Abstract
This study illustrates how the supply chain financing (SCF) ecosystem has responded to the COVID-19 crisis during the first year of its onset. The economic effects have manifested in disrupted supply chains, increased financial stress, and an overall negative impact on global trade. A multitude of organizations have struggled to manage their cash flow and working capital to survive the crisis. Overall, we explore how the SCF community as a business service ecosystem has intervened to provide organizations with opportunities to improve their liquidity. We use data obtained from a systematic, in-depth analysis of the practice-driven literature reporting on SCF activities as they unfolded in the early stages of the crisis. An inductive qualitative case study approach is applied to develop the grounded theoretical model that illustrates the SCF ecosystem's behaviors as it responded to the crisis. Furthermore, a post-hoc investigation, consisting of semi-structured interviews with 28 SCF experts, has been conducted to validate the theoretical model and to capture future developments. As we anticipate continuing global supply chain disruptions given the virus and its mutations, global warming, and trade wars among superpowers, our study provides insights for the behaviors of and relationships between the actors that comprise the SCF ecosystem in turbulent times.
Keywords: Supply chain financing, Working capital management, Financial crisis, Business service ecosystem, Pandemic, Liquidity, Qualitative research, COVID-19 case study
1. Introduction
Lockdowns, border closures, quarantine requirements, and social distancing have had a devastating impact on global trade in 2020. One estimate on global exports of manufacturing inputs due to COVID-19 was the reduction of about $230 billion (Solleder and Torres Velásquez, 2020). Disruptions to global SCs have brought attention to the critical role of SCs in our daily lives. In addition to the movement of goods and services, we must consider the flow of finances. Late payments and the risk of impending defaults have risen sharply as the pandemic unfolded. Suppliers struggled to collect unpaid invoices as buyers withheld payments to conserve cash (Wiggins, 2020). In the first half of 2020, the amount of money more than 10 days overdue grew 56% in France, 52% in Spain, and 82% in Italy (Kahn, 2020). In particular, small and medium-sized enterprises (SMEs) were severely affected. With less financial leeway, they are more dependent on timely payments from their buying companies (Fed Small Business, 2019).
As the COVID-19 crisis imposes financial stress worldwide, managing cash and working capital (WC) through supply chain financing (SCF) became critical in surviving the situation (Basquill, 2020b). SCF builds on traditional trade finance solutions to help organizations manage their WC at a low interest rate (Templar et al., 2020). SCF offers benefits for both buyers and suppliers (Rogers et al., 2020). From a buyer perspective, SCF provides an opportunity to finance suppliers on favorable terms increasing their business resilience. From a supplier perspective, SCF enables opportunities to reduce receivables risks. SCF offers a set of practices that typically include accounts payable solutions (e.g., reverse factoring, dynamic discounting), inventory solutions (e.g., on- and off-balanced inventory financing), and accounts receivable solutions (e.g., trade receivables solutions, factoring).
Currently, the SCF solutions market is highly fragmented (Sommer and O’Kelly, 2017). Financial technology (fintech) companies have emerged as critical players in this field.1 The COVID-19 crisis has accelerated a trend involving higher private and public debt, lower interest rates, and shrinking fiscal and monetary room for policy maneuvers (Borio, 2020). Banks have begun to incorporate anti-cash-hoarding provisions into their lending agreements (Lentz and Smith, 2020). Actions taken by major financial institutions indicate they believe that loss rates will exceed those from the economic crisis in 2008/2009 (McIntyre et al., 2020). To calm the markets and stabilize SCs, governments and their affiliated agencies (i.e., central banks) have intervened far beyond the usual (Krsanac, 2020). Billion-dollar rescue and stimulus packages were put together around the world.2 For example, the International Monetary Fund (IMF), with a lending capacity of about $1 trillion, offered emergency financing for debt relief, enhanced short-term liquidity lines, and policy advice concerning cash management, financial supervision, and economic governance (IMF, 2021).
During COVID-19, organizations from different industries used a wide range of SCF practices. While they were united in their quest to survive, they displayed different reactions to the emerging crisis. The media reported extensively on the course of the situation. Because of the ubiquitous media presence, we were able to track the behavior of the organizations exhibiting what appeared to us the characteristics of an ecosystem (Moore, 1993). For our analysis, we draw primarily on the SCF literature (e.g., Rogers et al., 2020; Templar et al., 2020), enriched by contributions from the business ecosystems field (e.g., Adner, 2006; Adner 2017; Jacobides et al., 2018). The group of actors of the SCF community has, according to our observation, revealed clear signs of an ecosystem. Therefore, we refer to the sum of interacting organizations as the SCF ecosystem. During COVID-19, the SCF ecosystem has shown observable reactions. Whereas this ecosystem was previously active in the background before the pandemic, it was as if the situation unveiled the curtain. The SCF ecosystem has come to the fore.
A review of the scant academic literature on this topic (e.g., Bals, 2019; Chen et al., 2021) left us wanting to learn more about this novel phenomenon, especially with its presence becoming more prominent during a global crisis. Our intent was to learn about what this SCF ecosystem looks like, which groups of actors are to be included and what roles these actors play in the SCF ecosystem. We set out to understand how the SCF ecosystem has responded to the uncertainties and challenges caused by the COVID-19 crisis. We investigated how innovations in technology, business models, and collaboration mechanisms helped companies secure liquidity in SCs.3 Further, we sought to look ahead as we were curious about how the SCF ecosystem would behave in the late phase of the crisis and beyond. Having the global dislocations regarding liquidity in mind, we developed three research questions.
RQ1
How did the key actors in the SCF ecosystem behave during the COVID-19 crisis?
RQ2
How did their behaviors interact within the SCF ecosystem to sustain themselves?
RQ3
What would be the future implications for the SCF ecosystem after the COVID-19 crisis?
Given the unique challenges during the crisis, our decision was to use the rich information in the practitioner literature, also known as the grey literature (Adams et al., 2017). The disease's epidemiological course was unclear, and it posed significant challenges to conduct a field investigation on site. The companies also had many other pressing issues. At the same time, global media outlets reported the crisis, its effects, and its consequences. From such grey literature, we would be able to observe the course of the crisis and the actors' behavior in the SCF ecosystem from a safe distance. We used these newspaper articles, company reports, and social media posts.
The answers to RQ1 and RQ2 were addressed through an inductive qualitative case study approach (Eisenhardt, 1989) combined with an adapted version of the Gioia method (Gioia et al., 2012). We aimed to develop a grounded theoretical model (Glaser and Strauss, 2017) of the SCF ecosystem's behavior during COVID-19. RQ3 was addressed by conducting a post-hoc study comprising semi-structured interviews with SCF experts following Villena et al. (2021). It served to validate the model and derive implications for future development in SCF ecosystems. With this approach, we were able to ascertain the trends in the rising SCF ecosystem. The contributions of the study are threefold. First, the study contributes to a deeper understanding of the SCF ecosystem phenomenon. Beyond “to source, make, and deliver,” we show a fourth purpose of SCs. This attribute, called “to fund,” was induced by the presence of the COVID-19 crisis. Second, the study provides insights into the liquidity-driven challenges in SCs and how they cope with extreme situations using SCF practices. In particular, as we anticipate more extreme weather patterns due to global warming, disruptions due to trade wars among superpowers, and lingering effects of COVID-19 and its variants, theorizing behaviors of the actors in the SCF ecosystem during the first-year of the onslaught of the pandemic appears salient. Third, the grounded theoretical model forms a contribution to the rising body of business ecosystems literature whereby we introduce a new business service ecosystem involving a global system of interacting actors.
The present study is structured as follows. After the introduction, the context of the study is presented in Section 2 in a literature review. Section 3 presents the methodology. The results of the cases are highlighted in Section 4. Section 5 develops the grounded theoretical model through cross-case analysis. Section 6 looks ahead with the help of a post-hoc study. The paper concludes with a discussion, including a research outlook in Section 7.
2. Literature review
We review what SCF is and its conceptual link to working capital management (WCM). We identify the key actors within the SCF community. Afterward, we consider how an SCF community operates as an ecosystem and how its actors interact to enhance buyers' and suppliers’ liquidity.
2.1. SCF practices
Following the aftermath of the global financial crisis about ten years ago, the topic of SCF began receiving significant attention (Pfohl and Gomm, 2009; Wandfluh et al., 2016; Liebl et al., 2016; Gelsomino et al., 2016; Xu et al., 2018; Bals, 2019; Wuttke et al., 2019; Wetzel and Hofmann, 2019). The topic is increasingly finding its way into specialist textbooks, edited volumes, and trade books (Hofmann and Belin, 2011; Templar et al., 2016, 2020; Tate et al., 2018; Zhao and Huchzermeier, 2018; Rogers et al., 2020). The increasing number of SCF special issues in operations management journals (i.e., Hofmann and Johnson, 2016; Caniato et al., 2019; Chen et al., 2020; Rogers and Leuschner, 2015) underscores the interest. SCF, as Rogers et al. (2020:3) state, is about “using the supply chain to fund the organization and using the organization to fund the supply chain.” Table 1 illustrates the range of SCF research areas.
Table 1.
SCF research areas.
| Stream | Themes | Sources |
|---|---|---|
| Collaboration | Influence of trust and power between suppliers and buyers in adopting an SCF practice | Wuttke et al. (2013) |
| Hofmann and Zumsteg (2015) | ||
| Organization | The influence of organizational setup on SCF practices | Wandfluh et al. (2016) |
| SCF knowledge and training on SCF practices adoption | More and Basu (2013) | |
| Design of incentive to promote cross-functional collaboration in SCF | Liebl et al. (2016) | |
| Financial | Analytical modeling and simulation | Yan and Sun (2013) |
| Benefits for stakeholders | Van Der Vliet et al. (2015) | |
| Lekkakos and Serrano (2016) | ||
| The intersection between “finance” and the “supply chain collaboration” | Viskari and Kärri (2012) | |
| Vázquez et al., 2016 | ||
| Technologies | Contributions mainly confirm IT's a crucial role in SCF practices' information sharing process | Fairchild (2005) |
| Blackman et al. (2013) | ||
| Contribution of technologies in SCF (e.g., blockchain technology, artificial intelligence) | Du et al. (2020) | |
| Zhu et al. (2019) | ||
| Regulation | The regulation dimension of SCF practices | Fairchild (2005) |
| Iacono et al. (2015) | ||
| Liebl et al. (2016) |
Both the Great Depression and now COVID-19 have demonstrated that cash management is the number one priority in a crisis (Cowling et al., 2020; Joseph et al., 2020; Brum and De Rosa, 2021). As we mentioned already, at its core, SCF is about funding at the strategic level of the organization. Funding an organization from the outside via debt and equity becomes more difficult during challenging times as the last couple of years have shown. But we can already see the seeds of future funding difficulties today. In that context, SCF is closely related to WCM at the operational level. It provides solutions that help buying companies and their suppliers achieve liquidity (Wetzel and Hofmann, 2019). WC is essential for a company to finance itself (Baños-Caballero et al., 2014). Liquidity, which can be defined as available WC, can be thought of as the lifeblood of a company and can be more than half the value of its non-current assets (Filbeck and Krueger, 2005).
The goal of WCM is to minimize tied-up capital in a company's operations. This goal can be attained by reducing inventories or accounts receivable or extending accounts payable (Hofmann and Belin, 2011). In particular, there is a set of tools and practices focused on payables, inventory, and receivables financing solutions (see Templar et al., 2020 for more detail). The most well-known practices include reverse factoring (Wuttke et al., 2013; Iacono et al., 2015; Liebl et al., 2016) and dynamic discounting (Caniato et al., 2016). They involve a financial service provider (FSP) (i.e., bank or fintech) that help avail early payment on approved invoices with suppliers accepting a small fee on the invoice's face value. By setting up such payable financing practices, buyers support their suppliers. In the case of reverse factoring, the buyers use their credit strength to ensure that suppliers have access to low-cost WC. In the case of dynamic discounting, they use their own cash to offer early payment based on the supplier's willingness for a discount. In both cases, suppliers drive the date on which they want to be paid. Inventory financing tools include on-balance sheet and off-balance sheet financing. Lastly, suppliers may be incentivized to seek their own receivables financing in the form of collective, sales offer financing, invoice discounting, as well as traditional factoring (Guida et al., 2021; Moretto and Caniato, 2021).
2.2. SCF actors
We have thus far mentioned four types of SCF actors: buyers, suppliers, FSPs, and fintechs. In addition, logistics service providers (LSPs) and governmental agencies also belong to the extended group of SCF actors (see Fig. 1 ). All these actors constitute the SCF community. The figure classifies the actors into the demand side, with actors demanding SCF solutions and the offer side, with actors providing SCF solutions (Herath, 2015; Sommer and O’Kelly, 2017). Governments are shown outside of the two main groups because they influence policy and regulation and are indirectly involved in commercial transactions.
Fig. 1.
SCF actors.
Buyers and suppliers are the primary drivers of SCF. Buyers and suppliers have a direct exchange relationship under a contractual agreement. In a SC, buyers are also suppliers to other buyers downstream, and suppliers are also buyers to those upstream. In particular, the dominant buying companies facing the consumer markets are called “focal companies” (i.e., Apple, Siemens, GM, etc.). As original equipment manufacturers (OEM), they tend to be endowed with good cash reserve and credit rating. It is often a focal company working with a bank or fintech that introduces an SCF solution. Such buyers are typically large in revenue and drive a trade relationship. They may be characterized as foreign or domestic, a parent company, exporters, licensors, importers of foreign products, or manufacturers who package and distribute under an international or domestic brand. Suppliers fulfill a buyer's request based on a purchase order (Rogers et al., 2020). If not paid upon delivery, they will issue an invoice to the buyer, customarily paid within a 30-to-90-day window. Smaller suppliers may have limited bargaining positions and few alternative customers, often restricting their flow of capital.
LSPs are involved across all tiers of SCs. They are traditionally focused on transportation, warehousing, and inventory management between a supplier and its buyer. Generally, LSP's value propositions are centered around improving their client's profitability by increasing asset utilization, specifically network optimization and outsourcing. They can enhance liquidity and WC by inventory reduction initiatives, such as postponement, just-in-time and vendor-managed inventory. Some also offer financial services (Maloni and Benton, 1997). Hofmann and Wetzel (2018) identify three types of financial services provided by LSPs in customs clearance services, freight payment services, and inventory financing. Blockchain technologies can provide LSPs with additional opportunities to develop new value-added SCF services that improve transparency, build trust, and increase their clients' efficiency (Brink et al., 2018).
FSPs are providers of external financing for payment transactions and risk mitigation (Greenbaum and Thakor, 2007; Brealey et al., 2020). FSPs is an umbrella term for a broad group of actors (Martin and Hofmann, 2017). Banks play an essential role in integrating financial transactions (Silvestro and Lustrato, 2014). They act as solution providers with their own SCF solutions. The Spanish Banco Santander was one of the first banks picking up reverse factoring in the late 1980s. Nowadays, many large banks offer it as part of a broader SCF solution portfolio. Banks also act as funders, providing cash in SCF programs of third parties. Therefore, we now find an increasing number of SCF solutions in the bank's product and service portfolio (Bryant and Camerinelli, 2013). However, the group of FSPs also includes specialists, such as factoring providers (Templar et al., 2020). Factoring is a revolving transfer of receivables of a company (the supplier with approved invoice) against a debtor (the buyer that owes money) to a funder (the factor) before the expected payment date.
Modern SCF practices are built on several technologies (Jia et al., 2020; Vousinas, 2019). These technologies enable connectivity across the focal company, its suppliers, customers, and the FSPs (IFC, 2020). In this context, fintechs are a new breed of companies specializing in integrated SCF practices (Rogers et al., 2016). They are typically independent of banks or other lenders (Martin and Hofmann, 2017). They facilitate the reconciliation of private orders, invoices, credit notes, and payments. They can offer additional services such as credit risk management and the setup of SCF programs. Fintechs can be IT-providers, B2B networks, and e-invoicing service providers, as well as marketplaces and hubs (Bryant and Camerinelli, 2013). IT-providers offer software applications and IT solutions in payments, cash management, workflow automation, and systems integration. Many are global IT-software providers of specific IT-technology (IBM, SAP). B2B networks offer collaborative solutions for the electronic exchange of purchase orders and invoices alone or in partnership with financial institutions (Bryant and Camerinelli, 2013). Finally, marketplaces connect all SCF parties using a platform-based technology (e.g., Basware, T Nexus, GXS, Tieto, Tradeshift, Elemica, E2open, Covisint). They enable collaboration between the focal company's internal systems, its suppliers and customers, and FSPs (Botta et al., 2020).
Governments and the public support SCF development through national or local initiatives (Bryant and Camerinelli, 2013). When SMEs face difficulties raising WC, public authorities can intervene by injecting liquidity in the corporate credit market through funding programs. Some governments also promote digitization and automation, encouraging the development of new SCF solutions. To support SCs, central and development banks use partial credit guarantees while letting private lenders originate, fund, and collect on credit. These governmental agencies can support legal, regulatory, and policy frameworks and provide technological platforms, supply-level advisory, and demand-level awareness building. They could kickstart an SCF framework and support commercial banks in developing new SCF solutions (World Bank, 2021). 4
The other actors include, among others, investors. For investors to participate in SCF programs, the receivables and inventories addressed must be made “tradable” and placed on the capital market. This process is known as asset securitization (Hofmann et al., 2017). This securitization is often implemented by fintechs and special purpose vehicles (SPVs).5 Consultants and analysts are another notable group. They provide advisory services to the different market players on the strategy, market research, business models, marketing, and systems development of SCF (Bryant and Camerinelli, 2013). Industry associations enable developing a shared cooperative or market players to exchange information and establish standard practices across the SCF network (Templar et al., 2020).
2.3. SCF ecosystem
A business ecosystem reflects an economic community working “cooperatively and competitively to support new products, satisfy customer needs, and eventually incorporate the next round of innovations” (Moore, 1993: 76). The SCF community is an example within the broader category of business ecosystems. Since providing liquidity is a service, we refer to the SCF ecosystem as a business service ecosystem. The characteristics of services, such as intangibility, heterogeneity, inseparability, and perishability, as well as the fact that customers provide significant input in the service creation process are important in these ecosystems (Nie and Kellogg, 1999; Sampson and Froehle, 2006). Moreover, we cannot just assume single dyadic relationships between a customer and a provider but rather numerous relationships and subcontracts. Therefore, the literature on multiple service relationships must also be considered (e.g., Choi and Wu, 2009; Wynstra et al., 2015).
The SCF ecosystem, at the center of the study, has some special features compared to other types of ecosystems. First of all, it should be mentioned that it is an ecosystem of business services. It differs in particular from pure innovation ecosystems, which are mainly focused on the creation of new products or services (Granstrand and Holgersson, 2020), and in relation to participating actors also from knowledge ecosystems, which are primarily based on the proximity to research institutions (Valkokari, 2015). Furthermore, the SCF ecosystem is built around the core value proposition of "to fund" as a service and that with direct reference to the supply chain (Rogers et al., 2020; Hofmann et al., 2021). The services created by the actions of the actors involved form a new market for professional SCF practices, which can no longer be described by classic industry sectors or industry designations as would be customary, for example, from banks (Martin and Hofmann, 2017). The companies operating in the SCF ecosystem can now serve markets, i.e., SCF demand from physical supply chain actors, on a scale that goes beyond the capabilities of the single firm. For example, fintechs provide the technological platform for settlement, investors contribute liquidity to SCF programs, insurance companies secure transactions, and rating agencies take over the evaluation of the involved actors. In this way, the providers in the SCF ecosystem create mutual value among themselves in the sense of interlinked business models. The actors in the SCF ecosystem are complementary value co-creators with different roles.
The SCF ecosystem, at the center of the study, has some special features compared to other types of ecosystems. Primarily, it should be mentioned that it is an ecosystem of business services. It differs in particular from pure innovation ecosystems, which are mainly focused on the creation of new products or services (Granstrand and Holgersson, 2020), and in relation to participating actors also from knowledge ecosystems, which are primarily based on the proximity to research institutions (Valkokari, 2015). Furthermore, the SCF ecosystem is built around the core value proposition of "to fund" as a service and that with direct reference to the supply chain (Rogers et al., 2020; Hofmann et al., 2021). The services created by the actions of the actors involved form a new market for professional SCF practices, which can no longer be described by classic industry sectors or industry designations as would be customary, for example, from banks (Martin and Hofmann, 2017). The companies operating in the SCF ecosystem can now serve markets, i.e., SCF demand from physical supply chain actors, on a scale that goes beyond the capabilities of the single firm. For example, fintechs provide the technological platform for settlement, investors contribute liquidity to SCF programs, insurance companies secure transactions, and rating agencies take over the evaluation of the involved actors. In this way, the providers in the SCF ecosystem create mutual value among themselves in the sense of interlinked business models (Fu et al., 2022). The actors in the SCF ecosystem are complementary value co-creators with different roles.
The SCF ecosystem as an example of a business service ecosystem shows four main characteristics that we draw upon. The first characteristic is that they help coordinate interrelated organizations with significant autonomy (Wareham et al., 2014). Several independent FSPs work together to enable SCF solutions (Templar et al., 2020). For example, a reverse factoring program usually requires a fintech and its technology platform, banks to provide funding for the program, insurance companies to cover a possible default of the funds injected, and a rating agency to measure the risks (Kouvelis and Xu, 2021).
Second, modularity describes complex systems’ decomposition into modules (Kast and Rosenzweig, 1972). In organizations, modularity means breaking down a complex value creation system into individual modules so that they can perform on their own. Ecosystems, in turn, enable these independent modules to be combined in new ways to form various service bundles (Baldwin, 2012). In SCF ecosystems, the buyers or suppliers can choose among the service components of SCF practices offered by each FSPs. Based on the modularity of service components, they can decide how the modules are combined (Jacobides et al., 2018). For example, SCF practices include technological elements (IFC, 2020): digital infrastructure (electronic invoicing, cloud computing, e-payments), standardization and interfaces (application program interfaces, distributed data management, identity verification), managed services and platforms (software as a service, platform as a service, online marketplaces), and analytics (diagnostic analytics, predictive analytics, artificial intelligence). In SCF ecosystems, these technological elements are put together via a series of nested subcontracting.
Third, ecosystems help coordinate interrelated organizations. Independent banks are brought together via technology platforms offered by fintechs. From the client's point of view (buyer or supplier), such multi-bank approaches can achieve a geographic coverage of SCF practices and reduce a bank's potential default risk (Hofmann et al., 2017). Thus, the ecosystems provide processes and rules on how to resolve coordination issues. In this regard, Gulati et al. (2012) considered ecosystem membership to be “open” as the actors work together of free will. This form of cooperation of the SCF solution providers is de jure and de facto decentralized. This means that no central actor guides the fate of the SCF ecosystems, sets standards, or defines interfaces. The latter is formed in cross-company working groups such as the Society for Worldwide Interbank Financial Telecommunication (see: https://www.swift.com/).
The final characteristic of ecosystems is non-generic complementarities, giving rise to a specific structure of relationships and alignment to create value (Jacobides et al., 2018). SCF ecosystems provide complementarities, and collaborative consumption of SCF solutions generate greater utility. For example, buying companies benefit from the fact that other competitors have already introduced SCF practices with the same suppliers. The onboarding of the shared suppliers to the SCF solution would be easier (Wuttke et al., 2019). From the SCF solutions providers’ perspective, different service components cannot be provided without coordination between the service providers. For example, several banks must be integrated to offer a reverse factoring solution to achieve the greatest possible geographical coverage. The individual FSPs might not have a banking license for all currency areas (Liebl et al., 2016). Due to the nested relationships between the service providers, the degree of non-generic complementarities in business service ecosystems can be increased more easily than in the case of simple service arrangements (Madhavan et al., 2004).
3. Methodology
This study's goal is to explore how the SCF community, with its actors across the world, has responded to the onslaught of COVID-19. We have adopted an inductive qualitative case study (Eisenhardt, 1989; Glaser and Strauss, 2017) combined with grounded theorizing based on the Gioia approach (Langley and Abdallah, 2011; Gioia et al., 2012; Gehman et al., 2018). The data were gathered from the practitioner literature encompassing trade magazine articles, press releases, and investor reports. The results were complemented with a post-hoc study using interviews with 28 SCF experts, following Villena et al. (2021). Our methodological approach was chosen given the scope and specificity of the COVID-19 crisis. While companies were under stress and, in some cases, in survival mode, the news and social media had reported ubiquitously about the situation. Time was crucial in this study because realities on the ground were shifting as the virus spread, and various countermeasures were taken worldwide. For these reasons, the practitioner-based grey literature (Adams et al., 2017) and contextualization via additional sources such as websites were used. Newswire services reported the widespread impact of the COVID-19 crisis on organizations and their SCs worldwide. Table 2 offers an overview of the methodological steps taken in this study.
Table 2.
Summary of methodology.
| Step | Key features |
|---|---|
| Overall research design |
|
| Data collection |
|
| Data coding |
|
| Data analysis |
|
| Grounded theoretical model |
|
| Post-hoc study |
|
3.1. Data collection and coding
The primary reason for adopting the grey literature as a data source is contemporaneity (Albino et al., 2011; Adams et al., 2017; Martin and Assenov, 2012). On the one hand, we followed Eisenhardt (1989) through the construction and analysis of each of the six cases. The credibility of the case insights was enhanced by sampling multiple perspectives and data sources within each case. On the other hand, we worked in the cross-case analysis to identify the overall patterns of actions and interactions across the cases, using the Gioia approach (Gioia et al., 2012). Although Langley and Abdallah (2011) explicate the fundamental differences between the two approaches for qualitative research, our study builds a bridge between the two approaches in a pragmatic sense.
Two rounds of data collection were conducted through round one (R1) and round two (R2). For R1, keyword searches were developed to extract news articles and press releases from the ABI INFORM database and included articles and news stories from sources such as the Financial Times, PR Newswire, Dow Jones, and other sources. The keywords and the exclusion criteria are outlined in Appendix A. Articles from January 2020 (start of the pandemic) to August 2020 (end of the first wave) were included.6 The list of keywords was developed in a recursive manner to elevate the possibility of finding all relevant articles, while minimizing the possibility of omissions. The research team settled on the final set of keywords after receiving multiple sets of other alternatives striving to increase the number of relevant articles. R2 was conducted to augment the articles from the formal search, following a snowballing approach. The dataset included a collection of social media posts on LinkedIn, trade journal articles, and company press releases. The data were added to capture different industries’ perspectives not covered by the traditional newswire services. In total, there were 136 articles identified (see Appendix D).
Before the formal coding, two researchers sampled ten articles and coded them independently. Then, they came together to discuss the differences and developed a consistent coding approach. After that, each article in the total pool of articles was randomly assigned and coded by two coders. The coders recorded their assessment in the spreadsheet (see Appendix E for a sample). The inter-coder agreement on the affected actor groups was 71.85% for R1 and 77.42% for R2. The inter-coder agreement was calculated as the ratio of number of agreements and total number of items (Miles and Huberman, 1994; Wu and Choi 2005). For every coding that showed discrepancies, the team discussed to resolve the differences.
3.2. Data analysis
The researchers met weekly from June 2020 until February 2021 to discuss and analyze the coded data. Overall, the research team collectively spent more than 240 person-hours (see Appendix C) and followed an iterative process in team discussions and consensus-building (Villena and Gioia 2018). The result was within-case analysis and cross-case analysis (Miles and Huberman 1994) with the eventual goal of creating the grounded theoretical model (see Villena and Gioia 2018). A case is defined as the collection of actions taken by a specific group of supply chain actors (SCA) as a reaction to the COVID-19 crisis within the bounded context of SCF activities (Miles and Huberman (1994). To each case, we applied the Gioia method to create data structures (Langley and Abdallah, 2011; Gioia et al., 2012; Gehman et al., 2018). Each data structure has three levels of abstractions: first-order categories, second-order themes, and third-order dimensions, with the latter being the most overarching. These data structures are then used to create our grounded theoretical model for the SCF ecosystem.
We began by examining each article individually to extract the key findings for our analysis. We first tagged each article based on the main actor who initiated this action or was most affected by the action and by various interactions between SCA. All these findings were compiled, compared, and enumerated into a single master document. Next, we split the team into three sub-teams of two researchers each to begin the case-building exercise from this master document. For every single item, each sub-team analyzed and extracted unique behaviors for each SCA. Each sub-team produced its own set of notable behaviors for the different SCA. Once we went over the entire list of items, we discussed the individual categorizations regarding how they could be organized into unique cases. Next, we reviewed the behaviors identified by the sub-teams and removed duplicate entries. Lastly, we continued to discuss the behaviors and their categorization into the cases until consensus on the categorization was reached. The result was six unique cases: (1) physical SCA, (2) financial SCA, (3) regulatory and governmental agency actors, (4) interactions between physical and financial SCA, (5) interactions between financial supply chain (FSC) and regulatory and governmental agency actors, and (6) interactions between physical SC and regulatory and governmental agency actors.
The next step was to begin the data structure building for each of these six cases, following the Gioia method (Langley and Abdallah, 2011; Gioia et al., 2012; Gehman et al., 2018). For each case, the whole research team grouped unique behaviors/actions to develop unique first-order categories. After the first order categories were identified for all cases, we extracted emerging patterns from the first order categories and identified them as the second-order themes. This occurred after several rounds of team discussions. The last part of this process was to create the third-order dimensions. The team met several times to discuss and arrive at a consensus. The entire coding process from behaviors all the way up to third-order dimensions is depicted in Fig. 2 .
Fig. 2.
From coded articles to dimensions.
3.3. Post-hoc investigation
We conducted a post-hoc investigation (Roh et al. 2016) to examine the grounded theoretical model for validation purposes (Cho and Linderman 2019) and to gauge the future, post-COVID-19 implications based on SCF experts' informed opinions. The data were gathered qualitatively via semi-structured interviews. Initially, 38 experts were contacted from three geographic areas (Asia, Europe, and the United States). The respondents who agreed to participate came from the following groups of ecosystem members: buyers/suppliers, banks, and fintech providers. The informant's job titles ranged from CEOs, other C-level executives, general managers, vice presidents, directors, and others. We received responses from 28 SCF experts representing 25 organizations. Their profiles are listed in Appendix G. The sample offered diversity in terms of industry, position in the SCF ecosystem, and geography. Each respondent was asked to respond to a set of seven questions (see Appendix F for the semi-structured interview tool). Respondents were given the option to reply either via emails or by hour-long interviews with the research team members.
4. Within-case analysis: six cases and underlying data structures
We introduce six cases, as shown in Fig. 3 . These cases emerged from our data analyses, with additional actors not demonstrated in our initial Fig. 1. At the core, the SCF community consists of buyers and suppliers, we add LSPs, and call them the physical SCA (Case 1). The next group, financial SCA, include banks, fintechs, and investors (Case 2). Regulatory and governmental agency actors include governments, regulatory agencies, and credit rating agencies (Case 3). In addition to the actors in this community, we identified interactions between the actors. These interactions create three additional cases (Cases 4, 5, and 6). The focus on both actors and interactions is required for a thorough analysis of the SCF community (Mollenkopf et al., 2021).
Fig. 3.
Listing of six cases.
For each within-case analysis, there are two parts. One, introducing a description of the case, and the other presenting the results of the within-case analysis. We follow the preparation and presentation style of Corley and Gioia (2004), Gioia et al. (2012), and Villena and Gioia (2018). Each data structure table highlights the emergent 1st-order categories (left column) leading to the 2nd-order themes (middle column). From the latter, we generate the overarching dimensions (right column).
4.1. Case 1: physical SCA
As the world became aware of the pandemic's inescapability, some buying companies began delaying or stopping payments to suppliers. To weather the crisis of undetermined length, they would need liquidity and WC. The immediate consequence was that suppliers had to cope with more unpaid invoices than usual. Suppliers became threatened and stressed. Some suppliers offered substantial discounts to get paid on time or demanded prepayments. LSPs supporting buyers and suppliers also suffered during the early days of the COVID-19 crisis because of the downturn. The flow of many types of products slowed to a trickle. The irony was that buyers still needed suppliers to ship goods. Just because they were delaying payments did not mean they did not need deliveries. In turn, many buyers began playing “hide and seek” with their suppliers. On the one hand, some of them hid from suppliers pleading for payments. On the other hand, they closely monitored suppliers for continuity of supply. Buyers often reduced the size of the orders, in fact, when their suppliers had already produced them. The consequence was suppliers taking on speculative inventory risk at a time of great uncertainty.
There were a few bright spots. Large buying companies such as Walmart, BHP, Unilever, and ASML took proactive measures to help suppliers, especially SMEs, be sustainable. Walmart waived or discounted rents for SMEs and adjusted delivery windows. They also adjusted their SCF program so that more suppliers qualified and would get paid more quickly, and BHP began paying suppliers right away. Unilever and ASML set aside cash to pay their suppliers early. Another bright spot was LSPs recovering from the early downturn from the COVID-19 crisis. They did well with consumers ordering more from home. However, this recovery was not without irony. One of the reasons warehousing businesses picked up was that buyers and suppliers needed extra storage for goods they could not sell.
In Case 1, we were able to identify 20 1st-order categories, which were then aggregated to a total of 6 2nd-order themes (see Table 4 ). The statement that buyers focusing on their own reflects buyers’ responses to preserve themselves. To do that, they have generally extended payment terms with their suppliers as a way of increasing liquidity. Such efforts can be enforced spontaneously and are usually interest-free. This trend of delaying payables to suppliers can already be seen in published financial figures. For example, from 2019 to 2020, DPOs extended from 60 to 76 days for Boeing and from 79 to 112 days for Coca-Cola (EuroFinance, 2020). These buying companies clearly were risking reputational damage.
Table 4.
Data structure Case 1.
| 1st-order categories | 2nd-order themes (practices) | Aggregate dimensions |
|---|---|---|
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Buyers focusing on their own | Reacting to survive |
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Buyers risking reputational damage | |
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Suppliers coping | |
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Ensuring continuity of supply | Elevating practices |
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Buyers sustaining suppliers | |
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Discovering new processes | |
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Interestingly, it seems that smaller buyers with fewer resources tend to pay their suppliers more on time. The reason may purely be economical. Suppose a smaller company with fewer financial resources suddenly extends its payment terms to suppliers. In that case, this may be interpreted as a sign of economic weakness than would be the case with large multinational companies. That is why we observed suppliers coping a lot with the consequences of the pandemic situation. Throughout the global economy, lockdowns during the COVID-19 crisis have caused sales to collapse abruptly. As our data show, SMEs struggled with unpaid invoices, and they were under pressure to pay their suppliers on time. Besides, there was a threat of numerous production stoppages due to a lack of materials. The consequence was suppliers taking on speculative inventory risk. To avert the threat of insolvency, suppliers have pursued two thrusts. Either they demanded their customers (the buyers) for prepayments. Or they have taken the initiative by offering customers a discount to get paid quickly.
Due to the COVID-19 crisis, major manufacturing hubs in China, such as Wuhan and Shenzhen, were locked down in the first quarter of 2020. Additionally, the borders were temporarily closed. As a result, buying firms around the world found themselves running out of inventories. Lockdowns in Europe and the US followed relatively quickly, which further worsened the supply situation. Consequently, the buyers globally tried to ensure continuity of supply. Furthermore, our analysis showed buyers and suppliers could not move inventory fast enough to replenish empty shelves. Buyers and suppliers increased safety stock, and LSPs were also affected. They notably experienced lowered demand first right after the COVID crisis began in early 2020 but soon faced an increasing demand afterward.
For one reason or another, we saw buyers sustaining suppliers. During the crisis, some buyers relaxed the requirements for suppliers. Operationally, they have adjusted individual delivery requirements, such as batch sizes or delivery dates. Financially, suppliers did not have to meet otherwise normal creditworthiness or rating requirements when making award decisions. The financial support measures also included early payments to suppliers. Some buyers took a more differentiated approach by offering varied payment terms to different suppliers. In some cases, SME suppliers were given preference with shortened payment terms. The crisis has also led to new and innovative patterns. We call it discovering new processes. One of the innovations observed was suppliers adopting digital forms of communication to reach buyer accounts payable. This can be seen as an attempt by supplier companies to stabilize their own financial situation through proactive approaches and technology use. LSPs also tried to innovate during the crisis. A logistics player tried to expand their business model to include financial services. In doing so, this LSP moved into the fintech business. Such financial add-ons included, for example, interim financing of inventories or the integrated handling of invoicing and payment processes.
4.2. Case 2: financial SCA
The struggles of buyers and suppliers sparked an opportunity for financial SCA. Fintechs saw a surge in demand because both buyers and suppliers needed their services. For instance, one fintech, PrimeRevenue, facilitated a record $10B in early payments in March of 2020. Another fintech, C2FO, estimated the liquidity needed to be $16T, with 75% of suppliers lacking cash globally.
The COVID-19 situation actually caused fintechs to increase their market value, and investors found fintechs attractive investment opportunities. Naturally, banks caught on to this trend and began introducing fintech services to their clients as well. The crisis hastened banks to start behaving as fintechs. For instance, banks would underwrite the loans to suppliers operating on purchase orders from the buyers (i.e., pre-shipment financing). Banks would actually absorb some of the risks by investing directly in the SCs. This shift was necessary because, with the crisis, banks saw a downturn in their cash management side of the business, and they remedied that situation by moving into the fintech business. However, lowering the water level by the COVID-19 crisis began exposing rocks hidden under the surface. For instance, a few clients of a fintech called Greensill collapsed due to the COVID-19 crisis. The bank, Credit Suisse, that had put up the money demanded repayment from Greensill. Overall, there was both optimism about new opportunities and nervousness about defaults.
In the second case, which relates to the FSC players, we were able to condense eight 1st-order categories down to a total of two 2nd-order themes from the data (see Table 5 ). One thing that stands out in our data is that banks have tried to mimic fintechs’ business models during the crisis. Some banks may fear being substituted by fintechs in the long-term (Erel and Liebersohn, 2020). During the COVID-19 crisis, fintechs experienced increased demand, and banks also wanted to enjoy this demand growth. This increased attention to fintechs and their range of services was also reflected by investors, who favored them for their increasing value (Cumming and Schwienbacher, 2018; Kolokas et al., 2022).
Table 5.
Data structure Case 2.
| 1st-order categories | 2nd-order themes (practices) | Aggregate dimensions |
|---|---|---|
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Banks mimicking fintech business models | Evolving processes and business models |
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FSPs adapting to increased uncertainty | |
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We found evidence that FSPs adapted to increased uncertainty during COVID-19. The data show that FSPs recognized that both opportunities and risks (co-existence of gains and pains) are simultaneously associated with the COVID-19 crisis. On the one hand, opportunities exist for FSPs, such as increased demand for liquidity from SCA. Threats of revenue losses elsewhere (e.g., mortgages) could be coped with by awarding trade and SCF credits. However, FSPs saw the pandemic's volatility as a threat to their SCF business. For example, the uncertainties caused by the COVID-19 crisis have also led FSPs to postpone specific initiatives, such as standardization activities.
4.3. Case 3: regulatory and governmental agency actors
The regulatory and governmental agency actors exhibited a heightened sense of keeping things afloat during COVID-19. Their interventions were well beyond the usual level given the seriousness of the circumstances. Many of them decided to finance stimulus packages to encourage goods and services to move across SCs. For instance, IMF and the US Congress both pledged $50B and $8.3B respectively to help the economy. In the UK, the government set up an intermediary to buy commercial paper from large buyers to facilitate a faster payment to SME suppliers’ invoices.
The governments tried to bring various options to banks and other SCF actors to provide more liquidity to buyers and suppliers. The US Federal Reserve Bank and other international banks sought to instill stability in SCs’ economic activities. The U.S. Federal Reserve Bank cut the interest rate by a full point and announced a new term asset-backed securities loan facility. Such funding schemes were designed to increase lending. Supranational agencies, such as the World Bank, tried to stabilize SC activities in developing economies. For instance, they issued loans to regional trade and development banks who would then lend money at lower rates.
As the COVID-19 crisis drew attention to the payment extension programs at buying companies, the regulatory and governmental agency actors voiced concern about the classification of SCF activities on books. The issue was the inconsistent accounting treatment of SCF transactions, for instance, and whether to record it as a loan or WC. The Australian government was particularly vocal about the lack of transparency in SCF practices and the potential for how SCF could be misused. The government promoted a policy of 30 days net for the SME suppliers so they could stay afloat.
In Case 3, we have seven 1st-order categories and two 2nd-order themes (see Table 6 ). The first practice that emerged from our analyses relates to how regulatory agencies intervened with temporary initiatives. Around the globe, various regulatory agencies were tasked with generating monetary stimuli in the markets. For instance, as countries used lockdowns to suppress the virus's spread, fiscal support measures were introduced to help businesses stay afloat. Regulatory agencies set up intermediaries to release liquidity. Governments themselves were active during COVID-19 by persuading FSPs in their countries to provide more liquidity to the economy. For example, the PPP loans backed by the U.S. government were disbursed by financial institutions (i.e., banks). Besides, actions were also taken at the supranational level. In particular, actors such as the World Bank or development banks (i.e., Asian Infrastructure Investment Bank or New Development Bank) should be mentioned in this context.
Table 6.
Data structure Case 3.
| 1st-order categories | 2nd-order themes (practices) | Aggregate dimensions |
|---|---|---|
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Regulatory agencies intervening with temporary initiatives | Guiding economic policies and practices |
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Regulatory agencies raising a red flag about SCF practices | |
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The second theme includes the collection of concerns about some SCF solutions. We observed how regulatory agencies raised a red flag about selected SCF practices. Some of the known challenges relate to classification issues and also to disclosure issues. First, securities and exchange commissions (SEC) could require a buyer to reclassify its trade payables as short-term bank debt. This would potentially impact loan covenants and leverage ratios of the company (Gustin, 2021). Second, regulatory agencies, particularly rating agencies, criticized the lack of SCF disclosures. They claimed that higher disclosure standards would give investors and auditors a better basis for making informed decisions concerning the companies’ risk, including financial position, liquidity, and cash flows (Basquill, 2020a). To date, the standard-setting agencies have not been able to issue guidance.
4.4. Case 4: Interactions between physical and financial SC actors
The financial SCA were playing both ends for the middle. To relieve the tension between the buyers and suppliers, in which the buyers want to delay payment and suppliers wish it sooner, the fintechs and banks were trying to offer innovative solutions. Much of the innovations came not as a result of prolonged creative engagement but as a remedy for the impending problems caused by COVID-19. One example of a key innovation is the digitization of information. Its implementation accelerated because of the crisis. For example, LSPs began adopting digital tools (i.e., Docu-sign) for digitized paperwork to facilitate cross-border shipping. Promoting a digital-first strategy, a freight forwarder called Beacon moved into fintech business. Since they had the relevant data on various players in the SC, they believed they were in the proper position to help suppliers by offering financial services along with shipping services. While this company integrated services in its own offering, other companies integrated across their company boundaries. To help retailers and their suppliers, a new partnership emerged between PrimeRevenue, a fintech, and a UK co-op organization made up of many retailers at the behest of Barclays Bank who would provide funding for the SCF program.
Further, there were considerable efforts to help SMEs in the long tail for the SC buy and supply goods. Alibaba offered on their online marketplace an innovative solution through which small buyers could also enjoy late payments (i.e., 60-day payment terms) that would have been reserved for large buyers with leverage. Mybank, a Chinese online bank part of Ant Group, rolled out a plan to help SMEs in rural China, offering financing through AI technologies that can be accessed from mobile phones. Some fintechs began offering financial services specifically for tier-2 and tier-3 suppliers. The circumstances these smaller suppliers are rather unique. They are often placed under the vendor-managed inventory program, and they cannot generate invoices until their parts are used by the buyer.
There have been cases where the FSC actors played both ends against the middle. A US-based fintech was cited by the Australian government for using AI to compute how much it can “squeeze” suppliers. Perhaps, a less obtrusive strategy was rolled out by an aerospace engine manufacturer in an SCF strategy that involved suppliers to co-invest in engine development and receive part of the revenue generated after the sale, resulting in the delay of paying the supplier. Fintechs continued to engage in innovative practices. For instance, they would generally use cash from the buyer to conduct dynamic discounting. However, with cash-strapped buyers, some fintechs found external funders. Thereby, the buyer would take short-term loans to pay their suppliers but gain from their suppliers’ discounts.
In the fourth case, we took a closer look at the collective activities of fintechs and banks. The data structure presented in Table 7 shows how we arrived at a total of four 2nd-order themes from the 17 1st-order categories. A first practice has emerged regarding the possibilities of early payments from buyers to suppliers created by FSP. Both fintechs and banks have implemented numerous initiatives during the crisis to strengthen the SC's liquidity situation. In our data, we also observed that support did not stop at tier-1 suppliers. Interestingly, we also identified intances where financial services were offered to tier-2 and tier-3 suppliers. Thus, a new type of phenomenon involving the use of new technologies, such as blockchain, known as “deep-tier financing,” was increasingly gaining traction. However, we saw more than a drive for early payments. We have also been able to learn how FSPs helped some buyers establish new ways to extend payables. However, this phenomenon was not observed across all industries, only in those that got hit particularly hard by COVID-19, such as airlines. Efforts to extend payments included novel technologies such as artificial intelligence (AI), which can help determine which suppliers are more likely to accept and tolerate a late payment than others.
Table 7.
Data structure Case 4.
| 1st-order categories | 2nd-order themes (practices) | Aggregate dimensions |
|---|---|---|
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FSPs facilitating early payments from buyers to suppliers | FSPs applying ambivalent payment mechanisms for buyers and suppliers |
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Buyers using FSPs devising new ways to extend payables | |
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FSPs offering in-depth integration with buyers and suppliers | FSPs developing novel practices and models |
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FSPs participating in new business models for physical SCA | |
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We saw FSPs offering in-depth integration with buyers and suppliers. During the crisis, there was increased partnering between banks, FSPs, and retailers. Solutions we discovered included online market offerings for SME business to extend their payment agreements, platform offerings from fintechs to finance both suppliers and consumers, and new solutions that increase transparency and efficiency in payments (e.g., credit card). We also saw the participation of FSPs in new business models of physical SCA. On the one hand, banks proactively approached buyers and suppliers hit by the crisis and involved other investors in the process. On the other hand, larger and financially stronger buyers sought cooperation with banks to be able to support their smaller SME suppliers. Furthermore, we found indications of the expansion of their own business model into new spheres. Fintechs, for example, were active in the real goods sector during COVID-19. These financial players were actively involved in the management of personal protective equipment (PPE). Actors in the physical SC expanded their business field into finance. For example, some LSPs have added fintech services to their traditional portfolio of transportation, handling, and warehousing services during the crisis.
4.5. Case 5: Interactions between financial SC and regulatory and governmental agency actors
The regulatory and governmental agency actors were trying to both control and help the financial SCA. They monitored financial actors to ensure they were adequately engaging SCs under the crisis. Controlling involved the measures encouraging certain practices while discouraging others. We do not mean by measures that the governments were passing new legislation. If anything, they were promoting the banks and fintechs to voluntarily provide liquidity in SCs. It was as if the governments were saying, “help us help you, so you can help them.” Encouraging targeted banks to extend loans to troubled companies. They did so by invoking the countercyclical capital buffer that banks use to help alleviate market downturns. Further, central banks promoted SCF programs. For instance, the European Central Bank worked to ensure enough liquidity to support various forms of SCF programs. There were state-backed loans and central banks purchasing assets through their pandemic emergency purchasing program with SCF assets. Discouraging certain practices was also part of the equation as well. In particular, the Australian government was preventing fintechs from supporting big buying companies mistreating smaller suppliers with unilateral term extensions beyond 30 days during COVID-19. Rating agencies discouraged fintechs and banks from supporting SCF practices that could mask financial stress in companies. S&P Global was particularly concerned whether larger buying companies were intentionally hiding problems by engaging in SCF, which could make it difficult to assess how much debt the buying company was carrying.
Besides controlling, helping was also part of this case. Regulatory and governmental agency actors tried to help the financial SCA do their part to keep SCs as healthy as possible. They sent signals to instill confidence in financial markets. They explained that governments were prepared to serve as a provider of additional credit insurance. Central banks lowered interest rates to calm markets. For example, the Bank of England used quantitative easing to help stimulate lending by banks. The Bank of China took things one step further. They required their local banks to lend money to SMEs that included what they called micro-businesses. The Federal Reserve in the US updated the term asset-backed securities loan facility (TALF) program from the economic crisis about ten years ago. The goal was to inject liquidity into banks, so they can lend more money. Overall, regulatory and governmental agency actors were firing on all cylinders to control and help the financial SCA.
We identified ten 1st-order categories, which were grouped into three 2nd-order themes (see Table 8 ). The first theme addresses the concerns expressed about SCF practices during COVID-19. In particular, rating agencies were concerned about SCF potentially hiding problems. The rating agencies accused SCF of providing a way for companies to conceal financial stress. Such concealment poses an increased risk to investors. We saw numerous government attempts to discourage fintechs from helping large and financially strong companies exploiting SME suppliers.
Table 8.
Data structure Case 5.
| 1st-order categories | 2nd-order themes (practices) | Aggregate dimensions |
|---|---|---|
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Concerns being expressed over SCF practices | Exposing dark side |
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Central banks injecting liquidity | Stabilizing financial markets |
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Governments intervening to calm financial markets | |
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We also observed the massive liquidity injections by central banks. Several banks have run into difficulties and had to adapt their lending practices, for instance, by reprioritizing credit lines. By injecting liquidity, central banks tried to ensure that FSP and buyers and suppliers can survive the resulting economic situation. Through a stimulus package, central banks supported SCF practices and saved them from collapse. Provisions taken by major financial institutions indicate they believe that loss rates will exceed those from the economic crisis in 2008/2009 (McIntyre et al., 2020).
More broadly, governments intervened in the pandemic to calm financial markets. An assortment of measures was initiated by governments. The activities undertaken by governments during the crisis extended further than usual. For example, the governments’ behavior was not only intended to create confidence in the financial markets. They were also actively intervening. For example, they encouraged banks to adjust their lending practices to the benefit of ailing companies. They also influenced their central banks, which lowered interest rates to avoid a credit crunch or actively support SCF programs.
4.6. Case 6: Interactions between physical SC and regulatory and governmental agency actors
The regulatory and governmental agency actors recognized the importance of keeping the core of SC activities healthy. They promoted SCF programs to bring liquidity to physical product SCs so that the buyers and suppliers could function and continue to produce goods and services. Central banks and regional development banks began acting as providers of SCF-related solutions to cash-strapped buyers and suppliers. They actively promoted faster payments of SME invoices, and one way was to purchase accounts receivables from SMEs. The U.S. Treasury Department provided direct loans to buyers and suppliers through paycheck protection programs (PPP). China Federation of Industry and Commerce launched an SCF service, in collaboration with several banks, that would provide close to $1B to over 100 buyers and suppliers in loans. The Bank of England rolled out a stimulus package to help SME suppliers that were affected by COVID-19 to be able to borrow at low interest rates.
In particular, Asian Development Bank actively facilitated medical and pharmaceutical goods transactions across the SC. They engaged in mapping SCs of critical goods needed for ventilators, N95 masks, and other essential goods. They then published the maps to the buyers and suppliers across the SC. EXIM bank of the US provided financial support to buyers and suppliers through their own SCF programs, loan guarantees, and credit insurance. The bridge-financing they offered would facilitate exports through short-term financing until the liquidity returns from private sectors. Nevertheless, it was clear that reviving the core was not merely to help the financial liquidity of the physical SCA. It was ultimately to ensure the well-being of the population. For instance, EXIM bank linked financial support with humanitarian aid. When the suppliers were poised to export critical medical supplies to make a profit, those suppliers were warned by EXIM bank that they would be excluded from financing coverage. These entities were reviving the core so that the stability and well-being of the society would be maintained.
Based on eight 1st-order categories, we derived four 2nd-order themes (see Table 9 ). We observed that governments directly provided money to suppliers. For example, central and regional banks have emerged as providers of SCF solutions. Besides, governments have facilitated lending to SME suppliers. We recorded how governments improved existing instruments to help buyers and suppliers. For example, the EXIM bank was expanded to help buyers and suppliers with their trade transactions. We uncovered that governments have established guidelines to protect SME suppliers. Under the guise of SCF, unethical business practices, bullying and manipulation of suppliers, and submission tactics can be hidden (ASBFEO, 2020). For example, a limit was set on the payment terms against suppliers. Finally, we observed how governments have regulated medical goods’ flow. Regional development banks (i.e., Asian DB) have proactively engaged in the trade of medical and pharmaceutical goods. They deliberately facilitated financing for suppliers exporting critical medical goods.
Table 9.
Data structure Case 6.
| 1st-order categories | 2nd-order themes (practices) | Aggregate dimensions |
|---|---|---|
|
Governments providing money directly to suppliers | Supporting supply chains financially |
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Governments enhancing existing solutions to help buyers and suppliers | |
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Governments setting policies to protect SME suppliers | Enabling supply chains administratively |
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Governments regulating the flow of medical goods | |
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5. Cross-case analysis: from data structures to a model
Next, we look across all six data structures presented previously to create a grounded theoretic model (Corley and Gioia, 2004; Gioia et al., 2012; Villena and Gioia, 2018). The goal is to use the collected data represented in the within-case analyses to create an overall model of the SCF ecosystem's response to the COVID-19 crisis. Our data covers the early months of this rather abrupt onset of the crisis with the lockdown measures, border closures, supply disruptions, demand shortfalls, and the like.
We have identified key actors within the SCF ecosystem and interventions at different levels articulated in our within-case analyses. Our emergent model consists of three large sections—the locus of interventions, patterns of activities, and interactions across sets of actors. The locus of interventions identifies the key actors across different levels of the SCF ecosystem— physical SCA, financial SCA, and regulatory and governmental agency actors (i.e., Cases 1, 2, and 3). The patterns of activities reflect the within-case analysis outcomes. We have shown the overarching dimensions from those cases that capture specific behavioral patterns. Lastly, the model's underlying dynamic nature is captured in the interactions across sets of actors, reflecting Cases 4, 5, and 6 showing how influence occurred across different levels of the SCF actors.
5.1. Locus of interventions
We begin with an initial list of key actors in the SCF community based on the literature (see Fig. 1). From there, we have incrementally modified them as the data unfolded in terms of who they are and how to classify them into different groups. Such a classification into largely homogeneous actor groups (Doty and Glick, 1994) should enable a better understanding of the various intervention levels. As such, the locus of interventions addresses the “who and where” (left side of Fig. 4 ).
Fig. 4.
Grounded theoretical model.
The first level under the locus of interventions contains the physical SCA. This group is the source of the demand for SCF solutions. As such, this group is at the center of all SCF activity. Buyers and suppliers form the nucleus of all SCF activities, and LSPs are intimately integrated with buyers and suppliers to facilitate the moving of goods and services. As discussed in Case 1, this group of actors struggled during the crisis. They, therefore, initiated measures for SCF solutions themselves or changed their demand behavior for SCF solutions. The second level occurs with the financial SCA. This group tried to meet the demand from the physical SCA. As SCF solutions providers, this group created opportunities, as discussed in Case 2. The third level consists of the regulatory and governmental agency actors, including governments, regulatory agencies, and credit rating agencies. They also include quasi-governmental regulatory organizations such as central banks. During the crisis, every country in the world enlisted some governmental or quasi-governmental actors to support the economy. The third group primarily tried to keep things flowing, as described in Case 3.
5.2. Patterns of activities
Emerging patterns of activities are listed under the “what” section of the model (middle of Fig. 4). These are as the overarching dimensions under the within-case analyses leading to the Gioia data structures (Table 4, Table 5, Table 6). They correspond with the four loci of interventions in Cases 1, 2, and 3.
Reacting to survive. The disruptions and business restrictions have had a massive impact on the physical SCA. They were hit by the crisis both on the supply side and on the demand side. In general, buyers and suppliers have been forced to reduce staffing to cut costs, due in part to a dramatic fall in demand for goods and services. For some industry sectors, such as hospitality, tourism, and airlines, most revenues were wiped out (Dua et al., 2020). Thus, during the crisis, buyers' and suppliers’ priority was to preserve liquidity. With sales plummeting due to lockdowns and credit markets tightening, they grappled with higher financing costs and more stringent borrowing structures. They were short on money and found it challenging to meet all WC requirements (Demmou et al., 2020). Buyers and suppliers had to consider alternative ways to fund their businesses.
Elevating SCF practices. In addition to the efforts to survive the crisis, the already known SCF practices have been boosted by the innovative responses of the physical SC players. For instance, suppliers learned to adopt digital forms of communications to reach the buyer's accounts payables. Further, the physical SCA appear to have recognized that SCF is an effective means to meet the sudden liquidity challenges. For instance, some buyers set aside cash and shortened payment terms to suppliers through SCF. Some LSPs expanded their services to include SCF solutions.
Evolving processes and SCF business models. The demand for SCF solutions triggered by COVID-19 affected the financial SCA in many different ways. While fintechs saw an increase in demand for their services, banks first experienced reduced revenue in the cash transaction business. Consequently, investors began favoring fintechs for their increased value and banks made up for their losses by expanding into the fintech business. Overall, the financial SCA recognized that the crisis was also an opportunity. They further developed WC processes and SCF business models to benefit from the increased demand.
Guiding economic policies and SCF practices. The regulatory and governmental agency actors were also found to have become much more involved in the crisis. They slipped out of their otherwise relatively passive orientation and started to intervene with temporary measures. For instance, regulatory agencies pledged monetary stimuli and set up intermediaries to release liquidity. Overall, governments actively conducted economic policies, including SCF practices. Further, supranational agencies such as the World Bank focused on stabilizing markets in less developed countries.
5.3. Interactions across sets of actors
At the center of the model are buyers and suppliers with LSPs supporting their activities. Most immediately, the financial SCA’ impact what these physical SCA do. In particular, there was ambivalence in what they did as if they were playing both ends (see Case 4). Then, we have regulatory and governmental agency actors controlling and helping the FSC activities (see Case 5). Lastly, we see the regulatory and governmental agency actors working to revive the physical SCA (see Case 6). In Table 7, Table 8, Table 9, one group of actors impacting on the other is depicted with an arrow with the core dimensions capturing the “how” (right side of Fig. 4).
Financial SCA applying ambivalent payment mechanisms. During COVID-19, both buyers and suppliers were hurting for cash. The ambivalence comes from the fact that fintechs and banks helped buyers find new ways to extend payables to suppliers. Still, they also facilitated early payments to the suppliers. To help suppliers, fintechs stepped in to support suppliers connect with buyers and funders. Some fintechs emerged specializing in helping SME suppliers further upstream in SCs.
Financial SCA developing novel practices and models. The financial SCA found new ways to integrate with buyers and suppliers. For instance, fintechs offered new solutions to improve transparency and efficiency in business-to-business credit card transactions. Some fintechs learned to act as a trading company by buying and selling personal protection equipment. Some banks worked with buyers to help finance suppliers of small contracts.
Regulatory and governmental agency actors stabilizing financial markets. During the early days of the crisis, the regulatory and governmental agency actors rose to the occasion and were highly active far beyond usual. For instance, governments intervened to calm the financial markets. Their agencies put pressure on banks to extend loans to troubled companies. Central banks lowered interest rates to prevent a credit crunch. Central banks supported the SCF practices with stimulus packages. In some instances, they injected liquidity into banks so these banks can then lend more money out.
Regulatory and governmental agency actors exposing the dark side. The government and monitoring actors voiced concerns over certain SCF practices. In particular, governments tried to discourage fintechs from enabling buyers to ‘bully’ SME suppliers into extending payment terms or offering discounts. Rating agencies also voiced concerns that certain SCF practices might potentially be masking financial stress. Such a potentially dark side of SCF practices under crisis was highlighted.
Regulatory and governmental agency actors supporting SCs financially. The state actors have been directly supporting actors in the physical SC with financial resources. For instance, governments provided SCF solutions to buyers and suppliers. They also channeled cash or loans directly to troubled suppliers. Wanting to protect jobs in the physical SC, the EXIM bank of the US tried to protect large buyers and their suppliers from remaining solvent.
Regulatory and governmental agency actors enabling SCs administratively. The state actors created policies to protect SME suppliers. For instance, the Australian government tried to enforce a limit on payment terms toward suppliers. Bank of China, in particular, intervened to help improve liquidity in SME suppliers. Further, governments regulated the flow of medical goods such as drugs and PPEs. Regional development banks (i.e., Asian Development Bank) actively facilitated medical and pharmaceutical goods. The EXIM bank tried to protect the domestic physical SCs of critical medical supplies by excluding financing to suppliers exporting those supplies.
6. Post-hoc investigation
The post-hoc investigation results cover two purposes: offering qualitative validation of our grounded theoretical model and painting a picture of what the future may hold for the SCF ecosystem (see Table 3). The importance of triangulation and validation has been stated numerous times (e.g. Golicic and Davis, 2012; Singhal et al., 2008) and has been a formula followed by a number of scholar in the recent past (e.g. Ried et al., 2021; Villena et al., 2021; Thomas et al., 2021). As previously mentioned, the 28 expert interviewees had job titles ranging from CEO, other C-level executives, general manager, vice president, and director. For more details, see Appendix G.
Table 3.
Procedure of the post-hoc study.
| Step | Key Features |
|---|---|
| Post-hoc instrument creation |
|
| Creation of the panel of experts |
|
| Interviewed the panel of experts |
|
| Compilation of responses from the panel of experts |
|
| Reporting findings |
|
6.1. Examining our grounded theoretical model
We compared the interviewee responses to our grounded theoretical model. Overall, consistent with the themes and dimensions in our model, the interviewees confirmed the following. One of the most commonly expressed notions is the lengthening of payables and the shortening of receivables for buyers and suppliers. In other words, buyers were looking to hold on to their cash longer, while suppliers were looking to get paid sooner. There was also increased demand for financing solutions by buyers and suppliers that FSP had to find ways to accommodate. Lastly, governments, being concerned with minimizing the economic decline during COVID-19, have taken active steps in ensuring that companies have access to liquidity. One significant event was the Bank of England's decision to recognize SCF assets as collateral for lending. The interviewees believed that this particular action strengthened the market for SCF assets and ensured adequate liquidity.
A few noteworthy findings emerged during the interviews that are not as clear in our grounded theoretical model. One issue has to do with the difference between traditional banks and fintech companies. The main difference between the two is related to the source of the funding. Specifically, a fintech can gather financing from multiple sources, while a bank predominantly relies on its own balance sheet. This enabled fintechs to be more flexible during the crisis. For example, if one source of funding diminished, others would fill the gap. This became especially clear when the appetite for cash was increased as it was during the first few months of the crisis. Another exciting trend comes from buying companies. Some of them voluntarily paid their smaller suppliers early during the most active phase of the COVID-19 crisis, April through June of 2020. This happened in industries where the buyer was not heavily affected by the pandemic or buyers with solid financials.
6.2. Moving forward
Regarding the future for the SCF ecosystem, several major trends were discovered. One major trend is the increasing scope and popularity of fintech services. We saw evidence that demand for SCF and fintech services increased during the crisis. This brings up the question of whether new companies are entering the market and whether those new business models are changing the ecosystem. For example, some fintechs chose to collaborate with LSPs. These collaborations would enable buyers and suppliers to have logistics services combined with financial services. Similarly, the combinations of different services seem to provide a win-win-win for the two service providers independently and for their customers.
Another future trend is digitalization. First, internally, companies with paper-based processes were forced to become digitalized because virtually all governments worldwide asked companies to allow their workers to work remotely. This move to remote work required that employees complete their work from home, as non-digital processes became unsustainable. At the inter-firm level, many global trade transactions that required paper-based documentation became difficult to execute. Therefore, the push for digitalization became inevitable. Lastly, the recognition that data is key to several SCF improvements has become more acute during the pandemic. Establishing an SCF program requires a fair amount of IT work. For instance, blockchain technologies are now being used to reach SME suppliers deep in the SC in an effort our interviewees referred to as “deep-tier financing.” Our informants generally believed that COVID-19 would be an inflection point in the development of digitalization. Some dynamic discounting providers are looking to use machine learning and artificial intelligence to streamline their offerings. Blockchain was often mentioned as the foundation of future innovation.
The final big topic that emerged from the interviews was in which ways the SCF ecosystem would expand. The most commonly mentioned extensions of traditional SCF were purchase order (PO) financing, inventory financing, and deep-tier financing. Some respondents viewed traditional reverse factoring-based SCF approaches as reasonably well understood and developed. In PO financing, the funding would be received by the supplier much earlier than reverse factoring. Another form of funding is inventory financing, enabling a company to have inventory held by a third party, removing it from the buying company's balance sheet, yet ensuring adequate supply and risk management. In particular, the supply disruptions during the pandemic have increased the need for such solutions. Further, deep-tier financing is about funding suppliers beyond tier 1 by leveraging a downstream buyer's credit rating. The solutions in this space are all built on blockchain technology. During the crisis, the need to help SMEs on the long tail of SCs became acute and gave rise to deep-tier financing.
Blockchain is seen as one of the key technologies that will enable inventory financing because it is critical that stock is correctly identified and monitored. Blockchain technology can be used in the context of SCF ecosystems to improve inventory management and thus liquidity requirements in the supply chain. This is because both the actors in the physical supply chain (e.g., supplier's suppliers, suppliers, manufacturers, and retailers) and the actors in the financial supply chain (e.g., banks, fintechs, or insurance companies) gain increased transparency about the transactions permanently recorded through the use of blockchain. The stored records can be used to improve demand forecasting for inventory, prove the authenticity of products, facilitate the provision of required liquidity, or automate transactions via the use of smart contracts (e.g., invoice audits and payments). Further, deep-tier financing is about funding suppliers beyond tier 1 by leveraging a downstream buyer's credit rating and promises-to-pay. The solutions in this space are all built on blockchain technology. Based on the blockchain, a payment guarantee from the end buyer can be recorded in the form of a non-falsifiable token (NFT). This NFT can be passed along the supply chain – from the buyer to the supplier and further to the sub-supplier and his suppliers. The “tokenization” is verified at each stage of the supply chain, so that the various companies along the chain can prove that they are actually being paid. During the crisis, the need to help SMEs on the long tail of SCs became acute and gave rise to deep-tier financing.
Overall, the COVID-19 crisis proved to be a watershed moment for the SCF ecosystem. Before the pandemic, it was a growing area of finance, but also one that was relatively obscure. Some major companies had established reverse factoring programs as a way to reduce their reliance on outside funding, and there were a growing number of companies offering services. However, it wasn't an ecosystem. Instead, it could be described as a community of actors that were loosely related to each other. The desperate need for liquidity and the lack of other solutions fully up to the task of keeping SCs gave rise to the SCF ecosystem.
7. Discussion
The study has identified how relevant actors in the SCF ecosystem behaved during the COVID-19 crisis (RQ1), how they interacted among them as an ecosystem during the early stages of the pandemic (RQ2), and implications for the use of SCF in the future (RQ3). In the course of uncovering the SCF business service ecosystem, the essence of SCF has come to the fore. It was as if the COVID-19 crisis unveiled the curtain and the fourth purpose of SCs came to light. Beyond the three purposes of the SC, “to source, make, and deliver,” the fourth purpose of “to fund” has become apparent.
7.1. Theoretical implications
Our study confirms how SCF practices and solutions provide organizations and their SCs with liquidity. While this theoretical contribution is not novel, this research supports the need for ability to generate liquidity. Demand remains relevant and support the SCs’ WC needs, with technologies playing a vital role (Hofmann and Johnson, 2016; IFC, 2020). We demonstrate the importance of the WC and the role of liquidity in times of global crisis (Kesimli and Günay, 2011; Ramiah et al., 2014; Haron and Nomran, 2016; Tsuruta, 2019), consistent with papers in this area (Lekkakos and Serrano, 2016; Wandfluh et al., 2016; Wuttke et al., 2019; Kouvelis and Xu, 2021).
Extending the literature, we first identified the relevant actors in the SCF community and how they form a business service ecosystem. Beyond a descriptive and conceptual presentation of the SCF ecosystem (Bals, 2019; Templar et al., 2020), our study shows evidence regarding its specifics. During the crisis, even FSPs received support from governmental agencies. These non-generic complementarities in the SCF ecosystem were necessary to ensure the flow of financial resources from drying up. The dynamic behaviors of the SCF ecosystem are complex and uncertain (Mäkinen and Dedehayir, 2014; Basole et al., 2015). For instance, different corporate buyers adopted different practices toward their suppliers during the crisis. They vary from self-centeredness on the one hand (trying to unilaterally extend the payment terms to the suppliers) to a more collaborative view on the other hand (trying to support the suppliers financially). This complementary behavior is moderated by the industry affiliation, the importance of suppliers, and cash constraints.
In Fig. 5 , the SCF ecosystem interactions are described. Activity is triggered by the need for liquidity. The crisis caused by COVID-19 slowed down the flow of money within SCs, increasing the need for SCF mechanisms to improve the cash flow. The SCF ecosystem then provided funding to companies with liquidity needs.
Fig. 5.
A model of ecosystem's dynamics in larger context.
What is unique about exploring the SCF ecosystem is not the response of a single actor or the interventions between specific groups of actors. The buyers and suppliers together engage in the co-production of services. They then joined in the SCF solutions by providing data and connect their IT systems with those of the service providers. The SCF ecosystem did not appear spontaneously; ecosystems result from deliberate experimentation and engineering from various actors (Jacobides et al., 2018). The SCF actors existed before COVID-19 and began adjusting to the new circumstances with the crisis's spread. COVID-19 was the trigger of change (Wu, 2021). This trigger activated the ecosystem. The global crisis impacted actor groups at various levels as an external shock.
We observed actions taken between organizations of different groups. Thus, with the “who and where?” question, the locus of interventions remains salient. Business service ecosystems consist of several autonomously operating organizations that interact with each other in a complementary sense (Gyrd-Jones and Kornum, 2013). As therapeutic measures addressing symptoms, the observed patterns correspond to SCF actors’ responses to the crisis. It is noticeable that there was not one singular conforming behavior by all actors in the situation (Gounaris and Prout, 2009); instead, the actions were distributed in the respective groups across the SCF ecosystem.7 The development of novel practices can be linked to the counter-cyclicality of innovations; for instance, recessions are a fertile environment for firms to innovate (Filippetti and Archibugi, 2011).8 As shown in our study, many innovations were driven by fintechs. They were open to technological advancements in times of crisis. Finally, numerous patterns of actions were identified concerning states and their institutions. What role the state should assume has been debated for decades. The neoclassical-liberal school and the Keynesian school typically oppose each other, with the former associated with a passive role and the latter associated with an active role. Without assigning regulatory agencies to one camp or the other, state interventions in the SCF ecosystem were examined. We observed states setting policy guidelines and taking risks to calm the markets. We would expect governmental agencies to retain a relevant role in the SCF ecosystem in the coming years as we anticipate continuing major disruptions (Flyvbjerg, 2020). In supply chain finance transactions at the operational level, different actors in the supply chain will take the lead. It is usually not small suppliers that take the lead, but either large buying companies or banks and fintechs. We have observed examples of both. Nonetheless, every therapy also has its side effects. The dark side of SCF practices was also discussed during the crisis. All in all, the patterns of activities in the SCF ecosystem form the core of the grounded theoretical model and can be expressed with the question “what?”
SCs, at their core, have been seen as serving the functions of “to source, make, and deliver” (see Supply Chain Operations Reference or SCOR model). We believe that there is another important function of SCs, and that is “to fund” (Rogers et al., 2020). SCF includes the strategies used by firms to fund internal operations as well as their external SC partners (Huff and Rogers, 2015). The complex dynamics present within the business service ecosystem underscore the impact SCF practices have on suppliers. So, this new purpose of the SC is to utilize the SC to fund the organization, and, using the organization's financial strength (i.e., lower cost of capital) to help fund suppliers. During COVID-19, this utilization of SCF as a tool to strengthen suppliers has clearly become visible in its criticality to the firms' survival and well-being.
Overall, COVID-19 has led to an increased level of interactions in the SCF ecosystem. The interactions describe the process of collaborative responses and can be characterized as “how?” The inter-actor group activities are aimed at dealing with the strained liquidity situation. The overreaching efforts of responses target buyers' and suppliers' financial rehabilitation via SCF practices. Thereby, the SCF community exhibited both business and innovation ecosystems traits in the crisis (Adner, 2006). This is where the complementary nature of the actors in ecosystems comes into play (Basole et al., 2015). The actors in different groups have synergistically complemented each other. Value creation in the SCF ecosystem has required orchestration among actors as a loosely coupled system (Dhanaraj and Parkhe, 2006). This can also be interpreted as a system's responsiveness to changing forces like a pandemic.
The SCF ecosystem's response has contributed to the economic processes to remain working during a crisis. The SCF ecosystem has proven to be robust. It has contributed to its own viability and that of various actor groups. The outcome of our model can be described as “when will the patient recover”? From the perspective of SCF, COVID-19 is not yet over by the time we conclude our data collection in late April 2021. Assuming that the SCF ecosystem's behavior is recursively dynamic, it would not be possible to map the ecosystem's development over time into post-COVID-19 situations. The selected investigation period refers broadly to the first wave of the COVID-19 crisis. Measures for subsequent waves, a possible new epidemic, or other significant crisis would be necessary to ensure sustainable viability. In this regard, the post-hoc study provided an insight into possible future directions which appear under future development. We anticipate blockchain technology and SCF will continue to integrate to offer expanded services in PO financing, inventory financing, and deep-tier financing. With accelerated digitization, the velocity and volume of SCF transactions will increase.
7.2. Managerial implications
As we have shown in this paper, the pandemic's global nature and the severe impact of the economic lockdown have profoundly impacted SCs. We have seen changes in the SCF community that otherwise would have taken possibly years. Decision-makers need to understand the integrated nature of their SC involving the financial flow. Without proper attention to the financial flow, SCs might be at risk for disruption. If the flow of payments upstream stops, suppliers might have to allocate deliveries. The worst-case would be the stoppage of their operations. This cascading ripple effect has the potential to spread across the entire SC. In the future, decision-makers must understand how the SC is financed and which practices can employ to solve short and long-term liquidity crises. It is also advisable that there are multiple ways in which companies can generate liquidity because, like in procurement, single sourcing of capital can lead to disruptions in times of crisis.
We have seen that buyers under a crisis have been eager to hold on to their cash longer, including lengthening payment cycles. This can have a potentially negative impact on the suppliers, who now have to find a way to make up that liquidity shortfall. This is where FSPs such as banks and fintech companies stepped in and provided much-needed relief. The characteristic of this business service ecosystem is that the different service providers in the SCF field complement each other. The prevailing complementarity serves to exploit synergies and drive innovation (e.g., new SCF practices like PO financing or sustainable supplier financing). LSPs also come into question here as SCF service providers. They should consider how far they can add financial services to their portfolio.9
From the perspective of governments, they sought to keep economies going by enabling the injection of liquidity. Doing so, they tried to remove barriers that were previously limiting capital to reach SCs in need. Policymakers should continue to use development banks to ensure sufficient liquidity in SCs, especially in regions and sectors where markets do not appear to be developing. In addition, they should create regulatory conditions for the global SCF ecosystem's frictionless functioning.
One crucial fact to remember is that all actors are part of the ecosystem. As a result of this, their decisions and actions are interconnected. This interconnectivity is of a long-term nature and is subject to interactions with other surrounding systems, triggered by an external shock such as a pandemic.
7.3. Limitations and future research
The procedure chosen by the study relied on the grey literature to formulate the observed patterns and interactions in analyzing the data. For example, during the coding process, there was a constant challenge to distinguish between concrete actions (“this really happened”) and speculations (“this could have been done”). Only activities that took place based on the team's judgment were included in the actual analysis. A few selective interviews with potential informants could have helped our data compilation. We made a few attempts to contact informants directly, but the tense COVID-19 situation did not allow this in the middle of the crisis.
The methodology used is not easily transferable to other emerging phenomena. The method requires an investigation subject that has a sufficiently sizeable visible horizon. Thus, a bottleneck of the process applied is the amount and variety of openly available information on a given topic. In this sense, our approach has some parallel to event studies' logic (Hendricks and Singhal, 2003), without explicitly addressing events’ timing. Additionally, there is a limitation in exploring only “coarser” patterns of behavior with the approach. The necessary immersion in the depth of the observation is lacking.
The study does not map a longitudinal section in the strict sense. The analysis covered the first wave of the COVID-19 crisis. Neither the state before (pre-COVID-19) nor the developments after (post-COVID-19) were assessed. The situation before COVID-19 could not have been understood easily, and the post situation has not yet occurred. At the time of writing, the consequences of measures on the SCF ecosystem could not be foreseen. For example, the suspension of the insolvency filing requirement was still in effect in many countries. The extent to which the players would have to deal with so-called “zombies” (i.e., already insolvent suppliers or buyers) could not be positively assessed.
Finally, we would like to suggest a few recommendations for future research projects. Initially, future studies should pay more attention to the formation and dynamics of the SCF ecosystem. In addition to comparative case study investigations (Eisenhardt, 1989), empirically-informed analytical modeling may be helpful for this purpose (Browning and de Treville, 2018). Such an analytical approach could, for example, model the behavior of the SCF ecosystem in crisis situations. Promising explanatory perspectives are the stakeholder approach (Moretto et al., 2019) and complex adaptive system theory (e.g., Choi et al., 2001; Choi and Hong, 2002; Kim et al., 2011). Future investigations using the stakeholder approach could explore how to adequately address the interests of the different groups in SCF programs. Particular attention should be paid to the stakeholder group of governmental agencies. It is expected that they will play a more significant role in the SCF ecosystem in the future. The complex adaptive system theory could help address the mentioned dynamics of the SCF ecosystem. An important point that we have identified but needs more in-depth study is the question of who is leading the ecosystem and how that leadership is taking shape. Future research should also further expand the scope of the study. We strongly recommend including the long tail of the SC in investigations. The sub-suppliers (so-called deep-tiers) in particular play a critical role in the economy but often have liquidity issues. They have not been sufficiently considered in SCF studies to date. Future research projects should focus on establishing a link between sustainability (circular economy) and SCF. The ethical side of SCF also deserves greater attention in the future, focusing on the potential dark sides of the SCF approach and how they can be converted to the bright side. This could be done, for example, in the development of a code of conduct at the global SCF ecosystem level.
Author satement
Thomas Y. Choi, Conceptualization, Methodology, Formal analysis, Writing - Original Draft, Writing - Review & Editing.
Erik Hofmann, Conceptualization, Formal analysis, Writing - Original Draft, Writing - Review & Editing.
Simon Templar, Conceptualization, Formal analysis, Writing - Original Draft, Writing - Review & Editing.
Dale S. Rogers, Conceptualization, Formal analysis, Writing - Original Draft, Writing - Review & Editing.
Rudolf Leuschner, Conceptualization, Formal analysis, Writing - Original Draft, Writing - Review & Editing.
Rohan Y. Korde, Data Curation, Formal analysis, Writing - Original Draft.
Conflict of interest statement
None
Footnotes
The group of the fintechs include companies such as Taulia (https://taulia.com/), PrimeRevenue (https://primerevenue.com/), CRX Markets (https://www.crxmarkets.com/), etc. The total value of investments into fintech companies worldwide in 2019 was $135.7 billion U.S. (Statista, 2021). The overall fintech funding dropped by $25.6 billion U.S. of investment globally during the first half of 2020 (Korobov, 2020).
The fiscal actions of countries around the world amounted to nearly $14 trillion as of end-December 2020 as well as central bank actions amounting to over $9 trillion (IMF, 2021). The key economic responses are summarized in the IMF policy tracker: https://www.imf.org/en/Topics/imf-and-covid19/Policy-Responses-to-COVID-19.
During the COVID-19 crisis, we observed an acceleration of adoption of digital tools and digital payment systems within supply chains. The digital tools offer possibilities to minimize person-to-person contact in market-based transactions as well as production systems. Advancements in information technology can be leveraged to assess risk and enable financial service providers to extend financing to an increasingly wide range of actors along the supply chain (United Nations, 2020).
Cases of SCF initiatives by central and development banks include Nacional Financiera (NAFIN, Mexico), “Cadenas Productivas”; Asian Development Bank (ADB), “Supply Chain Finance Program” (SCFP); Credit Reference Center of People's Bank of China (PBOC-CRC), “Receivables Finances Service Platform” (RFSP); European Bank for Reconstruction and Development (EBRD), “Trade Facilitation Program” (TFP); Hong Kong Monetary Authority (HKMA), “e-TradeConnect”; and others.
The basic structure of securitization is as follows. First, a creditor sells a pool of various loan receivables to a company (i.e., SPV) established for this purpose. This SPV issues securities and places them on the capital market. The securities are collateralized by the payment claims arising from the loans. Since investors are usually unable to assess the quality of the securities, a rating agency is commissioned to assess the risks. Interest and principal payments on the securities are essentially made from the cash flows generated by the loans. A servicing agent is contracted to collect the receivables and all related administrative activities.
At the writing of this study in September of 2021, the COVID-19 crisis was still ongoing. Despite initial successes with vaccinations, supply chains in some parts of the world were still experiencing lockdowns.
At the level of the physical SCA, two opposing directions are identified in the action patterns: one self-focused and the other collaborative practices. The former was evident where there were cash constraints (e.g., in industries at a complete standstill due to lockdowns). The latter was mainly observable in cases where some liquidity reserves were available.
The opposite thesis suggests that innovation is cyclical and therefore firms tend to reduce their innovation efforts during the downswing of the economy (Freeman, 2008).
We offer two examples of innovative and forward-looking companies. As a freight forwarder, Beacon pair their service with SCF. The integration of services that cover the flow of products, information and finances has proven a synthesis of the value a service provider can bring to their customers. Another example is the Singapore-based Agrocorp, which entered a partnership with two Dutch banks, the commercial Rabobank and development bank FMO, that collaborate to streamline the agricultural supply chain, by financing pre-payments to growers and enabling extended payment terms to buyers of those goods. This puts Agrocorp into the position where they can reduce risk and uncertainty while improving efficiency of the whole ecosystem.
APPENDIX A. List of keyword searches used
Keywords:
(COVID* OR Corona* OR Pandemic) AND (“Supply Chain Financing”.
OR “Supply Chain Finance”)
AND (Buyer* OR manufacturer* OR OEM OR “OEM.”)
AND (Seller* OR Supplier* OR “Tier 1 Supplier*” OR Vendor*)
AND (FinTech*)
AND (LSP OR “Logistics Service* Provider*” OR “3 PL” OR “4 PL” OR “Logistics”)
AND (FSP OR “Financial Service* Provider*” OR “Commercial Bank*” OR “Merchant Bank*” OR “Investment Bank*” OR “Accepting and Issuing House*”)
AND (Government OR Regulator* OR “Regulatory Body” OR “Central Bank*” OR “Federal Reserve” OR “The Fed” OR “European Central Bank” OR “Bank of England” OR “Swiss National Bank”)
Exclusion rules for keyword search.
-
•
No duplicates, i.e., exclude duplicate articles (articles with the same title).
-
•
Exclude the string “supply chain, finance” from the keyword search.
-
•
Exclude the strings (“Industry Snapshots” OR “Private company research report*” OR “News Bites*”) from the keyword search because this included news about companies that had nothing to do with supply chain financing.
-
•
We excluded “Industry Snapshots” and “News Bites” because these were aggregated news stories and not individual company stories.
-
•
We excluded “Private company research report” because these were mostly for stock analysis with Buy/Sell/Hold recommendations which were not relevant for the study.
-
•
The Source of the articles was limited to newswires and trade journals.
APPENDIX B. Complete list of first-order categories
-
I.
Actors in physical goods SC: 1. Buyers, 2. Suppliers, 3. LSPs
-
II.
Actors in financial SC: 4. Banks, 5. Fintechs, 6. Credit Ratings agencies, 7. Insurance
-
III.
Government-supported agencies: 8. Direct support (Central banks and regional development banks, etc.), 9. Indirect support (World Bank, EX-IM bank, etc.)
-
IV.
Government agencies: 10. Department of/Ministry of Commerce, 11. Department of Treasury/Finance, 12. Regulators (SEC, FASB and IASB, etc.)
-
V.
Interactions between players: 13. Country-centric, 14. Regional, 15. Global, 16. Triads and beyond, 17. Speculation
APPENDIX C. Time spent on the various activities
Weekly team meetings: 30 weeks, 1 h each, 6 people, Subtotal = 180 person hours.
Working on common/novel themes lists: Subtotal = 30 person hours.
Working on articles search and coding spreadsheet: Subtotal = 31.5 person hours.
APPENDIX D. Complete set of secondary data used in this study
A total of 136 articles were coded in two rounds in this study. Round 1 had 100 articles, and Round 2 had 36 articles. The tables listed below show the details of all these articles.
NOTE: The list of articles is available upon request.
APPENDIX E. Spreadsheet used to code articles
| Ar. No. | Ar. Title | Ar. Doc Link | Ar. Source | Ar. Date | Buyer (1 or 0) | Supplier (1 or 0) | FSP (1 or 0) | LSP (1 or 0) | Govt (1 or 0) | Fintech (1 or 0) | Coded Article Summary | Coded Interaction |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
APPENDIX F. Instrument used in the post-hoc study
-
1.
During the COVID-19 crisis, suppliers tried to accelerate the payment, and the buyers tried to delay payment. What have you seen in this regard? How do you think these strategies will evolve beyond COVID-19?
-
2.
We heard about how fintechs using technology (i.e., AI) when conducting dynamic discounting. What technology application have you seen during the COVID-19 crisis? How do you think the role technology will play evolve beyond COVID-19?
-
3.
The demand for fintech increased during the COVID-19 crisis. What have you seen? How do you think the SCF will evolve beyond COVID-19?
-
4.
There seems to be the integration of services during the COVID-19 crisis (i.e., LSP with fintech service, banks with fintech services). Have you seen this? Do you anticipate this trend to continue after COVID-19?
-
5.
We saw state and monitoring actors (i.e., governments, central banks, rating agencies) being very active during the COVID-19 crisis. What did you see? What do you think will happen after COVID-19?
-
6.
During the COVID-19 crisis, we began hearing about inconsistencies in SCF instruments' treatments (i.e., working capital vs. loan). What have you heard? What do you think will happen beyond COVID-19?
-
7.
Please tell us anything else you have seen as a significant development. Also, tell us what you see as a new normal in SCF beyond COVID-19.
APPENDIX G. List of interviewees included in the post-hoc study
| Title of the interviewee | Country | Stakeholder type |
|---|---|---|
Data availability
Data will be made available on request.
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