Trade credit research before and after the global financial crisis of 2008 – A bibliometric overview

Highlights

• •Applying bibliometrics and econometrics, the study analyses 1191 works of trade credit research published till 2019.
• •About 69 % of the literature are published after the global economic crisis of 2008.
• •Literature on trade credit converged to three major sub-domains.
• •The banking and financing cluster exhibits the highest growth followed by production and operations.
• •Reputation of publications, empirical works, and the domain of production and operations significantly influence its citations.

Abstract

This study presents an overview of the state-of-the-art in trade credit research by examining 1191 publications between 1955 and 2019. Applying bibliometrics and econometrics, the study compares the extant research across the three sub-domains of banking and finance, production and operations, and accounting. Findings suggest that the financial emergency in the global market had resulted in a watershed moment in trade credit research. About 69 % of the literature was found to have emerged after the global economic crisis of 2008. A network analysis grouped the trade credit articles into four major and four minor clusters. The banking and financing cluster exhibited the highest growth followed by the production and operation cluster while the perspectives of accounting are yet to gain traction. Conversely, reputation of the publishing hub, empirical studies, and the production and operational dimensions of the research positively and significantly influence citations. Alongside a thorough introspection, the study also provides new areas to direct the course of future research.

1. Introduction

Emerging from the field of Banking and Finance, research in trade credit has evolved as a multi-disciplinary scientific domain with contributions from Business Management, Industrial Engineering, Production and Operations, Finance, Economics, etc. (Paul and Boden, 2008). Prior reviews have provided partial qualitative and (or) quantitative perspectives of their respective disciplines (see Table 1 ), while the need for a holistic overview of the extant literal corpus is unaddressed. In addition, the precise factors contributing to the growth in literature in this fast-evolving academic domain lack empirical support.

Table 1.

Reviews on trade credit.

Author/s	TOS	Method	Period	NA	Focus
Paul and Boden (2008)	Qualitative	Literature Review	ND	ND	Trade credit management
Janssen et al. (2016)	Qualitative	Literature Review	2012–2015	ND	Perishable inventory models
Gelsomino et al. (2016)	Qualitative	Literature Review	2000–2014	119	Supply Chain Finance
Xu et al. (2018)	Qualitative and quantitative	SLR & Bibliometrics	1973–2016	112; 348	Supply Chain Finance
Del Gaudio et al. (2018)	Qualitative	Literature Review	ND	ND	Trade credit and SMEs
Pattnaik et al. (2020)	Qualitative and quantitative	SLR & Bibliometrics	1999–Feb, 2019	138	Financial and economic perspectives

Notes: This table presents some of the former reviews on trade credit. It includes the author(s) of the study, type of study (TOS), the study method, study period, number of articles analysed (NA) and the primary focus of the study. ND stands for not defined period/articles.

As an example, in one of the recent reviews, Pattnaik et al. (2020) provided a comprehensive overview of the literal corpus on trade credit covered in top Finance and Economics journals between 1999 and February, 2019. However, the study misses out the basic multi-disciplinary nature of the research domain. Therefore, views from other important disciplines e.g., Industrial Engineering, Production and Operations etc. excluded in the former are included in this endeavor. Alternatively, while the former evaluates only 138 articles covered in Web of Science (WOS); this work analyzes 1191 works of research available in Scopus. In addition, both the studies also differ in their analytical outfits. The primary concern of the first review is on network analysis and therefore exhibits limitations in the descriptive front. However, this endeavor is rich in both descriptive, network, and predictive contents of the multi-disciplinary research domain.

An evolving trend in a research stream concerns both academia and practitioners alike. Systematic reviews help to identify the specific areas that require future research attention; it also assists practitioners to gain holistic insight (Randhawa et al., 2016). Given these general issues, the evolution and growth in the corpus of trade credit research from multiple disciplines accentuate the need for a quantitative investigation to formulate an overview, identify key themes and find directions for future research.

Given the specific objectives, we apply a range of bibliometric and econometric tools to achieve our goals. Bibliometrics or, its most appropriate term, scientometrics is the “quantitative study of science, communication in science, and science policy” (Hess, 1997, p. 75). From peer-reviewed scientific publications, commonly referred to as the “bibliometric” source of science, this field has evolved to standardize, collect, report, and analyse a broad range of information sources (Gil-Doménech et al., 2020; Baker et al., 2020). Essentially, by applying mathematics and statistics, it constructs exceptional summaries of the bibliographic data (Prichard, 1969). This methodology has been extensively applied in the field of Management Research (Podsakoff et al., 2008), Entrepreneurship Education (Kakouris and Georgiadis, 2016), Sciences and Social Sciences (Glänzel, and Schoepflin, 1999), Humanities (Nederhof et al., 1989) etc. to analyse topics, subjects and (or) themes (Chung and Cox, 1990; Blanco-Mesa et al., 2017; Xu et al., 2018), journals (Baker et al., 2020; Donthu et al., 2020), and educational institutions (Merigó et al., 2019). A bibliometric overview of the extant literature on trade credit can present invaluable empirical insights which may be useful to both existing and aspiring researchers for expanding the boundaries of knowledge in their respective domains.

This study presents an in-depth investigation of the broader research domain and its sub-domains; recognizes the most influential thinkers, academic outlets while their intellectual clustering exposes their semantic (dis)association. Further, we also explore the thematic components and introspect on the extant literature to provide some future research directions. In addition, we also identify a number of factors influencing the quantum of citations, thus indicating a growth of trade credit research. Therefore, apart from the de novo researchers, such a detailed introspection may also inform the established academics in the field about theories which require future validation and the study methods they could deploy. Moreover, this study contributes significantly to the extant research by asking the following research questions (RQs):

RQ1. What is the state-of-the-art of the research front—in terms of publications, authorship and citation structure, influence, impact, activity, and productivity of its contributing authors and publishing sources—and how does it vary in the pre and post crisis era of 2008?

RQ2. What is the thematic layout of the research domain?

RQ3. How do the extant literature, its prolific contributors, publishing sources, and highly cited references converge intellectually?

RQ4. What factors significantly contribute to the growth of the research domain?

RQ5. What is the direction for actionable research in future?

In the remaining sections, we summarise the research methods, discuss the study outcomes, provide key summaries, and conclude the paper.

2. A synthesis

2.1. Research areas of trade credit

Trade credit—in form of receivables—is a financing provision by non-financing firms (García-Teruel and Martínez-Solano, 2010) with or without bank intermediation. In the context of a complex and competitive global market, credit supply is inevitable to retain B2B relations and impart business growth (Chowdhury and Lang, 1996) while financing supply chain is must for obtaining competitive advantage (Pirttilä et al., 2019). Enhanced fluctuations in the level of industrial production backed by disruptive technologies significantly distress trade credit policies both for the upstream and downstream supply chain. The nature of the product, fluctuations in market demand and the level of hostility in the business environment further impact its demand and supply. Buyers benefit from the time value of money by increasing their demand for credit supplies while suppliers, on the other hand, register a growth in sales (Schwartz, 1974). Simultaneously, empirical evidences assert the influential role of a number of qualitative indicators such as the national culture (El Ghoul and Zheng, 2016), the cultural background of finance managers (Bedendo et al., 2020), and level of social trust (Levine et al., 2018) impacting the demand and supply of trade credit.

Demand for trade credit is also influenced by the level of financial market development and financing constraints. More importantly, trade credit substitutes bank credit during a crisis (Bastos and Pindado, 2013) and therefore is a viable source of finance both in the developed and developing world (McMillan and Woodruff, 1999; Afrifa and Gyapong, 2017; Li et al., 2018). Suppliers' credit played a crucial role in the survival of SMEs during the crisis. Empirical evidence substantiates the argument that trade credit reduces the likelihood of financial distress among SMEs (McGuinness et al., 2018). Simultaneously, it impacts the production cycles, optimal ordering quantity, level of inventory, firm performance, and industry growth (Haley and Higgins, 1973; Barth et al., 2001; Huang, 2003, 2007; Huang and Hsu, 2008; Fisman and Love, 2003; Chung and Huang, 2003; Chung et al., 2005; Liao, 2008; Allen et al., 2019).

In the chronological progression of the research domain, the intertwined financing and accounting perspectives of trade credit shows signs of separation. Few notable thinkers such as Brick and Fung (1984) proposed the tax theory of trade credit, hypothesising that businesses offer trade credit to obtain tax benefit. Other themes explored in this area include revenue management and budgetary participation among others.

Thus, the literature analysed exhibit three broader academic domains: first, the banking and financing aspects, and second, the production and operational dimensions, and third the accounting perspectives of trade credit.

2.2. Factors contributing to the growth of trade credit literature

In order to delineate the potential factors contributing to the popularity of trade credit literature in academia this study applies both quantitative and qualitative tools. In the field of scientometrics citations are predominantly considered to be the indicators of quantity versus that of quality (Aksnes et al., 2019). On the basis of citations, Hirsch (2005) proposed the "h-index" while Egghe (2006) proposed the "g-index". Both Hirsch and Egghe provide authorial performance indicators measuring the quantitative performance of authors. Drawing from such prior evidence, we examined citation as a growth factor for a research domain as every new citation is the birth of a new publication. Alternatively, it also adds to the influence and impact of the academic source cited, the specific theme cited, and contributes to the intellectual influence of its contributor(s).

Given the debatable qualitative indications of citations (Aksnes et al., 2019), we present qualitative perspectives i.e. journal's academic reputation, an author's academic reputation, etc. by using the indicators of the Chartered Association of Business School's (CABS) Academic Journal Guide, 2018 (AJG, 2018).

Alternatively, Ball and Tunger (2006) argue that growth in publications indicates a growing research area while Acedo et al. (2006) and Finardi and Burati, 2016 Finardi and Burati (2016) associate growth with authorial collaborations. Other factors include an increase in global submissions and publications in prestigious academic outlets (Merigó et al., 2019; Baker et al., 2020), etc. Alongside the hypothetical constructs, this study also applies some of the tested factors affecting citations (growth in publications) by drawing upon Schwert’s work (1993).

2.3. Conceptual model of trade credit research

By combining all the theoretical perspectives, the conceptual model of our study is proposed in Fig. 1 .

Fig. 1.

Conceptual model.

Note: This figure provides the conceptual model regarding the factors contributing to the growth in trade credit literature.

During academic emergency or external threats to industry (environment)—established research domains grow faster. Growth of the broader academic domain is also due to the upsurge in research activities of the interrelated disciplines. In such circumstances, published articles in reputed journals positively affect growth. The growing inter-disciplinary variable mediates between the primary established academic domain and the discipline's overall growth. In other words, by drawing wisdom from the (reputed) publications and the established academic domain, the growing inter-disciplinary sub-domain(s) expand(s) the broader horizon of knowledge further.

3. Study methods

3.1. Corpus of trade credit articles

Data analysed in this study was furnished from Scopus. It is one of the largest databases (Bartol et al., 2014) of peer-reviewed works in social science (Norris and Oppenheim, 2007), extensively accessed for quantitative analyses (Durán-Sánchez et al., 2019; Baker et al., 2020; Donthu et al., 2020). Fig. 2 summarises the study design.

Fig. 2.

Study design.

Note: The figure provides an overview of the research design.

To carry out the investigation, a broad search term was finalised. The term, “trade credit” OR “account* receivable*” OR “account* payable*”, searched in the title, abstract or keywords enabled access to the extant literature in Scopus. Redundant documents were eliminated using subject filters. Literature confined to Business Management, Accounting, Economics, Econometrics, Finance, Social Sciences, Arts and (or) Humanities were included. Since our focus was accessing the core bibliographic records on trade credit; only those documents classified as articles, reviews, or conference proceedings were included and the rest excluded. Further, due to the dynamic nature of the research domain literature published in 2020 is omitted. Finally, a total of 1191 documents published between 1955 and 2019 were considered for analyses.

3.2. Descriptive analysis

The descriptive analysis in this study was conducted manually using Microsoft (MS) Excel under the four broad categories of: 1. Publication trend. 2. Authorship pattern. 3. Citation structure. 4. The influence, impact, activity, and productivity indicators (Donthu et al., 2020; Baker et al., 2020). Refer Appendix A for definitions of the descriptive variables.

3.3. Factor analysis

The study explores the thematic factors of the research domain using Principal Component Analysis (PCA) with Varimax rotation under Kaiser Normalisation. It was conducted with the help of the Statistical Packages for the Social Sciences (SPSS) software. The core of such an analysis is based on the co-occurrence counts of the keywords indicating themes (Callon et al., 1983; Marrone, 2020). Ponzi (2002) applied a similar technique to explore the intellectual structure among frequently co-occurring authors.

To carry out the analysis, prolific themes appearing at least five times in the shortlisted articles are identified. Using MS-Excel, the co-occurrence matrix of those themes was later processed in SPSS to obtain the Pearson's correlations. Finally, we used the correlations' matrix to explore the thematic components.

3.4. Network analyses

When two distinct works cite one or more common documents, they exhibit intellectual similarities (Kessler, 1963); whereas, co-citation is the citation of two or more existing researches in a later article (Small, 1973). The network analyses in the forms of bibliographic couplings (Kessler, 1963) and co-citations (Small, 1973) unveil the semantic clustering among the citing and cited documents, contributing authors, publishing sources, etc. (Merigó et al., 2019; Donthu et al., 2020; Baker et al., 2020).

The networks in this study are visualized using VOSviewer and Gephi software (van Eck and Waltman, 2017v, 2010; Bastian et al., 2009). Both the programs apply two standardized weights for visualising the networks e.g. the total number of links and the total strength of the links. The size of the nodes in a network indicates its relevance; whereas, the number and size of the interlinking lines represent the strength of the association among the nodes (van Eck and Waltman, 2017v).

3.5. Regression analysis

We apply the Ordinary Least Square (OLS) regression in SPSS to explore the potential growth factors of the research domain (Dragos et al., 2014). Drawing primarily from previous research (Schwert, 1993), the following model is proposed:

$$\mathrm{Y}=\mathrm{a}+\mathrm{\beta }{\mathrm{\Sigma }}_{i=1}^n{X}_i+\mathrm{\epsilon }$$ (1)

Where, Y is the dependent variable, total citations. Xi denotes the independent variables i.e. the article length, number of contributing authors, authorship type (sole-authored or co-authored), publication year (before or after 2008), the AJG 2008 ratings, bibliographic cluster of the article (major or minor), the study method (primary or secondary), the thematic components’ score. And, ε is the error term.

3.5.1. Dependent variable

The dependent variable, total citations, is defined as the total quantitative growth of the research domain.

3.5.2. Independent variables

• •Article length—defined as the number of pages of the article.
• •Number of contributing authors—defined as the total number of authors who contributed to the article.
• •Thematic score—respective thematic component scores of the articles.

Dummy variables include, authorship type (sole-authored or co-authored), publication year (before or after 2008), academic reputation (AJG, 2018 ratings of 4*, 4, 3, 2, or 1), bibliographic clustering of the article (major or minor). Research method of the trade credit article i.e. primary (empirical) or secondary (review, conceptual, model building, etc.).

4. Results

Scopus revealed that 3813 documents were published on trade credit between 1955 and 2019. Application of filters reduced the final number to 1191. The shortlisted articles include 1063 articles, 53 reviews, and 75 conference papers cited 22,444 times in Scopus. For the remainder of the study, these documents are interchangeably termed as publications, research, articles or more specifically, trade credit articles/research, etc.

4.1. State-of-the-art in trade credit research

Table 2 presents the number of publications; authorship pattern; citation structure; and the influence, impact, activity, and productivity of trade credit research published between 1955 and 2019. Along with the consolidated figures, the table also compares and contrasts the state-of-the-art in trade credit research published before and after 2008. Fig. 3 shows the yearly growth in publications, its influence (h-index), and the average annual citations of the articles published between 1955 and 2019. Fig. 4 depicts the annual trend of the intelligentsia, while Fig. 5 distributes the trade credit articles by its number of contributors. It also compares the authorial distribution of the articles before and after 2008.

Table 2.

State of the art in trade credit research before and after 2008.

		1955 to 2019	Before 2008	After 2008
Publications	TP	1191	368	823
Authorship Pattern	NCA	2654	627	2,027
	GA	2062	535	1527
	CI	1.23	0.70	1.46
	SA	334	173	161
	CA	857	195	662
Citation structure	NCP	874	302	572
	PCP	0.73	0.82	0.70
	TC	22,444	14,055	8389
	C/P	18.84	38.19	10.19
	C/CP	25.68	46.54	14.67
	C/CA	8.46	22.42	4.14
	CT1	823	260	563
	CT2	49	40	9
	CT3	2	2	–
Influence, impact, activity, & productivity	H	71	60	45
	G	120	110	66
	NAY	53	42	11
	PAY	22.47	8.76	74.82

Notes: This table presents the publication trend, authorship pattern, citation structure, influence, impact, activity, and productivity in trade credit research between 1955 and 2019 and compares the indicators before and after 2008. Here, TP = total publications; NCA = number of contributing authors; GA = growth in the number of unique authors; CI = collaboration index; SA = number of sole-authored articles; NCP = number of cited publications; PCP = proportion of cited publications; TC = total citations; C/P = average citations per publication; C/CP = average citations per cited publication; C/CA = average citations per contributing author; CT1 = first citation threshold i.e. between 1 and 99 citations; CT2 = second citation threshold i.e. between 100 and 499 citations; CT3 = third citation threshold i.e. 500 citations and above; h = h-index; g = g-index; NAY = number of active years; and PAY = productivity per active year.

Fig. 3.

Trend in trade credit research.

Notes: This figure depicts the annual trend in the publications on trade credit, its influence and popularity between 1955 and 2019. Here, TP = total publication, and CPY = average citations per year.

Fig. 4.

Authorship trend in trade credit research.

Notes: This figure depicts the annual authorship trend in the area of trade credit research. Here, CuNA = cumulative number of authors (a variable which includes the repetition of authors contributing more than one research in any given year), and GA = growth in authorship (excluding repetition of authors).

Fig. 5.

Distribution of trade credit research by the number of its contributors.

Note: This figure compares the distribution of trade credit research by the number of its contributing authors before and after 2008.

Of note, 2019 is the most productive year with 162 publications followed by 2018 with 97. Fig. 3 depicts an exponential growth in publications since 2008. As indicated in Table 1, about 69 % (TP: 823 of 1191) of the study articles are published after 2008. Thus, the majority of the research on trade credit followed the global economic crisis of 2008. Such a trend is not surprising as—during the collapsing phase of banking—academia largely proclaimed trade credit as the primary source of alternate finance that sustained the global economy (Giannetti et al., 2011; Chor and Manova, 2010).

Conversely, as the demand was non-decreasing post 2008, the proposition of the economic order quantity model by Teng et al. (2012) attracted further research attention (see Table 3 ). Not only did academia recognise trade credit as a major source of finance over these years, trade credit also managed to establish itself as one of the predominant factors influencing factors like economic order quantity (EOQ), economic production quantity (EPQ), the shifts in the volume of production, etc.

Table 3.

Influential articles published before and after 2008.

R	CPY	Title	Author(s)	Year	TC	C/CA
Published before 2008
1	31.87	"Trade credit: Theories and evidence"	Petersen M.A., Rajan R.G.	1997	733	366.50
2	23.82	"The relation between earnings and cash flows"	Dechow P.M., Kothari S.P., Watts R.L.	1998	524	174.67
3	20.67	"The disclosure of material weaknesses in internal control after the Sarbanes-Oxley Act"	Ge W., McVay S.	2005	310	155.00
4	18.82	"Optimal retailer's ordering policies in the EOQ model under trade credit financing"	Huang Y.-F.	2003	320	320.00
5	17.84	"Accruals and the prediction of future cash flows"	Barth M.E., Cram D.P., Nelson K.K.	2001	339	113.00
6	17.31	"Trade credit: Suppliers as debt collectors and insurance providers"	Cuñat V.	2007	225	225.00
7	17.12	"Trade credit, financial intermediary development, and industry growth"	Fisman R., Love I.	2003	291	145.50
8	14.85	"Trade credit and bank credit: Evidence from recent financial crises"	Love I., Preve L.A., Sarria-Allende V.	2007	193	64.33
9	14.69	"In-kind finance: A theory of trade credit"	Burkart M., Ellingsen T.	2004	235	117.50
10	14.47	"Bank discrimination in transition economies: Ideology, information, or incentives?"	Brandt L., Li H.	2003	246	123.00
11	14.28	"Knowledge spillover in corporate financing networks: Embeddedness and the firm's debt performance"	Uzzi B., Gillespie J.J.	2002	257	128.50
12	13.82	"Are accruals during initial public offerings opportunistic?"	Teoh S.H., Wong T.J., Rao G.R.	1998	304	101.33
13	13.59	"A joint approach for setting unit price and the length of the credit period for a seller when end demand is price sensitive"	Abad P.L., Jaggi C.K.	2003	231	115.50
14	13.08	"An EOQ model with noninstantaneous receipt and exponentially deteriorating items under two-level trade credit"	Liao J.-J.	2008	157	157.00
15	12.90	"Evidence on the determinants of credit terms used in interfirm trade"	Ng C.K., Smith J.K., Smith R.L.	1999	271	90.33
16	11.91	"Trade credit and credit rationing"	Biais B., Gollier C.	1997	274	137.00
17	11.62	"Credit chains and bankruptcy propagation in production networks"	Battiston S., Delli Gatti D., Gallegati M., Greenwald B., Stiglitz J.E.	2007	151	30.20
18	11.17	"An EOQ model under retailer partial trade credit policy in supply chain"	Huang Y.-F., Hsu K.-H.	2008	134	67.00
19	11.06	"The optimal cycle time for EPQ inventory model under permissible delay in payments"	Chung K.-J., Huang Y.-F.	2003	188	94.00
20	10.95	"The exploitation of relationships in financial distress: The case of trade credit"	Wilner B.S.	2000	219	219.00
21	10.33	"Trade credit and the bank lending channel"	Nilsen J.H.	2002	186	186.00
22	10.15	"Financial development, bank discrimination and trade credit"	Ge Y., Qiu J.	2007	132	66.00
23	9.46	"The optimal retailer's ordering policies for deteriorating items with limited storage capacity under trade credit financing"	Chung K.-J., Huang T.-S.	2007	123	61.50
24	8.93	"The optimal inventory policies under permissible delay in payments depending on the ordering quantity"	Chung K.-J., Goyal S.K., Huang Y.-F.	2005	134	44.67
25	8.82	"Trade credit and informational asymmetry"	Smith J.K.	1987	291	291.00
Published after 2008
1	25.00	"Off the cliff and back? Credit conditions and international trade during the global financial crisis"	Chor D., Manova K.	2012	200	100.00
2	24.67	"Country-level institutions, firm value, and the role of corporate social responsibility initiatives"	El Ghoul S., Guedhami O., Kim Y.	2017	74	24.67
3	23.50	"Trade credit, risk sharing, and inventory financing portfolios"	Yang S.A., Birge J.R.	2018	47	23.50
4	22.33	"What you sell is what you lend? Explaining trade credit contracts"	Giannetti M., Burkart M., Ellingsen T.	2011	201	67.00
5	17.00	"Retailer's economic order quantity when the supplier offers conditionally permissible delay in payments link to order quantity"	Chen S.-C., Cárdenas-Barrón L.E., Teng J.-T.	2014	102	34.00
6	17.00	"Inventory models for deteriorating items with maximum lifetime under downstream partial trade credits to credit-risk customers by discounted cash-flow analysis"	Wu J., Al-Khateeb F.B., Teng J.-T., Cárdenas-Barrón L.E.	2016	68	17.00
7	16.50	"Supply chain finance: A literature review"	Gelsomino L.M., Mangiaracina R., Perego A., Tumino A.	2016	66	16.50
8	16.25	"Trade credit, the financial crisis, and SME access to finance"	Carbó-Valverde S., Rodríguez-Fernández F., Udell G.F.	2016	65	21.67
9	15.50	"A partial credit guarantee contract in a capital-constrained supply chain: Financing equilibrium and coordinating strategy"	Yan N., Sun B., Zhang H., Liu C.	2016	62	15.50
10	15.14	"Firms as liquidity providers: Evidence from the 2007–2008 financial crisis"	Garcia-Appendini E., Montoriol-Garriga J.	2013	106	53.00
11	15.00	"Economic order quantity model with trade credit financing for non-decreasing demand"	Teng J.-T., Min J., Pan Q.	2012	120	40.00
12	14.75	"Impact of trade credit and inflation on retailer's ordering policies for non-instantaneous deteriorating items in a two-warehouse environment"	Tiwari S., Cárdenas-Barrón L.E., Khanna A., Jaggi C.K.	2016	59	14.75
13	14.50	"Joint pricing and inventory model for deteriorating items with expiration dates and partial backlogging under two-level partial trade credits in supply chain"	Tiwari S., Cárdenas-Barrón L.E., Goh M., Shaikh A.A.	2018	29	7.25
14	14.33	"Bank lending constraints, trade credit and alternative financing during the financial crisis: Evidence from European SMEs"	Casey E., O'Toole C.M.	2014	86	43.00
15	14.17	"An inventory model with non-instantaneous receipt and exponentially deteriorating items for an integrated three layer supply chain system under two levels of trade credit"	Chung K.-J., Cárdenas-Barrón L.E., Ting P.-S.	2014	85	28.33
16	14.00	"Who should finance the supply chain? Impact of credit ratings on supply chain decisions"	Kouvelis P., Zhao W.	2018	28	14.00
17	13.40	"The response of corporate financing and investment to changes in the supply of credit"	Lemmon M., Roberts M.R.	2010	134	67.00
18	13.00	"The roles of bank and trade credits: Theoretical analysis and empirical evidence"	Cai G., Chen X., Xiao Z.	2014	78	26.00
19	13.00	"Joint effects of variable carbon emission cost and multi-delay-in-payments under single-setup-multiple-delivery policy in a global sustainable supply chain"	Sarkar B., Ahmed W., Kim N.	2018	26	8.67
20	13.00	"Supply chain finance: A systematic literature review and bibliometric analysis"	Xu X., Chen X., Jia F., Brown S., Gong Y., Xu Y.	2018	26	4.33
21	12.25	"Equilibrium financing in a distribution channel with capital constraint"	Jing B., Chen X., Cai G.G.	2012	98	32.67
22	12.00	"The collapse of international trade during the 2008-09 crisis: In search of the smoking gun"	Levchenko A.A., Lewis L.T., Tesar L.L.	2010	120	40.00
23	12.00	"Delay-in-payments - A strategy to reduce carbon emissions from supply chains"	Aljazzar S.M., Gurtu A., Jaber M.Y.	2018	24	8.00
24	12.00	"Understanding informal financing"	Allen F., Qian M., Xie J.	2019	12	4.00
25	11.43	"Optimal production lot with imperfect production process under permissible delay in payments and complete backlogging"	Ouyang L.-Y., Chang C.-T.	2013	80	40.00

Notes: This table ranks the top articles in trade credit research published before and after 2008. Ranking (R) of the articles are based on their respective average citations per year (CPY). Here, TC = total citations, and C/CA = citations per contributing author.

From another perspective, an evolving research domain triggers further research attention by attracting and engaging more scholars (Ball and Tunger, 2006). The notion is evidentially affirmed in Fig. 4. The figure depicts an increasing trend in the number of thinkers. 2062 unique researchers have contributed to the domain between 1955 and 2019 (see Table 2, GA: 2062) (which increases to 2654 authors upon including repeated authors), who contributed the 1191 articles.

About 74 % of the growth in authorship in the area of trade credit occurred after 2008 (GA: 1527) with each of the lead contributors collaborating with 1.46 others to contribute a work of research (CI: 1.46). Fig. 5 depicts that the majority of the post global financial crisis research on trade credit are by two or three authors. Such figures may also indicate the requirements for multiple research skills in the area of trade credit, which can explain the rising number of co-authored articles (CA: 662) with a simultaneous dip in sole-authored publications (SA: 161). However, Acedo et al. (2006) and Baker et al. (2020) believe that modern research is more collaborative due to the ease of networking among researchers in the era of internet and information technology.

Considering citations as an indicator of popularity or intellectual influence (Tsay, 2009; Donthu et al., 2020), trade credit research published before 2008 was found to be more influential (TC: 14,055) compared to the citations of articles published after 2008 (TC: 8389). Apart from popularity, the total citations also partially portray the intellectual dimension of trade credit research. In other words, the total citations to the 1191 articles on trade credit studied have directly influenced about 22,444 works of research (TC: 22,444).

In addition, the average indicators of citations suggest that on an average, a trade credit article published between 1955 and 2019 influenced about 19 other works of research (C/P: 18.84) and the figure increases to about 26 per publication that was cited (C/CP: 25.68). The articles published before 2008 were found to have influenced about 38–47 other works of research (C/P: 38.19; C/CP: 46.54).

Each of the contributors on trade credit influenced around 8 other thinkers between 1955 and 2019 (C/CA: 8.46) while the same increases to about 22 per author who were cited before 2008 (C/CP: 22.42). However, the majority of the studied articles (about 69 %) received between 1 and 99 cites (CT1: 823), about 4 % were cited between 100 and 499 times (CT2: 49), while only two articles breached the level of 500 and above citations in Scopus (CT3: 2). Notably both the highly cited articles were published before 2008.

Excluding the number of cited publications (NCP: 572 vs. 302) and the first citation threshold (CT1: 563 vs. 260), it was found that for most of the remaining parameters of citations, the articles published before 2008 dominated over those published after 2008. Such indicators affirm that the most influential content on trade credit prevailed before the global economic crisis.

Conversely, the intellectual aura of trade credit research extends to its 71 articles being cited at least 71 times while about 120 of the highly cited articles received at least 14,400 citations between 1955 and 2019 (h: 71; g: 120). We also found that the research domain has been active for the past 53 years by contributing 22 yearly articles (NAY: 53; PAY: 22.47). Interestingly, in the past 11 years—between 2009 and 2019—the domain contributes about 75 articles per active year (NAY: 11; PAY: 74.82).

In conclusion, the protective mechanism of trade credit as an alternative source of finance during banking emergency has historically triggered academic attention between 2008 and 2019. With the pandemic onset of COVID-19 shutting down operations, we foresee a similar trend in the production and operational area of trade credit research in the near future.

4.2. The most influential papers on trade credit

Table 3 presents the top articles published before and after 2008.

The seminal paper by Michell A. Petersen and Raghuram G. Rajan (1997) titled: “Trade Credit: Theories and evidence” is by far the most influential work in the research domain followed by Patricia M. Dechow, S.P.Kothari, & Ross L. Watts’s “The relation between earnings and cash flows” (1998). Acknowledging trade credit as a significant source of finance to small American firms, Petersen and Rajan (1997) empirically validated the financing and marketing theories of trade credit. Apart from introducing a new research area to global academia (i.e. small and medium enterprises or SMEs), the study provided some of the methodologies widely followed in later empirical investigations (García-Teruel and Martínez-Solano, 2010; Afrifa and Gyapong, 2017, etc.). On average, the study has influenced about 32 yearly articles published between 1998 and 2019. Precisely, the aura of its intellectual influence extends to 733 academic researches published till 2019 (TC: 733). On the other hand, assuming accounts receivables, payables, and inventory as the only accruals; Dechow et al. (1998) proposed a seminal model of earnings which affirmed that the current earnings are better predictors of the future operating cash flows of firms than the current operating cash flows. The study influences an average of 24 articles every year (CPY: 23.82) while the total citations reveal an intellectual outreach of 524 works of research published between 1999 and 2019 (TC: 524). While following Dechow et al. (1998); Barth et al. (2001) proposed their model of accrual processing by investigating the precise roles of accruals in predicting the future cash flows.

Interestingly, the top three highly cited works exhibit convergence in their respective philosophical outlooks. Petersen and Rajan (1997) predominantly present the banking and financing views of trade credit research while both Dechow et al. (1998) and Barth et al. (2001) navigate the broader finance and accounting dimensions. However, the model proposed in the note by Huang (2003) modified the production and operational dimensions concerning the trade credit policies of retailers. While earlier notions predominantly argued against the extension of credit period by retailers, Huang (2003) proposed that like suppliers, retailers stimulate their demand by extending the credit period to their customers.

Thus, the highly cited articles, depicting the existing knowledge in the established academic domain, presumably influence their respective research sub-domains. However, a deeper investigation of the highly cited articles reveal that the accounting dimension of trade credit is dormant without any significant contribution in the post-crisis era thereby presenting scope for future research. In the subsequent discussion, we recognize some of the most prolific and influential thinkers in the broader domain.

4.3. Prolific authors in the area of trade credit

Table 4 lists some of the most prolific authors who have contributed at least five works of research between 1955 and 2019.

Table 4.

Top contributors in trade credit.

	Publications			Authorship pattern				Citation structure						IIAP
Author	TP	B08	A08	NCA	CI	SA	CA	PCP	TC	C/CP	C/CA	CT1	CT2	h	g	NAY	PAY
K. -J. Chung	15	9	6	34	1.27	2	13	1.00	883	58.87	389.56	11	4	11	15	12	1.25
J.-T. Teng	15	3	12	48	2.20	1	14	1.00	880	58.67	275.00	12	3	12	15	9	1.67
L.-Y. Ouyang	13	8	5	37	1.85	–	13	0.92	394	32.83	138.43	12	–	9	12	8	1.63
N.H. Shah	12	2	10	31	1.58	2	10	0.83	102	10.20	39.48	10	–	5	10	8	1.50
C.K. Jaggi	11	2	9	32	1.91	–	11	0.82	412	45.78	141.63	8	1	6	9	10	1.10
R. Uthayakumar	11	–	11	23	1.09	–	11	0.73	54	6.75	25.83	8	–	4	7	7	1.57
Y.-C. Tsao	10	1	9	17	0.70	6	4	0.90	173	19.22	101.76	9	–	6	9	8	1.25
S. Mateut	9	3	6	19	1.11	2	7	0.89	243	30.38	115.11	8	–	7	8	8	1.13
G. Yano	9	–	9	19	1.11	–	9	0.56	13	2.60	6.16	5	–	2	3	7	1.29
Y.-F. Huang	8	7	1	16	1.00	3	5	0.88	910	130.00	455.00	3	4	6	7	6	1.33
M. Shiraishi	8	–	8	17	1.13	–	8	0.63	13	2.60	6.12	5	–	2	3	7	1.14
D. Tsuruta	8	1	7	9	0.13	7	1	0.75	46	7.67	40.89	6	–	4	6	6	1.33
P.J. García-Teruel	7	–	7	18	1.57	–	7	1.00	179	25.57	69.61	7	–	6	7	4	1.75
T. Kärri	7	–	7	25	2.57	–	7	1.00	35	5.00	9.80	7	–	4	5	5	1.40
P. Martínez-Solano	7	–	7	18	1.57	–	7	1.00	179	25.57	69.61	7	–	6	7	4	1.75
R.P. Tripathi	7	–	7	10	0.43	4	3	0.71	22	4.40	15.40	5	–	3	4	4	1.75
N. Wilson	7	7	–	16	1.29	–	7	1.00	270	38.57	118.13	6	1	7	7	4	1.75
C.-T. Chang	6	1	5	17	1.83	–	6	1.00	255	42.50	90.00	6	–	5	6	6	1.00
M.D. Hill	6	–	6	21	2.50	–	6	0.83	48	9.60	13.71	5	–	4	5	6	1.00
J.-J. Liao	6	2	4	16	1.67	1	5	0.83	371	74.20	139.13	3	2	5	5	5	1.20
G.C. Mahata	6	1	5	12	1.00	1	5	1.00	92	15.33	46.00	6	–	5	6	5	1.20
S. Paul	6	1	5	15	1.50	–	6	0.83	59	11.80	23.60	5	–	4	5	5	1.20
S. Tiwari	6	–	6	21	2.50	–	6	0.50	100	33.33	28.57	3	–	3	3	3	2.00
Y.-W. Zhou	6	–	6	18	2.00	–	6	0.67	66	16.50	22.00	4	–	4	4	4	1.50
L.E. Cárdenas-Barrón	5	–	5	18	2.60	–	5	1.00	343	68.60	95.28	4	1	5	5	3	1.67
X. Chen	5	–	5	16	2.20	1	4	1.00	267	53.40	83.44	5	–	5	5	5	1.00
M.C. Cheng	5	1	4	13	1.60	–	5	0.80	26	6.50	10.00	4	–	3	4	5	1.00
M. Deloof	5	2	3	12	1.40	–	5	1.00	203	40.60	84.58	4	1	4	5	5	1.00
M. Pirttilä	5	–	5	19	2.80	–	5	1.00	26	5.20	6.84	5	–	3	5	4	1.25
B. Sarkar	5	–	5	16	2.20	–	5	0.80	95	23.75	29.69	4	–	4	4	3	1.67
S.R. Singh	5	–	5	16	2.20	–	5	1.00	37	7.40	11.56	5	–	3	5	3	1.67
C.-H. Su	5	2	3	10	1.00	2	3	1.00	105	21.00	52.50	5	–	3	5	4	1.25
B. Summers	5	5	–	11	1.20	–	5	1.00	211	42.20	95.91	4	1	5	5	3	1.67
S. Viskari	5	–	5	17	2.40	–	5	1.00	29	5.80	8.53	5	–	3	5	4	1.25
D. Yazdanfar	5	–	5	9	0.80	1	4	0.80	35	8.75	19.44	4	–	2	4	4	1.25

Notes: This table presents the top contributors in trade credit research. Here, IIAP = influence, impact, activity, and productivity; TP = total publications; B08 = number of publications before 2008; A08 = number of publications after 2008; NCA = number of contributing authors; CI = collaboration index; SA = number of sole-authored articles; CA = number of co-authored articles; PCP = proportion of cited publications; C/CA = citations per contributing author; CT1 = first citation threshold i.e. between 1 and 99 cites; CT2 = second citation threshold i.e. between 100 and 499 cites; h = h-index; g = g-index; NAY = number of active years; and PAY = productivity per active year.

In terms of the number of publications, Kun-Jen Chung affiliated to the National Taiwan University of Science and Technology and Jinn-Tsair Teng affiliated to the William Paterson University in the United States share the top rank for contributing 15 articles each. However, 60 % of Chung's contributions were before 2008 (TP: 6) while Jinn-Tsair Teng dominated the post-crisis era of trade credit research (TC: 12). Interestingly, both the researchers work in the area of operational trade credit influencing about 883 and 880 fellow researchers in the specialized research domain (TC: 883 and 880, respectively).

The table also reveals that Teng associates with the highest number of co-authors to contribute the highest number of his co-authored articles (NCA: 48; CA: 14) while Yu-Chung Tsao, affiliated to Tatung University in Taiwan is the top contributor of sole-authored articles. Interestingly, with only 8 publications, Yung-Fu Huang affiliated to the Chaoyang University of Technology in Taiwan, is the most influential contributor in our study (TC: 910; C/CP: 130; C/CA: 455). The author shares the highest count of articles with Chung by being cited between 100 and 499 times in Scopus (CT2: 4 each). However, with 12 articles cited at least 12 times and all the 15 contributions receiving at least 225 citations, Teng is identified as the most influential and impactful researcher in the broader research domain (h: 12; g: 15). We also found that Chung is the most active thinker for contributing at least one article in 12 of the 53 years of trade credit research (NAY: 12) while Sunil Tiwari affiliated to the National University of Singapore, is the most productive thinker by contributing at least 2 articles in each of his active years in trade credit research.

4.4. Prolific academic outlets publishing trade credit research

Table 5 lists some of the most prolific academic hubs publishing on trade credit while Figs. 6 & 7 portray the broader picture of evolutionary trends by analysing the quality indicators of the journals publishing on trade credit before and after 2008.

Table 5.

Top sources publishing on trade credit.

	Publications			Authorship pattern					Citation structure			IIAP				AJG
Source	TP	B08	A08	NCA	NAA	CI	SA	CA	TC	C/CP	C/AA	h	g	NAY	PAY	rating
International Journal of Production Economics	56	12	44	148	105	1.64	11	45	3,074	54.89	29.28	34	55	17	3.29	3
Journal of Banking and Finance	25	7	18	63	60	1.52	5	20	826	37.55	13.77	13	22	14	1.79	3
Journal of the Operational Research Society	18	11	7	38	36	1.11	6	12	691	46.07	19.19	10	15	11	1.64	3
Journal of Corporate Finance	14	1	13	34	34	1.43	1	13	289	22.23	8.50	9	13	6	2.33	4
Journal of Financial and Quantitative Analysis	14	9	5	24	22	0.71	7	7	809	62.23	36.77	9	13	11	1.27	4
The Journal of Finance	13	10	3	22	21	0.69	6	7	1765	147.08	84.05	10	12	12	1.08	4*
Review of Financial Studies	12	5	7	27	26	1.25	2	10	322	26.83	12.38	10	12	7	1.71	4*
Journal of Business Finance And Accounting	11	9	2	22	20	1.00	3	8	191	17.36	9.55	7	11	11	1.00	3
Small Business Economics	11	2	9	26	26	1.36	3	8	269	24.45	10.35	8	11	8	1.38	3
Auditing	10	9	1	19	17	0.90	2	8	140	14.00	8.24	7	10	9	1.11	3
Applied Economics	9	5	4	18	17	1.00	3	6	440	55.00	25.88	6	8	8	1.13	2
Journal of Financial Economics	9	1	8	21	19	1.33	1	8	136	17.00	7.16	6	8	7	1.29	4*
Managerial and Decision Economics	9	7	2	15	14	0.67	3	6	284	31.56	20.29	6	9	5	1.80	2
Financial Management	8	4	4	13	12	0.63	4	4	314	44.86	26.17	7	7	5	1.60	3
Production Planning and Control	8	7	1	18	17	1.25	1	7	840	105.00	49.41	6	8	7	1.14	3
Journal of International Economics	6	3	3	9	9	0.50	3	3	283	47.17	31.44	4	6	6	1.00	4
Journal of Money, Credit and Banking	6	1	5	14	14	1.33	1	5	266	66.50	19.00	3	4	5	1.20	4
Manufacturing and Service Operations Management	6	–	6	14	12	1.33	–	6	231	38.50	19.25	6	6	4	1.50	3
World Development	6	5	1	9	9	0.50	5	1	585	97.50	65.00	5	6	6	1.00	3
Accounting Horizons	5	1	4	14	14	1.80	–	5	173	43.25	12.36	3	4	4	1.25	3
Contemporary Accounting Research	5	4	1	13	13	1.60	–	5	321	80.25	24.69	3	4	5	1.00	4
Production and Operations Management	5	–	5	14	12	1.80	–	5	149	37.25	12.42	3	4	3	1.67	4
Transportation Research Part E: Logistics and Transportation Review	5	1	4	12	11	1.40	1	4	188	37.60	17.09	5	5	4	1.25	3

Notes: This table lists the top contributing sources on trade credit research. Here, IIAP = influence, impact, activity, and productivity; TP = total publications; B08 = number of publications before 2008; A08 = number of publications after 2008; NCA = number of contributing authors (includes authors' repetition); NAA = number of affiliated authors (excludes authors' repetition); CI = collaboration index; SA = number of sole-authored articles; CA = number of co-authored articles; TC = total citations; C/CP = citations per cited publication; C/AA = citations per affiliated author; h = h-index; g = g-index; NAY = number of active years; PAY = productivity per active year; and AJG = Chartered Association of Business Schools' (CABS) Academic Journal Guide 2018.

Fig. 6.

Publication trend based on the quality indicators of the sources of trade credit articles.

Notes: This figure compares the publication trend based on the quality indicators of the sources of trade credit articles published before and after 2008. Here, CABS = Chartered Association of Business School's (UK) and AJG 2018 = Academic Journal Guide 2018. The rating of 4* depicts the academically excellent research on trade credit based on its publication in the journals of distinction; 4 represents the most original and best-executed trade credit research; 3 indicates the original and well-executed trade credit research; 2 shows the original trade credit research of an acceptable standard, and 1 represents the acceptable research published in sources which meets the normal scholarly standards of a double-blind peer-review.

Fig. 7.

Citation structure based on quality of the sources of trade credit articles.

Notes: This figure compares the citations to the trade credit research domain based on the quality indicators of the sources of trade credit articles published before and after 2008. Here, CABS = Chartered Association of Business School's (UK) and AJG 2018 = Academic Journal Guide 2018. The rating of 4* depicts the citations to the academic master-piece research on trade credit published in the academic journals of distinction; 4 represents the citations to the most original and best-executed research in trade credit; 3 indicates the citations to the original and well-executed research; 2 shows the citations to the original trade credit research of an acceptable standard, and 1 represents the acceptable trade credit research published in sources which meets the normal scholarly standards of a double-blind peer-review.

As depicted in Table 5, the International Journal of Production Economics (IJPE) has contributed the highest number of trade credit articles between 1955 and 2019 contributed by 105 unique researchers though the total number of contributors in the journal add up to 148 (TP: 56; NAA: 105; NCA: 148). It is followed by the Journal of Banking and Finance (JBF) publishing 25 articles on the financing views of trade credit contributed by 63 lead authors (TP: 25; NCA: 63). Conversely, on the basis of total citations, IJPE is the most popular academic source (TC: 3074). From another perspective we find that about 56 lead thinkers published their research in the IJPE and influence over 3074 others in the multi-disciplinary research domain. Similarly, only 13 lead thinkers who have published their research in The Journal of Finance (JF), on average, influence about 147 others or 1765 lead contributors (C/CP: 147.08; TC: 1765). However, with the highest count of h-index, g-index, NAY, and PAY, our study crowns IJPE as the most influential, impactful, active, and productive academic outlet publishing on trade credit (h: 34; g: 55; NAY: 17; PAY: 3.29).

As shown in Fig. 6, majority of the publishing sources before 2008 are rated 3 in the Chartered Association of Business School's (UK) Academic Journal Guide (2018). Such a rating affirmed that about 35 % of the articles on trade credit (129 of 368 total publications) are recognized as original and well-executed research (AJG: 3); about 8 % (30 of 368) are identified as the most original and best-executed research (AJG: 4); and about a similar proportion of them (29 of 368) are noted as academic excellence (AJG: 4*). The figure demonstrates that the academic quality of the publishing sources of the majority of trade credit research in the post-crisis era is un-traceable in the AJG, 2018 (precisely, 338 of 823, about 41 %). However, about 22 % of the publications are still original and well-executed research (180 of 823 published in AJG 3 rated journals); about 5 % (44 of 823) are counted as the most original and best-executed, while only about 3 % of the publications (23 of 823) are recognised as academic masterpieces. Such indicators should encourage aspiring contributors to contribute original and innovative work in the area. Conversely, in terms of citations indicating popularity and influence (see, Fig. 7), articles published before 2008 in journals bearing an AJG, 2018 rating of 3 dominate in influence with 5646 citations being followed by the sources rated 4* receiving 5646 citations. Such indicators suggest that the quality rankings of the journals may have significantly influenced citations of the published articles. In other words, drawing knowledge from the reputed academic sources, the interdisciplinary research sub-domains have expanded further.

4.5. The most prolific and influential themes

Table 6 enlists the prominent themes of the extant literature on trade credit which have been discussed in at least 10 articles.

Table 6.

Top themes in trade credit research.

	Publications			Authorship pattern				Citation structure				IIAP
Theme	TP	B08	A08	NA	CI	SA	CA	PCP	TC	C/CP	C/A	h	g	NAY	PAY
Trade credit	370	60	310	658	1.38	71	299	0.76	6658	23.53	10.12	44	70	28	13.21
Inventory	110	24	86	139	1.31	28	82	0.83	3,162	34.75	22.75	31	55	23	4.78
Accounts receivable	53	8	45	110	1.26	15	38	0.57	272	9.07	2.47	8	15	17	3.12
Supply chain	44	5	39	85	1.30	8	36	0.80	885	25.29	10.41	15	29	12	3.67
Deterioration	39	1	38	71	1.54	9	30	0.85	321	9.73	4.52	10	16	11	3.55
Working capital management	35	–	35	72	1.54	8	27	0.83	298	10.28	4.14	9	16	10	3.50
Accounts payable	33	–	33	79	1.64	7	26	0.48	193	12.06	2.44	6	13	10	3.30
SMEs	30	–	30	59	1.63	3	27	0.67	329	16.45	5.58	9	18	7	4.29
Profitability	30	–	30	76	1.77	4	26	0.67	276	13.80	3.63	9	16	10	3.00
Financial crisis	27	–	27	57	1.37	6	21	0.74	514	25.70	9.02	10	20	9	3.00
Finance	25	11	14	49	1.40	4	21	0.84	873	41.57	17.82	12	21	19	1.32
Working capital	24	–	24	63	1.79	3	21	0.63	219	14.60	3.48	8	14	9	2.67
EOQ	23	11	12	36	1.13	5	18	0.96	1,557	70.77	43.25	14	22	15	1.53
Permissible delay in payments	22	8	14	28	1.18	5	17	0.95	1,560	74.29	55.71	15	21	13	1.69
Bank credit	21	–	21	38	1.10	7	14	0.76	270	16.88	7.11	7	16	9	2.33
China	19	2	17	43	1.42	2	17	0.53	294	29.40	6.84	8	10	9	2.11
Supply chain management	19	–	19	56	2.16	1	18	0.95	310	17.22	5.54	9	17	8	2.38
Capital structure	19	1	18	42	1.26	3	16	0.68	227	17.46	5.40	7	13	7	2.71
Partial trade credit	18	2	16	32	1.28	3	15	0.89	444	27.75	13.88	9	16	11	1.64
Deteriorating items	16	4	12	28	1.31	3	13	0.94	498	33.20	17.79	8	15	12	1.33
Supply chain finance	16	–	16	49	2.06	1	15	0.81	224	17.23	4.57	6	13	5	3.20
Credit	15	8	7	18	0.20	13	2	0.73	162	14.73	9.00	7	11	12	1.25
Trade credits	14	2	12	32	1.71	2	12	0.93	308	23.69	9.63	7	13	11	1.27
Financial constraints	14	1	13	29	1.07	5	9	0.71	300	30.00	10.34	5	10	7	2.00
Default risk	13	–	13	27	1.31	3	10	0.92	125	10.42	4.63	4	11	9	1.44
Inflation	13	–	13	29	1.62	3	10	0.77	176	17.60	6.07	6	10	7	1.86
Cash conversion cycle	12	2	10	27	1.42	3	9	0.83	154	15.40	5.70	6	10	11	1.09
Factoring	12	7	5	20	1.17	4	8	0.75	131	14.56	6.55	7	9	11	1.09
Firm performance	12	–	12	28	1.42	3	9	0.67	139	17.38	4.96	5	8	7	1.71
Credit risk	10	2	8	24	1.40	3	7	0.70	155	22.14	6.46	5	7	8	1.25
Earnings management	10	2	8	20	1.00	3	7	0.90	224	24.89	11.20	5	9	9	1.11
Bank loans	10	–	10	21	1.10	2	8	0.80	99	12.38	4.71	5	8	7	1.43
Financing	10	1	9	22	1.30	2	8	0.80	199	24.88	9.05	6	8	5	2.00

Notes: This table lists the top themes presented in at least 10 articles published between 1955 and 2019. Here, IIAP = influence, impact, activity, and productivity; TP = total publications; B08 = number of publications before 2008; A08 = number of publications after 2008; NA = number of contributing authors (excludes repetitions); CI = collaboration index; SA = sole-authored publications; CA = co-authored publications; PCP = proportion of cited publications; TC = total citations; C/CP = citations per cited publication; C/A = citations per contributing author; h = h-index; g = g-index; NAY = number of active years; and PAY = publications per active year.

The term “trade credit” leads the table by a huge margin over all others. We found 370 articles (TP: 370) presented by the highest number of researchers (NA: 658) to explore perspectives on “trade credit”. The theme has predominantly evolved in the post crisis era of 2008 (TP: 310 vs. 60) and leads in the categories of sole-authored and co-authored publications (SA: 71; CA: 299). This multi-disciplinary research theme is widely popular and cited in 6658 works of research (TC: 6658). Invariably, it is also the most influential, impactful, active, and productive researched item (h: 44; g: 70; NAY: 23; and PAY: 13.21). Among its most influential works, using the precise term in their published titles, Huang (2003) presents a contrary opinion against the former researchers and argues that retailers receiving extension in credit period would pass on the same benefit down their supply chain to retain their customers; Petersen and Rajan (1997) empirically investigated the factors influencing trade credit demand and supply from the perspective of small firms, while Fisman and Love (2003) acknowledged the role of trade credit in fostering industrial growth in developing economies with an absence or modest development of financial intermediaries.

Apart from “factoring”, almost all the enlisted items exhibited growth in their respective pre versus post crisis numbers. Such indications affirm the views of Merigó et al. (2019) and Baker et al. (2020) that a growing research discipline explodes further as a greater number of researchers try exploring newer dimensions in their respective areas. However, for a de-novo researcher it is important to have a holistic picture of the broader research domain. With such a large number of themes explored, de-novo researchers need guidance on the intellectual convergence of the thematic items which is addressed in our subsequent discussion.

4.6. Exploratory factor analysis exposing the thematic factors of trade credit

Drawing from the guidelines of Ponzi (2002), we have explored the thematic factors published in at least 5 articles on trade credit which extends to 95 initial themes. PCA carried out in SPSS on the correlation matrix of trade credit themes revealed a high Kaiser-Meyer-Olkin (KMO) value (≥ 0.700) indicating sampling adequacy; while rejection of the null-hypothesis of the Bartlett's test of sphericity (p-value ≤ 0.01) confirmed non-identical items. Thus, both the tests confirmed the applicability of exploratory factor analysis (Hair et al., 2012).

The preliminary analysis under Oblique rotation revealed lower correlations among the thematic factors and therefore the analysis proceeded under Varimax rotation with Kaizer normalisation. In addition, the preliminary analysis suggested five thematic components explaining about 97.08 % of the thematic variance which is substantially higher than the recommended range of 60 % in Social Science research (Hair et al., 2012). However, due to cross-loadings suggesting similar usage of keywords within the intellectual sub-domains of trade credit research, we excluded some of the variables such as “trade credit” which loaded in two principal components.

Post exclusion of the cross-loaded variables, our analysis was finalized with 68 thematic items loaded under three principal components explaining about 94.29 % of the thematic variance (KMO: 0.837 with a significant Bartlett's test of sphericity at 99 % confidence interval). Table 7 presents the communalities of the 68 thematic items. Table 8 shows the loading of the thematic items to their respective components and also presents the reliability of the thematic scale.

Table 7.

Communalities of trade credit themes.

Sl.	Theme	Initial	Ext.	Sl.	Theme	Initial	Ext.
1	Trade credit	1.000	.951	35	Two-level trade credit	1.000	.994
2	Accounts receivable	1.000	.927	36	Bankruptcy	1.000	.946
3	SME	1.000	.995	37	Economic order quantity	1.000	.975
4	Deteriorating item	1.000	.995	38	Financial management	1.000	.973
5	Deterioration	1.000	.992	39	Optimization	1.000	.964
6	Accounts payable	1.000	.911	40	Risk management	1.000	.924
7	China	1.000	.995	41	Asymmetric information	1.000	.992
8	EOQ	1.000	.995	42	EPQ	1.000	.983
9	Profitability	1.000	.953	43	Nigeria	1.000	.919
10	Delay in payments	1.000	.985	44	Firm size	1.000	.969
11	Cash flow	1.000	.935	45	Value chain	1.000	.900
12	Financial crisis	1.000	.993	46	Bank lending	1.000	.993
13	Bank credit	1.000	.996	47	Cash discount	1.000	.989
14	Permissible delay in payments	1.000	.972	48	Collateral	1.000	.981
15	Receivables	1.000	.840	49	Information asymmetry	1.000	.981
16	Partial trade credit	1.000	.985	50	Supply chain coordination	1.000	.821
17	Credit period	1.000	.989	51	Contagion	1.000	.959
18	Capital structure	1.000	.946	52	Financing constraints	1.000	.730
19	Liquidity	1.000	.984	53	Partial backlogging	1.000	.986
20	Pricing	1.000	.997	54	Private firms	1.000	.853
21	Supply chain finance	1.000	.456	55	Bank financing	1.000	.928
22	Monetary policy	1.000	.988	56	Corporate finance	1.000	.717
23	Panel data	1.000	.982	57	Defective items	1.000	.982
24	Small business	1.000	.991	58	Financial crises	1.000	.984
25	Small firms	1.000	.991	59	Financial development	1.000	.992
26	Firm performance	1.000	.976	60	Firm value	1.000	.972
27	Bank loans	1.000	.994	61	Shortages	1.000	.979
28	Competition	1.000	.971	62	Advance payment	1.000	.861
29	Financial constraints	1.000	.988	63	Bargaining power	1.000	.983
30	Financial distress	1.000	.983	64	Maximum lifetime	1.000	.981
31	Inflation	1.000	.980	65	Return on assets	1.000	.974
32	Cash conversion cycle	1.000	.966	66	Emerging markets	1.000	.908
33	International trade	1.000	.982	67	Non-instantaneous deterioration	1.000	.955
34	Leverage	1.000	.933	68	Sweden	1.000	.730

Note: Using principal component analysis (PCA) in SPSS, this table presents the communalities of the trade credit themes presented in at least 5 articles published between 1955 and 2019.

Table 8.

Rotated component matrix, factor loadings and reliability of the three thematic components (C1, C2, and C3) in trade credit research.

Theme	CI	Theme	C2	Theme	C3
Firm performance	.917	Maximum lifetime	.959	Cash conversion cycle	−.930
Panel data	.916	Permissible delay in payments	.952	Nigeria	−.910
Financial management	.912	Delay in payments	.942	Profitability	−.885
SME	.894	Shortages	.932	Return on assets	−.777
Financial crisis	.890	Inflation	.922
Small firms	.881	EPQ	.918
Financial development	.879	Non-instantaneous deterioration	.913
Financial constraints	.876	Deterioration	.901
Liquidity	.875	Defective items	.898
Capital structure	.874	Two-level trade credit	.893
Firm size	.865	Partial backlogging	.892
Accounts payable	.863	Optimization	.890
Private firms	.860	Deteriorating item	.886
Firm value	.858	EOQ	.880
China	.858	Partial trade credit	.874
Bank lending	.857	Cash discount	.873
Bank financing	.854	Cash flow	.867
Accounts receivable	.852	Economic order quantity	.862
Sweden	.851	Credit period	.847
Small business	.850	Pricing	.801
Asymmetric information	.845	Advance payment	.740
Emerging markets	.844	Supply chain coordination	.702
Bank loans	.840
Collateral	.837
Financial distress	.833
International trade	.831
Bargaining power	.827
Monetary policy	.822
Corporate finance	.820
Financing constraints	.817
Bank credit	.817
Value chain	.816
Financial crises	.816
Receivables	.811
Information asymmetry	.805
Contagion	.799
Competition	.787
Risk management	.783
Bankruptcy	.775
Leverage	.770
Supply chain finance	.539
Reliability
Number of items	41		22		4
Cronbach's alpha	.997		.998		.938
Inter-items correlation	.888		.953		.806

Note: This table presents the factor loadings of the trade credit themes using principal component analysis under Varimax rotation with Kaiser normalization. Here C = component.

As shown, the prolific themes of trade credit research converged into three thematic components suggesting three major sub-domains in the broad research discipline. The first thematic component loaded the highest count of items (41) with a Cronbach's alpha score of 0.997, suggesting a highly reliable component. It is named as the banking and financing views of trade credit. The second thematic component loaded 22 thematic items, a highly reliable component with Cronbach's alpha value of 0.998, named as the production and operational views on trade credit. Finally, the third principal component loaded only 4 items suggesting a widely unexplored area and hence a hot-avenue for future research. The component is reliable with a Cronbach's alpha value of 0.938 named as the core accounting views on trade credit.

Unfortunately, the thematic components present a very broad overview while the de novo thinkers may appreciate a more microscopic view to unveil the precise intellectual sub-domains and its current status—active, dormant, or dead. Such analysis is not only critical to gain perspectives; it can effectively guide academics for expanding their respective fields of research.

5. Network analysis on the State of the Art in Trade Credit research

5.1. Network analyses

Kessler (1963) explained that scientific works express intellectual (dis)association through their citations while Small (1973) made co-citations as the basis of his principal philosophy. Precisely, when two or more works of literature cite similar articles, they form a bibliographic couple (Kessler, 1963) while Small (1973) argued that frequent co-citations of published articles in the subsequent research evidentially confirms intellectual association. Unfortunately, the argument of Small (1973) may suffer a time lag effect as more recent publications require time to influence academic evidence in the form of citations (Marrone, 2020). Since our study incorporates articles till 2019, we applied bibliometric coupling analysis on the trade credit articles, authors, and publishing sources. However, to expose the prior seminal ideas, we applied co-citation analysis on the cited references as explained by Small (1973).

Table 9 presents the descriptive analyses of the bibliographic clusters, Fig. 8 reveals the publication trend of the clusters in the pre and post crisis era, while Fig. 9 shows the kind of studies that prevailed within the respective bibliographic clusters.

Table 9.

Descriptive of the bibliographic clusters of trade credit research.

		Major clusters				Minor clusters
		1	2	3	5	4	6	7	8
Publications	Before 1979	1	6	2	–	1	–	–	–
	1980 to 1989	3	2	7	6	1	–	–	–
	1990 to 1999	17	3	21	3	2	–	–	–
	2000 to 2009	46	13	72	35	1	2	–	–
	2010 to 2019	110	79	313	199	–	–	2	3
	TP	177	103	415	243	5	2	2	3
Authorship Pattern	NCA	379	258	926	623	6	4	4	6
	CI	1.14	1.50	1.23	1.56	0.20	1.00	1.00	1.00
	SA	53	22	94	39	4	–	1	–
	CA	124	81	321	204	1	2	1	3
Citation structure	NCP	136	75	322	203	5	2	1	1
	PCP	0.77	0.73	0.78	0.84	1.00	1.00	0.50	0.33
	TC	4140	1,285	9,844	5,197	27	37	4	1
	C/CP	30.44	17.13	30.57	25.60	5.40	18.50	4.00	1.00
	C/CA	10.92	4.98	10.63	8.34	4.50	9.25	1.00	0.17
	CT1	126	74	297	191	5	2	1	1
	CT2	9	1	24	12	–	–	–	–
	CT3	1	–	1	–	–	–	–	–
II.AP	h	30	19	49	40	3	2	1	1
	g	62	33	90	66	5	2	1	1
	NAY	30	25	34	26	5	2	2	1
	PAY	5.90	4.12	12.21	9.35	1.00	1.00	1.00	3.00
AJG 2018	4*	9	1	34	–	–	–	–	–
	4	12	2	46	–	–	–	1	–
	3	46	16	123	93	2	–	–	–
	2	34	21	82	13	2	–	–	1
	1	16	13	44	39	–	1	–	–
	NA	60	50	86	98	1	1	1	2

Notes: This table presents the important descriptive of the bibliographic clusters of trade credit articles published till 2019. Here, IIAP = influence, impact, activity, and productivity; TP = total publications; NCA = number of contributing authors of those publications; CI = collaboration index; SA = sole-authored articles; CA = co-authored articles; PCP = proportion of cited publication; TC = total citations; C/CP = citations per cited publication; C/CA = citations per contributing author; CT1 = first citation threshold i.e. between 1 and 99 citations; CT2 = second citation threshold i.e. between 100 and 499 citations; CT3 = third citation threshold i.e. above 500 citations; h = h-index; g = g-index; NAY = number of active years; and PAY = publications per active year; and NA = not available.

Fig. 8.

Publication trend of the bibliographic clusters.

Notes: This figure shows the publication pattern of the bibliographic clusters of trade credit articles published before and after 2008. Among the eight reported, clusters 1, 2, 3, and 5 denote the major bibliographic clusters of the research domain while clusters 4, 6, 7, and 8 are minor.

Fig. 9.

Types of the studies on trade credit.

Notes: This figure presents the types of studies presented in the bibliographic clusters of trade credit research published till 2019. Clusters 1, 2, 3, and 5 are the recognized major bibliographic clusters while clusters 4, 6, 7, and 8 are minor.

Bibliographic coupling analysis exhibits 4 major and 4 minor clusters formed by 950 bibliographic couples based on their identical patterns of referencing. The clusters are numbered chronologically and named as per the naming of factors in factor analyses (Baker et al., 2020)

5.1.1. Major clusters

5.1.1.1. Cluster 1: trade debt and financial management

As presented in Table 9, the first cluster consists of 177 articles contributed by 379 researchers (TP: 177; NCA: 379) of which the majority are co-authored (SA: 53 vs CA: 124) with a collaboration index of 1.14 (CI: 1.14). The cluster exhibits an evolving trend with about 65 % (115 of 177) of the research published after 2008 (see Fig. 8). The citation structure of the cluster suggests that each of its cited articles influenced about 30 works of research on average, with a broader intellectual influence on about 4,140 other lead thinkers/researchers (C/CP: 30.44; TC: 4,140). The cluster is important as it occupies the third rank in citations with each of the contributing authors receiving at least 11 citations in a conservative form (C/CA: 10.92). The majority of the articles (CT1: 126) are cited between 1 and 99 times. However, the most influential work of research received above 500 citations (CT3: 1). Of note, the article is already recognised as the second most influential work of research in Table 2. Further, the impact of the cluster is extended by its 30 articles cited at least 30 times with 62 highly cited articles receiving at least 3844 citations (h: 30; g: 62). The cluster has been active for 30 years while contributing about 6 articles per active year (NAY: 30; PAY: 5.90). Based on the reported quality indicators of the publications, the majority of the works constituting the cluster are identified as original which justified its publication in journals rated 2 and above.

Further, as indicated in Fig. 9, most of the articles are empirical investigations while the cluster contributes a smaller number of qualitative researches such as conceptual papers, reviews etc., thereby suggesting few methodological gaps for the aspiring contributors to fulfil.

Of note is that some of the influential themes of the cluster include terms such as firm value, corporate social responsibility, earnings and cash flows, prediction of future cash flows, etc. Some of its recent and original publications draw upon transactions cost theory; for eg. Ghoul et al. (2017) empirically validate that corporates' valuation is moderated by their socially responsible behaviours i.e. corporate social responsibility (CSR). On such insightful findings, researchers could introduce and validate the hypothesis of a social behaviour of trade credit explaining the controversial findings of the financing theory among the large and listed firms in the emerging economies (Ahmad et al., 2018). Future research can try associating the financing behaviour of large firms to their degree of social activity. Perhaps, more than the firm size indicating the financing capability of larger firms to the smaller firms—crucial in emerging economies—CSR activity may better explain the controversial findings of the financing theory noted in the emerging economies.

5.1.1.2. Cluster 2: supply chain finance and working capital management

The second major cluster contributes the least number of studies engaging the least number of researchers compared to the other major clusters (TP: 103; NCA: 258). Though its authorship, citations, influence, impact, activity, and productivity indicators are the lowest compared to the other major clusters (SA: 22, CA: 81; PCP: 0.73, TC: 1,285, C/CP: 17.13, C/CA: 4.98, CT1: 74, CT2: 1; CT3: 0; h: 19, g:33, NAY: 25, PAY: 4.12); its latest publications trend shows a high level of evolution, thus suggesting recent academic attention and therefore a highly lucrative avenue for future research with about 77 % of its contributions having been made between 2000 and 2019 (79 of 103). Interestingly, Fig. 9 suggests that future contributions could be a review, conceptual paper, a mathematical or theoretical model, empirical investigations in the form of regression analysis, case studies, survey-based or experiment-based research, etc.

Some of the influential themes of the cluster include terms such as working capital management, supply chain finance, receivables management, firm profitability, etc. Paul et al. (2018) associated trade credit supply as an investment in receivables. However, explaining receivables as a social investment, future research could measure its impact on the accounting or market-based performances. Evidential positive outcome may encourage larger firms in emerging economies to extend the in-kind finance (Giannetti et al., 2011) to smaller firms.

5.1.1.3. Cluster 3: banking and financing cluster of trade credit

The third cluster is the most competitive cluster of the research front occupying the top-most rank in the majority of the indicators reported in Table 9 (TP: 415; NCA: 926, SA: 94, CA: 321; TC: 9,844, C/CP: 30.57, CT1: 297, CT2: 24, CT3: 1; h: 49; g: 90; NAY: 34; PAY: 12.21). Interestingly, most of the articles are published in high quality academic outlets— good news for aspiring contributors—with an evolving research trend. As per Fig. 8, about 77 % of the published research followed the era of the global economic crisis (321 of 415) while Fig. 9 suggests that most of the publications are empirical investigations. Thus, future research could contribute reviews, conceptual models, predictive regression models, case studies, survey-based research, etc.

Some of the influential themes addressed in this cluster include credit conditions, international trade, global financial crisis, risk sharing, etc. As an example of what could be done in the cluster, future research can further explore the concern raised by Chod et al. (2019). The problem of free-riding among retailers, frequently switching highly substitutive and competitive suppliers, and significantly moderating trade credit supply. Such problems are particularly important in emerging economies where large suppliers are the second level source of primary credit supply (Li et al., 2018). Chod et al. (2019) empirically validated their proposed model. In addition to its empirical validations in evolving economies, future research could also test how the supplier-retailer's relation mediate the above proposition. We argue that apart from establishing the credit quality of the new buyers, the incremental increase of credit supply with age may naturally eliminate the risk of free-riding.

5.1.1.4. Cluster 5: production and operational dimensions of trade credit

Cluster 5 is the second most productive but the top-most collaborative research front on trade credit (TP: 243; CI: 1.56). It occupies the highest rank in the proportion of cited publications with every 84 of its 100 articles cited at least once (PCP: 0.84). However, it is ranked two in most of the indicators reported in Table 9 (TP: 243; NCA: 623, SA: 39, CA: 204; TC: 5,197, C/CP: 25.60, CT1: 191, CT2: 12; h: 40; g: 66; NAY: 26; PAY: 9.35). In terms of the AJG, 2018 indicators, the gap of some of the most original and unique proportions in the domain could be filled through future research. Simultaneously, as per Fig. 8, about 84 % of the published research follows the era of the global economic crisis (203 of 243) suggesting a new and fast-evolving research dimension while Fig. 9 suggests that the majority of the articles are mathematical models. Thus, future research could contribute review articles, conceptual models, predictive regression-based research articles, case studies, etc. to fill potential gaps in the published research methods.

Most of the latest articles published in the cluster work in the area of green supply chains, operational management, optimal ordering quantity, optimal wholesale price, optimal carbon emissions, etc. As an example, the amalgamative supply chain operations model of Dash Wu et al. (2019) addressing the concern of optimal carbon emissions (OCE), optimal ordering quantity (OOQ) of a capital-constraint retailer, and the optimal wholesale price (OWP) for a manufacturer operating in a viable trade credit financing and bank financing market environment proposed and validated that lower levels of carbon emissions fosters win-win outcomes. Future empirical validations of such models can sensitise controlled carbon emissive behaviour as it positively impacts profitability.

5.1.2. Minor clusters

5.1.2.1. Cluster 4, 6,7 and 8: minor areas of trade credit

Clusters 4, 6, 7 and 8 are the minor areas of trade credit which have their respective unique patterns of referencing. Because of the lower number of articles, we reported them under one head. The publication trend suggests that cluster 4 and 6 reported their last article between 2000 and 2009; however, all the articles of Clusters 7 and 8 appeared between 2010 and 2019 suggesting some emerging dimensions. In addition, the quality indicators of the publishing sources suggest the emergence of major original research in cluster 4. Of note, the clusters have contributed research in the area of trade credit management, industry specific dimensions on the impact of trade credit in textile industry, etc. Thus, the cluster is a specialized research dimension of the broad area of the financing aspect of trade credit.

Similarly, cluster 6 presents some unique ideas of cash-to-cash management and budgetary participation of employees—some specialized accounting views—which can be substantially explored in the future. Cluster 7 attempts exploring the historic perspectives of trade credit. One of its articles published in 2014 attempts expanding the neglected area of medieval sovereign debt in Europe and links sovereign debt to the repayment habit of the existent Kings. Good repayment history of the medieval Kings positively influenced trade credit extension by merchants. Future research may consider its expansion by weighing its relevance to modern international trade credit. However, tracing the appropriate historical financial data is a major challenge to expand the dimension. Cluster 8 introduces a new sub-domain of the significance of trade credit in the capital structure of small, medium, and micro enterprises (SMMEs) for their sustainable economic performance. Future research could further expand this area as the growth of the sector is crucial for emerging economies.

It can be concluded that the bibliographic coupling analysis affirms three major sub-domains of trade credit (banking and financing, accounting, and production and operations) gradually expanding to eight specialized sub-domains.

The following section visualizes the intellectual epicentres of trade credit research, and the network among the most prolific authors and contributing sources.

5.2. Networks' visualisation

Fig. 10 reveals the semantic association among the most co-cited articles on trade credit depicting the intellectual epicentres of the research domain till 2019, Fig. 11 unveils the semantic association among the most prolific contributors while Fig. 12 exposes the intellectual similarities among the most prolific sources. The spatial distance between any two nodes depict their intellectual closeness.

Fig. 10.

The intellectual epicenters of trade credit research.

Note: Using VOSviewer and Gephi software, this figure reveals the semantic association among the most co-cited articles on trade credit depicting the intellectual epicenters of the research domain till 2019.

Fig. 11.

Bibliographic couplings among the prolific authors of trade credit.

Note: Using VOSviewer and Gephi software the figure reveals the intellectual association among the most prolific contributors of trade credit research.

Fig. 12.

Co-citation among the most prolific sources of trade credit research.

Note: Using VOSviewer and Gephi software, this figure reveals the intellectual association among the most prolific sources of trade credit research.

A minute observation of all the figures reveal that each of them exhibits three broad clusters (denoted by the three colours) confirming the notion on the existence of three broad intellectual sub-domains influencing the intelligentsia on trade credit.

Fig. 10 presents the articles co-cited at least 150 times between 1975 and 2019. The oldest reported article in the figure is of Schwartz (1974). Interestingly, excluding Goyal (1985); Aggarwal and Jaggi (1995), and Teng (2002) all the remaining articles are closely knitted. It suggests that the operational dimension of trade credit stand out from the remaining perspectives.

Interestingly, Fig. 11 also presents three broad groups of intellectuals. However, X. Chen and D. Yazdanfar stand out in their referencing pattern exhibiting similarities with N. H. Shah and L. -Y. Ouyang, respectively.

In Fig. 12, the majority of the journals present the financial perspectives of trade credit followed by the sources publishing the operational dimensions. Unfortunately, the accounting views are limited by numbers. As indicated, IJPE occupy a prominent position exhibiting strong affiliation with the European Journal of Operational Research (EJOR). As already indicated IJPE has contributed the maximum research post 2008 and therefore its centrality isn't surprising. We also found distinct and strong linkages between JF and Review of Financial Studies (RFS) and, JF and The Journal of Financial Economics (JFE) suggesting the prevalence of similar ideas among them.

In the subsequent section we report the outcome of the regression analysis revealing some key variables influencing the total citations of the discipline over the years.

6. Factors influencing citations: regression analysis

Table 10 presents the description of the studied variables, Table 11 presents the correlation matrix of the studied variables while Table 12 presents the regression coefficients of the variants.

Table 10.

Descriptive of the regression variables.

Variable	Mean	SD	N
Total citations	25.70	53.98	745
Number of contributing authors	2.30	0.97	745
Article length	17.15	8.61	745
First thematic component	0.02	0.02	745
Second thematic component	0.02	0.05	745
Third thematic component	0.01	0.08	745
Dummies	NA	NA	745

Notes: This table presents the descriptive statistics of the potential variables influencing the citation of trade credit articles published till 2019. Here, SD = standard deviation, N = number of cases, and NA = not applicable. The dummies include: authorship type of the trade credit articles i.e. sole-authored or co-authored; publication year of the articles i.e. before or after 2008; AJG 2018 ratings of the publishing sources of the trade credit articles such as 4*, 4, 3, 2, or 1; bibliographic clustering of the trade credit articles i.e. major or minor cluster; and research type of the trade credit articles i.e. primary (empirical) or secondary (review, conceptual, model building, etc.).

Table 11.

Correlation matrix of the variables.

	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19
1	1.00
2	0.15	1.00
3	−0.05	0.02	1.00
4	0.01	0.08	0.72	1.00
5	−0.01	−0.08	−0.72	−1.00	1.00
6	−0.34	0.00	0.31	0.23	−0.23	1.00
7	0.34	0.00	−0.31	−0.23	0.23	−1.00	1.00
8	0.43	0.32	−0.05	−0.01	0.01	−0.12	0.12	1.00
9	0.09	0.14	−0.03	−0.02	0.02	−0.02	0.02	−0.07	1.00
10	0.10	−0.09	0.05	0.02	−0.02	−0.14	0.14	−0.18	−0.20	1.00
11	−0.11	−0.04	−0.02	−0.01	0.01	−0.01	0.01	−0.11	−0.13	−0.32	1.00
12	−0.13	0.05	0.01	0.03	−0.03	0.09	−0.09	−0.09	−0.11	−0.27	−0.17	1.00
13	−0.04	−0.02	−0.07	−0.06	0.06	−0.09	0.09	−0.03	0.02	−0.03	0.05	0.00	1.00
14	0.04	0.02	0.07	0.06	−0.06	0.09	−0.09	0.03	−0.02	0.03	−0.05	0.00	−1.00	1.00
15	0.00	−0.13	0.04	−0.03	0.03	0.02	−0.02	−0.02	0.03	0.09	−0.13	−0.03	−0.02	0.02	1.00
16	0.00	0.13	−0.04	0.03	−0.03	−0.02	0.02	0.02	−0.03	−0.09	0.13	0.03	0.02	−0.02	−1.00	1.00
17	−0.10	0.12	0.01	0.02	−0.02	0.21	−0.21	−0.03	0.06	−0.06	0.08	0.05	−0.08	0.08	−0.30	0.30	1.00
18	0.06	−0.13	0.08	0.05	−0.05	0.08	−0.08	−0.10	−0.12	0.04	−0.14	0.09	−0.05	0.05	0.39	−0.39	−0.31	1.00
19	−0.05	−0.09	−0.01	−0.02	0.02	0.10	−0.10	−0.05	−0.06	−0.12	0.00	0.08	−0.02	0.02	−0.15	0.15	0.03	−0.09	1.00

Notes: This table presents the Pearson's correlation among the potential variables influencing the citations of trade credit articles. Here 1 = total citations (the dependent variable); 2 = length of the trade credit article; 3 = number of contributing authors of the articles; 4 = authorship category (sole-authorship or co-authored); 5 = sole-authored articles; 6 = categories of the publication year (before or after 2008); 7 = publication year before 2008; 8 through 12 present the AJG 2018 ratings of the publishing source of the trade credit article i.e. 4*, 4, 3, 2, or 1, respectively; 13 = bibliographic clustering of the trade credit article (major or minor cluster); 14 = major cluster; 15 = type of the trade credit research (empirical or secondary); 16 = empirical research; and 17 through 19 = the thematic component scores of the articles.

Table 12.

Regression coefficients of the study variables.

Variable	SC			CS
	Beta	t	Sig.	T	VIF
(Constant)		−2.75	0.01
Article length	0.01	0.29	0.77	0.82	1.22
Number of contributing authors	0.00	0.08	0.94	0.45	2.21
Authorship type (sole-authored or co-authored)	0.07	1.50	0.13	0.47	2.12
Publication year before 2008	0.29	8.70	0.00	0.79	1.27
AJG4*	0.46	13.04	0.00	0.72	1.39
AJG4	0.19	5.62	0.00	0.75	1.33
AJG3	0.20	4.99	0.00	0.58	1.74
AJG2	0.04	1.17	0.24	0.67	1.50
AJG1	0.00	−0.08	0.93	0.73	1.37
Bibliographic cluster (major)	0.04	1.35	0.18	0.97	1.03
Empirical research	0.07	2.09	0.04	0.77	1.30
First thematic component	0.00	−0.03	0.97	0.79	1.27
Second thematic component	0.18	5.15	0.00	0.74	1.36
Third thematic component	0.05	1.50	0.13	0.92	1.08

Notes: This table presents the Ordinary Least Square (OLS) regression outcome under the enter method in SPSS software. Apart from reporting the standardized coefficients (SC) of the independent variables influencing the dependent (total citations), the table also presents the collinearity statistics (CS) such as tolerance (T) and the variance inflation factors (VIF) of the regressors. Of note, the R² of the model is 0.34 with an adjusted R² value of 0.33. The regression is significant at 99 % confidence interval (p-value ≤ 0.01). Here, T = tolerance; and VIF = variance inflation factor.

The dependent variable, total citations, reports a mean of 25.70 (SD: 53.98) suggesting that each of the articles shortlisted for the regression received on average about 26 citations. However, the corpora of articles exhibit higher variance in citations (explained in the bibliographic coupling analyses section). The mean number of the contributing authors of the selected articles and the average article length is 2.30 (SD:0.97) and 17.15 (SD: 8.61), respectively. However, the thematic variances of the selected corpora exhibit an ascending order which is not surprising as the majority of the shortlisted articles are from the first thematic factor and lowest from the third.

The correlation matrix presents the magnitude and direction of relationship between the variables. The regression coefficients make some interesting revelations: First, all the reported variables have positively influenced the citations (growth) of trade credit research. Second, articles published before 2008 significantly influence growth of the academic domain as most of the evolving articles are based on the intellectual foundations laid in the former articles. Third, in particular, articles published in more qualitative journals based on their AJG, 2018 ratings have significantly influenced the evolving sub-domains of the research front. Fourth, validating and confirmatory empirical research positively influences the growth of publications. Fifth, the evolving research in the area of production and operations is a significant contributor to the growth of trade credit articles. And finally, the time-lag effect of citations is evident in the banking and financing sub-domain of trade credit research which is a non-significant but positive influencer of trade credit citations.

7. Direction for future research

Although the directions for future research are already presented during the analysis of the bibliographic networks, we summarise some of the key aspects here.

While analysing the gaps in research methods, we suggest future research to be more qualitative in nature. Researchers should consider providing more conceptual and theoretical models, specialized pathways, survey-based studies etc. in the emerging clusters to fortify the domains. Simultaneously, we call for more studies with empirical insights from emerging economies that would educate global academia which has largely observed this phenomenon from the developed world (Ahmad et al., 2018).

Drawing from Ghoul et al. (2017), we suggest researchers to introduce and validate the hypothesis of a social behaviour of trade credit as an explanation to the pitfalls of the financing theory among the large and listed firms in the emerging economies (Ahmad et al., 2018). Simultaneously, Paul et al. (2018) presented receivables as an alternative investment. However, the social nature of trade credit is unexplored. Given its importance, we argue that trade credit is not only commercial, it also has social implications, especially when the relation is between the large-scale lender to the micro, small, and medium enterprises (MSMEs). Further, drawing from the model of Dash Wu et al. (2019), future research could also explore the impact of green trade credit on firm's accounting, market, and social performances.

Conversely the behavioral dimensions in the research front is unexplored. With regard to the application of theories in this area, we suggest that there are opportunities to carry out researches using theories such as the prospect theory and dynamic capability theory. Prospect theory was originally developed by economists Kahneman and Tversky (1979). It constitutes one of the first economic theories formulated using experimental methods. Prospect describes how individuals asymmetrically assess their loss and gain perspectives relative to their specific situation. They found that individuals are risk-averse when facing gains but risk-lovers when facing losses. Accordingly, the prospect theory describes the actual behavior of people. In the original formulation of the theory, the term prospect referred to the predictable results of a lottery. However, the prospect theory can also be applied to the prediction of other forms of behaviors and decisions in areas such as trade credit. Similarly, tenets of dynamic capability framework (Teece, 2007) can be applied in trade credit research to analyze how the firm capabilities and trade credit influence firm performance.

8. Conclusions

The state-of-the-art in trade credit literature indicates an evolving growth trend. A growing discipline attracts more researchers from multiple disciplines resulting in synergetic output. We found 2019 as the most productive year for the research domain (TP: 192). The number of articles grew from 368 in the pre-crisis period to 828 after 2008. Simultaneously, about 74 % of the growth in authorship in the area of trade credit occurred after the global economic crisis of 2008. Thus, it is concluded that the area of trade credit is fast evolving. The major themes of the trade credit literature converged into three intellectual sub-domains: banking and finance, production and operations, and accounting. The bibliographic coupling analysis of the extant literature grouped the articles into four major and four minor clusters. The major clusters include: trade credit and financial management, supply chain finance and working capital management, the core banking and financing cluster of trade credit, and the production and operational dimensions of trade credit. The four minor clusters of research in the area of trade credit management are its specialized industrial applications, the historical perspectives, and the most current and evolving small, medium, and micro-enterprises.

As analysed, threat in the external environment cause academic emergence contributing to the growth of trade credit articles. So far, academia has been investigating the impact of the global economic crisis; however, with the onset of COVID-19 locking down operations globally, the role of trade credit in such situations may attract more scholarly attention in the area. On analysing the factors affecting the growth of trade credit publications as indicated in its citations we found that the reputation of articles based on its publishing source, empirical studies, rising publications in emerging sub-domains etc. positively influence citations. Thus, aspiring contributors should carefully consider where they should publish their article so as to be a more impactful and influential researcher in the domain.

Appendix A. Definitions of descriptive variables

Variable	Definition
Publication
TP	Defined as the sum of total publications
B08	Defined as the number of publications before 2008
A08	Defined as the number of publications after 2008
Authorship pattern
NCA	Number of authors contributing the research article(s).
GA	Annual increment of authors added to the research domain.
CI	Ratio between the number of contributing authors to total publications less the number of total publications ($\frac{NCA}{TP}-1$).
SA	Number of articles contributed by a single author.
CA	Number of articles contributed by multiple authors.
Citation structure
NCP	The number of articles cited at least once in Scopus.
PCP	Ratio between the number of cited publications to the total number of publications.
TC	Sum of the citations accredited to an article, an author, a journal, a cluster, etc.
C/P	Average citations per publication.
C/CP	Average citations per cited publication.
C/CA	Average citations per contributing author.
CT1	Citations between 1 and 99 times.
CT2	Citations between 100 and 499 times.
CT3	500 citations and above.
Influence, impact, productivity, and activity
h	h number of publications cited at least h times.
g	Sum of g number of highly cited publications cited at least g2 times.
NAY	Number of years an article or a theme on trade credit was published or the number of years an academic source &/or a cluster published on trade credit.
PAY	Number of publications in each of the active years.

Appendix B. Supplementary data

The following is Supplementary data to this article: