intelligence (RI)	RI1	0.855	0.876	0.924	0.803	48.143
		RI2	0.928
		RI3	0.903
Entrepreneurial Orientation (EO)	Risk Taking (RT)	RT1	0.773	0.809	0.888	0.726	45.805
		RT2	0.906
		RT3	0.871
	Innovativeness (Innov)	Innov1	0.811	0.815	0.891	0.732	56.728
		Innov2	0.920
		Innov3	0.831
	Pro-activeness (Pro)	Pro1	0.903	0.766	0.895	0.810	20.545
		Pro2	0.898
Data Analytical Capability (DAC)	DAC 1	0.891	0.833	0.928	0.811
	DAC 2	0.930
	DAC 3	0.880
Internal Integration (II)	II1	0.711	0.667	0.811	0.589
	II2	0.799
	II3	0.790
External Integration (EI)	Customer Integration (CI)	CuI1	0.891	0.862	0.916	0.783	33.456
		CuI2	0.866
		CuI3	0.897
	Supplier Integration (SI)	SI1	0.853	0.862	0.916	0.785	35.347
		SI2	0.883
		SI3	0.920
Supply Chain Hyperagility (HAG)	Short Term Perspective (STP)	STP1	0.851	0.656	0.853	0.744	31.454
		STP2	0.874
	Time Pressure (TP)	TP1	0.972	0.943	0.972	0.946	65.223
		TP2	0.973

Table 2.

Profile of the respondents' organizations

Variables	Frequency	Percentage (%)
Respondents Work Experience (yrs.)
0 to 5	36	30
6 to 10	31	25.80
11 to 15	33	27.50
16 to 25	17	14.17
Above 25	3	2.50
Total	120
Type of Industry
Automotive	5	4.2
E-Commerce, Retail, and Services	13	10.8
Consumer Goods	8	6.7
Machinery and Electronics	9	7.5
Mining and Infrastructure	13	10.8
Healthcare and Pharmaceuticals	7	5.8
Petrochemical	9	7.5
Others	56	46.7
Total	120
Number of Employees in the Organization (Organization Size)
0–50	13	10.83
51–100	4	3.33
101–200	10	8.33
201–500	14	11.67
501–1000	8	6.67
Above 1001 +	71	59.17
Total	120
Ownership of the Organization
Public owned	23	19.17
Privately owned	82	68.33
Public–private partnership	6	5
Others	9	7.50
Total	120

Instrument development

The measures used for most of the variables in this study are adapted from already established scales in the literature. The measure of market orientation is adapted from Kohli et al. (1993). Data analytics capability is measured using scale by Srinivasan and Swink (2018). The scale for entrepreneurial orientation is based on two studies by Engelen (2010) and Lee and Sukoco (2007). The supply chain integration is measured using a scale by Demeter et al., (2016). The existing scales are aligned with the objective of the study through some minor modifications in the language.

Scale for the construct SCH is developed by following the steps suggested by Hinkin (1998). The content validity of its instrument items was established by identifying items through a literature review and validated through the feedback of the domain experts. Since supply chain hyperagility is a new construct in the literature, its operationalization is based on the organization's ability to fulfill market demand under extreme time pressure with a short-term perspective (Müller et al., 2022). As a result, this study considers SCH as a second-order reflective construct. Specifically, the measurement of time pressure is based on the ability of internal and external capabilities to sustain the time pressure (Müller et al., 2022). Thus, it is measured using items that record internal and external time pressure to supply products to the market during the disruption. The measurement of the short-term perspective is based on prioritizing the response to the disruption over the organization's long-term goals (Müller et al., 2022). It is measured using items that record the priority given to the speed over long-term performance parameters and deferring the organization's long-term goals to remain focused on fulfilling immediate market demand during the disruption.

The inputs of five academic and three industrial professionals were used to establish the face validity of the scale. The professionals from academics were faculty of operations management from two different business schools. The industrial professionals were supply chain managers. The experts were requested to evaluate the items based on their representativeness and redundancy. Thus, the content validity of the scales in this study is established. The construct validity (composite and discriminant) and reliability of SCH, along with other constructs, are evaluated in Sect. 5.1. The operationalization of each construct is shown in Appendix A. The responses are recorded using a seven-point Likert scale which varies from 1 = strongly agree to 7 = strongly disagree.

Additionally, the study uses a set of control variables. First, the industry type is controlled, as the likelihood of disruptions can vary across industries. For example, organizations belonging to uncertain and dynamic industries may be better prepared to manage disruptions due to their past experiences with environmental disruption (Dubey et al., 2019). The survey records the nature of the industry in which the organization lies. Second, the organization's size is controlled due to the availability of fewer resources, with smaller organizations reacting quickly to any external disruption (Ramaswami et al., 2009). It is measured using the number of employees in the organization. Third, the organization's ownership is controlled as some significant differences exist between publicly-owned and privately-owned organizations. Processes in a publicly owned organization are often complicated by multiple tiers and conflicting interests between bureaucrats and politicians. Whereas privately-owned organizations consistently compete in the market to gain control while focusing on the business's profitability. As a result, managers in privately owned organizations are more proactive in reacting to any changes in the market (Iswajuni et al., 2018). Thus, the response to a disruption in a publicly owned organization could be slow compared to privately owned organizations (Backx et al., 2002). Fourth, the age of the organization is controlled as the older organizations have extensive experience in dealing with several past disruptions. Thus, such organizations tend to recover quickly and efficiently from any disorder (Thornhill & Amit, 2003). It is recorded by asking the respondent about the number of years since the organization's inception.

Systematic measurement bias

Common method bias (CMB) can be an issue in survey-based design. First, we performed Harman's one factor, which explains 36.05% of the total variance. Second, we adopted the common marker variable technique to check the common method bias as recommended by Lindell and Whitney (2001). We considered an unrelated variable to partial out the correlation resulting from CMB. We found minimal differences between adjusted and unadjusted correlations. Hence, we conclude that CMB is not a major issue.

We examined the nonresponse bias suggested by Armstrong and Overton (Armstrong & Overton, 1977). We compared the difference between; (a) early and late respondents and (b) completed versus incomplete surveys. A sample of 65 respondents was selected from early responses. The t-test of the difference between early (65 responses) and late response (55 responses) had a p-value = 0.315. The t-test of the difference between completed and incomplete surveys had a p-value = 0.612. This suggests that nonresponse bias is not a major issue in our results.

Data analyses and results

The study uses SmartPLS 3.3, which works on Partial Least Squares estimates (PLS). This study investigates the relationship between SCH and its antecedents for the first time. Thus, it is an exploratory study. According Gupta and George (2016), the PLS method is recommended as an appropriate tool for analyzing such exploratory studies. Peng and Lai (2012) also support this method's relevance due to its ability to predict the exogenous variables. This study attempts to predict SCH based on several antecedents. Additionally, PLS is considered an effective method for estimating a complex research model (Moshtari, 2016). Therefore, the PLS technique is relevant and appropriate for the analysis of this study. The study is conducted in two phases. In the first phase, the validity and reliability of the measurement model are established. It is followed by analyzing the structural model (Moshtari, 2016; Peng & Lai, 2012).

Measurement model

The measurement model is assessed by constructs' composite reliability, Cronbach's alpha, and average variance extracted (AVE). The composite reliability and Cronbach's alpha are greater than 0.8 and 0.7 for all the constructs except II and STP, whose Cronbach's alpha is still greater than 0.6, signifying acceptable measurement reliability (Hair, 2006). AVE is noted as greater than 0.5, indicating that the latent constructs account for at least 50% of the variance in the items. The multi-collinearity among the items is analyzed using collinearity statistics in which all items are under acceptable value of five. It ensures that there is no overlap between two or more regressors while explaining a single construct (Hair, 2006).

The discriminant validity of the measures is examined to show that no item is loaded higher on another construct than its original construct. The study uses Fornell and Larcker's Criterion to demonstrate discriminant validity. It highlights that the square root of each construct's AVE (on the diagonal of the correlation matrix) is higher than the correlations between the focal construct and all other constructs (see Table 4). Altogether, the evaluation of the measurement model suggests that the psychometric properties of the model are significantly strong to measure and estimate the structural model.

Table 4.

Discriminant validity

	CI	DAC	Innov	II	ID	IG	Pro	RI	RT	STP	SI	TP
CI	0.885
DAC	0.183	0.900
Innov	0.431	0.154	0.856
II	0.578	0.208	0.460	0.767
ID	0.202	0.077	0.402	0.309	0.899
IG	0.332	0.205	0.455	0.450	0.543	0.836
Pro	0.440	0.157	0.721	0.390	0.245	0.411	0.900
RI	0.465	0.127	0.592	0.537	0.630	0.740	0.446	0.896
RT	0.446	0.200	0.787	0.476	0.385	0.489	0.669	0.612	0.852
STP	0.116	0.636	0.209	0.232	0.200	0.191	0.240	0.240	0.168	0.863
SI	0.628	0.205	0.444	0.550	0.223	0.295	0.444	0.369	0.474	0.200	0.886
TP	0.056	0.425	0.141	0.280	0.041	0.092	0.179	0.140	0.079	0.594	0.161	0.973

Hypotheses testing

Since the PLS technique does not assume the multivariate normal distribution, the traditional parametric methods to test significance are unsuitable (Henseler et al., 2014). In comparison, the PLS uses bootstrapping process to estimate standard errors and parameter significance (Moshtari, 2016; Peng & Lai, 2012). The path coefficients and $p$-values are presented in Table 5. The computation is done in SmartPLS 3.3 using 500 bootstrapping runs. $\beta$ values (in Ordinary Least Square) refers to the estimated path coefficients that explains the changes in dependent variable if one unit of independent variable changes. The sign of $\beta$ value signify the nature of relationship between the variables. It could be positive or negative. The $p$-value demonstrates the significance levels of the relationship based on the proposed confidence levels.

Table 5.

Results

Hypotheses	Relationship	$f^2$	Β	$p$-values
H1	DAC- > MO	0.031	0.175	0.043**
H2	DAC- > SCH	0.484	0.551	0.000***
H3	DAC—> EO	0.038	0.191	0.075*
H4a	MO- > II	0.11	0.337	0.001***
H4b	MO- > EI	0.019	0.140	0.093*
H5a	EO- > II	0.084	0.296	0.003***
H5b	EO- > EI	0.207	0.465	0.000***
H6a	II- > SCH	0.062	0.244	0.018**
H6b	EI- > SCH	0.012	− 0.107	0.256
	Control Variables
1	Ownership—> SCH	0.03	0.136	0.036**
2	Size—> SCH	0.021	0.122	0.032**
3	Organization Age—> SCH	0	0.014	0.827
4	Industry—> SCH	0.011	− 0.081	0.290

*p < 0.1; **p < 0.05; ***p < 0.01

Looking at the $p$-values and the sign of $\beta$ values, eight out of nine relationships are significant and positive (Refer to Fig. 2). DAC has a significant positive relationship with MO, EO, and SCH. However, the relationship between DAC and EO is significant at a confidence level of only 90%. Similarly, the associations of MO with II and EI are also positively significant. EO shares a significant positive relationship with both types of integrations. Additionally, II has a significant relationship with SCH. However, the relationship between EI with SCH is not empirically significant. Thus, the results don't support H6b.

Fig. 2.

Research model showing linkage beta values. *p < 0.1; **p < 0.05; ***p < 0.01

The study also looks into the explanatory power of the model by examining the $R^2$ value of the constructs. It ensures the fulfillment of the PLS objective to maximize the variance explained in endogenous constructs. $R^2$ of all the endogenous variables are presented in Table 6. All latent constructs have a value greater than 0.5, which lies in the category of moderately strong(Chin, 1998). Additionally, the study uses Cohen $f^2$ to evaluate the effect size of each predictor. According to Cohen (J. Cohen, 1988), $f^2$ values of 0.35, 0.15, and 0.02 are considered large, medium, and small, respectively. As per Table 5, the effect size of DAC on SCH is large, whereas EO on EI is medium. The effect size of DAC on MO, EO on II, II on HAG, MO on EI, and MO on II lie under the small category. Finally, the study evaluates the model's capability to predict using Stone-Geisser's $Q^2$ for endogenous constructs. The value of $Q^2$ for all the endogenous constructs is greater than 0, which assures acceptable predictive relevance (Peng & Lai, 2012) (See Table 5).

Table 6.

$R^2$ and Prediction ($Q^2$)

Latent constructs	Dimensions	$R^2$	$Q^2$	Overall $Q^2$
Market Orientation	IG	0.864	0.592	0.017
	ID	0.550	0.428
	RI	0.829	0.659
Entrepreneurial Orientation	Innov	0.875	0.623	0.02
	Pro	0.727	0.554
	RT	0.845	0.596
External Integration	CuI	0.812	0.627	0.191
	SI	0.816	0.628
Hyperagility	STP	0.754	0.547	0.260
	TP	0.837	0.783
Internal Integration	Unidimensional	0.319		0.148

Discussion and implications of the study

The motivation of this study is to understand how organizations can successfully navigate environmental disruptions through SCH. Such an understanding is a precursor to designing an effective supply chain. This is especially important for a pandemic like COVID-19, where supply chain operations must adjust extremely fast. Our results suggest that strategic orientations, analytical capability, and integration are important drivers for hyperagility. We examine the role of entrepreneurial orientation (EO) and Market Orientation (MO) on SCH. Our result suggests that EO and MO are not directly linked with SCH; rather, they influence SCH through supply chain integration. The study also acknowledges the important role of analytics capability in strengthening the SCH. The following sections discuss the theoretical and managerial implications of this study.

Theoretical implications

This study enriches the understanding of supply chain hyperagility and its antecedents. It operationalizes the hyperagility construct and empirically validates its antecedents for the first time in literature. The results make several contributions to the literature. First, the study extends the relevance of the dynamic capability perspective for establishing the SCH construct. The dynamic capabilities perspective suggests that organizations need to develop capabilities to deal with uncertain environments (Teece et al., 1997). The study has identified antecedents of SCH that help organizations develop dynamic capabilities. These antecedents conform with the three dimensions of dynamic capabilities: sensing, seizing, and reconfiguring (Teece, 2007). The notion of sensing aligns with the capabilities developed by data analytics and market orientation. The seizing aspect aligns with the entrepreneurial orientation, and reconfiguration aligns with the supply chain integration.

Second, the findings contribute to the disruption management literature by suggesting that effective use of data and intelligence can create new organizational capabilities and enable rapid response under time pressure. IT facilitates intelligence generation through the digital availability of information and data. IT-related capability is an important dimension of analytical capability (Dubey et al., 2021; Liu et al., 2013). Prior literature suggests that IT-related capability is positively associated with supply chain agility since it is important in enabling organizations to improve sensing and response capability (Liu et al., 2013). IT-related technology such as Industry 4.0, Blockchain, and RFID help organizations collect data on a real-time basis. The organization can adjust inventory, improve forecasting accuracy, and supply chain resilience. IT improves flexibility and supply chain coordination (Srinivasan & Swink, 2018). It also facilitates knowledge sharing along the supply chain (Liu et al., 2013). IT-related capabilities can reduce response time to unforeseen events and market changes and thereby improve supply chain agility (Liu et al., 2013).

However, some studies suggest that IT capability may not be sufficient to deal with external disruptions such as COVID-19, but instead, analytical capabilities may be more useful (Dubey et al., 2021). IT capability may be utilized to monitor the business environment efficiently. However, the analytical capability is required to generate meaningful and actionable insights from such data (Dubey et al., 2021; Liu et al., 2013). An organization's analytical capability helps to develop a business continuity plan that may help managers sense the changes in the business environment (Xu et al., 2021). Thereby managers may quickly respond to an uncertain environment like COVID-19. Analytical capability facilitates end-to-end real-time information and transparency, which leads to better supply chain decisions, and thus, it is associated with hyperagility (Gupta et al., 2021; Xu et al., 2021). Some researchers argue that to deal with disruptions similar to COVID-19 in the future, the adoption of digital technology is a must to improve transparency and trust among supply chain participants (Gupta et al., 2021). Similarly, our result suggests that analytical capability is associated with supply chain hyperagility.

Third, the study conceptually establishes and empirically tests the role of supply chain integration in achieving SCH. Supply chain integration is a dynamic capability that helps organizations find new value-creation methods under unforeseen situations (Jajja et al., 2018). In the supply chain literature, supply chain integration is considered one of the critical capabilities (Demeter et al., 2016). It helps form a network of supply chain partners that collaborate and facilitate knowledge sharing and generation within the supply chain. Supply chain integration enables an efficient and effective flow of information and materials at low cost and high speed (Jajja et al., 2018). Such characteristics ease the way for the supply chain to react quickly under unforeseen situations. The prior result suggests that supply chain integration positively affects organizational performance. However, some research suggests that it is indirectly associated with business performance. It improves supply chain agility, and agility improves business performance (Tse et al., 2016). Further, some authors also categorize supply chain integration into two parts: internal and external integration (Tse et al., 2016). Our result also sheds light on a similar direction and suggests that internal and external integration are positively associated with hyperagility.

Anecdotal evidence suggests that organizations utilized their internal and external capabilities to deal with supply chain disruptions due to COVID-19. For instance, some automobile and aerospace organizations started manufacturing ventilators on their assembly line by utilizing their internal and external capabilities (Kamar et al., 2021). Internal capabilities are linked with exploiting internal capacities, leveraging knowledge, and engaging employees to accentuate manufacturing and transportation activities within the supply chain. This capability helps accelerate new product development and speed up manufacturing activity, sourcing decisions, and distribution, which are essential components to make the supply chain hyperagile (Kapadia, 2021). Similar to internal integration, external integration is also positively associated with SCH. An organization's external capability is associated with the network, trusted collaboration, and transparent flow of information within the supply chain network. With these capabilities, organizations can quickly build and operate a supply chain in an uncertain environment. Organizations can get raw materials and transport the component and raw materials at an extreme speed (Müller et al., 2022). Those organizations that lack these capabilities need more time to respond at an extreme speed. While extant literature suggests that dynamic capability helps improve supply chain agility, we extend this literature by examining how it affects SCH. Our empirical analysis broadly suggests that internal integration is significantly associated with hyperagility.

Fourth, we examine the impacts of entrepreneurial orientation (EO) and Market Orientation (MO) on SCH. Prior studies suggest that EO and MO are positively associated with new product development (Morgan & Anokhin, 2020) and improve supply chain responsiveness. However, the recent disruptions have highlighted the significance of new product development under immediate need for short period, which SCA does not endorse, so we extend this line of inquiry in the context of hyperagility and examine the effect of EO and MO on SCH. Our result suggests that EO and MO are not directly linked with SCH. Rather, they influence supply chain integration and, further, are influenced by analytical capability. As EO signifies organizations' ability to identify new opportunities through innovativeness, proactiveness, and risk-taking capabilities, prior studies suggest that EO helps organizations under turbulent environments (Engelen, 2010). The prior result suggests that EO improves organizational performance, and some studies suggest that EO is not directly connected with organizational performance.

However, there is an agreement in the literature that EO needs to be properly managed within the organization to reap its full potential (Engelen, 2010). More recently, Müller et al. (2022) suggest that EO allows organizations to leverage its internal and external capabilities in developing an advanced supply chain agility based on qualitative study. EO helps to capitalize resources to enter into new and unknown markets. During times of disruption, such as COVID-19, many organizations started to manufacture products that were new for many markets. However, some organizations managed their operations well due to their risk-taking, proactive and innovative capabilities, which are essential parts of EO (Morgan & Anokhin, 2020). The risk-taking characteristics of organizations indicate that their management value disruption as a new opportunity for a new business that can cover lost sales due to core business (Engelen, 2010). Due to EO, the organizations proactively develop an outsourcing strategy that improves responsiveness. Interestingly, our result suggests that it directly does not influence SCH but rather indirectly affects through supply chain integration. The reason behind this could be the critical role of integrating and aligning mechanisms for successful strategy implementation. Thus, the integration and alignment helps supply chain to realize the full potential of EO.

Similar to EO, prior research suggests that MO helps in product innovation; however, there is little understanding of how it shapes hyperagility. We extend the literature in this direction. MO indicates the capability of generating and creating market intelligence for creating superior product value and reduces the chance of product failure (Kohli et al., 1993). Our result suggests that MO helps inter-organizational coordination and indirectly impacts hyperagility. Internal and external integration provides better inter-organizational coordination, which leads to better communication and exchange of information and resources associated with customers and competitors (Ciampi et al., 2021). Integration also removes the barriers that hinder the flow of tacit knowledge. This will create trust and transparency within departments, providing a more receptive and proactive environment to deal with uncertain situations like COVID-19 at extreme speed (Müller et al., 2022).

Results of this study establish the role of analytical capabilities toward EO and MO as dynamic capabilities that support decision-making, foster knowledge sharing and create new knowledge, and improve analytical skills. Thus, it increases the strategic propensity to entrepreneurship. Our result is in line with prior results that suggest that analytical capabilities help collect the data in real-time and update customer requirements by more accurate forecasting, thus enhancing EO and MO (Ciampi et al., 2021).

Managerial implications

This study has crucial implications regarding the adoption of SCH for supply chain and operations managers. First, the results inform managers of the potential of hyperagility to deal with future large-scale demand disruptions (such as COVID-19). During the pandemic, managers worldwide faced the challenge of addressing demand fluctuations. They depended on the existing knowledge of supply chain agility. However, its long-term orientation stopped managers from making significant changes in their operations to address short-term needs. Now, the concept of hyperagility in this study prepares managers to bring short-term perspective while managing unprecedented and immediate market demand.

Second, the findings help managers who faced a constant dilemma during the pandemic on how to develop hyperagility. This study identifies specific organizational capabilities that could foster hyperagility in supply chains. For example, supply chain integration allows organizations to achieve synergy between different functions that allow them to absorb information and react quickly as per market requirements. Thus, managers must effectively and jointly work with supply chain partners, including planning and scheduling, material selections, selection of mode of transportation, and goal setting. Additionally, the market and entrepreneurial orientations encourage organizations to leverage market intelligence and proactiveness. These orientations help managers to reinforce supply chain integration that would influence hyperagility. This study suggests that managers incorporate these capabilities in advance so that if the need arises, their organizations can exhibit hyperagile characteristics by responding to immediate market demands with extreme speed. The empirical validation of the antecedents of hyperagility provides established pieces of evidence of the potential contribution of these capacities towards the hyperagile nature of supply chains.

Third, our findings highlight the role of analytical capability in improving SCH. Thus, managers can focus on developing analytical capabilities. They can improve organizations' analytical capability by investing in digital technology, which enhances information processing and intelligence generation. Managers can strategically invest in digital technology that helps collect large amounts of data from cloud computing and the Internet of Things (IoT). However, organizations need to focus on both soft and hard enablers since both are important in order to develop analytics capability. These enablers allow organizations to track and monitor supply chain processes, which allows real-time information sharing. This would, in turn, increase the agility of organizations.

Fourth, our results suggest that analytical capability is associated with market orientation and entrepreneurial orientation. Thus, managers need to understand the role of data analytical capability towards dynamic capabilities of market orientation and entrepreneurial orientation. It allows managers to generate market-related intelligence, operate business proactively, and take calculated risks. Through effective adoption, DAC managers can position their organization strategically to react at tremendous speed to exploit short-term business opportunities.

Conclusion and future research directions

Many organizations failed to survive the external disruptions such as COVID-19. Thus, it is critical to formulate strategies that manage organizations to survive these disruptions. To survive future disruptions and uncertainty like COVID-19 pandemic, the organization should be able to respond to immediate market demand even if it is for short period under extreme time pressures. While past research has highlighted the role of supply chain agility under uncertainty and examined their antecedents, the recent demand surge, and time pressure because of external disruptions call for a fresh look at organizations' supply chain capabilities. Recently, a few scholars have indicated that organizations should realize an extended supply chain agility to enhance survival characteristics. This study extends the notion of extended supply chain agility through ‘Supply Chain hyperagility’. It demonstrates the ability to fulfill immediate and unforeseen demands with a short term perspective to remain competitive in the market (Müller et al., 2022; Sodhi & Tang, 2021). However, there is little theoretical and empirical understanding regarding the antecedents of supply chain hyperagility. We addressed this gap by analyzing the antecedents using the structural equation modeling approach. The theoretical underpinning of this study is the dynamic capability theory which considered four antecedents of supply chain hyperagility: (i) data analytical capability, (ii) integration, (iii) entrepreneurial orientation, and (iv) market orientation. We collected data from Indian manufacturing organizations to test the hypothesized model.

This exploratory study has some limitations which provide avenues for future research. First, this study is based on cross-sectional data from single informants. In such empirical settings, common method bias is difficult to avoid, and it is hard to establish causality. We suggest a future longitudinal study to establish a link between SCH and its antecedents. Data should be gathered using a multi-informant questionnaire to deal with common method bias in the survey-based study. Second, we have not considered any contingent factors that could influence the effect of antecedents on SCH. Thus, additional moderating factors can be included in the future to control for potentially unobserved heterogeneity. Third, we have not investigated the performance consequences of SCH. In the future effect of hyperagility on organizational performance can be examined.

Appendix A

See Table 7.

Table 7.

Items for the variables

Second order	First order	Items	Description
Market Orientation (Kohli & Jaworski, 1990)	Intelligence Generation	IG1	We meet with customers at least once a year to find out what products or services they will need in the future
		IG2	We can detect fundamental shifts in our industry (e.g., competition, technology, regulation)
		IG3	We conduct interviews often to detect changes in our customers' product preferences
		IG4	We poll end users at least once a year to assess the quality of our products and services
		IG5	We periodically review the likely effect of changes in our business environment (e.g., regulation) on customers
	Intelligence Dissemination	ID1	We have interdepartmental meetings at least once a quarter to discuss market trends and developments
		ID2	Our organization periodically circulates documents (e.g., reports, newsletters) that provide information on our customers
	Response to Intelligence	RI1	If a major competitor were to launch an intensive campaign targeted at our customers, we would implement a response immediately
		RI2	Several departments get together periodically to plan a response to changes taking place in our business environment
		RI3	When we find that customers would like us to modify a product of service, the departments involved make concerted efforts to do so
	Internal Integration (Demeter et al., 2016)	II1	We share Information between our manufacturing plants
		II2	We ensure joint decision making between our manufacturing plants
		II3	We follow joint innovation strategy
External Integration (Demeter et al., 2016)	Supplier Integration	SI1	We share information with key suppliers
		SI2	We adopt collaborative approaches with key suppliers
		SI3	We ensure joint decision making with key suppliers
	Customer Integration	CuI1	We share information with key customers
		CuI2	We adopt collaborative approaches with key customers
		CuI3	We ensure joint decision making with key customers
Entrepreneurial Orientation (Engelen, 2010; Lee & Sukoco, 2007)	Risk Taking	Ri1	Our organization stresses a fully delegated policy for employees
		Ri2	Our organization gives the freedom for individuals or teams to develop new ideas
		Ri3	In general, the top managers of our organization have a strong tendency to be ahead of others in introducing novel products or ideas
	Innovativeness	Innov1	Our organization encourages and stimulates technological, product/service-market, and administrative innovation
		Innov2	Our organization stimulates creativity and experimentation
		Innov3	Our organization’s innovative initiatives are hard for competitors to successfully imitate
	Proactiveness	Pro1	In dealing with competitors, our organization typically initiates actions which competitors respond to
		Pro2	In dealing with competitors, our organization is very often the first business to introduce new products/services, administrative techniques, operating technologies, etc
	Data Analytical Capability (Srinivasan & Swink, 2018)	DAC 1	We use advanced analytical techniques (e.g., simulation, optimization, regression) to improve decision making
		DAC 2	We routinely use data visualization techniques (e.g., dashboards) to assist users or decision‐maker in understanding complex information
		DAC 3	We deploy dashboard applications/information to our managers’ communication devices (e.g., smart phones, computers
Supply Chain Hyper Agility (Müller et al., 2022)	Time Pressure	TP1	Our organization operated under high internal time pressure to manufacture our products due to COVID19
		TP2	Our organization operated under high external time pressure to manufacture our products due to COVID19
	Short Term Perspective	STP1	Our organization quickly prioritized speed goal during COVID 19
		STP2	Our organization deferred common goals during COVID 19

Declarations

Conflict of interest

Authors declare that there is no conflict of interest.

Research involving human participants and/or animals

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

This study is not funded.