Government Supports, Digital Capability, and Organizational Resilience Capacity during COVID-19: The Moderation Role of Organizational Unlearning

Abstract

This paper provides an investigation into how different types of government supports can be used to enhance organizational resilience capacity during the COVID-19 pandemic. Based on resource orchestration theory, this study examines the effects of direct government support and indirect government support on organizational resilience capacity, the mediation role of digital capability, and the moderation effects of organizational unlearning. The empirical results from 205 Chinese firms show that direct government support and indirect government support have positive effects on organizational resilience capacity, which were mediated by digital capability. In addition, organizational unlearning positively and negatively moderates the positive relationship between direct government support, indirect government support and digital capability. Our theoretical discussion and empirical results contribute to the literature related to organizational resilience, digital capability, government support, and organizational unlearning.

1. Introduction

During the COVID-19 pandemic, firms across the world are trapped in increasingly chaotic business systems with considerable vulnerability, uncertainty, complexity, and ambiguity (VUCA) in social (e.g., lockdown, social distancing) and economic (e.g., deteriorated market order, restrained demand, disordered supply chain) trajectories [1]. Thus, firms are facing striking shifts and reductions in market demands [2]), turbulences in supply chains [3], and shortages in the human resource supply [1,4] as well as disturbances in their internal operations [5], which increase the operational costs of firms and decrease their expected revenues [6,7]. In this situation, Organizational Resilience Capacity (hereafter ORC for short), as the ‘ability to absorb strain and preserve or improve its functioning despite the presence of adversity [8]’, is vital for firms to ‘handle unexpected event effectively, bounce back from crises that could potentially threaten their survival, and even foster growth [8]’. The previous research focuses on the question of what drives the ORC of firms, and identifies the internal (e.g., human resource management [9,10], organizational culture [11], strategic orientation [12]) and external (e.g., business networks [8]) factors of firms as the effective drivers of ORC, arguing that ‘organizational characteristics, resources, or processes seem to be significant for resilience capacity [8]’.

Although insightful, due to the aforementioned adversities that endanger the revenue and survival of firms, the affected firms, in general, lack the necessary financial and managerial resources to enhance their own ORC [13], and/or have little to offer for the ORC improvements of their partner firms. Thereby, government support, such as financial (e.g., tax reduction and subsidies [14]) and non-financial (e.g., law enforcement and favorable policies [15]) supports that governments provide to absorb disruption and maintain economic and social orders [16,17] may potentially fuel the ORC of firms to a certain extent [18]. In fact, Direct Government Support (hereafter DGS for short) represents the financial (e.g., tax reduction, subsidies, and loans) and operational (e.g., permissions and licenses) benefits that an individual firm can actively solicit from government [14,15,19], which may reduce the financial burden of the firm and secure the market position of the firm [20]. Indirect Government Support (hereafter IGS for short) denotes the endeavors (e.g., policy interpretation, law enforcement, and R&D prompting) that central and local governments actively provide to a certain range of firms/industries [15], which may improve market efficiencies [21] and lower the bureaucratic costs that the firms experience [22]. Thus, the differences in the benefits provided by DGS and IGS may result in affecting ORC in potentially diversified ways. However, previous studies that investigate the effects of government support in the pandemic context mainly treat government support as a unidimensional construct [13,18], or focus on the individual elements (e.g., policies [7]) of government support. To date, there is limited evidence about the roles that DGS and IGS play in the scenario of ORC enhancement, and their potentially diversified functional ways are yet to be investigated.

Secondly, digital technologies (i.e., the internet of things, big data analytic, artificial intelligence and cloud computing) are recognized as effective tools to deal with shifts and adversities (e.g., lockdown and social distancing, offline-online shifting in demand, supply, and internal operations) during the pandemic [2,3,9,20]. However, not all firms can benefit from the adoption of digital technologies [16,23]; rather, only those that have a high level of digital capability, namely the capacity to effectively ‘use digitally relevant infrastructure, resources, and platforms to achieve entrepreneurial goals [7]’, can realize such benefits [24,25]. Since the establishment and enhancement of the digital capability of firms relies on the construction of digital infrastructures, the adopting of digital technologies, and the cultivating of digital mindsets and talents [26], it depends on firms continuously investing in managerial and financial resources [25,27], and the resultant resource burden would become more striking during the pandemic [20]. Ergo, it would be hard for most firms to independently establish and develop digital capability, and the soliciting of government support could thus be treated as feasible solution for firms.

More importantly, putting to one side the noted effects to counteract the adversities during the pandemic, the revolutionary effects of ‘being digital’ in changing business logic and competitive rules before the outbreak of COVID-19 [25] and in continuously shaping the economic and social lives in the post-pandemic era [28,29] has long been perceived. Thus, many countries have launched initiatives to stimulate the digital transformation of firms before (e.g., Indonesia 4.0 map, Smart nation 2025 (Singapore) [30]) and/or during (e.g., guidance on prompting the adoption of interactive technologies (Chinese Ministry of Science and Technology) [1]) the pandemic. Thereby, digital capability is the ‘‘competitive necessity’ for many firms to ensure their survival through pandemic [20]’ by transforming the supports they have obtained (i.e., DGS and IGS) into competitive advantages (i.e., ORC) during the pandemic and/or even in the post-pandemic world [30]. As resource orchestration theory (ROT) [31,32] proposes, when facing environmental uncertainties, firms should effectively structure their resource portfolio, bundle them to build necessary capabilities, and leverage those capabilities to maintain and create value. However, previous research has advocated how digital technologies [6,7,33] and digitalization [34] may affect the performance-related outcomes of firms, while the direct, and more importantly, the intermediate roles that digital capability plays in government support–ORC links remain inadequately addressed.

Thirdly, the building and developing of the digital capability of firms would make the current skills and knowledge of employees and managers obsolete [35], and require drastic changes to their current routines and processes [9,36], which may induce considerable internal cognitive, normative, and behavioral resistance against such a process [35,37,38]. More importantly, the intensified competitions and the shifting or reducing demands require firms to maintain or strengthen their current competencies to secure revenue, so they can be in a position to invest in the establishment and development of digital capability to create new competencies to boost new market and innovation opportunities [2,39]. Therefore, firms should effectively divest the outdated or unnecessary knowledge resources to make room for the new ones [22], while properly maintaining their current competencies. In this situation, organizational unlearning, namely, ‘practices to change or discard traditional or obsolete beliefs and routines within organizations [40]’, should be properly configured to: (1) lay suitable internal foundations for the building and developing of digital capability, and (2) manage the trade-off between maintaining the existing competency and creating the new competency. However, to date, we have limited knowledge about how organizational unlearning may affect the building and developing of the IGS/DGS-based digital capability of firms.

To address these knowledge inadequacies, we incorporated ROT and wisdom from related fields to investigate the direct and indirect effects that different types of government support (i.e., DGS and IGS) have on the digital capability and ORC of firms, and the moderation effects of organizational unlearning on government support–digital capability links. Our theoretical discussion and empirical results add to previous research in the following ways. Firstly, this work identifies the differences between direct and indirect government support, and empirically investigates their direct and indirect effects on digital capability and ORC. Thus, we advance the taxonomy research of government support and enrich the antecedent research of digital capability and ORC. Secondly, we illustrate the contingent role of organizational unlearning such as the resource divestment of firms in affecting IGS/DGS–digital capability relations, thereby adding a ROT-based logic in explaining the varying moderation effects of organizational unlearning and adding empirical evidence for the inadequately addressed ‘negative influence of organizational unlearning’ [41,42]. Thirdly, we identify the intermediate role of digital capability in transforming the supports provided by central and local governments to enhance the ORC of firms, thus providing a ROT-based framework, namely the ‘resource structuring-capability bundling-competitive advantage path paradigm’ for the ‘how to improve firms’ ORC’ [43] inquiry.

2. Literature Review

2.1. Government Support

Government support represents the resource, policies, and other favorable treatments that central and local governments provide to qualified firms and industries [15,17,22,27]. It aims at fueling firm development, directing industrial upgrading, and stimulating economic growth [20,21], as well as absorbing shocks and disruptions when unexpected events in the areas of public health, economic systems, and national security occur [1,4,44]. Traditional government support research has investigated its positive effects on firm-level outcomes (e.g., performance, innovation [45], internationalization [46], entrepreneurial orientation [47], and capability building [48]).

Recent advances in this field have begun to explore the potential benefits that government support may bring in fueling the digital transformation-related outcomes of firms. Specifically, financial supports (e.g., tax reduction, subsidies, low interest loans, etc.) would ease the financial burden of firms, and facilitate the purchasing of the digital infrastructure and devices of firms [21]; the operation supports (e.g., government contracts, permissions of market entry and land use; licenses for export and import) improve the competitive position of firms, shelter firms against external uncertainties and secure the revenue of firms [21]; the intellectual supports (e.g., training programs, technical assistance, etc.) increase the technological expertise and talents of firms in digital-related fields [49]; the policy supports (e.g., IP protection, market transaction governance, industrial and national policies, rules and plans, etc.) would create a competition-friendly market that reduce the bureaucratic and transaction costs of firms [47], secure the return of the digital innovation of firms [4], and establish a sense of necessity for ‘being-digital’ [1]. They also have irreplaceable roles in constructing the prerequisite of the digitalization of firms, for instance, the formulating of rules and laws to protect individual privacy and ownership of digital resources [43,50].

2.2. Digital Capability

Digital capability is the ability of firms to use digital-related technologies, infrastructure, and resources to achieve entrepreneurial goals [7,25]. Previous research has identified the benefits of the adoption of different digital technologies by firms to assist their survival through and recovery from the pandemic. Specifically, digital technologies are ‘the combination and connectivity of innumerable, dispersed information, communication and computing technologies [9]’. Big data analytics and the internet of things increase the information gathering, processing, and analyzing capabilities of firms [2]; block chain ensures the accuracy and security of the business transactions of firms [29]; and social media increases the daily work-flow agility of firms [20], even in work-at-home scenarios and enables the timely interaction of firms with customers and stakeholder [51]. Could computing and big data analytics reduce the infrastructural costs of firms, and facilitate the cross-functional and external collaboration and integration of firms, by which the flexibly and cost-efficiency of firms would be improved [52]?

However, as suggested by Khin and Ho [25], ‘no matter how well technology has been deployed within an organization, its usage and services still need to be managed effectively and efficiently’. Thereby, digital capability, built on the digital infrastructure, mindset and talent as well as the knowledge and skills of firms [53], may determine the extent to which firms can derive benefits from their digital technologies and infrastructures [24]. In particular, digital capability was found to enhance the digital innovation [52] and organizational agility of firms [52]. In terms of its mediation effect, Zhu et al. [2] identified how digital capability mediates the relations between digital technology and entrepreneurial support policies and competitive advantages, while Pan et al. [53] examined the mediation roles of digital capability in the strategic orientation–NPD performance link.

2.3. Organizational Unlearning

Organizational unlearning is the prerequisite for effective learning activities of firms to occur [32,54]. In particular, previous research identified the cognitive, behavioral, and normative influences that organizational unlearning may shed upon firms and individuals: cognitive influence eliminates existing cognitive structures that may filter out new ideas and knowledge, thus enabling individuals and firms to receive previously unperceived and/or perceived-as-inappropriate new knowledge [55]. Behavioral influence stimulates changes in current routines, process, and behavioral patterns, thus consciously and subconsciously detaching employees from habitual practices [56]. Normative influence eliminates the legitimacies of old routines, processes, and knowledge in the value system of firms, thus accelerating the process of establishing new ones [57]. The confluence of these sets of influences enable firms to discard ‘obsolete routines, beliefs, knowledge, and value to make room for new ones [58]’ and results in the positive and negative effects of organizational unlearning.

While the positive effects of organizational unlearning upon the innovation [40,59] and performance [57] of firms have been extensively addressed, its negative effects receive relatively scant attention [56]. In fact, with regards to the intentional and/or conscious process of individuals [41,60], the consequence of the value judgement of individuals to ‘ascertain whether the unlearned knowledge is inferior to the newly acquired [61]’ is uncertain. Thus, inevitable unintended knowledge loss may occur: (1) when firms inappropriately select the ‘still-valuable’ knowledge as the objects of unlearning [32], and (2) when the loss of one part of knowledge may decrease the value of other parts [62]. Also, the ‘unlearning of supposedly outdated practices could obstruct organizational functioning [63]’, thus leading to temporary weakness in the current competencies of the firm [57]. In addition, organizational unlearning would engage firms with more innovative learning and innovating activities, thus it may compete with their other sets of activities for financial, operational, and managerial resources [56,61].

3. Theory and Hypotheses

3.1. Resource Orchestration Theory: An Integrative Theoretical Framework

Based on the talents from the resource-based view, dynamic capability theory, and resource dependent theory, Sirmon et al. [31,64] suggests that, merely the possession of resources that are valuable, rare, inimitable, and unable to be substituted is insufficient to guarantee the competitive advantage of firms. Rather, firms should orchestrate their resources via the process of structuring resource portfolios (i.e., externally acquiring, internally accumulating, actively divesting), bundling the resources to build capabilities for the purpose of stabilizing, enriching, and/or pioneering, and leveraging the built capabilities to maintain and/or create value for customers [64,65]. In this way, ROT unlocks the connection between resources and value creation under environmental uncertainties [28], and has been increasingly applied to explain how the digital-related attempts of firms may generate competitive advantages in the pandemic context (e.g., [53,65]).

On this basis, we argue that ROT provides a convenient theoretical lens for this research to explore the direct, indirect, and moderation relations among DGS/IGS, digital capability, organizational unlearning, and ORC in the pandemic context. In particular, during the pandemic, as we discussed above, firms are, in general, lacking sufficient resources to alter or enhance current competencies or develop new competencies to maintain or create value [3]. In this situation, as firms potentially access external support, DGS and IGS are valuable resources that firms can actively solicit to structure their resource portfolio [2,35]. Digital capability, given its vital means in securing and increasing the revenue of firms during the pandemic via the strengthening of current competencies or new competency creation [25,26], is the critical capability for firms to bundle on the basis of the structured resources to leverage competitive advantages (e.g., ORC) [6,50]. Meanwhile, organizational unlearning, representing firms active divestment of unnecessary technological resources [54,57], would affect their process of bundling newly acquired resources (i.e., DGS and IGS) into necessary capability (i.e., digital capability) [41,66]. Therefore, this article develops the resource-structuring capability-bundling–competitive advantage model amid the pandemic context (see Figure 1).

3.2. Government Support and Organizational Resilience Capacity

Organizational resilience capacity reflects the extent to which firms are well-prepared when faced with crisis [66]. It is the capability of firms to ‘anticipate, adapt, respond, and recover promptly from unpredictable events [8]’ in internal and external environments [6]. According to previous studies, the enhancement of ORC depends on the following in relation to firms: (1) financial resources to maintain financial liquidity, leverage, and solvency [8]; (2) intellectual (e.g., knowledge, skill, and technology) and material (e.g., raw materials and equipment) resources that maintain their production and operation for earning revenue [67,68]; (3) routines and processes that can maintain cost-efficiency in the face of external pressures [6]; and (4) supply chain efficiency and product quality that can secure the current revenue [69]. In other words, the improvement of ORC of firms tightly links with being able to maintain and/or increase revenue during the pandemic, which is determined by the extents to which firms can: (1) access various types of supportive resources [8,16], (2) improve current competency to maintain the efficiencies of internal operation and/or value delivery [6], and/or (3) establish and develop new competencies [1,20].

In this situation, government support would improve the ORC of firms in the pandemic context. Specifically, DGS denotes the financial and the business operation-related support that firms solicit from central and local governments, while IGS represents the policy and innovation-related support that these governments actively grant to create an amicable market infrastructure that underpins the business operations of firms and fuels the innovative endeavors of firms. Their diversities in term of the origins, objectives, and associated benefits would be mirrored in the different underlying influences that constitute their effects on ORC.

In terms of DGS, as noted, firstly, the financial support of DGS would be offered to firms with relatively abundant financial capital, thus safeguarding the liquidity of firms to maintain their business operation when their sales are disturbed by reduced demands during the pandemic [68]. Secondly, the operation-related support would secure the revenue of firms via the government purchase contracts that are immune to the pandemic influence [3] and reduce operational expenditures (e.g., applications for land use) of firms to increase their financial liquidity [14]. Thirdly, the permission of market entry would be offered to firms with first mover advantages, via which an above-the-average return would be obtained [70,71], while exclusive ones may even enable firms to pursue monopolistic profits [72,73].

In terms of IGS, firstly, the enforcement of business-transaction law would help to create a relatively competition-friendly market environment to reduce the transaction costs of firms [47]. Secondly, the provision of regulation and policy-related information would increase the transparency of regulatory institutions, which would mitigate the regulatory uncertainties and associated bureaucratic costs experienced by firms and spare the managerial attentions and resources needed for firm to comprehend such uncertainty [18]. Thirdly, the enhanced IP protection would secure the expected return of the R&D investment of firms [18], while the prompted R&D collaboration between industrial sectors and research institutions may facilitate their knowledge searching and acquisition [1,45].

In this case, IGS and IGS enable the focal firm to be more capable of and willing to solve experienced adversities via the strengthening of existing competencies (e.g., incremental innovation, cost reduction, and efficiency improvement), and/or the exploring of new competencies (e.g., new market exploring, product and business model innovation). Therefore, we argue that:

H1. 

DGS has a positive effect on ORC.

H2. 

IGS has a positive effect on ORC.

3.3. The Mediation Role of Digital Capability

As noted, digital capability represents the skill, talent, and expertise of firms to manage their digital technologies and infrastructures [25] to improve the timing, speed, and accuracy of their information gathering, processing, and decision making [6]. Thus, digital capability would improve the ORC of firms due to the following reasons.

Firstly, in terms of internal operations, the use of information collecting (e.g., IoT, social media, mobile), analyzing (e.g., artificial intelligence, cloud computing, machine learning), and real-time control technologies would increase the internal efficiencies of firms (e.g., production, operation, resource allocation, and managing) and reduce the associated costs [33,74]. Secondly, in terms of marketing competency, digital capability increases the speed, accuracy, and comprehensiveness of the market information (e.g., supply chain information, competition information, consumer feedbacks, etc.) collection of firms [33]. Combined with the use of big data analytics [26], the picturing of market conditions [8], the comprehension of external uncertainties [25], and the identification and action on embedded opportunities (e.g., innovation ideas from consumer feedbacks) by firms would be improved [75,76]. Specifically, firms could increase the values of their offerings (e.g., products, services) through newly developed digitalized product and services and integrating digital techniques into current products and services [19]. Thirdly, in terms of adaptation to the influences of the pandemic, as the pandemic compels the business actors to adopt social isolation practices [8], firms with a high level of digital capability would be better able to maintain their operations via a remote working strategy [20] and to conduct business transactions via the connectivity technologies that link them with business partners and consumers in a real-time manner [77]. Together,

H3a. 

Digital capability has a positive effect on ORC.

As noted, the establishing and enhancing of digital capability require firms to: (1) spend considerable expenditure to purchase and install material digital infrastructures [34]; (2) cultivate and accumulate digital-related knowledge, skills, and managerial expertise, as well as digital mindsets and talents that could effectively utilize their established digital infrastructures [78], during which the training, learning, and recruiting costs rise [74]; (3) modify internal routines and processes to couple with the establishment and operation of the digital system [2,79]; and (4) bear the losses when failure and malfunction occur [68,80]. Therefore, the relationships between DGS/IGS and digital capability are rather straightforward.

Firstly, the financial- and business operation-related support of DGS secures and/or increases the expected revenue of firms, while the reduced transaction and bureaucratic costs brought by IGS increase distributable financial and managerial resources. Together, the focal firm would be more capable of undertaking the purchasing and learning, as well as the training and recruiting costs for constructing their material and intellectual digital infrastructures [4,20] and be more resilient when risks and failures occur in the establishment and strengthening of its digital capability [47].

Secondly, the policy- and law-related support of IGS would help to create a competition-friendly market, in which the competitive advantages of firms depend on the extent to which they can produce superior consumer values with lower costs than competitors [73]. Digital capability, as noted, would increase the values of the products or services offered by firms through ‘digitalized products and integrating digital technologies into products [20]’. Thus, firms may easily reach a conclusion about the ‘competitive necessities’ of digital capability to create competitive advantages [67,81] and be more inclined to the establish and strengthen such capability.

In addition, government supports demonstrate the legitimacy of the focal firm, indicating the compliance of the activities of firms towards the expectations of the Government [71,72,73]. In this case, it would be easier for the focal firm to obtain collaboration, assistance, and support from business systems to establish and strengthen its digital capability [21]. More importantly, as the Chinese Government has strived to stimulate the digital transformation of firms to absorb the disruption of the pandemic and to advance the pace of digital economy construction [1] for the sake of maintaining and strengthening legitimacy, the focal firm would be more willing to cultivate digital capability to demonstrate its compliance [5]. Therefore,

H3b. 

DGS has a positive effect on digital capability.

H3c. 

IGS has a positive effect on digital capability.

So far we have hypothesized the relations between government support, digital capability, and ORC. However, it only reveals part of the whole picture.

As noted, during the COVID-19 pandemic, the production and marketing of firms, as well as other business operations were constrained by the adverse effects of the pandemic, thus endangering revenue that was vital for survival [3,8,34,82]. In this situation, the competitive advantage of the firms lie in their ORC, namely, their capacity to anticipate, adapt, respond to the adverse influences to secure their revenue and/or to develop new competencies to recover and bounce back [66]. According to ROT, faced with external uncertainty, firms should properly structure their resource portfolio with externally obtained and internally accumulated resources [31]. Since firms, in general, are lacking distributable resources during the pandemic, government supports have become a feasible solution for firms to structure their resource portfolio to create competitive advantage. However, the structured resource of firms should be bundled into the proper capability that could be leveraged to address external challenges by maintaining and/or creating customer values [64]. In this way, digital capability, given its revolutionary influences to shape the business landscape before the pandemic [25], to increase the robustness and competencies of firms during the pandemic [1,21] and to cope with digital-dominant business logics after the pandemic [28,29], is the necessary choice of firms that can transform their structured resources into competitive advantage amid the pandemic context (i.e., ORC) and may even lay a solid foundation for their new value creation in the post-pandemic world. Thus,

H3d. 

Digital capability mediates the relationships between government support and ORC.

3.4. The Moderation Effect of Organizational Unlearning

Organizational unlearning, as aforementioned, functioning as the active divestment of unnecessary and/or obsolete knowledge resources, and the discarding of outdated beliefs, routines, and behaviors of firms, may present both positive and negative influences on firms. Thus, organizational unlearning may have diversified influences upon the DGS/IGS–digital capability links.

In terms of the DGS–digital capability relation, firstly, when firms have a high level of organizational unlearning, as opposed to low, its normative influences, as noted, would legitimize the unlearning activities as the right thing to do, thus increasing the confidence and self-motivation of managers and employees to implement such practices [58]. In this case, the followed learning and using of those digital-related knowledge, technologies, and skills would be accelerated [41,57]. Secondly, under the cognitive influences of organizational unlearning, firm employees and managers would become amicable to the newly established routines, beliefs, and value system [62], thus being more adaptive to such changes [55], which in turn, synergizes with their newly learned digital-related knowledge and skills to fuel the creation and developing of digital capability. Thirdly, the behavioral influences would help to erase unconscious and subconscious habitual practices [56], thus advancing the digital capability use of the firms to a greater extent.

Although it is the case that the ‘current competency damaging’ and the ‘resource competing’ effects of high level unlearning may occur, as DGS fuels digital capability via the revenue-related benefits, and those benefits largely depend on exclusive opportunities (e.g., market entry) and favorable treatments (e.g., tax reduction and subsidies, government purchase contracts) that are immune to market competition [3], the negative influences that organizational unlearning present to the current competencies of firms may have trivial damage versus the benefits that DGS has on digital capability. Therefore,

H4a. 

Organizational unlearning positively moderates the relationship between DGS and digital capability.

In comparison, in the scenario of the IGS–digital capability relation, as noted, IGS enhances digital capability by increasing the motivation of firms to invest in distributable resources for the building and developing of digital capability. However, an important premise exists that firms should maintain their current competencies to secure their revenue for the purposes of maintaining business operations, and more importantly, make investments to establish and enhance their digital capability [23,34]. In this case, the ‘competency damage’ effect may hamper the distributable profits of firms that ought to invest in the establishment and enhancement of new competencies (e.g., digital capability), while the ‘resource competing’ effects may decrease the resources and managerial attentions that ought to be allocated to the project of establishing and developing digital capability. In this way, the cost-reduction benefits of IGS would be largely counteracted.

Although it is true that a high level of organizational unlearning may increase the efficiency of the establishment and enhancement of the digital capability of firms, considering the vital roles that cost-saving and revenue-securing plays in the investment decisions of firms during the pandemic [8], the noted damage may inhibit the financial inputs of firms for building their material and intellectual digital infrastructures at the starting point, thus decreasing the benefits that IGS has on digital capability. Thus,

H4b. 

Organizational unlearning negatively moderates the relationship between IGS and digital capability.

4. Methodology

4.1. Sampling and Data Collection

In this study, we conducted a questionnaire survey from June 2021 to February 2022 using self-report scales of managers that are more appropriate for measuring self-evaluated or perceived constructs. We selected China, a typical midrange economy entity [70] as the empirical context. In the past decade, China has strived to develop its digital economy and legitimized it as the national development strategy in 2021 [43]. As one of the first pioneered countries experiencing the pandemic, the central and the local governments have formulated a series of policies to stimulate firm recovery and economic growth, and Chinese firms have extensively relied on digital infrastructure, technologies, and business systems to alleviate the adverse of influences of the pandemic [7,8]. As a result, China has managed to maintain an average national growth rate of 5.1% (2.2% in 2020; 8/1% in 2021) during the pandemic, showing a decent level of economic resilience. In this case, China presents an ideal context for the exploration of the relations among government support, digital capability, organizational unlearning, and organizational resilience.

We used the translation-back-translation method to maintain cross-cultural equivalence and conducted pilot tests in 15 firms (about 2 h for each test) to collect comments and suggestions about the design and wording of our measurement items and accordingly revised the questionnaire to increase the clarity and validity [42]. To decrease economic development bias and geographic bias, we divided the 32 provinces, autonomous regions, and municipalities of China into three groups based on their location and GPD rankings, namely the eastern and coastal region, the middle region, and the north-western region [83]. According to industrial directories, we randomly selected 200 firms in each region, and assigned ten profession interviewers to contact the top managers of these firms to solicit their participation via the following endeavors. The interviewers introduced our intended research and informed the managers that there were no correct or incorrect answers and that confidentiality would be strictly guaranteed. Considering the influences of COVID-19 pandemic, we sent electronic questionnaires to the firm mangers that agreed to participate and informed them that they could contact our interviewers at any time if they had any question about the items. After removing responses with missing data, we were left with 205 (34.2%) valid responses. The sampled firms covered: various ownership structures (i.e., state-owned enterprises (36.6%), private-owned enterprises (34.1%), foreign-invested enterprises (13.1%), collectively owned enterprises (10.2%), and others (5.9%)); geographic regions (i.e., eastern and coastal region (39.5%), the middle region (29.3%), and the north-western region (31.2%)); and industrial sectors (i.e., manufacturing (35.1%), chemistry (14.6%), industrial services (11.2%), textile and clothing (7.3%), food (4.9%), information technology (2.9%), electronics (9.8%), automobile (10.7%), and others (3.9%)). To test nonresponse bias, T-tests were conducted by comparing the participating and nonparticipating firms in terms of firm size, firm age, and the results indicate no significant differences (p > 0.1).

4.2. Measures

The constructs in our research were measured based on the existing literature, and all of them contained multiple items with the 7-point Likert scale (from ‘1’ for strongly disagree to ‘7’ for strongly agree, see Appendix A). Following Marcucci et al. [6], organizational resilience capacity included internal resilience and external resilience. We adopted the measurement scales and adjusted them to fit the Chinese context and used five items to measure internal resilience and three items to measure external resilience. A similar measurement was also used in Xie et al. [8]. Following Khin and Ho [25], digital capability was measured using four items. Based on previous studies around government support, we adapted the measures from Han et al. [71], Nakku et al. [15], and Shu et al. [14] and used four items to measure direct government support and indirect government support. Following Akgun et al. [62] and Zhang and Zhu [40], organizational unlearning was measured using eight items. We also controlled factors that might influence the ORC of a firm, including: firm age (the operational years); firm size (the natural logarithm of the number of employees); ownership structure (coding 1 when the focal firm was a foreign-invested enterprise and coding 0 otherwise); industrial sectors (coding 1 when firms operated in the manufacturing industry and coding 0 otherwise) [84]; and prior innovation performance (assessing ‘relative prior innovation performance comparing with their major competitors in the past three years’ [85]).

In

Table 1. Descriptive Statistics and Correlations (n = 205).

Variables	Mean	S.D	1	2	3	4	5	6	7	8	9	10
1. Firm age	11.57	4.92	--	0.28 **	−0.03	0.01	0.05	−0.07	−0.12	−0.02	−0.12	0.03
2. Firm size	5.85	1.69	0.28 **	--	−0.02	−0.07	0.08	−0.04	−0.00	0.02	0.07	0.02
3. Indusry	0.23	0.42	−0.03	−0.02	--	0.13	−0.02	0.03	0.01	0.06	0.12	0.12
4. Ownership	0.13	0.34	0.01	−0.07	0.13	--	−0.13	0.10	0.01	0.01	0.01	0.06
5. P.P	4.1	1.55	0.05	0.08	−0.02	−0.13	--	0.04	0.16 *	0.05	0.16 *	−0.08
6. IGS	4.86	1.17	−0.07	−0.04	0.03	0.10	0.04	0.92	0.33 **	0.22 **	0.24 **	0.17 *
7. DGS	4.62	1.18	−0.12	−0.00	0.01	0.01	0.16 *	0.33 **	0.88	0.30 **	0.32 **	0.16 *
8.ORC	4.55	1.13	−0.02	0.02	0.06	0.01	0.05	0.22 **	0.30 **	0.81	0.28 **	0.29 **
9. OU	4.30	0.95	−0.12	0.07	0.12	0.01	0.16 *	0.24 **	0.32 **	0.28 **	0.87	0.32 **
10. DC	4.77	1.28	0.03	0.02	0.12	0.06	−0.08	0.17 *	0.16 *	0.29 **	0.32 **	0.94
11. MV	5.22	4.04	0.04	−.03	−0.00	−0.10	0.01	−0.02	0.07	0.04	−0.00	−0.06

Notes: * p < 0.05, ** p < 0.01; Diagonal elements (in bold) are square roots of the AVE values; Zero-order correlations are below the diagonal; adjusted correlations are above the diagonal; PP stands for prior performance; IGS/DGS stands for indirect/direct government support; ORC stands for organizational resilience capacity; OU stands for organizational unlearning; DC stands for digital capability.

, we present descriptive statistics, in terms of the standard deviations, means, correlations, and square roots of average variance extracted (AVE) from each construct.

5. Analysis and Results

5.1. Construct Validity and Reliability

We employed several procedures to assess the psychometric properties of all latent constructs used in this study. Firstly, we calculated the composite reliability (C.R.) values to examine the reliability. As Table 1 indicates, the minimal value was 0.94, well above the cutoff value (0.70), indicating good composite reliability [86]. Secondly, structural equation modeling using AMOS 16.0 was employed to assess the unidimensionality and validity of our variables. The results show that the four-factor confirmatory measurement model reached a satisfactory fit to the empirical (χ² = 246.67, df = 160, p < 0.001; CFI = 0.97; GFI = 0.90; SRMR = 0.05; RMSEA = 0.05; TLI = 0.97). Moreover, all the items significantly loaded onto the variables, and Cronbach’s α values of all constructs exceeded 0.84, which indicated good internal consistency. Thirdly, as Table 1 shows, the square roots of the AVE values of all constructs were well above the correlations between each variable and every other one, in support of discriminant validity [87]. Fourthly, pair-wise chi-square tests were used on all latent variables to assess whether the unconstrained and constrained models significantly differed [87]. The results indicate that all the chi-square difference tests are significant, which provides further evidence of discriminant validity.

We employed the following method to decrease the potential CMV. Firstly, a marker variable (MV) (respondent tenure) which is theoretically unrelated to most of the constructs was added, and we calculated the adjusted correlations and statistical significances by using the lowest positive correlation (r = 0.008) between the MV and other variables [10]. None of the significant correlations turned out to be insignificant after the adjustment (see Table 1 for details). Secondly, one latent CMV variable was added into the measurement model, and the variance extracted by the CMV was 0.22, below the cutoff value (0.50) [87]. Thirdly, Harman’s single-factor test by using EFA (exploratory factor analysis) without rotation was conducted to examine the structure of the variables. Seven factors emerged, and the first factor accounts for 27.73% variance, suggesting no overwhelming factor emerged. Therefore, the results of our tests suggest that CMV was not a serious concern.

5.2. Hypothesis Testing and Results

We conducted hierarchical linear regression to test our proposed hypotheses. We mean-centered all the interaction terms to mitigate potential multicollinearity.

Table 2. Summary of Results from Hypotheses Testing (n = 205).

Variables	Organizational Resilience Capacity				Digital Capability
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7
Control Variables
Firm age	−0.01 (0.02)	0.00 (0.02)	−0.01 (0.02)	0.00 (0.02)	0.01 (0.02)	0.02 (0.02)	0.02 (0.02)
Firm size	0.02 (0.05)	0.01 (0.05)	0.01 (0.05)	0.01 (0.05)	0.02 (0.06)	0.02 (0.06)	−0.01 (0.06)
Ownership	0.08 (0.25)	0.00 (0.24)	0.03 (0.24)	−0.03 (0.24)	0.22 (0.29)	0.14 (0.28)	0.14 (0.26)
Industry	0.15 (0.20)	0.12 (0.19)	0.08 (0.19)	0.06 (0.18)	0.31 (0.22)	0.27 (0.22)	0.08 (0.21)
Prior performance	0.04 (0.05)	0.00 (0.05)	0.06 (0.05)	0.02 (0.05)	−0.06 (0.06)	−0.09 (0.06)	−0.11 (0.06)
Direct effects
DGS	(H1)	0.26 *** (0.07)	(H3d)	0.23 ** (0.07)	(H3b)	0.18 * (09)	0.22 * (0.09)
IGS	(H2)	0.14 * (0.07)	(H3d)	0.11 (0.07)	(H3c)	0.17 * (08)	0.01 (0.09)
DC		(H3a)	0.25 *** (0.06)	0.20 ** (0.06)
OU							0.35 *** (0.10)
Interactions
DGS × OU						(H4a)	0.22 ** (0.07)
IGS × OU						(H4b)	−0.14 * (0.07)
Constant	4.32 ***	4.40 ***	3.12 ***	3.46 ***	4.73 ***	4.78 ***	4.94 ***
Max VIF	1.12	1.14	1.12	1.16	1.12	1.14	1.20
R2	0.01	0.11	0.09	0.16	0.02	0.08	0.20
ΔR2		0.03 ***	0.08 *** M1	0.04 ** M2		0.06 ***	0.12 ***
F-Value	0.31	3.47 **	3.11 **	4.46 ***	0.87	2.35*	4.52 ***

Notes: * p < 0.05, ** p < 0.01, *** p < 0.001; Hypotheses in the parentheses in bold are supported; DC: digital capability, DGS: direct government support, IGS: indirect government support, ORC: organizational resilience capacity.

shows our empirical results.

In Table 2, we present the results for the standardized regression estimates from Model 1 to Model 7. The mean values of variance inflation factors (VIF) for each regression were much lower than the benchmark of 10, which means that multicollinearity was not a serious concern [88]. We also conducted F-tests to examine the significance of R square changes, and the results indicate that newly added independent variables in each model could account for significant variances in dependent variables. The empirical results show support for our theoretical hypotheses. Firstly, in terms of the direct effects hypotheses, as shown in Table 2, we found positive relationships between: DGS/IGS and ORC (β = 0.26, p < 0.001/β = 0.14, p < 0.05, Model 2); digital capability and ORC (β = 0.25, p < 0.001, Model 3); and DGS/IGS and digital capability (β = 0.18, p < 0.05/β = 0.17, p < 0.05, Model 6), in support of H1, H2, and H3a, b, c. Secondly, in terms of the mediation effect, H3d predicted the mediation role of digital capability in the relationship between government support (i.e., DGS and IGS) and ORC. As shown in Model 4, when digital capability was entered, the effect of DGS on ORC decreased and became less significant (β = 0.23, p < 0.01, Sobel Z = 1.85, p < 0.05 (one-tailed); p < 0.1 (two-tailed)), (Model 4), and the effect of IGS on ORC became insignificant (β = 0.1, p > 0.1, Sobel Z = 1.83, p < 0.05 (one-tailed); p < 0.1 (two-tailed)), (Model 4), in support of H3d. Thirdly, regarding the moderation effects, in Model 7, organizational unlearning was found to strengthen and weaken the relationship between DGS/IGS and ORC (β = 0.22, p < 0.01/β = −0.14, p < 0.05, Model 7), in support of H4a and H4b.

To test the robustness of our mediation test results, we followed the procedure in Preacher and Hayes [89] to examine the mediation effects. The mediation model (Model 4) was subjected to bootstrap analyses with 1000 bootstrap samples and a 95% confidence interval (CI). According to Preacher and Hayes [89], when the 95% CI contains zero, one can conclude that the effects are nonsignificant, and when CI do not contains zero, one can conclude that the effects are significant. Therefore, full mediation effects are established when nonsignificant direct effects and significant indirect effects are simultaneously observed, and partial mediation effects are established when significant direct effects and significant indirect effects are simultaneously observed.

As shown in

Table 3. Results of Hypotheses Testing (n = 205).

Path	Direct Effects			Indirect Effect
	PE	CI	p-Value	PE	CI	p-Value
DGS→DC→ORC	0.235	0.084, 0.393	0.010	0.049	0.004, 0.139	0.033
IGS→DC→ORC	0.076	−0.064, 0.233	0.277	0.026	0.002, 0.111	0.032

Notes: DC: digital capability, DGS: direct government support, IGS: indirect government support, ORC: organizational resilience capacity.

, the direct effect of DGS/IGS on ORC had point estimates (PEs) of 0.235/0.076 and 95% bias-corrected confidence intervals (CIs) of (0.084, 0.393)/(−0.064, 0.233). The indirect effect of DGS/IGS on ORC through digital capability had PEs of 0.049/0.026 and 95% bias-corrected CIs of (0.004, 0.139)/(0.002, 0.111). Therefore, digital capability fully mediated the effect of IGS on ORC and partially mediated the effects of DGS on ORC. These results support H3d.

6. Discussions and Implications

6.1. Discussions of the Results

The key purpose of this study, guided by ROT, was to shed light on the question of ‘how to improve firms’ ORC in the pandemic context’. The findings of the survey data show support for the proposed hypotheses.

DGS and IGS were found to have positive effects on the ORC and digital capability of firms, which is consistent with previous theoretical predictions about the vital roles that governments play in prompting the digital transformation of firms [4,9,18] and in assisting in the absorption of disruption to recover from the pandemic [1,20,27]. More importantly, we theoretically distinguished DGS from IGS by illustrating the different underlying influences that constitute their actual effects: DGS secures and/or increase the revenue of firms via the financial- and operation-related benefits it provides, and such benefits may contain certain degrees of immunities in the face of adversities; IGS improves the market order and law enforcements, and facilitates the R&D collaboration of firms with external entities. Thus, the bureaucratic and transaction costs experienced by firms would be reduced. On this basis, our findings further reveal the diversified ways that firms can adopt to build on the government support they have obtained and to improve their ORC. Specifically, the ORC of firms would benefit from their DGS and IGS via the building and developing of digital capability, while DGS may also directly improve their ORC.

In line with previous theoretical predictions that propose the capacity of firms to utilize digital technology to improve their robustness, flexibility, and adaptability during the pandemic [20,30], our findings indicate the positive effect of digital capability on ORC, thus confirming the critical role of digital capability [65], rather than individual digital technology, in enhancing the ORC [4,28]) of firms. Moreover, organizational unlearning was found to positively and negatively moderate DGS/IGS–ORC relations. Our theoretical discussion and empirical results confirm the positive influences of organizational unlearning, as ‘the disposal of technological assets from firms’ technological resource portfolio [22], on firms’ process of bundling resources to develop capability‘ for remaining as state of the art in a given industry [22]. The negative moderation effect we found reveals the contextual premise for the proposition that in the pandemic context, firms should have sufficient resources and/or secured revenue to endure the revenue loss caused by the ‘competency damage’ and ‘resource competing’ effects of organizational unlearning.

6.2. Theoretical Contributions

Our theoretical discussion and empirical results add to previous research in the following ways. Our primary contribution derives from the relatively overarching framework we introduced to investigate the ORC of firms in the pandemic context. Given the crucial role of ORC to secure the survival of firms, previous studies pay much attention to the question of ‘what factors drive firms’ ORC’ [8,12,90], yet the specific mechanism via which ‘ORC can be achieved in practice [8]’ was inadequately addressed. As resource orchestration theory ‘could be useful for developing a holistic view of how resources and capabilities can be bundled and mobilized [82]’ to create competitive advantage, we employed ROT as our the theoretical framework to incorporate the wisdom from studies of government support, digital capability, and organizational unlearning, and to delineate the ‘resource portfolio structuring (DGS and IGS)—capability bundling (digital capability) and its contingencies (organizational unlearning)—competitive advantage in the pandemic context (ORC)’ path. In this way, we theoretically illustrated, and empirically examined the direct, indirect, and contingent effects of government support and digital capability on ORC, thus enriching the antecedent research of organizational resilience, adding a ROT-based path paradigm to answer the question of ‘how firms may enhance their ORC’, and further contributing to the ongoing inquiries in related fields.

In terms of government support studies, recent advances in this field majorly treat government support as a whole (e.g., [15,17]) or focus on its macro policies and/or financial stimuli (e.g., [45,46]) subsets to investigate its effects on the outcome variables. Our research makes a deliberate attempt by specifying DGS and IGS as the subdivisions of government support, elaborating their varying direct and indirect effects on digital capability and ORC, as well as their contingencies upon organizational unlearning, thus providing empirical evidence to address the call that ‘the study of government roles in the process of digital transformation is limited’ [20]. The diversified mediation paths (full vs. partial) and contingent effects (positive vs. negative) provide evidence for the validation of our classification, thus enriching the taxonomy research of government support.

In terms of digital capability research, by theoretically illustrating the ‘value maintaining and creating’ effects that digital capability may have before [25], during [20,21], and after [28,29] the pandemic, our research identifies the critical role of digital capability in transforming the structured resources (DGS and IGS) of firms into competitive advantage (ORC) amid the COVID-19 pandemic. In this way, we were able to situate digital capability, a nascent digital business concept, into ROT (the classic managerial research framework), thus adding nuance to ROT research. Moreover, the effects that DGS and IGS have on digital capability enriches the antecedent research of digital capability with evidence from the pandemic context, while their contingencies upon organizational unlearning, on the other hand, add a plausible explanation for the discussion around the ‘digitalization paradox’ [23].

In terms of organizational unlearning research, considering that ‘imbalance has emerged between the number of theoretical papers and the empirical testing [91]’, and previous studies mainly hold a positive view about the direct effects of organizational unlearning on the outcome variables [32,40,59], we identified the ‘divesting of obsolete knowledge resource’ nature of organizational unlearning, and echo Klammer and Gueldenberg [63] by investigating its effects in shaping the new competency establishing and strengthening process of firms [57], namely, their process of bundling resources to create capability [65]. Therefore, we add a knowledge resource-divesting logic for the interpretation of the moderation effects, alongside the direct contribution that organizational unlearning may have in affecting the learning of new knowledge by firms [54]. The opposite moderation effects of organizational unlearning on the DGS/IGS–digital capability links validate our proposition and add evidence for the contingent roles of organizational unlearning [58,61,91].

6.3. Managerial Implications

With the adverse influences of the COVID-19 pandemic, one important issue faced by firms is how to improve their organizational resilience capacity in this hostile world. Thereby, the theoretical discussions and empirical results of this research provide several implications for mangers. Primarily, mangers should not be cautious about the facilitating roles that government supports play in enhancing ORC and not hesitate to solicit them from governments. Also, they should be aware about the intermediate role that digital capability plays i.e., the efficient ways to realize the benefits of their solicited support would be the creation and developing of digital capability. Moreover, managers should properly initiate and prompt unlearning activities during the building and developing of their government support-based digital capability. Specifically, managers should endeavor to prompt their unlearning activities to improve the establishment and strengthening of their digital capability when firms have solicited DGS from the Government, while avoiding the damage that unlearning may cause on their current competencies in the IGS–digital capability scenario.

7. Conclusions, Limitations, and Future Research Directions

Faced with the adversities of the COVID-19 pandemic, how to improve the ORC of firms has become the central theme in recent organizational and managerial research. While previous research provided insightful knowledge about ‘what factors drives ORC’, ‘in which way would firms’ ORC be enhanced’ received limited attention [7]. Therefore, based on ROT, this paper examines the relationships between government support (i.e., DGS, IGS), digital capability, organizational unlearning, and ORC. The hypotheses were tested based on the survey data of 205 Chinese companies, with hierarchical linear regression analysis and SEM analysis being performed. The results show that DGS and IGS have positive effects on organizational resilience capacity, which were mediated by digital capability. In addition, organizational unlearning positively and negatively moderates the positive relationship between DGS/IGS.

Although this research makes a number of important findings with significant theoretical and managerial implications, it has several limitations that may provide direction for future research.

Firstly, in terms of the generalizability of our research findings, since the pandemic context in China contains both similar and different traits compared to other countries, whether our research findings could be applied to others is in question. Future research undertaken in other midrange economies and/or developed countries that retest our research findings and discuss the potential heterogeneities in terms of the contextual differences between countries is highly recommended. Secondly, though our research provides general understandings of how firms could build on government support to improve their digital capability and ORC amid the pandemic context, the inherent characteristics of the firms, for instance, their ownership structure, industrial sector, geographic region may have a contingent impact on the direct, indirect, and moderation relations we investigated. Future research may consider to retest and compare our research findings across these contingencies (e.g., born digital vs. traditional; SOEs vs. FIEs) to attain a more nuanced understanding. Thirdly, empirical tests using cross-sectional data suffer from the incapacity of addressing the issue of causality and dynamics. Future research that uses panel data or the tracking research method should retest the robustness of our findings and address the weaknesses associated with cross-sectional, survey-based research methods.