The impact of information technology usage on supply chain resilience and performance: An ambidexterous view

Abstract

Scholars and practitioners have recognized the importance of supply chain (SC) resilience. However, it remains unclear how to build SC resilience and whether SC resilience can enhance firm performance and bring values to customers. By analyzing data collected from 206 manufacturers in China, this study empirically examines how firms implement different information technology (IT) patterns (exploitative versus explorative) with SC partners to achieve supplier and customer resilience from information processing theory, and examines the performance implications of these two dimensions of SC resilience. In addition, this study also investigates how IT ambidexterity reconciles the paradox between IT exploitation and IT exploration in enhancing SC resilience. The results show that both supplier and customer resilience could improve SC performance. To achieve the two aspects of SC resilience, only explorative use of IT with suppliers and customers have significant effects. The results also show that the ambidextrous use of IT on the customer side takes effect. The exploitative and explorative use of IT complement each other to improve customer resilience. The findings of this study contribute to IT and SC resilience literature.

1. Introduction

The COVID-19 pandemic has caused severe global disruptions of supply and demand in many companies, leading to huge financial losses to them. Some companies, such as Haier, have made full use of information technology (IT) such as the industrial Internet platform to effectively address the disruption and quickly recover from it. Consequently, as the ability of the supply chain (SC) to recover and maintain the continuity of material, information and cash flow in the presence of SC disruptions, SC resilience is attracting increasing attention from scholars and practitioners (Brandon-Jones et al., 2014; Brusset and Teller, 2017; Chowdhury and Quaddus, 2017; Jüttner and Maklan, 2011; Sawik, 2013; Yu et al., 2019). Especially in today's vulnerable business environment under COVID-19, firms are likely to be disrupted by some unexpected events in different parts of a SC (Hendricks and Singhal, 2005; Narasimhan and Talluri, 2009; Wagner and Bode, 2008). Based on the nodes where disruptions may occur and the continuity of operations that needs to be guaranteed in the SC, SC resilience can be classified into internal, supplier and customer resilience. Extant research, however, mainly addresses SC resilience from the perspective of a single firm. Few empirical studies simultaneously investigate different types of resilience and their performance implications, which lends us a research opportunity. In this study, we mainly focus on supplier and customer resilience and their performance implications. They are recognized as external resilience that could ensure continuous material supply and stable product delivery in a three-level SC setting, and are critical for firm survival and performance (Pournader et al., 2016; Voss et al., 2009). For instance, when faced with the same chip crisis caused by a fire disaster at their chip supplier Philips, Nokia and Ericsson ended up differently. Nokia integrated with Philips to adjust production plans in time and captured the market share while Ericsson stopped the mobile phone business in the following year due to lack of such external resilience (Lee, 2004). Consequently, we mainly focus on external resilience and its performance implications.

Previous literature suggested that IT has revolutionized SCs to achieve numerous benefits such as coordination, increased efficiency, responsiveness, and competitive advantages (Huo et al., 2015; Prajogo and Olhager, 2012; Rai et al., 2012; Singh, 2020; Subramani, 2004). Firms implement IT to share information and knowledge across functions and organizational boundaries. It improves sensing and information processing capabilities so that firms can deal with unforeseen events rapidly, and compete successfully in the changing environment (Ngai et al., 2011). Some studies examined the effects of IT-related factors (e.g., IT infrastructure capability, big data analytics) on firm's response to disruptions and unpredicted changes (Dubey et al., 2019a; Singh and Singh, 2019; Wamba and Akter, 2019; Wamba et al., 2020). However, these studies considered IT factors as general capabilities and have not specifically investigated how IT implementation at different nodes of a SC influences SC resilience. It provides us the opportunity to explore how IT implementation on supplier and customer sides (i.e., external IT use) affects SC resilience differentially. In order to effectively address disruptions, it is critical for firms to share information and integrate with upstream and downstream partners via IT use that crosses firm boundary. In addition, we classify supplier/customer IT implementation into patterns of exploitative and explorative use to test their effects in enhancing SC resilience respectively.

To recover from SC disruptions, firms should excel at using IT to exploit their current structured processes (IT exploitation) or explore unstructured processes (IT exploration) (Andriopoulos and Lewis, 2009; Tushman and O'Reilly, 1996). According to information processing theory (IPT), the use of IT is an effective approach to enhancing information sharing and processing capabilities that are conducive for disruption recoveries (Dubey et al., 2019a; Galbraith, 1974; Premkumar et al., 2005; Zhou and Benton Jr, 2007). Specifically, the exploitative use of IT standardizes information formats to improve information processing capability, which enables focal firm and its SC partners to make rapid decisions and take immediate actions when addressing SC disruptions. While explorative use of IT strengthens extensive interfirm information sharing, which enables them to collaboratively develop novel solutions towards SC disruptions. But operations managers are often faced with a paradox between exploitation and exploration (Andriopoulos and Lewis, 2009; He and Wong, 2004; Lee et al., 2015). On the one hand, they are contradictory yet interrelated operational processes as they compete for scarce firm resources when managing SC disruptions (Chiu, 2014; Gupta et al., 2006). On the other hand, they orient the organization in the pursuit of different goals as IT exploitation addresses efficiency and IT exploration emphasizes flexibility in operations, which may have different impacts on firm recovery (Chiu, 2014; He and Wong, 2004; Koryak et al., 2018). IT ambidexterity that firms simultaneously use IT exploitation and IT exploration in a way that both complement each other or are balanced has been recognized as a fundamental IT implementation mode in SCs (Gibson and Birkinshaw, 2004; Lee and Rha, 2016; Li et al., 2013; Ojha et al., 2018; Sanders, 2008; Subramani, 2004). It is prized as a means of resolving the exploitation-exploration paradox, rather than increasing the tension between the two, and enables firms to recover from disruptions quickly and efficiently (Andriopoulos and Lewis, 2009; Chiu, 2014; He and Wong, 2004; Lee et al., 2015). Although the ambidextrous use of IT could reconcile the paradox and becomes vital to the maneuvers of SC disruption recovery, the extant literature has seldom examined the relationship between explorative, exploitative and ambidextrous use of IT and SC resilience.

To fill these research gaps, this study grounds in IPT to underpin the relationships between patterns of IT use, SC resilience and SC performance. Specifically, our research questions are as follows: (1) How will the exploitative, explorative, and ambidextrous use of IT influence customer and supplier resilience respectively? (2) How will supplier and customer resilience influence SC performance? By answering the above questions, this study contributes to the literature in several ways. First, this study extends the extant IT-enabled SC resilience literature by revealing comprehensive mechanisms between different patterns of IT use (i.e., exploitative and explorative) and SC resilience (i.e., supplier and customer resilience). Second, this study applied ambidexterity perspective to shed light on how IT ambidexterity reconciles the paradox between IT exploitation and IT exploration in improving SC resilience. Third, this study provides empirical evidence on performance implications of supplier and customer resilience. In addition, this study provides practical guidelines for managers to adopt appropriate patterns of IT to recover from disruptions with SC partners more effectively, which is extremely useful and critical for SCM practices under the global SC disruptions caused by the COVID-19 pandemic.

The rest of this paper is organized as follows. First, it will introduce the definitions of the relevant constructs and provide the theoretical foundation. Hypotheses will be developed based on these contents. Next, it will depict the research methodology and analyze the data to get results. Then, it will discuss the findings and provide theoretical and managerial implications. Finally, limitations and suggestions for future research will be provided.

2. Theoretical background and hypotheses

2.1. Patterns of IT use in SCM

Previous studies distinguished two categories of IT use in supply chain management (SCM): internal and external IT use (Savitskie, 2007; Zhang et al., 2016b). Internal IT use is conceptualized as the implementation of IT throughout manufacturing processes to share information within the firm (Savitskie, 2007; Zhang et al., 2016b). It generally includes the applications within the focal firm that facilitate its internal operations and enhance collaboration among different functions. For instance, traditional systems like ERP is the most common case of internal IT use (Zhang et al., 2016b). In contrast, external IT use does not refer to the use of one specific IT tool. It is defined as the extent of using IT such as EDI, CRM, Internet, or cloud computing to integrate SC partners and digitize activities beyond firm boundaries (Xue et al., 2013; Zhang et al., 2016b). It acts as an electronic linkage across firm boundaries, which is embedded in business routines with suppliers and customers (Gonzalvez-Gallego et al., 2015; Zhang et al., 2016b). Specifically, supplier IT use is the use of IT for integrating suppliers and digitizing supply-side activities such as purchasing and material inventory management. In contrast, customer IT use is the use of IT for integrating customers and digitizing customer-side activities such as delivering and retailing (Xue et al., 2013).

This study mainly focuses on external IT use and considers supplier and customer IT use separately. On the one hand, although internal IT use (e.g., ERP system) can help the focal firm to cope with disruptions to some extent by controlling and monitoring internal processes within the firm, and the software applications and IT infrastructure for internal operations are now available to most of the firms (Zhang et al., 2016b). Firms still suffers from coping with SC disruptions due to the weakness in collecting external information by internal IT usage. On the other hand, IT linkage with suppliers and customers extends information breadth and depth, which will determine the successfulness of responding, adapting and recovering from SC disruptions. For instance, although the COVID-19 pandemic has caused global SC disruptions to many companies, Haier can still rely on their solid external IT linkage with global partners for daily production and sales planning, which leads to stable customer services during the challenging days.

In addition, according to organizational learning theory, the methods of how firms leverage resources and capabilities can be considered into two patterns, exploitation and exploration (Levinthal and March 1993). Exploitation is defined by terms such as refinement, choice, execution, selection and implementation. The primary purpose of exploitation is to improve operational efficiency. It can be achieved through standardization, process control, streamlined activities with variance reduction and a high level of consistency. In contrast, exploration is defined by terms such as risk-taking and experimentation, search, innovation, discovery and flexibility. The primary pursuit of exploration is to create new possibilities and establish distinctive competency. It can be achieved through reassessment of current solution with variance-seek and novel solutions development (Andriopoulos and Lewis, 2009; Chiu, 2014; March, 1991; Sanders, 2008; Subramani, 2004). Based on this theory, we extend the exploitation versus exploration construct to define a new typology of external IT use: (1) external IT use for exploitation and (2) external IT use for exploration (Sanders, 2008; Subramani, 2004). Specifically, supplier IT use for exploitation is defined as firms using IT to automate structured processes (e.g., purchase processing, invoicing, warehouse and inventory management, shipment and delivery) with their suppliers. It aims to improve capabilities incrementally and achieve definable benefits in supplier management, such as purchasing cost reduction, supply continuity and efficiency enhancement. In contrast, supplier IT use for exploration is defined as firms using IT to digitalize unstructured processes (e.g., forecasting market demands, coordination and integration, suppliers’ expertise exploration and leverage) with their suppliers. It aims to generate novel solutions to supply-side problems and harvest benefits in the long-run. Customer IT use for exploitation and exploration are defined similarly. Customer IT use for exploitation automates customer-side structured processes, and improves efficiency in information and knowledge exchange with customers. Customer IT use for exploration is implemented in unstructured processes. It aims to uncover new methods of problem-solving and develop long-run benefits with customers (Sanders, 2008; Subramani, 2004).

2.2. SC resilience

SC resilience is defined as the capability of the SC to recover from SC disruptions and maintain the continuity of material, information, and cash flow (Day, 2014; Johnson et al., 2013; Sawik, 2013). It requires the focal firm to work together with its suppliers and customers to guarantee the integrity of cooperative structures and processes (Brandon-Jones et al., 2014; Jüttner and Maklan, 2011; Ponomarov and Holcomb, 2009).

Both supply and customer side may be disrupted due to severe catastrophes, such as natural disasters, wars, terrorist attacks, and economic crises, as well as operational uncertainties, such as stockouts, quality problems, production fluctuations, and order cancellations (Kumar et al., 2010; Lin and Zhou, 2011; Tang, 2006; Trkman and McCormack, 2009). According to the node in the SCs that disruptions may occur and the continuous operations to be maintained, SC resilience can be classified into internal, supplier, and customer resilience (Pournader et al., 2016; Sawik, 2013; Voss et al., 2009). This study mainly focuses on supplier and customer resilience, which are commonly recognized as external resilience. Because from the practical view, compared with internal operations, it is more difficult for firms to control external activities and to recover from disruptions with their suppliers and customers in unpredictable environments. While external resilience is recognized as the important capability of the focal firm and its SC partners to maintain upstream material supply and downstream product delivery after disruptions in a three-level SC setting. It determines firm's survival and performance improvement. Specifically, supplier resilience is the capability embedded between the focal firm and its suppliers to maintain the continuity of supply and guarantee the integrity of upstream structures and functions. Customer resilience is the capability embedded between the focal firm and its customers to preserve the continuity of demand and ensure the integrity of downstream structures and functions.

2.3. Information processing theory

IPT views every firm as an open information-processing system which must deal with several sources of uncertainties and fluctuations. Firms can mitigate the negative impacts of uncertainties by improving information processing capability (Galbraith, 1974). In the volatile environment, streamlined information is needed to generate more synchronized decision making and coordinated actions. In other words, firms will suffer from misunderstandings of environmental stimuli and conflict in risk management practices if their information processing capability is poor (Daft and Lengel, 1986). Applying this theory in SCM, firms need to improve interfirm information processing capabilities to cope with uncertainties and achieve competitiveness (Mason-Jones and Towill, 1997). Wang et al. (2013) suggested two types of information processes to respond to SC disruptions. The first type is to share more information between SC partners to mitigate information distortions and generate more solutions. Another type is to reduce information sources by standardizing information formats so that rapid decisions and responsive actions can be made.

As an essential intermediary for information sharing in SCs, IT helps firms to diffuse information across organizational boundaries effectively (Huo et al., 2015; Iyer, 2011; Li et al., 2009; Patnayakuni et al., 2006; Song et al., 2007; Yu et al., 2017). It connects suppliers and customers with formalized language and streamlined information flow, which facilitates information processing capability and allows firms to cope with uncertainties with their SC partners quickly (Srinivasan and Swink, 2015; Yao et al., 2009). IPT provides a theoretical lens to understand how firms implement different patterns of IT with suppliers and customers to build SC resilience. This study considers that the exploitative use of IT improves information processing capability by standardizing information formats. It facilitates firms to make immediate reactions to uncertainties with their SC partners. While the explorative use of IT allows for abundant information sharing. Firms generate novel solutions with their SC partners and they can win the competitiveness in the long-run.

3. Conceptual model and research hypotheses

3.1. Exploitative use of IT and SC resilience

Based on IPT, syntactic (common language), semantic (common meaning), and pragmatic (means for value assessing and information sharing) boundaries impede the transfer of information across firms (Carlile, 2004). The exploitative use of IT automates data recording and overcomes misunderstandings between SC partners. Therefore, it promotes coherence among activities and the efficient utilization of resources (Im and Rai, 2008; Malhotra et al., 2007). In the highly volatile environment, especially when SC disruptions threaten firms, SC partners need to build standardized information formats and garner shared understandings of mutual concerns, which will reduce conflicts and time in disruption recovery processes. Therefore, the exploitative use of IT with suppliers and customers will improve each resilience respectively.

Specifically, supplier IT use for exploitation automates structured processes in the upstream, such as purchasing, invoicing, inventory management, and material shipment. When the upstream endures the hardship of disruptions, firms can quickly search for substitutive materials by the standardized and institutionalized information, which allows firms to remedy material shortage and recover from disruptions quickly, thus leading to improved supplier resilience (Wang and Wei, 2007). In addition, the focal firm and its suppliers can obtain highly visible and accessible searching for information through the exploitation use of IT. When their operations are disrupted, they will have a better understanding of the situation they are facing. Congruent decisions can be made and the following actions can be taken by the two parties quickly, leading to improved supplier resilience (Kim et al., 2011).

Similarly, we also propose that customer IT use for exploitation improves customer resilience. Firms and their customers can search for and exchange needed information quickly, which is standardized and institutionalized recorded in integrated IT platforms (Rai and Tang, 2010). For example, firms can keep aware of demand fluctuations while customers can trace distribution and logistics information over time. It will create the opportunity for both parties to take immediate action when any of them find abnormal signals of potential disruptions (Subramani, 2004). When disruptions happen, customer IT use for exploitation improves customer resilience by structuring inter-firm transactions with standard protocols (Qrunfleh and Tarafdar, 2014). They can follow standardized routines for disruption recovery. Based on the aforementioned argumentations, we propose the following two hypotheses:

H1a

Supplier IT use for exploitation is positively related to supplier resilience

H1b

Customer IT use for exploitation is positively related to customer resilience

3.2. Explorative use of IT and SC resilience

IT use for exploration encourages broad and deep information sharing between SC partners for unstructured tasks (Subramani, 2004). It facilitates the generation of new knowledge and ideas to idiosyncratic environments through experimentation and innovation (Im and Rai, 2008; Qrunfleh and Tarafdar, 2014). This information and expertise promote responsiveness and are more valuable when environments are dynamic (Kim and Lee, 2010; Rai and Tang, 2010). When disruptions occur, the environment becomes highly volatile. Firms and their SC partners need sufficient information about their mutual operations and external environment so that they can initiate actions immediately. IT use for exploration catalyzes more vibrant information sharing. Therefore, firms can make mutual process reconfigurations dynamically with evolving environmental requirements, leading to a higher level of supplier and customer resilience.

Specifically, firms and their suppliers will have more excellent information processing capability to execute uninformed plans and unstructured processes when they implement IT for exploration. IT infrastructure integrated into a higher level of interfirm collaboration can facilitate thorough information sharing and streamline unordered upstream activities (Gosain et al., 2004; Rai et al., 2006). Once an unexpected disruption arises in the upstream, firms and their suppliers use IT to share specific knowledge effectively and adjust purchase planning and material delivery accordingly. Therefore, they can react to unforeseen SC disruptions rapidly and keep the continuity of upstream operations in the long-run. In addition, firms integrate with their suppliers to a greater extent by leveraging IT exploration. They can routinize unstructured processes, accelerate problem-solving, and succeed in disruption recovery (Wang and Wei, 2007; Zhang et al., 2016a). Therefore, supplier IT use for exploration improves supplier resilience. Similarly, customer IT use for exploration improves customer resilience by enriching information sharing and enhancing integration in unstructured processes. The collecting and analyzing data of disruptions through the support of the explorative use of IT enhance firms’ ability to respond to demand changes and meet customer specifications (Tarafdar and Qrunfleh, 2017). It also reduces information processing lead time to cope with downstream disruptions. Therefore, customer resilience is improved. Based on the aforementioned argumentations, we propose the following two hypotheses:

H2a

Supplier IT use for exploration is positively related to supplier resilience

H2b

Customer IT use for exploration is positively related to customer resilience

3.3. Ambidextrous use of IT and SC resilience

The interplay between exploitative and explorative use of IT with SC partners can be a paradox (Lee et al., 2015; Luo et al., 2015). However, from organizational learning literature on ambidexterity, the two patterns can be achieved simultaneously in a way that both complement each other or are balanced (He and Wong, 2004). The collective impact of exploitation and exploration will be maximized when the two patterns are balanced (comparable in magnitude) or complementary (reinforce each other's marginal effect), especially in dynamic environments (Gibson and Birkinshaw, 2004; Raisch and Birkinshaw, 2008; Venkatraman, 1989). Based on the ambidexterity perspective and the patterns of IT use proposed by Sanders (2008); Subramani (2004) and Lee et al. (2015), we introduced the concept of “ambidextrous IT use” and test its impact on SC resilience. It indicates that firms simultaneously pursue IT exploration and IT exploitation. We define ambidextrous IT use in two alternative ways: balanced use of IT patterns and complementary use of IT patterns (Cao et al., 2009; He and Wong, 2004; Mehrabi et al., 2019; Venugopal et al., 2020; Wong et al., 2013). Specifically, balanced IT use is defined as the case in which firms maintain relatively similar levels of exploitative and explorative use of IT with their SC partners. It indicates that a match between supplier/customer IT use for exploitation and exploration could enhance performance. In contrast, we define the complementary IT use as the case in which firms complement two patterns use of IT with SC partners to leverage their combined strengths (Cao et al., 2009; He and Wong, 2004; Lee et al., 2015; Patel et al., 2012; Venugopal et al., 2020; Wong et al., 2013; Zhang et al., 2016a). It signifies that supplier/customer IT use for exploitation and exploration can be supportive of one another, which enables greater marginal effect of each other in improving performance.

The ambidextrous use of IT for SC resilience has not been addressed by extant literature (Han et al., 2017). We propose that the balanced and complementary use of IT patterns leads to enhanced supplier and customer resilience. It allows firms to simultaneously achieve standardized and streamlined processes for the rapid response, as well as abundant knowledge and tighter integration for the long-run recovery (Goo et al., 2015). Specifically, the balanced use of IT exploitation and exploration increases resilience by searching and obtaining essential information in a standardized format easily, and also by integrating and adjusting processes with SC partners effectively. The complementary use of two IT patterns also improves supplier and customer resilience. Firms use explorative IT to share a large amount of information and collaborate with suppliers and customers to recover from disruptions. The effectiveness for the recovery can be improved if the SC is visible and firms can obtain the essential information structurally through IT exploitation (Johnson et al., 2013; Pettit et al., 2013; Scholten and Schilder, 2015). For instance, Haier records daily transaction information such as procurement, production, and sales with customers and suppliers based on exploitative IT use. Simultaneously, based on the structured information, it takes advantage of its industrial Internet platform-COSMOPlat to track demand changes and fit customers’ requirements with its supply network. Through the balanced and complementary use of IT patterns, Haier successfully builds a resilient SC and maintains stable operations during the COVID-19 pandemic. Therefore, we propose the following two hypotheses:

H3a

Ambidextrous (balanced and complementary) supplier IT use is positively related to supplier resilience

H3b

Ambidextrous (balanced and complementary) customer IT use is positively related to customer resilience

3.4. SC resilience and SC performance

SC performance measures the extent to which the whole SC can keep products available and make on-time delivery to meet end customers' requirements (Huo et al., 2014; Tarafdar and Qrunfleh, 2017). When assaulted by disruptions, supplier and customer resilience facilitate a quick recovery and ensures continuity of material supply and product delivery, which reduces the negative impacts of SC disruptions and enhances end customers’ value and satisfaction (Chowdhury and Quaddus, 2017; Fiksel et al., 2015; Singh, 2020). Specifically, the continuous material supply is the prerequisite for production activities and customer service. SC operations cannot sustain and customer demands cannot satisfied without stable material flows from suppliers. Therefore, supplier resilience lays the foundation for SC performance enhancement. Comparatively, customer resilience ensures continuous products and services delivered to customers under SC disruptions, which will safeguard their value and loyalty. Therefore, we propose the following two hypotheses:

H4a

Supplier resilience is positively related to SC performance

H4b

Customer resilience is positively related to SC performance

4. Research methodology

4.1. Questionnaire design

The questionnaire was designed based on the adaptation of a number of extant valid instruments. First, we developed an English version questionnaire by an extensive literature review on SC resilience and IT patterns. Then, the questionnaire was translated into Chinese. Two doctoral students were asked to translate the Chinese version back into English dependently. After that, we compared the translated version with the original one so that the conceptual equivalence can be achieved. Minor revisions would be made in the Chinese version if any differences were found. Finally, a pre-test of the questionnaire was conducted in 18 companies. Face-to-face interviews were held with managers who hold executive positions in departments such as SCM, purchasing, and distribution. They were asked to fill out the preliminary version of the questionnaire. They also provide feedback on the descriptions of the items and difficulties in answering the questionnaire. We made some modifications based on their feedback to make sure all items were understandable and relevant to practices in China. All measures are shown in Appendix A.

All constructs were measured using 7-point Likert reflective scales. Specifically, the measures for SC performance were adopted from (Huo et al., 2014) and Beamon (1999). We asked the respondents to indicate the degree to which they agreed with the statements that their SC could meet customer requirements, introduce new products, speed up the SC process, have an outstanding delivery performance, and provide high-level customer service, with “1” for “strongly disagree” and “7” for “strongly agree”.

The measures for supplier and customer resilience were mainly adapted from Ambulkar et al. (2015), who developed a four-item scale to measure firm resilience which is internally focused. We extended these measures into the SC level. We asked the respondents to evaluate their perceptions of how they agree that their company can maintain high situational awareness, provide a quick response, cope with changes, and adapt to SC disruptions with its major supplier and customer to represent supplier and customer resilience respectively. Furthermore, based on the review of previous literature on SC resilience and the interview with managers, we also added one additional item that was adapted from Brandon-Jones et al. (2014), and asked how they agree that their company can speedily recover to normal operations with its major supplier and customer after the SC disruption, with “1” for “strongly disagree” and “7” for “strongly agree”. We provided definitions of supplier and customer resilience at the beginning of related questions so that the respondents would better comprehend the measures.

The measures for supplier and customer IT use for exploitation and exploration were adapted from Sanders (2008) and Subramani (2004). We asked the respondents to evaluate the extent to which their company implemented specific IT tools (e.g., ERP, EDI, IOS, SCMS, CRM, Intranet, and Extranet) for the basic operations with the major supplier and customer to represent IT use for exploitation. These operations included order processing, invoicing, accounts settling, shipment and delivery information exchanging, warehouse stock and inventories management, and document processing. To measure supplier and customer IT use for exploration, we asked the respondents to evaluate the extent to which their company use these IT tools with the major supplier and customer in the explorative activities such as predicting trends in sales and customer's preferences, integrating and coordinating, and creating new business opportunities, with “1” for “not at all usage” and “7” for “extensive usage”.

We also added the respondent's character as the marker variable (Podsakoff et al., 2003; Williams et al., 2010). It was evaluated by the respondents' perceptions of how they agreed on the feeling to have a lot of friends, a cheerful person full of energy, and the sense of talking with others, with “1” for “strongly disagree” and “7” for “strongly agree”. In addition, we added company size (measured by the number of employees and fixed assets) as a control variable since larger companies tend to have more resources. They may achieve a higher level of SC resilience and performance compared with smaller competitors (Huo et al., 2015; Revilla and Saenz, 2017).

4.2. Sampling and data collection

We randomly selected sample companies that were located in four representative regions (Bohai Bay Economic Rim, Pearl River Delta, Yangzi River Delta, and other areas) in China. Specifically, other areas include northeastern, central, and western parts of China, which represent a lower stage of economic development compared with the other three coastal areas (Zhao et al., 2006). The contact information was listed in the directory provided by the National Bureau Statistics of China. A wide range of industries such as automobiles, electronics, computers, food, chemicals, and so on are included in the directory. Therefore, generalized results can be obtained based on this dataset.

We first contacted those randomly selected companies to identify a key informant who was knowledgeable about SCM practices. We explained the research objective to enhance their willingness to participate in this survey. 2820 companies were contacted and 812 of them agreed to participate in the survey. Then we sent out 812 questionnaires to the key informant who was willing to participate in, along with a cover letter to guide them to fill out. Simultaneously, we clarified the purpose of the questionnaire in the cover letter and promised not to divulge their information. Respondents were encouraged to participate by entitlement to a summary report, which will finally be sent to them according to the information they provide. To further improve the response rate, we made a second wave of phone calls. We got 298 returned responses and finally obtained 206 useable responses after eliminating 92 invalid responses with a lot of missing values, reflecting a response rate of 25.4%. The profiles of the companies and the essential information of the informants are shown in Table 1 and Table 2 . Various companies with different company sizes, from different regions and industries, have been investigated. In addition, most of the informants were middle or top managers and had been in their position for more than five years, indicating that they were knowledgeable about the questions (Flynn et al., 2010; Zhao et al., 2011). We made follow up phone calls to further ensure that the informants understood each of the questions and they answered the questionnaire according to the actual practices.

Table 1.

Profiles of responding firms.

Industry	Percentage	Region	Percentage
Metal, Mechanical & Engineering	40.78%	Bohai Bay Economic Rim	35.44%
Electronics & Electrical	19.42	Yangzi River Delta	24.76
Textiles & Apparel	10.19	Pearl River Delta	19.90
Chemicals & Petrochemicals	7.77	Other areas in China	19.90
Food, Beverage, Alcohol & Cigarettes	6.31
Building Materials	4.85
Publishing and Printing	4.37
Rubber & Plastics	3.88
Pharmaceutical & Medicals	2.43
Number of employees		Ownership
<50	0.97%	State-owned	16.02%
50–99	0.97	Privately-owned	53.88
100–199	23.30	Foreign-owned	19.42
200–499	33.98	Joint venture	10.68
500–999	17.96
1000–4999	18.45
5000 or more	4.37

Table 2.

Respondent characteristics.

Tenure of the current position in firm (years)	Percentage
≤1	0%
2–5	23.3
6–10	39.8
11–15	18.9
≥16	18.0
Position of respondent
Top manager (e.g., presidents, CEO, director, and deputy of these positions)	22.3%
Middle manager (e.g., manager of purchasing, marketing, production, and other operations related positions)	76.2
Others (e.g., purchaser and salesman)	1.5

4.3. Bias

We searched the basic information from the website of the non-respondent companies, such as company age, ownership, fixed asset, region, industry, and the number of employees. We compared the values with those of respondent companies (Schilke, 2014). The results of t-tests showed no significant differences (p > 0.05), indicating non-response bias is not a problem in this study. We also compared the early and late responses in terms of the mean values of the measurements (Armstrong and Overton, 1977). No significant differences (p > 0.05) were found in the results of t-tests, further indicating non-response bias is not a concern in this study.

Because only one informant answered all questions in each questionnaire, common method bias could be a potential problem. First, we performed Harman's one-factor test with exploratory factor analysis (EFA) (Podsakoff et al., 2003; Podsakoff and Organ, 1986). The EFA results revealed seven factors with eigenvalues above 1.0, explaining 72.2 percent of the total variance, and the first factor did not explain the majority of the total variance. The results of Harman's one-factor test indicated common method bias is not a problem in this study. Second, confirmatory factor analysis (CFA) was applied to Harman's one-factor test (Sanchez and Brock, 1996). The model fit indices were χ ² = 3121.35 with d. f. = 594, RMSEA = 0.18, SRMR = 0.12, NNFI = 0.78, and CFI = 0.79, which were much worse than those of the measurement model, indicating that a single factor was not acceptable. Third, we used the respondent's character as the marker variable to assess the potential common method bias (Lindell and Whitney, 2001). It was theoretically unrelated to other variables in this study. The lowest positive correlation between marker variable and other latent variables (r = 0.26) served to adjust the correlations among the variables. Table 3 shows that only one of the 21 significant correlations became nonsignificant after the partial correlation adjustment. These results provide evidence that common method bias is unlikely to be a concern in this study.

Table 3.

Descriptive statistics and correlations.

Construct	Mean	S.D.	1	2	3	4	5	6	7	8
1. Customer resilience	5.36	0.984	.74	.50**	.14*	.23**	.17*	.26**	.32**	.09
2. Supplier resilience	5.15	1.048	.63**	.82	.38**	.36**	.23**	.35**	.35**	.09
3. Customer IT use for exploitation	5.45	1.185	.37**	.54**	.82	.63**	.47**	.43**	.19**	.01
4. Customer IT use for exploration	5.13	1.266	.43**	.53**	.72**	.87	.27**	.50**	.19**	.00
5. Supplier IT use for exploitation	5.13	1.252	.39**	.43**	.61**	.47**	.84	.60**	.07	.03
6. Supplier IT use for exploration	4.87	1.317	.46**	.52**	.58**	.63**	.71**	.88	.22**	.06
7. Supply chain performance	5.16	0.963	.50**	.52**	.41**	.41**	.32**	.43**	.75	.08
8. Marker variable	5.64	0.945	.33**	.33**	.27**	.26**	.28**	.31**	.32**	.75

Note: Zero-order correlations are below the diagonal; adjust correlation for potential common method bias are above the diagonal; square root of AVE shown on the diagonal of the matrix in bold; *p < 0.05, **p < 0.01.

4.4. Reliability and validity

The two-step method was applied to test reliability (Narasimhan and Jayaram, 1998). First, EFA was performed to ensure the unidimensionality of the constructs. Table 4 and Table 5 show that all items had higher loadings on the constructs they were intended to measure and had low cross-loadings on other factors, indicating unidimensionality. Second, we calculated Cronbach's alpha and composite reliability for the constructs. The values exceeded the threshold of 0.70, indicating the reliability of the constructs (Table 6 ) (Hair et al., 2010).

Table 4.

EFA results of supplier IT use patterns, supplier resilience, and supply chain performance.

	Factor Loadings
	Supply chain performance	Supplier resilience	Supplier IT use for exploration	Supplier IT use for exploitation
Sres1	.257	.712	.268	.156
Sres2	.234	.833	.200	.164
Sres3	.237	.806	.127	.193
Sres4	.242	.773	.198	.097
Sres5	.183	.811	.092	.106
SITexploi1	.127	.285	.217	.807
SITexploi2	.132	.165	.403	.780
SITexploi3	.103	.015	.453	.742
SITexploi4	.075	.183	.225	.843
SITexplor1	.213	.236	.771	.354
SITexplor2	.202	.181	.791	.315
SITexplor3	.141	.288	.764	.364
SITexplor4	.174	.219	.823	.286
SCperf1	.745	.282	-.092	.139
SCperf2	.729	.195	.030	.176
SCperf3	.610	.182	.111	.002
SCperf4	.820	.117	.265	.070
SCperf5	.814	.102	.222	.025
SCperf6	.794	.135	.163	.029
SCperf7	.713	.288	.131	.182
Eigenvalues	4.386	3.744	3.308	3.158
Total variance explained 72.98%

Sres: supplier resilience; SITexploi: supplier IT use for exploitation; SITexplor: supplier IT use for exploration; SCperf: supply chain performance.

Table 5.

EFA results of customer IT patterns, customer resilience, and marker variable.

	Factor Loadings
	Customer IT use for exploration	Customer resilience	Customer IT use for exploitation	Marker variable
Cres1	.078	.747	.162	.011
Cres2	.100	.818	.233	.100
Cres3	.093	.751	.259	.237
Cres4	.285	.770	-.020	.095
Cres5	.202	.758	-.077	.157
CITexploi1	.329	.131	.783	.091
CITexploi2	.529	.181	.679	.044
CITexploi3	.486	.071	.677	.151
CITexploi4	.242	.146	.823	.094
CITexplor1	.775	.171	.349	.103
CITexplor2	.854	.194	.240	.082
CITexplor3	.828	.196	.286	.091
CITexplor4	.801	.207	.324	.068
MV1	-.054	.170	.127	.778
MV2	.306	.112	.005	.800
MV3	.052	.121	.109	.864
Eigenvalues	3.585	3.240	2.757	2.163
Total variance explained 73.40%

Cres: customer resilience; CITexploi: customer IT use for exploitation; CITexplor: customer IT use for exploration; MV: marker variable.

Table 6.

Reliability and validity analysis.

Construct	No. of items	Cronbach's alpha	Composite reliability	AVE
1. Customer resilience	5	0.859	0.859	0.55
2. Supplier resilience	5	0.907	0.909	0.67
3. Customer IT use for exploitation	4	0.886	0.888	0.67
4. Customer IT use for exploration	4	0.923	0.926	0.76
5. Supplier IT use for exploitation	4	0.903	0.906	0.71
6. Supplier IT use for exploration	4	0.930	0.929	0.77
7. Supply chain performance	7	0.896	0.897	0.56
8. Marker variable	3	0.784	0.791	0.56

To assess convergent validity, we employed CFA that all items were linked to the corresponding constructs, with the covariance freely estimated. The model fit indices were χ2 = 1056.58 with d. f. = 566, RMSEA = 0.066, NNFI = 0.97, CFI = 0.97, and SRMR = 0.047. The results indicated that the model was acceptable (Hu and Bentler, 1999). In addition, the CFA results showed that all factor loadings were larger than 0.50, and they were significant at the 0.01 level. These results indicated that convergent validity was satisfied. We calculated the average variance extracted (AVE) for all constructs. Table 6 showed that AVE for all constructs was greater than 0.50, further indicating convergent validity (Flynn et al., 2010).

To assess discriminant validity, we compared the square root of AVE (the bold diagonal of the matrix in Table 3) with the correlation coefficient between the focal construct and all other constructs. The values were higher than the correlation coefficients, indicating discriminant validity (Fornell and Larcker, 1981). We also employed heterotrait-monotrait ratio (HTMT) of correlations approach to assess discriminant validity (Henseler et al., 2015). HTMT indicates a comparison of the average of the heterotrait-heteromethod correlations (i.e., the correlations of indicators across constructs measuring different phenomena) and the average of the monotrait-heteromethod correlations (i.e., the correlations of indicators within the same construct). Table 7 shows that the HTMT ratio of correlations is lower than the predefined threshold of 0.85, which satisfies the HTMT_0.85 criteria and indicates discriminant validity (Clark and Watson, 2016; Henseler et al., 2015; Kline, 2015).

Table 7.

HTMT results.

Construct	1	2	3	4	5	6	7	8
1. Customer resilience
2. Supplier resilience	0.720
3. Customer IT use for exploitation	0.425	0.602
4. Customer IT use for exploration	0.485	0.576	0.797
5. Supplier IT use for exploitation	0.442	0.479	0.681	0.506
6. Supplier IT use for exploration	0.509	0.568	0.635	0.680	0.771
7. Supply chain performance	0.566	0.583	0.458	0.446	0.354	0.465
8. Marker variable	0.408	0.394	0.323	0.315	0.334	0.365	0.388

5. Analyses and results

5.1. Hypotheses testing

Structural equation modeling was performed to test the direct effect hypotheses (H1a, H1b, H2a, H2b, H4a, and H4b) (see Fig. 1). The model fit indices were χ ² = 1086.12 with d. f. = 537, RMSEA = 0.070, NNFI = 0.97, CFI = 0.97, and SRMR = 0.093, indicating the model was acceptable (Hu and Bentler, 1999). Fig. 2 Shows significant paths with standard coefficients. Neither supplier nor customer IT use for exploitation had a significant effect on supplier and customer resilience. Therefore, H1a and H1b were rejected. Both supplier and customer IT use for exploration were significantly and positively related to supply and customer resilience respectively. Therefore, H2a and H2b were supported. The impact of supplier and customer resilience on SC performance was positive and significant, supporting H4a and H4b.

Fig. 1.

The conceptual model.

Fig. 2.

Structural equation modeling results. + p < 0.1, *p < 0.05, **p < 0.01, ***p < 0.001.

We performed the hierarchical regression analysis to test the ambidextrous hypotheses (H3a and H3b). Based on previous studies, we used two alternative measures to depict IT ambidexterity (Cao et al., 2009; He and Wong, 2004; Mehrabi et al., 2019; Venugopal et al., 2020; Wong et al., 2013). Specifically, when operationalizing the concept of balanced IT use, we adapted the method for measuring balance between exploitation and exploration by subtracting the absolute difference between IT use for exploration and exploitation from 7 (since IT use for exploration and exploitation were measured on scales from 1 to 7). A higher value indicates a better balance of IT use for exploitation and exploration without overemphasizing one of the two activities (Cao et al., 2009; He and Wong, 2004; Patel et al., 2012; Wong et al., 2013). Complementary IT use was operationalized as the interactive term of IT use for exploitation and exploration. We mean-centered the constructs to mitigate the potential of multicollinearity (He and Wong, 2004; Wong et al., 2013). Such an approach to operationalizing balanced and complementary ambidexterity of exploitation and exploration has been widely used in previous studies in different research contexts (Cao et al., 2009; He and Wong, 2004; Mehrabi et al., 2019; Patel et al., 2012; Venugopal et al., 2020; Wong et al., 2013). In hierarchical regression, we brought variables into the model in three steps. First, we introduced control variables (i.e., employees, fixed assets) into Model 1. Second, we introduced independent variables (i.e., supplier/customer IT use for exploitation and exploration) into Model 2. Third, we introduced two measures of IT ambidexterity (i.e., balanced/complementary supplier IT use, balanced/complementary customer IT use) into Model 3 to further identify their influence on SC resilience. Table 8 demonstrates the results of the hierarchical regression. The ambidextrous hypotheses were partially supported. Specifically, neither balanced nor complementary supplier IT use had a significant effect on supplier resilience. Therefore, H3a was rejected. The balanced customer IT use had a significant and negative effect on customer resilience, while the complementary customer IT use had a marginally significant and positive effect on customer resilience, which partially supported H3b.

Table 8.

Hierarchical regression results.

Independent variable	Dependent variable: supplier resilience			Independent variable	Dependent variable: customer resilience
	Model 1	Model 2	Model 3		Model 1	Model 2	Model 3
Constant	5.11*** (.282)	3.05*** (.353)	3.12*** (.633)	Constant	5.37*** (.266)	3.40*** (.381)	4.49*** (.656)
Control variable				Control variable
Employees	.01 (.075)	.06 (.065)	.06 (.066)	Employees	.03 (.071)	.01 (.064)	.01 (.063)
Fixed assets	-.09 (.061)	-.07 (.052)	-.07 (.053)	Fixed assets	-.03 (.058)	-.00 (.052)	.00 (.052)
Independent variable				Independent variable
H1a: Supplier IT use for exploitation		.09 (.071)	.09 (.077)	H1b: Customer IT use for exploitation		.10 (.077)	.09 (.076)
H2a: Supplier IT use for exploration		.35*** (.068)	.36*** (.079)	H2b: Customer IT use for exploration		.27*** (.072)	.39*** (.083)
Ambidextrous variable				Ambidextrous variable
H3a: Balanced supplier IT use			-.02 (.101)	H3b: Balanced customer IT use			-.27** (.099)
H3a: Complementary supplier IT use			.01 (.039)	H3b: Complementary customer IT use			.07+ (.036)
R2	.011	.286	.286		.002	.193	.225
Change in R2	–	.274	.001		–	.192	.032
F	1.176	20.088	13.294		.155	12.023	9.610
Change in F	–	38.565	0.075		–	23.857	4.054
p-value (change)	–	.000	.928		–	.000	.019

Note: ⁺p < 0.10; *p < 0.05; **p < 0.01; ***p < 0.001.

The standard error for each unstandardized parameter estimate is shown in parentheses.

Significant parameter estimates and changes in F-values are in bold.

5.2. Tests for endogeneity

We addressed the possible endogeneity concerns in the following ways. First, we addressed simultaneity by collecting temporally lagged data for dependent variables in different parts of the questionnaire (Rindfleisch et al., 2008). Consequently, we minimized the potential threats of endogeneity due to measurement error that mainly resulted from common method bias (Wang et al., 2016).

Second, following Hamilton and Nickerson (2003), we conducted a two-stage least square (2SLS) regression to test potential endogeneity caused by omitted variables (Li et al., 2010; Zhou and Li, 2012). Supplier IT use for exploration/exploitation and customer IT use for exploitation/exploration are exogenous role in our model. In Stage 1, we employed the instrumental variable approach by asking the respondent to evaluate the extent of “the level of strategic partnership with major supplier” and “significant investments in equipment dedicated to the relationship with major supplier”. Previous literature suggested that strategic relationship and specific asset investments with suppliers enable the focal firm to implement IT to interact and integrate with them. These two one-item variables created predicted values for supplier IT use for exploitation and exploration. The regression results showed that the two instrumental variables were significantly related to supplier IT use for exploitation (β = 0.36, p < 0.001 for the first instrumental variable; and β = 0.17, p < 0.001 for the second) and supplier IT use for the exploration (β = 0.45, p < 0.001 for the first instrumental variable; and β = 0.26, p < 0.001 for the second). Similarly, we asked the respondent the extent “the level of strategic partnership with our major customer” and “significant investments in equipment dedicated to the relationship with major customer”. Previous literature indicated that high involvement of customers and specific asset investments with them encourage the focal firm to adopt IT to integrate with them to avoid opportunism. These two one-item variables created predicted values for customer IT use for exploitation and exploration. The regression results showed that the two instrumental variables relate positively to customer IT use for exploitation (β = 0.30, p < 0.001 for the first instrumental variable; and β = 0.16, p < 0.001 for the second) and customer IT use for the exploration (β = 0.22, p < 0.01 for the first instrumental variable; and β = 0.17, p < 0.01 for the second). In Stage 2, we added instrumental variables as indicators of supplier/customer IT use for exploration and exploitation into model and estimated their effects on supplier/customer resilience. The results of 2SLS regression were consistent with our previous results, indicating that we did not omitted important variables in our conceptual model. Thus, we find no evidence of endogeneity problems to our findings.

6. Discussion and implications

The COVID-19 pandemic has caused severe global disruptions in supply and demand. Firms such as Haier that could establish external resilience based on external IT implementation to ensure supply and demand are the one that can survive and grow under such SC disruptions. Consequently, this study aims to complement SC resilience literature by investigating impacts of different patterns of IT use on supplier and customer resilience and their effectiveness on SC performance. First, this study examined the performance implications of supplier and customer resilience. It echoes the call of Chowdhury and Quaddus (2017) for more empirical research to explore the relationship between SC resilience and SC performance in different nations. Our results find that both supplier and customer resilience are positively related to SC performance, verifying the importance of external resilience in mitigating SC disruptions and enhancing SC performance. Firms need to build resilience capability with their SC partners to ensure stable material supply as well as continuous product and service delivery so that the end customers' needs can be satisfied and they can improve SC performance. This result is supported by previous studies (Chowdhury and Quaddus, 2017; Dubey et al., 2019a; Ortiz-De-Mandojana and Bansal, 2016; Ruiz-Benitez et al., 2018; Yu et al., 2019). For instance, Ortiz-De-Mandojana and Bansal (2016) emphasized that firm resilience contributes to firms’ survival and sustainability by helping firms to behave as complex dynamic systems, operating within dynamic systems of SC partners.

Second, this study examined the impacts of supplier/customer IT use for exploitation and exploration in improving supplier and customer resilience respectively. It answers the call of Dubey et al. (2019b) for more research to consider other enablers of SC resilience, such as technical resources. The results demonstrate that only IT use for exploration with SC partners can improve supplier and customer resilience, while IT use for exploitation shows no significant effects. This finding is in line with Sanders (2008) to some extent that although firms use IT more for exploitation than exploration, the exploitation will subject firms to the risk of obsolescence. IT use for exploitation results in immediate positive feedback that will produce a strong path dependence and ultimately harms the long-run survival in the face of turbulent and unpredictable environments. Our findings indicate that IT use for exploitation mainly standardizes information formats in the structured transaction process between SC partners. It improves SC visibility and helps firms to search for information conveniently. However, IT use for exploitation is not sufficient to build SC resilience. When SC disruption occurs, firms have to conduct the unstructured process with suppliers and customers in the volatile environment. IT use for exploration provides the platform for absorbing more diversified information and developing more integrated relationship between SC partners. It will therefore improve SC resilience capability and keep the continuity of upstream and downstream operations successfully.

Third, this study applied the ambidexterity perspective to shed light on how IT ambidexterity reconciles the paradox between IT use for exploitation and IT use for exploration in enhancing SC resilience. It echoes the call of He and Wong (2004) to test the ambidexterity hypothesis in other management research domains. The regression results show that the ambidextrous (balanced and complementary) use of IT with suppliers cannot improve supplier resilience. However, the balanced use of IT with customers shows a negative effect on customer resilience and complementary use shows a positive effect. Our findings underscore the complexity of IT use in different parts of a SC, and further highlight the importance of the explorative use of IT with SC partners to improve SC resilience. Specifically, when coping with frequent demand changes, firms need to make IT use for exploration and IT use for exploitation in the customer side complement each other to jointly contribute to disruption recovery with its customers. It indicates that the focal firm needs to enhance extensive information sharing via explorative use of IT with customers. It helps the focal firm to sense downstream changes in the market and better integrate with customers to reconfigure structures and processes towards disruptions. At the same time, if firms could improve efficiency in information processing and recording via exploitative IT use with customers (i.e., integrated IT platforms), they could quickly call up relevant information to effectively manage SC disruptions (Johnson et al., 2013; Pettit et al., 2013; Scholten and Schilder, 2015). However, the regression results indicate that when coping with disruptions in the supplier side, ambidextrous (balanced and complementary) use of IT with suppliers does not take effect. Combined with the previous SEM results, it suggests that the focal firm should rely on supplier IT use for exploration to share information and integrate with suppliers, which enables the focal firm and its suppliers to address disruptions responsively and maintain stable materials supply (Sanders, 2008).

To summarize, on the one hand, these findings demonstrate that ambidextrous IT use (i.e., complementary customer IT use) could help the focal firm to reconcile the paradox between IT exploitation and IT exploration in the customer side in managing SC disruptions. In other words, efforts in implementing IT for exploitation can improve firm's effectiveness in exploring new information and initiating reconfigurations towards SC disruptions. Proficiency in firm's explorative IT use enhance its capability to engage in successful exploitation. On the other hand, the findings indicate that it is necessary for the focal firm and its SC partners to manage the paradox by laying relatively more emphasizes on IT exploration when resources are limited (Goo et al., 2015). Because the tension between two patterns of IT use actually results from their competition for scarce resources (Cao et al., 2009; Chiu, 2014; Gupta et al., 2006; March, 1991), and IT use for exploration with SC partners, however, has been empirically shown to improve supplier and customer resilience in most configurations. Such findings also complement to the tenets of IPT (Daft and Lengel, 1986; Galbraith, 1974). To improve information processing capability in the volatile environment, extensive information sharing that generates more solutions and fewer information distortions is more important than information format standardization that reduces information source and the difficulty to search and analyze needed information (Wang et al., 2013).

For managers, this study provides some practical guidelines. First, both supplier and customer resilience are important for boosting SC performance. Firms should pay equal attention to keep the continuity of upstream and downstream operations of their SC when they are attacked by disruptions. Second, to achieve supplier and customer resilience simultaneously, firms should consider the difference between the exploitative and explorative use of IT with their SC partners. Resources need to be invested more in IT exploration. Firms should use IT to share rich information so that they can better understand the trends in environmental changes and create new solutions to recover from SC disruptions. They should also use IT to build integrated relationships with suppliers and customers so that more synchronized and efficient actions can be taken. Third, although the exploitative use of IT with customers such as recording customer-related data does not affect customer resilience directly, it still has some merits. It could complement the benefits of explorative use in effectively reconfiguring and recovering from disruptions with customers. These practical implications are extremely useful and critical for firms to manage SCM practices under the global disruptions of COVID-19 pandemic. Since although the COVID-19 pandemic has caused severe global disruptions in supply and demand, some companies such as Haier relied on their solid external IT linkage to quickly respond and stood out with stable customer service while others had to suspend production lines.

7. Conclusions, limitations, and future research directions

This study investigates impacts of different IT patterns (exploitative versus explorative and ambidextrous) with SC partners on supplier and customer resilience and their effectiveness on SC performance. Our results indicate that only explorative use of IT with suppliers and customers can facilitate supplier and customer resilience, while exploitative use of IT demonstrates no significant effects. Our results also show that the ambidextrous (balanced and complementary) use of IT with suppliers cannot improve supplier resilience. However, the ambidextrous use of IT patterns on the customer side takes effect. Specifically, the balanced use of IT with customers shows a negative effect on customer resilience and complementary use shows a positive effect. In addition, this study shows that both supplier and customer resilience can improve SC performance in the highly volatile environment.

Although this study makes contributions to theory and practice, some limitations should be considered. First, this study only examined the antecedents and outcomes of external resilience while omitting the role of internal resilience. Future research can investigate how internal resilience will influence firm performance. Second, this study only investigated how inter-firm IT implementation improves SC resilience. Future research can take intra-firm IT into considerations and explore how intra-firm IT patterns improve SC resilience. Third, this study treated IT as non-specific, and firms may implement different IT tools with suppliers and customers. Future research can examine the impact of some specific and emerging IT tools usage (e.g., big data analytics, blockchain technology) on SC resilience. Fourth, the hypotheses were tested with cross-sectional data collected from limited samples. Future research can verify the casual relationship between the adoption of a specific or emerging technology and firm's recovery from SC disruptions by using difference-in-difference approach with panel data. Fifth, although we used Harman's one-factor test and marker variable methods to eliminate the concern about common method bias, it is unlikely to completely resolve this concern resulted from the single-informant questionnaire design. Future research can involve managers in different functions to fill in the most relevant parts of the survey, or collect some objective data to mitigate the common method bias. In addition, although we have strictly controlled the questionnaire design and data collection processes, there is still a problem that the information in the non-anonymous questionnaire may be not true. Future studies could collect multi-source and secondary data to verify the results.

CRediT authorship contribution statement

Minhao Gu: Writing - original draft, Writing - review & editing. Lu Yang: Methodology, Writing - review & editing, Visualization. Baofeng Huo: Supervision, Resources.

Appendix A. Constructs measurement

Supply chain performance

SCperf1: Our supply chain has the ability to quickly modify products to meet customers’ requirements.

SCperf2: Our supply chain allows us to quickly introduce new products into our markets.

SCperf2: The length of the supply chain process is getting shorter.

SCperf2: We are satisfied with the speediness of the supply chain process.

SCperf2: Based on our knowledge of the supply chain process, we think that it is efficient.

SCperf2: Our supply chain has an outstanding on-time delivery record.

SCperf2: Our supply chain provides high-level customer services.

Customer resilience

Cres1: We and our main customer are able to maintain high situational awareness at all times.

Cres2: We and our main customer are able to provide a quick response to the supply chain disruption.

Cres3: We and our main customer are able to cope with changes brought by the supply chain disruption.

Cres4: We and our main customer are able to adapt to the supply chain disruption easily.

Cres5: We and our main customer can recovery to normal operations speedily after the supply chain disruption.

Supplier resilience

Sres1: We and our main supplier are able to maintain high situational awareness at all times.

Sres2: We and our main supplier are able to provide a quick response to the supply chain disruption.

Sres3: We and our main supplier are able to cope with changes brought by the supply chain disruption.

Sres4: We and our main supplier are able to adapt to the supply chain disruption easily.

Sres5: We and our main supplier can recovery to normal operations speedily after the supply chain disruption.

Customer IT use for exploitation

CITexploi1: We use specific IT based support for order processing, invoicing and settling accounts with our major customer.

CITexploi2: We use specific IT based support for exchange of shipment and delivery information with our major customer.

CITexploi3: We use specific IT based support for managing warehouse stock and inventories with our major customer.

CITexploi4: We use specific IT based support for our daily work, such as document processing with our major customer.

Customer IT use for exploration

CITexplor1: We use specific IT based support with our major customer for understanding trends in sales and customer's preferences.

CITexplor2: We use specific IT based support for integrating our company and major customer.

CITexplor3: We use specific IT based support to coordinate with our major customer.

CITexplor4: We use specific IT based support for leveraging our customer's expertise to create new business opportunities.

Supplier IT use for exploitation

SITexploi1: We use specific IT based support for order processing, invoicing and settling accounts with our major supplier.

SITexploi2: We use specific IT based support for exchange of shipment and delivery information with our major supplier.

SITexploi3: We use specific IT based support for managing warehouse stock and inventories with our major supplier.

SITexploi4: We use specific IT based support for our daily work, such as document processing with our major supplier.

Supplier IT use for exploration

SITexplor1: We use specific IT based support with our major supplier for understanding trends in sales and customer's preferences.

SITexplor2: We use specific IT based support for integrating our company and major supplier.

SITexplor3: We use specific IT based support to coordinate with our major supplier.

SITexplor4: We use specific IT based support for leveraging our supplier's expertise to create new business opportunities.

Marker variable (Respondent's character)

MV1: I am feeling very good to have a lot of friends.

MV2: I am a cheerful person full of energy.

MV3: I enjoy talking with others.